Reviewerly/exHarmony huggingface dataset:
INSET: Sentence Infilling with INter-SEntential Transformer

Missing sentence generation (or sentence infilling) fosters a wide range of applications in natural language generation, such as document auto-completion and meeting note expansion.This task asks the model to generate intermediate missing sentences that can syntactically and semantically bridge the surrounding context.Solving the sentence infilling task requires techniques in natural language processing ranging from understanding to discourselevel planning to generation.In this paper, we propose a framework to decouple the challenge and address these three aspects respectively, leveraging the power of existing largescale pre-trained models such as BERT and GPT-2.We empirically demonstrate the effectiveness of our model in learning a sentence representation for generation and further generating a missing sentence that fits the context.* These authors contributed equally to this work.She was extremely happy with our hotel and we had a complimentary buffet.... The food was just phenomenal!I can't recall what everything was called, but we rolled out of there stuffed and happy.My husband had the rib eye dumpling as an appetizer and he said it was the best dumpling he has ever had.Beautiful beachside boutique hotel with great views and modern decoration.My favorite part about this hotel is definitely the
RMM: A Recursive Mental Model for Dialogue Navigation

Language-guided robots must be able to both ask humans questions and understand answers.Much existing work focuses only on the latter.In this paper, we go beyond instruction following and introduce a two-agent task where one agent navigates and asks questions that a second, guiding agent answers.Inspired by theory of mind, we propose the Recursive Mental Model (RMM).The navigating agent models the guiding agent to simulate answers given candidate generated questions.The guiding agent in turn models the navigating agent to simulate navigation steps it would take to generate answers.We use the progress agents make towards the goal as a reinforcement learning reward signal to directly inform not only navigation actions, but also both question and answer generation.We demonstrate that RMM enables better generalization to novel environments.Interlocutor modelling may be a way forward for human-agent dialogue where robots need to both ask and answer questions.
Complaint-driven Training Data Debugging for Query 2.0

As the need for machine learning (ML) increases rapidly across all industry sectors, there is a significant interest among commercial database providers to support "Query 2.0", which integrates model inference into SQL queries. Debugging Query 2.0 is very challenging since an unexpected query result may be caused by the bugs in training data (e.g., wrong labels, corrupted features). In response, we propose Rain, a complaint-driven training data debugging system. Rain allows users to specify complaints over the query's intermediate or final output, and aims to return a minimum set of training examples so that if they were removed, the complaints would be resolved. To the best of our knowledge, we are the first to study this problem. A naive solution requires retraining an exponential number of ML models. We propose two novel heuristic approaches based on influence functions which both require linear retraining steps. We provide an in-depth analytical and empirical analysis of the two approaches and conduct extensive experiments to evaluate their effectiveness using four real-world datasets. Results show that Rain achieves the highest [email protected] among all the baselines while still returns results interactively.
Charting Coordination Needs in Large-Scale Agile Organisations with Boundary Objects and Methodological Islands

Large-scale system development companies are increasingly adopting agile methods. While this adoption may improve lead-times, such companies need to balance two trade-offs: (i) the need to have a uniform, consistent development method on system level with the need for specialised methods for teams in different disciplines (e.g., hardware, software, mechanics, sales, support); (ii) the need for comprehensive documentation on system level with the need to have lightweight documentation enabling iterative and agile work. With specialised methods for teams, isolated teams work within larger ecosystems of plan-driven culture, i.e., teams become agile "islands". At the boundaries, these teams share knowledge which needs to be managed well for a correct system to be developed. While it is useful to support diverse and specialised methods, it is important to understand which islands are repeatedly encountered, the reasons or factors triggering their existence, and how best to handle coordination between them. Based on a multiple case study, this work presents a catalogue of islands and the boundary objects between them. We believe this work will be beneficial to practitioners aiming to understand their ecosystems and researchers addressing communication and coordination challenges in large-scale development.
The interplay between local and non-local master equations: exact and approximated dynamics

Abstract Master equations are a useful tool to describe the evolution of open quantum systems. In order to characterize the mathematical features and the physical origin of the dynamics, it is often useful to consider different kinds of master equations for the same system. Here, we derive an exact connection between the time-local and the integro-differential descriptions, focusing on the class of commutative dynamics. The use of the damping-basis formalism allows us to devise a general procedure to go from one master equation to the other and vice versa, by working with functions of time and their Laplace transforms only. We further analyze the Lindbladian form of the time-local and the integro-differential master equations, where we account for the appearance of different sets of Lindbladian operators. In addition, we investigate a Redfield-like approximation, that transforms the exact integro-differential equation into a time-local one by means of a coarse graining in time. Besides relating the structure of the resulting master equation to those associated with the exact dynamics, we study the effects of the approximation on Markovianity. In particular, we show that, against expectation, the coarse graining in time can possibly introduce memory effects, leading to a violation of a divisibility property of the dynamics.
BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks

Images of handwritten digits are different from natural images as the orientation of a digit, as well as similarity of features of different digits, makes confusion. On the other hand, deep convolutional neural networks are achieving huge success in computer vision problems, especially in image classification. Here, we propose a task-oriented model called Bengali handwritten numeral digit recognition based on densely connected convolutional neural networks (BDNet). BDNet is used to classify (recognize) Bengali handwritten numeral digits. It is end-to-end trained using ISI Bengali handwritten numeral dataset. During training, untraditional data preprocessing and augmentation techniques are used so that the trained model works on a different dataset. The model has achieved the test accuracy of 99.78% (baseline was 99.58%) on the test dataset of ISI Bengali handwritten numerals. So, the BDNet model gives 47.62% error reduction compared to previous state-of-the-art models. Here we have also created a dataset of 1000 images of Bengali handwritten numerals to test the trained model, and it giving promising results. Codes, trained model and our own dataset are available at C :https://github.com/Sufianlab/BDNet.
Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network

Automatic recognition of Urdu handwritten digits and characters, is a challenging task. It has applications in postal address reading, bank's cheque processing, and digitization and preservation of handwritten manuscripts from old ages. While there exists a significant work for automatic recognition of handwritten English characters and other major languages of the world, the work done for Urdu language is extremely insufficient. This paper has two goals. Firstly, we introduce a pioneer dataset for handwritten digits and characters of Urdu, containing samples from more than 900 individuals. Secondly, we report results for automatic recognition of handwritten digits and characters as achieved by using deep auto-encoder network and convolutional neural network. More specifically, we use a two-layer and a three-layer deep autoencoder network and convolutional neural network and evaluate the two frameworks in terms of recognition accuracy. The proposed framework of deep autoencoder can successfully recognize digits and characters with an accuracy of 97% for digits only, 81% for characters only and 82% for both digits and characters simultaneously. In comparison, the framework of convolutional neural network has accuracy of 96.7% for digits only, 86.5% for characters only and 82.7% for both digits and characters simultaneously. These frameworks can serve as baselines for future research on Urdu handwritten text.
Solving the Blind Perspective-n-Point Problem End-to-End with Robust Differentiable Geometric Optimization

Blind Perspective-n-Point (PnP) is the problem of estimating the position and orientation of a camera relative to a scene, given 2D image points and 3D scene points, without prior knowledge of the 2D–3D correspondences. Solving for pose and correspondences simultaneously is extremely challenging since the search space is very large. Fortunately it is a coupled problem: the pose can be found easily given the correspondences and vice versa. Existing approaches assume that noisy correspondences are provided, that a good pose prior is available, or that the problem size is small. We instead propose the first fully end-to-end trainable network for solving the blind PnP problem efficiently and globally, that is, without the need for pose priors. We make use of recent results in differentiating optimization problems to incorporate geometric model fitting into an end-to-end learning framework, including Sinkhorn, RANSAC and PnP algorithms. Our proposed approach significantly outperforms other methods on synthetic and real data.
Combining split and federated architectures for efficiency and privacy in deep learning

Distributed learning systems are increasingly being adopted for a variety of applications as centralized training becomes unfeasible. A few architectures have emerged to divide and conquer the computational load, or to run privacy-aware deep learning models, using split or federated learning. Each architecture has benefits and drawbacks. In this work, we compare the efficiency and privacy performance of two distributed learning architectures that combine the principles of split and federated learning, trying to get the best of both. In particular, our design goal is to reduce the computational power required by each client in Federated Learning and to parallelize Split Learning. We share some initial lessons learned from our implementation that leverages the PySyft and PyGrid libraries.
Real-time virtual mouse system using RGB-D images and fingertip detection

Abstract A real-time fingertip-gesture-based interface is still challenging for human–computer interactions, due to sensor noise, changing light levels, and the complexity of tracking a fingertip across a variety of subjects. Using fingertip tracking as a virtual mouse is a popular method of interacting with computers without a mouse device. In this work, we propose a novel virtual-mouse method using RGB-D images and fingertip detection. The hand region of interest and the center of the palm are first extracted using in-depth skeleton-joint information images from a Microsoft Kinect Sensor version 2, and then converted into a binary image. Then, the contours of the hands are extracted and described by a border-tracing algorithm. The K-cosine algorithm is used to detect the fingertip location, based on the hand-contour coordinates. Finally, the fingertip location is mapped to RGB images to control the mouse cursor based on a virtual screen. The system tracks fingertips in real-time at 30 FPS on a desktop computer using a single CPU and Kinect V2. The experimental results showed a high accuracy level; the system can work well in real-world environments with a single CPU. This fingertip-gesture-based interface allows humans to easily interact with computers by hand.
Application of Lithological Mapping Based on Advanced Hyperspectral Imager (AHSI) Imagery Onboard Gaofen-5 (GF-5) Satellite

The Advanced Hyperspectral Imager (AHSI), carried by the Gaofen-5 (GF-5) satellite, is the first hyperspectral sensor that simultaneously offers broad coverage and a broad spectrum. Meanwhile, deep-learning-based approaches are emerging to manage the growing volume of data produced by satellites. However, the application potential of GF-5 AHSI imagery in lithological mapping using deep-learning-based methods is currently unknown. This paper assessed GF-5 AHSI imagery for lithological mapping in comparison with Shortwave Infrared Airborne Spectrographic Imager (SASI) data. A multi-scale 3D deep convolutional neural network (M3D-DCNN), a hybrid spectral CNN (HybridSN), and a spectral–spatial unified network (SSUN) were selected to verify the applicability and stability of deep-learning-based methods through comparison with support vector machine (SVM) based on six datasets constructed by GF-5 AHSI, Sentinel-2A, and SASI imagery. The results show that all methods produce classification results with accuracy greater than 90% on all datasets, and M3D-DCNN is both more accurate and more stable. It can produce especially encouraging results by just using the short-wave infrared wavelength subset (SWIR bands) of GF-5 AHSI data. Accordingly, GF-5 AHSI imagery could provide impressive results and its SWIR bands have a high signal-to-noise ratio (SNR), which meets the requirements of large-scale and large-area lithological mapping. And M3D-DCNN method is recommended for use in lithological mapping based on GF-5 AHSI hyperspectral data.
Edge AR X5: An Edge-Assisted Multi-User Collaborative Framework for Mobile Web Augmented Reality in 5G and Beyond

Multi-user mobile Augmented Reality (AR) has been successfully used in various fields as a novel visual interaction technology. But current mainstream wearable device-based and app-based solutions are still facing cross-platform, real-time communication, and intensive computing requirements. Mobile Web technology is envisioned to be a promising supporting technology for cross-platform application of mobile AR especially in 5G networks, which provide pervasive communication and computing resources thereby forming a formidable framework for the practical application of multi-user mobile Web AR. However, the problem of how to use these new techniques properly to achieve efficient communication and computing collaboration is obviously paramount in order for multi-user mobile Web AR to be realized in 5G networks. In this article, we propose the first edge-assisted multi-user collaborative framework for mobile Web AR in the 5G era. First, we propose a heuristic mechanism BA-CPP for efficient communication planning, which allows multi-user interaction synchronization to be achieved. Second, we introduce a motion-aware key frame selection mechanism called Mo-KFP to optimize the computational efficiency of the edge system, and simultaneously alleviate the initialization problem by collaborating with nearby mobile devices using the Device-to-Device (D2D) communication technique. Experiments are conducted in a real-world 5G network, and the results demonstrate the superiority of our proposed collaborative framework.
The Slippery Path to Total Presence: How Omnidirectional Virtual Reality Treadmills Influence the Gaming Experience

Researchers, game designers, and consumers place great hopes into the potential benefits of virtual reality (VR) technology on the user experience in digital games. Indeed, initial empirical research has shown that VR technology can improve the gaming experience in a number of ways compared to traditional desktop gaming, for instance by amplifying immersion and flow. However, on the downside, a mismatch between physical locomotion and the movements of the avatar in the virtual world can also lead to unpleasant feelings when using VR technology—often referred to as cybersickness. One solution to this problem may be the implementation of novel passive repositioning systems (also called omnidirectional treadmills) that are designed to allow a continuous, more natural form of locomotion in VR. In the current study, we investigate how VR technology and the use of an omnidirectional treadmill influence the gaming experience. Traditional desktop gaming, VR gaming, and omnidirectional treadmill gaming are compared in a one-factorial experimental design (&lt;em&gt;N&lt;/em&gt; = 203). As expected, we found that VR gaming on the one hand leads to higher levels of flow, presence, and enjoyment, but at the same time also is accompanied by higher levels of cybersickness than traditional desktop gaming. The use of the omnidirectional treadmill did not significantly improve the gaming experience and also did not reduce cybersickness. However, this more physically demanding form of locomotion may make omnidirectional treadmills interesting for exergame designers.
Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild

Scene text recognition is an important task in computer vision. Despite tremendous progress achieved in the past few years, issues such as varying font styles, arbitrary shapes and complex backgrounds etc. have made the problem very challenging. In this work, we propose to improve text recognition from a new perspective by separating the text content from complex backgrounds, thus making the recognition considerably easier and significantly improving recognition accuracy. To this end, we exploit the generative adversarial networks (GANs) for removing backgrounds while retaining the text content . As vanilla GANs are not sufficiently robust to generate sequence-like characters in natural images, we propose an adversarial learning framework for the generation and recognition of multiple characters in an image. The proposed framework consists of an attention-based recognizer and a generative adversarial architecture. Furthermore, to tackle the issue of lacking paired training samples, we design an interactive joint training scheme, which shares attention masks from the recognizer to the discriminator, and enables the discriminator to extract the features of each character for further adversarial training. Benefiting from the character-level adversarial training, our framework requires only unpaired simple data for style supervision. Each target style sample containing only one randomly chosen character can be simply synthesized online during the training. This is significant as the training does not require costly paired samples or character-level annotations. Thus, only the input images and corresponding text labels are needed. In addition to the style normalization of the backgrounds, we refine character patterns to ease the recognition task. A feedback mechanism is proposed to bridge the gap between the discriminator and the recognizer. Therefore, the discriminator can guide the generator according to the confusion of the recognizer, so that the generated patterns are clearer for recognition. Experiments on various benchmarks, including both regular and irregular text, demonstrate that our method significantly reduces the difficulty of recognition. Our framework can be integrated into recent recognition methods to achieve new state-of-the-art recognition accuracy.
Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion

Natural language processing (NLP) task has achieved excellent performance in many fields, including semantic understanding, automatic summarization, image recognition and so on.However, most of the neural network models for NLP extract the text in a fine-grained way, which is not conducive to grasp the meaning of the text from a global perspective.To alleviate the problem, the combination of the traditional statistical method and deep learning model as well as a novel model based on multi model nonlinear fusion are proposed in this paper.The model uses the Jaccard coefficient based on part of speech, Term Frequency-Inverse Document Frequency (TF-IDF) and word2vec-CNN algorithm to measure the similarity of sentences respectively.According to the calculation accuracy of each model, the normalized weight coefficient is obtained and the calculation results are compared.The weighted vector is input into the fully connected neural network to give the final classification results.As a result, the statistical sentence similarity evaluation algorithm reduces the granularity of feature extraction, so it can grasp the sentence features globally.Experimental results show that the matching of sentence similarity calculation method based on multi model nonlinear fusion is 84%, and the F1 value of the model is 75%.
Quantum Circuits with Classical Versus Quantum Control of Causal Order

Quantum supermaps are transformations that map quantum operations to quantum operations. It is known that quantum supermaps which respect a definite, predefined causal order between their input operations correspond to fixed-order quantum circuits. A systematic understanding of the physical interpretation of more general types of quantum supermaps--in particular, those incompatible with a definite causal structure--is however lacking. Here we identify two new types of circuits that naturally generalise the fixed-order case and that likewise correspond to distinct classes of quantum supermaps, which we fully characterise. We first introduce "quantum circuits with classical control of causal order", in which the order of operations is still well-defined, but not necessarily fixed in advance: it can in particular be established dynamically, in a classically-controlled manner, as the circuit is being used. We then consider "quantum circuits with quantum control of causal order", in which the order of operations is controlled coherently. The supermaps described by these classes of circuits are physically realisable, and the latter encompasses all known examples of physically realisable processes with indefinite causal order, including the celebrated "quantum switch". Interestingly, it also contains new examples arising from the combination of dynamical and coherent control of causal order, and we detail explicitly one such process. Nevertheless, we show that quantum circuits with quantum control of causal order can only generate "causal" correlations, compatible with a well-defined causal order. We furthermore extend our considerations to probabilistic circuits that produce also classical outcomes, and we demonstrate by an example how our characterisations allow us to identify new advantages for quantum information processing tasks that could be demonstrated in practice.