Reviewerly/exHarmony huggingface dataset:
Temporal phase unwrapping with a lightweight deep neural network

Absolute phase unwrapping is important for optical phase-based three-dimensional (3D) measurements of isolated objects or discontinuous surfaces. For fringe projection profilometry, the fringe order of each stripe can be obtained uniquely by temporal phase unwrapping (TPU) that employs at least two different phase maps of the same measured scene. In this work, we present a novel TPU strategy using deep learning. Our idea is to treat the calculation of fringe order as a classification problem, which can be solved by a lightweight fully connected neural network. Consequently, the training process of our network can be finished within an hour, which saves a large amount of time and makes it possible to deploy the network on mobile devices. Moreover, rather than obtaining the training data from time-consuming real experiments, we simulate the data with software under different noises.
Digital Quantum Simulation of Nonadiabatic Geometric Gates via Shortcuts to Adiabaticity

Geometric phases are used to construct quantum gates since it naturally resists local noises, acting as the modularized units of geometric quantum computing. Meanwhile, fast nonadiabatic geometric gates are required for reducing the information loss induced by decoherence. Here, we propose a digital simulation of nonadiabatic geometric quantum gates in terms of shortcuts to adiabaticity (STA). More specifically, we combine the invariant-based inverse engineering with optimal control theory for designing the fast and robust Abelian geometric gates against systematic error, in the context of two-level qubit systems. We exemplify X and T gates, in which the fidelities and robustness are evaluated by simulations in ideal quantum circuits. Our results can also be extended to constructing two-qubit gates, for example, a controlled-PHASE gate, which shares the equivalent effective Hamiltonian with rotation around the Z-axis of a single qubit. These STA-inspired nonadiabatic geometric gates can realize quantum error correction physically, leading to fault-tolerant quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era.
Fast simulation of quantum algorithms using circuit optimization

Classical simulators play a major role in the development and benchmark of quantum algorithms and practically any software framework for quantum computation provides the option of running the algorithms on simulators. However, the development of quantum simulators was substantially separated from the rest of the software frameworks which, instead, focus on usability and compilation. Here, we demonstrate the advantage of co-developing and integrating simulators and compilers by proposing a specialized compiler pass to reduce the simulation time for arbitrary circuits. While the concept is broadly applicable, we present a concrete implementation based on the Intel Quantum Simulator, a high-performance distributed simulator. As part of this work, we extend its implementation with additional functionalities related to the representation of quantum states. The communication overhead is reduced by changing the order in which state amplitudes are stored in the distributed memory, a concept analogous to the distinction between local and global qubits for distributed Schroedinger-type simulators. We then implement a compiler pass to exploit the novel functionalities by introducing special instructions governing data movement as part of the quantum circuit. Those instructions target unique capabilities of simulators and have no analogue in actual quantum devices. To quantify the advantage, we compare the time required to simulate random circuits with and without our optimization. The simulation time is typically halved.
Two Dimensional Sign Language Agent

extended-abstract Share on Two Dimensional Sign Language Agent Authors: Matthew McConnell School of Computing Science, University of Glasgow School of Computing Science, University of GlasgowView Profile , Mary Ellen Foster School of Computing Science, University of Glasgow School of Computing Science, University of GlasgowView Profile Authors Info & Claims IVA '20: Proceedings of the 20th ACM International Conference on Intelligent Virtual AgentsOctober 2020 Article No.: 39Pages 1–3https://doi.org/10.1145/3383652.3423898Published:19 October 2020Publication History 2citation101DownloadsMetricsTotal Citations2Total Downloads101Last 12 Months14Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Systematic Literature Review of Natural Language Processing: Current State, Challenges and Risks

In this research paper, a comprehensive literature review was undertaken in order to analyze Natural Language Processing (NLP) application based in different domains. Also, by conducting qualitative research, we will try to analyze the development of the current state and the challenge of NLP technology as a key for Artificial Intelligence (AI) technology, pointing out some of the limitations, risks and opportunities. In our research, we rely on primary data from applicable legislation and secondary public domain data sources providing related information from case studies. By studying the structure and content of the published literature, the NLP-based applications have been clearly classified into different fields which include natural language understanding, natural language generation, voice or speech recognition, machine translation, spell correction and grammar check. The development trend, open issues and limitations have also been analyzed.
Research on Application Effect in Physical Education Teaching Based on Computer-aided Technology

Abstract With the development and popularization of modern teaching methods, computer-aided instruction (CAI) has become a hot spot in current classroom teaching. CAI overcomes the defects of traditional teaching methods, and it has become an important way to optimize teaching. In college physical education classroom teaching, CAI makes up the textbook illustration and the writing explanation limitation. CAI simplifies complex actions, and it presents non-decomposable actions. CAI not only helps students to understand, master knowledge and develop intelligence, but also enables teachers to better grasp information and achieve good teaching results. This paper first analysis the necessity of the CAI application in physical education, and then analysis the recognition and application effect of CAI, and finally this paper puts forward some suggestion.
A Facial Expression Recognition Model Based on Texture and Shape Features

With the development of computer vision, facial expression recognition has become a research hotspot. To further improve the accuracy of facial expression recognition, this paper probes deep into image segmentation, feature extraction, and facial expression classification. Firstly, the convolution neural network (CNN) was adopted to accurately separate the salient regions from the face image. Next, the Gaussian Markov random field (GMRF) model was improved to enhance the ability of texture features to represent image information, and a novel feature extraction algorithm called specific angle abundance entropy (SAAE) was designed to improve the representation ability of shape features. After that, the texture features were combined with shape features, and trained and classified by the support vector machine (SVM) classifier. Finally, the proposed method was compared with common methods of facial expression recognition on a standard facial expression database. The results show that our method can greatly improve the accuracy of facial expression recognition.
Molecular MR Multiplayer: a cross-platform collaborative interactive game for scientists

We present Molecular MR Multiplayer – an interactive, collaborative solution for material design on the edge devices on flat screens and in Mixed Reality (MR) [3]. The application provides a concept of emerging gaming-like remote collaboration experience for researchers, helping explore and design chemical compounds, Both single-user and multi-user modes are implemented. The multi-user mode allows mixed collaborative sessions between local and distant users. A concept of the digital overlays over physical world known as Metaverse is developed.
Region-of-Interest Guided Supervoxel Inpainting for Self-supervision

Self-supervised learning has proven to be invaluable in making best use of all of the available data in biomedical image segmentation. One particularly simple and effective mechanism to achieve self-supervision is inpainting, the task of predicting arbitrary missing areas based on the rest of an image. In this work, we focus on image inpainting as the self-supervised proxy task, and propose two novel structural changes to further enhance the performance. Our method can be regarded as an efficient addition to self-supervision, where we guide the process of generating images to inpaint by using supervoxel-based masking instead of random masking, and also by focusing on the area to be segmented in the primary task, which we term as the region-of-interest. We postulate that these additions force the network to learn semantics that are more attuned to the primary task, and test our hypotheses on two applications: brain tumour and white matter hyperintensities segmentation. We empirically show that our proposed approach consistently outperforms both supervised CNNs, without any self-supervision, and conventional inpainting-based self-supervision methods on both large and small training set sizes.
Glove Based American Sign Language Interpretation Using Convolutional Neural Network and Data Glass

To live in a society, it is very important to communicate with each other. But this poses a grave problem for people with hearing disabilities. As they can converse using only sign language, it becomes very difficult for others who don't know the sign language to understand them. So, the purpose of this paper is to create an interpreter which can convert american sign language into the Engilsh language. Through convolutional neural network we were able to create such an interpreter which can interpret the american sign language. The Sign Language interpretation system was created using image processing, convolutional neural network and data glass. First, we created a database of 16500 samples (500 samples for each letter/gesture) for the basic sign language letters and gestures. Then using convolutional neural network, we were able to create a model which can distinguish between 33 hand gestures (26 English alphabets and 7 gestures). The images are taken through a raspberry-pi camera and then they are processed and matched against the model by a raspberry pi and the interpreted result is shown through a data glass.
SecurePay: Strengthening Two-Factor Authentication for Arbitrary Transactions

Secure transactions on the Internet often rely on two-factor authentication (2FA) using mobile phones. In most existing schemes, the separation between the factors is weak and a compromised phone may be enough to break 2FA. In this paper, we identify the basic principles for securing any transaction using mobile-based 2FA. In particular, we argue that the <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">computing system</inf> should not only provide <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">isolation</inf> between the two factors, but also the <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">integrity</inf> of the transaction, while involving the user in confirming the <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">authenticity</inf> of the transaction. We show for the first time how these properties can be provided on commodity mobile phones, securing 2FA-protected transactions even when the operating system on the phone is fully compromised. We explore the challenges in the design and implementation of SecurePay, and evaluate the first formally-verified solution that utilizes the ARM TrustZone technology to provide the necessary integrity and authenticity guarantees for mobile-based 2FA. For our evaluation, we integrated SecurePay in ten existing apps, all of which required minimal changes and less than 30 minutes of work. Moreover, if code modifications are not an option, SecurePay can still be used as a secure drop-in replacement for existing (insecure) SMS-based 2FA solutions.
Designing Context-Based Services for Resilient Cyber Physical Production Systems

Dynamicity and complexity of Cyber Physical Production Systems (CPPS) increase their vulnerability and challenge their ability to react to faults. Ensuring resilience of CPPS is even more difficult when on-field workers operating on the machines are engaged in the production line, according to the Human-in-the-Loop paradigm. In this scenario, we present a service-oriented approach to model resilient CPPS. Specifically, we design runtime recovery actions as a set of services. Services are context-based, i.e., they are associated with the steps of the production process as well as with the CPPS involved in the recovery actions. In the context model, different factors that could influence CPPS resilience, namely the functional degradation of one or more parts of the CPPS and changes in the environment in which the production process is executed, are monitored through proper parameters. The approach provides runtime selection of services among the ones associated with the process steps and CPPS involved in the recovery procedure. The values of monitored parameters are used as service inputs, while service outputs are displayed to the operators supervising the CPPS on which recovery actions must be performed, enabling fast and effective resilience also in a Human-In-the-Loop scenario. The approach is validated in a food industry case study.
Analysis of Models for Decentralized and Collaborative AI on Blockchain

Machine learning has recently enabled large advances in artificial intelligence, but these results can be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and maintain them. Published proposals to provide models and data for free for certain tasks include Microsoft Research's Decentralized and Collaborative AI on Blockchain. The framework allows participants to collaboratively build a dataset and use smart contracts to share a continuously updated model on a public blockchain. The initial proposal gave an overview of the framework omitting many details of the models used and the incentive mechanisms in real world scenarios. In this work, we evaluate the use of several models and configurations in order to propose best practices when using the Self-Assessment incentive mechanism so that models can remain accurate and well-intended participants that submit correct data have the chance to profit. We have analyzed simulations for each of three models: Perceptron, Na\"ive Bayes, and a Nearest Centroid Classifier, with three different datasets: predicting a sport with user activity from Endomondo, sentiment analysis on movie reviews from IMDB, and determining if a news article is fake. We compare several factors for each dataset when models are hosted in smart contracts on a public blockchain: their accuracy over time, balances of a good and bad user, and transaction costs (or gas) for deploying, updating, collecting refunds, and collecting rewards. A free and open source implementation for the Ethereum blockchain and simulations written in Python is provided at https://github.com/microsoft/0xDeCA10B. This version has updated gas costs using newer optimizations written after the original publication.
A clustering framework for lexical normalization of Roman Urdu

Roman Urdu is an informal form of the Urdu language written in Roman script, which is widely used in South Asia for online textual content. It lacks standard spelling and hence poses several normalization challenges during automatic language processing. In this article, we present a feature-based clustering framework for the lexical normalization of Roman Urdu corpora, which includes a phonetic algorithm UrduPhone, a string matching component, a feature-based similarity function, and a clustering algorithm Lex-Var. UrduPhone encodes Roman Urdu strings to their pronunciation-based representations. The string matching component handles character-level variations that occur when writing Urdu using Roman script.
Revealing Secrets in SPARQL Session Level

Based on Semantic Web technologies, knowledge graphs help users to discover information of interest by using live SPARQL services. Answer-seekers often examine intermediate results iteratively and modify SPARQL queries repeatedly in a search session. In this context, understanding user behaviors is critical for effective intention prediction and query optimization. However, these behaviors have not yet been researched systematically at the SPARQL session level. This paper reveals secrets of session-level user search behaviors by conducting a comprehensive investigation over massive real-world SPARQL query logs. In particular, we thoroughly assess query changes made by users w.r.t. structural and data-driven features of SPARQL queries. To illustrate the potentiality of our findings, we employ an application example of how to use our findings, which might be valuable to devise efficient SPARQL caching, auto-completion, query suggestion, approximation, and relaxation techniques in the future (Code and data are available at: https://github.com/seu-kse/SparqlSession .).
Two-dimensional chiral-waveguide quantum electrodynamics: Long-range qubit correlations and flat-band dark polaritons

We consider a two-dimensional extension of the one-dimensional waveguide quantum electrodynamics and investigate the nature of linear excitations in two-dimensional arrays of qubits coupled to networks of chiral waveguides. We show that the combined effects of chirality and long-range photon-mediated qubit-qubit interactions lead to the emergence of the two-dimensional flat bands in the polaritonic spectrum, corresponding to slow strongly correlated light.
Multiple-scale perturbation method on integro-differential equations: Application to continuous-time quantum walks on regular networks in non-Markovian reservoirs

The authors introduce a new multiple-scale perturbation method that works on integro-differential equations. The method is particularly useful for studying general non-Markovian effects. They propose an open-system model of a continuous-time quantum walk on different network topologies and show that non-Markovianity can, indeed, speed up the quantum walk.
RestKB: A Library of Commonsense Knowledge about Dining at a Restaurant

This paper presents a library of commonsense knowledge, RESTKB, developed in modular action language ALM and containing background knowledge relevant to the understanding of restaurant narratives, including stories that describe exceptions to the normal unfolding of such scenarios.We highlight features that KR languages must possess in order to be able to express pertinent knowledge, and expand action language ALM as needed.We show that encoding the knowledge base in ALM facilitates its piecewise construction and testing, and improves the generality and quality of the captured information, in comparison to an initial ASP encoding.The knowledge base was used in a system for reasoning about stereotypical activities, evaluated on the restaurant domain. IntroductionIn this paper, we introduce a library of general commonsense knowledge related to the restaurant domain, RESTKB, which is a result of our research on understanding narratives about stereotypical activities, including stories that describe exceptional scenarios.In previous work [33,22,32], we introduced a methodology for reasoning about such stories and demonstrated the advantages of using ASP [16] in implementing it.The knowledge in RESTKB was also originally written in ASP, but this encoding soon became difficult to manage and test.The next alternative was an action language, i.e., a highlevel logic programming language dedicated to the representation of knowledge about actions and their effects [17].Particularly appealing was a modular action language, ALM [21], that allows for the structuring and reuse of knowledge, and facilitates the gradual development and testing of knowledge modules.ALM's semantics is described in terms of a translation into ASP{f} [2], an extension of ASP with non-Herbrand functions, which means that an ALM knowledge base can be seamlessly integrated into our ASP-based methodology for reasoning about stories involving stereotypical activities.However, ALM was not immediately suitable for our purpose, as its syntax did not provide means for specifying non-deterministic effects of actions, which was necessary for reasoning about exceptional scenarios that require diagnosis.To better manage the growing amount of encoded knowledge, we decided to expand ALM with the missing feature, which was facilitated by the close connection with ASP{f}.Background knowledge bases are important for solving a variety of reasoning tasks, but they are even more important when dealing with narratives about stereotypical activities (e.g., dining at a restaurant, going to the doctor, etc.).Stories about stereotypical activities omit many more details about actions that take place, compared to other texts.These details are assumed to be filled in by the reader, based on a common understanding of how such activities should unfold.For instance, in Example 1 below, the fact that Nicole paid for her meal is not explicitly mentioned but assumed to have happened.
Effects Without Monads: Non-determinism – Back to the Meta Language

We reflect on programming with complicated effects, recalling an undeservingly forgotten alternative to monadic programming and checking to see how well it can actually work in modern functional languages.We adopt and argue the position of factoring an effectful program into a first-order effectful DSL with a rich, higher-order 'macro' system.Not all programs can be thus factored.Although the approach is not general-purpose, it does admit interesting programs.The effectful DSL is likewise rather problem-specific and lacks general-purpose monadic composition, or even functions.On the upside, it expresses the problem elegantly, is simple to implement and reason about, and lends itself to non-standard interpretations such as code generation (compilation) and abstract interpretation.A specialized DSL is liable to be frequently extended; the experience with the tagless-final style of DSL embedding shown that the DSL evolution can be made painless, with the maximum code reuse.We illustrate the argument on a simple but representative example of a rather complicated effect -non-determinism, including committed choice.Unexpectedly, it turns out we can write interesting non-deterministic programs in an ML-like language just as naturally and elegantly as in the functional-logic language Curry -and not only run them but also statically analyze, optimize and compile.The richness of the Meta Language does, in reality, compensate for the simplicity of the effectful DSL.The key idea goes back to the origins of ML as the Meta Language for the Edinburgh LCF theorem prover.Instead of using ML to build theorems, we now build (DSL) programs.