Reviewerly/exHarmony huggingface dataset:
The Capacity of Private Information Retrieval From Uncoded Storage Constrained Databases

Private information retrieval (PIR) allows a user to retrieve a desired message from a set of databases without revealing the identity of the desired message. The replicated database scenario, where N databases store each of the K messages was considered by Sun and Jafar, and the optimal download cost was characterized as (1+ 1/N+1/N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + ⋯ + 1/N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-1</sup> ). In this work, we consider the problem of PIR from uncoded storage constrained databases. Each database has a storage capacity of μKL bits, where L is the size of each message in bits, and μ ∈ [1/N, 1] is the normalized storage. The novel aspect of this work is to characterize the optimum download cost of PIR from uncoded storage constrained databases for any “normalized storage” value in the range μ ∈ [1/N, 1]. In particular, for any (N, K), we show that the optimal trade-off between normalized storage, μ, and the download cost, D(μ), is a piece-wise linear function given by the lower convex hull of the N pairs (t/N,(1 + 1/t + 1/t <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + ⋯ + 1/t <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-1</sup> ))for t=1,2,...,N. To prove this result, we first present a storage constrained PIR scheme for any (N, K). Next, we obtain a general lower bound on the download cost for PIR, which is valid for any arbitrary storage architecture. The uncoded storage assumption is then applied which allows us to express the lower bound as a linear program (LP). Finally, we solve the LP to obtain tight lower bounds on the download cost for different regimes of storage, which match the proposed storage constrained PIR scheme.
Exploiting Code Knowledge Graph for Bug Localization via Bi-directional Attention

Bug localization automatic localize relevant source files given a natural language description of bug within a software project. For a large project containing hundreds and thousands of source files, developers need cost lots of time to understand bug reports generated by quality assurance and localize these buggy source files. Traditional methods are heavily depending on the information retrieval technologies which rank the similarity between source files and bug reports in lexical level. Recently, deep learning based models are used to extract semantic information of code with significant improvements for bug localization. However, programming language is a highly structural and logical language, which contains various relations within and cross source files. Thus, we propose KGBugLocator to utilize knowledge graph embeddings to extract these interrelations of code, and a keywords supervised bi-directional attention mechanism regularize model with interactive information between source files and bug reports. With extensive experiments on four different projects, we prove our model can reach the new the-state-of-art(SOTA) for bug localization.
A Secure Data Aggregation Strategy in Edge Computing and Blockchain-Empowered Internet of Things

With the rapid development of the Internet of Things (IoT), more and more data are generated by smart devices to support various edge services. Since these data may contain sensitive information, security and privacy of data aggregation has become a key challenge in IoT. To tackle this problem, a blockchain-based secure data aggregation strategy, namely (BSDA), is proposed for edge computing empowered IoT. Specifically, in order to restrict task receivers [i.e., mobile data collectors (MDCs)] to search and accept tasks, the block header is intergraded with a security label including task security level (SL) and task completion requirement. Accordingly, new block generation rules are developed to improve system performance in throughput and transaction latency. Furthermore, BSDA decomposes both sensitive tasks and task receivers into groups against privacy disclosure. On the other hand, a deep reinforcement learning method, the improved self-adaptive double bootstrapped deep deterministic policy gradient (IDDPG), is developed to design energy-efficient MDC routes under the constrains that the SLs of MDCs should be higher than the SLs of data aggregation tasks. Simulation results indicate that 1) as a privacy-preserving strategy, BSDA obtains high throughput and low transaction latency and 2) BSDA outperforms certain contemporary strategies in aggregation ratio and energy cost.
Weakly Supervised Histopathology Image Segmentation With Sparse Point Annotations

Digital histopathology image segmentation can facilitate computer-assisted cancer diagnostics. Given the difficulty of obtaining manual annotations, weak supervision is more suitable for the task than full supervision is. However, most weakly supervised models are not ideal for handling severe intra-class heterogeneity and inter-class homogeneity in histopathology images. Therefore, we propose a novel end-to-end weakly supervised learning framework named WESUP. With only sparse point annotations, it performs accurate segmentation and exhibits good generalizability. The training phase comprises two major parts, hierarchical feature representation and deep dynamic label propagation. The former uses superpixels to capture local details and global context from the convolutional feature maps obtained via transfer learning. The latter recognizes the manifold structure of the hierarchical features and identifies potential targets with the sparse annotations. Moreover, these two parts are trained jointly to improve the performance of the whole framework. To further boost test performance, pixel-wise inference is adopted for finer prediction. As demonstrated by experimental results, WESUP is able to largely resolve the confusion between histological foreground and background. It outperforms several state-of-the-art weakly supervised methods on a variety of histopathology datasets with minimal annotation efforts. Trained by very sparse point annotations, WESUP can even beat an advanced fully supervised segmentation network.
Multi-turn intent determination and slot filling with neural networks and regular expressions

Intent determination and slot filling are two prominent research areas related to natural language understanding (NLU). In a multi-turn NLU system, contextual information from dialogue history is exploited to mitigate the ambiguity of user utterance. State-of-the-art models employ memory networks to encode dialogue context, which is used by neural networks for determining user intent and associated slots. However, these methods rely on a large amount of labelled data, whereas we often have limited labelled data. To address this problem, we propose a multi-task learning model based on neural networks and regular expressions (REs), to jointly perform intent determination and slot filling tasks. The proposed model integrates neural networks with REs to encode domain knowledge and handle cases with a limited amount of labelled data in an end-to-end trainable manner. More specifically, the model employs a pre-trained BERT model to obtain contextual word representations of user utterances. These representations are utilized by a memory network to encode multi-turn information which is shared by the tasks. Furthermore, the convolutional neural network (CNN) and the recurrent neural network (RNN) are applied to contextual word representations and dialogue context for intent determination and slot filling tasks, respectively. These neural networks are then combined with REs which encode domain knowledge about a particular intent or slot value. Finally, the two neural networks are trained simultaneously by minimizing the joint loss. Extensive experiments on Key-Value Retrieval and Frames datasets show that the proposed model outperforms baseline methods in both tasks while requiring modest human effort.
Deconvolutional neural network for image super-resolution

This study builds a fully deconvolutional neural network (FDNN) and addresses the problem of single image super-resolution (SISR) by using the FDNN. Although SISR using deep neural networks has been a major research focus, the problem of reconstructing a high resolution (HR) image with an FDNN has received little attention. A few recent approaches toward SISR are to embed deconvolution operations into multilayer feedforward neural networks. This paper constructs a deep FDNN for SISR that possesses two remarkable advantages compared to existing SISR approaches. The first improves the network performance without increasing the depth of the network or embedding complex structures. The second replaces all convolution operations with deconvolution operations to implement an effective reconstruction. That is, the proposed FDNN only contains deconvolution layers and learns an end-to-end mapping from low resolution (LR) to HR images. Furthermore, to avoid the oversmoothness of the mean squared error loss, the trained image is treated as a probability distribution, and the Kullback–Leibler divergence is introduced into the final loss function to achieve enhanced recovery. Although the proposed FDNN only has 10 layers, it is successfully evaluated through extensive experiments. Compared with other state-of-the-art methods and deep convolution neural networks with 20 or 30 layers, the proposed FDNN achieves better performance for SISR.
Leveraging Deep Learning in Global 24/7 Real-Time Earthquake Monitoring at the National Earthquake Information Center

Abstract Machine-learning algorithms continue to show promise in their application to seismic processing. The U.S. Geological Survey National Earthquake Information Center (NEIC) is exploring the adoption of these tools to aid in simultaneous local, regional, and global real-time earthquake monitoring. As a first step, we describe a simple framework to incorporate deep-learning tools into NEIC operations. Automatic seismic arrival detections made from standard picking methods (e.g., short-term average/long-term average [STA/LTA]) are fed to trained neural network models to improve automatic seismic-arrival (pick) timing and estimate seismic-arrival phase type and source-station distances. These additional data are used to improve the capabilities of the NEIC associator. We compile a dataset of 1.3 million seismic-phase arrivals that represent a globally distributed set of source-station paths covering a range of phase types, magnitudes, and source distances. We train three separate convolutional neural network models to predict arrival time onset, phase type, and distance. We validate the performance of the trained networks on a subset of our existing dataset and further extend validation by exploring the model performance when applied to NEIC automatic pick data feeds. We show that the information provided by these models can be useful in downstream event processing, specifically in seismic-phase association, resulting in reduced false associations and improved location estimates.
Improved fuzzy‐assisted hierarchical neural network system for design of computer‐aided English teaching system

Abstract The world has enhanced the development of education with the rapid development of information technology in modern society to facilitate the information process. Educational technology has gained experience through the advancement of digital information network technology, from modern teaching technology to computer technology and communication technology. In this paper, an English learning system has been developed based on an improved fuzzy‐assisted hierarchical neural network system (IF‐HNNS). Improving the standard of English education is particularly important in improving intelligent education, especially the efficiency of classroom teaching. The development of an interactive educational system has changed the conventional way of teaching and facilitated individual independent learning. This English teaching system is a network updating computer‐aided learning platform. The computer‐aided system has the following advantages: (i) Easy‐to‐actualize computer‐aided learning media will provide more teaching content and teach satisfaction. (ii) The interaction of computer‐aided learning will raise interest in learning. (iii) Computer helped learners are rich in educational resources and are easy to set teaching goals. (iv) It will improve the monitoring of the education and teaching cycle using a computer‐aided learning with human‐computer interaction method.
Error rates and resource overheads of repetition cat qubits

We estimate and analyze the error rates and the resource overheads of the repetition cat qubit approach to universal and fault-tolerant quantum computation. The cat qubits stabilized by two-photon dissipation exhibit an extremely biased noise where the bit-flip error rate is exponentially suppressed with the mean number of photons. In a recent work, we suggested that the remaining phase-flip error channel could be suppressed using a 1D repetition code. Indeed, using only bias-preserving gates on the cat-qubits, it is possible to build a universal set of fault-tolerant logical gates at the level of the repetition cat qubit. In this paper, we perform Monte-Carlo simulations of all the circuits implementing the protected logical gates, using a circuit-level error model. Furthermore, we analyze two different approaches to implement a fault-tolerant Toffoli gate on repetition cat qubits. These numerical simulations indicate that very low logical error rates could be achieved with a reasonable resource overhead, and with parameters that are within the reach of near-term circuit QED experiments.
Speech-Driven Facial Animation Using Cascaded GANs for Learning of Motion and Texture

Speech-driven facial animation methods should produce accurate and realistic lip motions with natural expressions and realistic texture portraying target-specific facial characteristics. Moreover, the methods should also be adaptable to any unknown faces and speech quickly during inference. Current state-of-the-art methods fail to generate realistic animation from any speech on unknown faces due to their poor generalization over different facial characteristics, languages, and accents. Some of these failures can be attributed to the end-to-end learning of the complex relationship between the multiple modalities of speech and the video. In this paper, we propose a novel strategy where we partition the problem and learn the motion and texture separately. Firstly, we train a GAN network to learn the lip motion in a canonical landmark using DeepSpeech features and induce eye-blinks before transferring the motion to the person-specific face. Next, we use another GAN based texture generator network to generate high fidelity face corresponding to the motion on person-specific landmark. We use meta-learning to make the texture generator GAN more flexible to adapt to the unknown subject’s traits of the face during inference. Our method gives significantly improved facial animation than the state-of-the-art methods and generalizes well across the different datasets, different languages, and accents, and also works reliably well in presence of noises in the speech.
Experimental realization of continuous-time quantum walks on directed graphs and their application in PageRank

PageRank is an algorithm used by Google Search to rank web pages in their search engine results. An important step for quantum networks is to quantize the classical protocol as quantum mechanics provides computational resources that can be used to outperform classical algorithms. In this paper, we experimentally realize continuous-time quantum walks for directed graphs with non-Hermitian adjacency matrices by using linear optical circuits and single photons. We find that the node classical centrality in a directed graph is correlated with the maximum node probability resulting from a continuous-time quantum walk and then demonstrate PageRank. Our work opens up an avenue of applications of quantum information in real-life tasks.
ENSURE: Efficient Scheduling and Autonomous Resource Management in Serverless Environments

An imminent challenge in the serverless computing landscape is the escalating cost of infrastructure needed to handle the growing traffic at scale. This work presents ENSURE, a function-level scheduler and autonomous resource manager designed to minimize provider resource costs while meeting customer performance requirements. ENSURE works by classifying incoming function requests at runtime and carefully regulating the resource usage of colocated functions on each invoker. Beyond a single invoker, ENSURE elastically scales capacity, using concepts from operations research, in response to varying workload traffic to prevent cold starts. Finally, ENSURE schedules requests by concentrating load on an adequate number of invokers to encourage reuse of active hosts (thus further avoiding cold starts) and allow unneeded capacity to provably and gracefully time out. We implement ENSURE on Apache OpenWhisk and show that, across several serverless applications and compared to existing baselines, ENSURE significantly improves resource efficiency, by as much as 52%, while providing acceptable application latency.
Discrete Deep Reinforcement Learning for Mapless Navigation

Our goal is to investigate whether discrete state space algorithms are a viable solution to continuous alternatives for mapless navigation. To this end we present an approach based on Double Deep Q-Network and employ parallel asynchronous training and a multi-batch Priority Experience Replay to reduce the training time. Experiments show that our method trains faster and outperforms both the continuous Deep Deterministic Policy Gradient and Proximal Policy Optimization algorithms. Moreover, we train the models in a custom environment built on the recent Unity learning toolkit and show that they can be exported on the TurtleBot3 simulator and to the real robot without further training. Overall our optimized method is 40% faster compared to the original discrete algorithm. This setting significantly reduces the training times with respect to the continuous algorithms, maintaining a similar level of success rate hence being a viable alternative for mapless navigation.
OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning

The recent breakthroughs in computer vision have benefited from the availability of large representative datasets (e.g. ImageNet and COCO) for training. Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non-varying distributions for a fixed set of tasks. Fully retraining models each time a new task becomes available is infeasible due to computational, storage and sometimes privacy issues, while naïve incremental strategies have been shown to suffer from catastrophic forgetting. It is crucial for the robots to operate continuously under open-set and detrimental conditions with adaptive visual perceptual systems, where lifelong learning is a fundamental capability. However, very few datasets and benchmarks are available to evaluate and compare emerging techniques. To fill this gap, we provide a new lifelong robotic vision dataset ("OpenLORIS-Object") collected via RGB-D cameras. The dataset embeds the challenges faced by a robot in the real-life application and provides new benchmarks for validating lifelong object recognition algorithms. Moreover, we have provided a testbed of 9 state-of-the-art lifelong learning algorithms. Each of them involves 48 tasks with 4 evaluation metrics over the OpenLORIS-Object dataset. The results demonstrate that the object recognition task in the ever-changing difficulty environments is far from being solved and the bottlenecks are at the forward/backward transfer designs. Our dataset and benchmark are publicly available at https://lifelong-robotic-vision.github.io/dataset/object.
Beyond the Code

Self-admitted technical debt (SATD) is a particular case of Technical Debt (TD) where developers explicitly acknowledge their sub-optimal implementation decisions. Previous studies mine SATD by searching for specific TD-related terms in source code comments. By contrast, in this paper we argue that developers can admit technical debt by other means, e.g., by creating issues in tracking systems and labelling them as referring to TD. We refer to this type of SATD as issue-based SATD or just SATD-I. We study a sample of 286 SATD-I instances collected from five open source projects, including Microsoft Visual Studio and GitLab Community Edition. We show that only 29% of the studied SATD-I instances can be tracked to source code comments. We also show that SATD-I issues take more time to be closed, compared to other issues, although they are not more complex in terms of code churn. Besides, in 45% of the studied issues TD was introduced to ship earlier, and in almost 60% it refers to DESIGN flaws. Finally, we report that most developers pay SATD-I to reduce its costs or interests (66%). Our findings suggest that there is space for designing novel tools to support technical debt management, particularly tools that encourage developers to create and label issues containing TD concerns.
Depth Estimation From Single Image And Semantic Prior

The multi-modality sensor fusion technique is an active research area in scene understating. In this work, we explore the RGB image and semantic-map fusion methods for depth estimation. The LiDARs, Kinect, and TOF depth sensors are unable to predict the depth-map at illuminate and monotonous pattern surface. In this paper, we propose a semantic-to-depth generative adversarial network (S2D-GAN) for depth estimation from RGB image and its semantic-map. In the first stage, the proposed S2D-GAN estimates the coarse level depthmap using a semantic-to-coarse-depth generative adversarial network (S2CD-GAN) while the second stage estimates the fine-level depth-map using a cascaded multi-scale spatial pooling network. The experimental analysis of the proposed S2D-GAN performed on NYU-Depth-V2 dataset shows that the proposed S2D-GAN gives outstanding result over existing single image depth estimation and RGB with sparse samples methods. The proposed S2D-GAN also gives efficient results on the real-world indoor and outdoor image depth estimation.
Importance-driven deep learning system testing

Deep Learning (DL) systems are key enablers for engineering intelligent applications due to their ability to solve complex tasks such as image recognition and machine translation. Nevertheless, using DL systems in safety- and security-critical applications requires to provide testing evidence for their dependable operation. Recent research in this direction focuses on adapting testing criteria from traditional software engineering as a means of increasing confidence for their correct behaviour. However, they are inadequate in capturing the intrinsic properties exhibited by these systems. We bridge this gap by introducing DeepImportance, a systematic testing methodology accompanied by an Importance-Driven (IDC) test adequacy criterion for DL systems. Applying IDC enables to establish a layer-wise functional understanding of the importance of DL system components and use this information to assess the semantic diversity of a test set. Our empirical evaluation on several DL systems, across multiple DL datasets and with state-of-the-art adversarial generation techniques demonstrates the usefulness and effectiveness of DeepImportance and its ability to support the engineering of more robust DL systems.