Reviewerly/exHarmony huggingface dataset:
Attention and Memory-Augmented Networks for Dual-View Sequential Learning

In recent years, sequential learning has been of great interest due to the advance of deep learning with applications in time-series forecasting, natural language processing, and speech recognition. Recurrent neural networks (RNNs) have achieved superior performance in single-view and synchronous multi-view sequential learning comparing to traditional machine learning models. However, the method remains less explored in asynchronous multi-view sequential learning, and the unalignment nature of multiple sequences poses a great challenge to learn the inter-view interactions. We develop an AMANet (Attention and Memory-Augmented Networks) architecture by integrating both attention and memory to solve asynchronous multi-view learning problem in general, and we focus on experiments in dual-view sequences in this paper. Self-attention and inter-attention are employed to capture intra-view interaction and inter-view interaction, respectively. History attention memory is designed to store the historical information of a specific object, which serves as local knowledge storage. Dynamic external memory is used to store global knowledge for each view. We evaluate our model in three tasks: medication recommendation from a patient's medical records, diagnosis-related group (DRG) classification from a hospital record, and invoice fraud detection through a company's taxation behaviors. The results demonstrate that our model outperforms all baselines and other state-of-the-art models in all tasks. Moreover, the ablation study of our model indicates that the inter-attention mechanism plays a key role in the model and it can boost the predictive power by effectively capturing the inter-view interactions from asynchronous views.
Session Types with Arithmetic Refinements

Session types statically prescribe bidirectional communication protocols for message-passing processes. However, simple session types cannot specify properties beyond the type of exchanged messages. In this paper we extend the type system by using index refinements from linear arithmetic capturing intrinsic attributes of data structures and algorithms. We show that, despite the decidability of Presburger arithmetic, type equality and therefore also subtyping and type checking are now undecidable, which stands in contrast to analogous dependent refinement type systems from functional languages. We also present a practical, but incomplete algorithm for type equality, which we have used in our implementation of Rast, a concurrent session-typed language with arithmetic index refinements as well as ergometric and temporal types. Moreover, if necessary, the programmer can propose additional type bisimulations that are smoothly integrated into the type equality algorithm.
<i>Pytheas</i>

CSV is a popular Open Data format widely used in a variety of domains for its simplicity and effectiveness in storing and disseminating data. Unfortunately, data published in this format often does not conform to strict specifications, making automated data extraction from CSV files a painful task. While table discovery from HTML pages or spreadsheets has been studied extensively, extracting tables from CSV files still poses a considerable challenge due to their loosely defined format and limited embedded metadata. In this work we lay out the challenges of discovering tables in CSV files, and propose Pytheas: a principled method for automatically classifying lines in a CSV file and discovering tables within it based on the intuition that tables maintain a coherency of values in each column. We evaluate our methods over two manually annotated data sets: 2000 CSV files sampled from four Canadian Open Data portals, and 2500 additional files sampled from Canadian, US, UK and Australian portals. Our comparison to state-of-the-art approaches shows that Pytheas is able to successfully discover tables with precision and recall of over 95.9% and 95.7% respectively, while current approaches achieve around 89.6% precision and 81.3% recall. Furthermore, Pytheas's accuracy for correctly classifying all lines per CSV file is 95.6%, versus a maximum of 86.9% for compared approaches. Pytheas generalizes well to new data, with a table discovery F-measure above 95% even when trained on Canadian data and applied to data from different countries. Finally, we introduce a confidence measure for table discovery and demonstrate its value for accurately identifying potential errors.
Foreground Objects Detection Using a Fully Convolutional Network With a Background Model Image and Multiple Original Images

Visual surveillance aims to reliably extract foreground objects. Traditional algorithms usually use a background model image which is generated through the probabilistic modeling of changes over time and space. They detect foreground objects by comparing a background model image with a current image. Hard shadows, illumination changes, camouflage, camera jitter, and ghost object motion make the robust detection of foreground objects difficult in visual surveillance. Recently, various methods based on deep learning have been applied to visual surveillance. It has been shown that deep learning approaches can stably extract salient features, and they give a superior result compared to traditional algorithms. However, they show a good performance only for scenes that are similar to a scene used in training. Without retraining on a new scene, they give a worse result compared to traditional algorithms. In this paper, we propose a stable foreground object detection algorithm through the integration of a background model image used in traditional methods and deep learning methods. A background model image generated by SuBSENSE and multiple images are used as the input of a fully convolutional network. Also, it is shown that it is possible to improve a generalization power by training the proposed network using diverse scenes from an open dataset. We show that the proposed algorithm can have a superior result compared to deep learning-based and traditional algorithms in a new scene without retraining the network. The performance of the proposed algorithm is evaluated using various datasets such as the CDnet 2014, SBI, LASIESTA, and our own datasets. The proposed algorithm shows improvement of 17.5%, 8.9%, and 4.3%, respectively, in FM score compared to three deep learning-based algorithms.
A Multitask Cascading CNN with MultiScale Infrared Optical Flow Feature Fusion-Based Abnormal Crowd Behavior Monitoring UAV

Visual-based object detection and understanding is an important problem in computer vision and signal processing. Due to their advantages of high mobility and easy deployment, unmanned aerial vehicles (UAV) have become a flexible monitoring platform in recent years. However, visible-light-based methods are often greatly influenced by the environment. As a result, a single type of feature derived from aerial monitoring videos is often insufficient to characterize variations among different abnormal crowd behaviors. To address this, we propose combining two types of features to better represent behavior, namely, multitask cascading CNN (MC-CNN) and multiscale infrared optical flow (MIR-OF), capturing both crowd density and average speed and the appearances of the crowd behaviors, respectively. First, an infrared (IR) camera and Nvidia Jetson TX1 were chosen as an infrared vision system. Since there are no published infrared-based aerial abnormal-behavior datasets, we provide a new infrared aerial dataset named the IR-flying dataset, which includes sample pictures and videos in different scenes of public areas. Second, MC-CNN was used to estimate the crowd density. Third, MIR-OF was designed to characterize the average speed of crowd. Finally, considering two typical abnormal crowd behaviors of crowd aggregating and crowd escaping, the experimental results show that the monitoring UAV system can detect abnormal crowd behaviors in public areas effectively.
A dynamic programming approach for distributing quantum circuits by bipartite graphs

There are many challenges involved in building near-term large-scale quantum computers. Some of these challenges can be overcome by partitioning a quantum circuit into smaller parts and allowing each part to be executed on a smaller quantum unit. This approach is known as distributed quantum computation. In this study, a dynamic programming algorithm is proposed to minimize the number of communications in a distributed quantum circuit. This algorithm consists of two steps: first, the quantum circuit is converted into a bipartite graph model, and then a dynamic programming approach is proposed to partition the model into low-capacity quantum circuits. The proposed approach is evaluated on some benchmark quantum circuits, and a remarkable reduction in the number of required teleportations is obtained.
Deep-Learning Assisted High-Resolution Binocular Stereo Depth Reconstruction

This work presents dense stereo reconstruction using high-resolution images for infrastructure inspections. The state-of-the-art stereo reconstruction methods, both learning and non-learning ones, consume too much computational resource on high-resolution data. Recent learning-based methods achieve top ranks on most benchmarks. However, they suffer from the generalization issue due to lack of task-specific training data. We propose to use a less resource demanding non-learning method, guided by a learning-based model, to handle high-resolution images and achieve accurate stereo reconstruction. The deep-learning model produces an initial disparity prediction with uncertainty for each pixel of the down-sampled stereo image pair. The uncertainty serves as a self-measurement of its generalization ability and the perpixel searching range around the initially predicted disparity. The downstream process performs a modified version of the Semi-Global Block Matching method with the up-sampled perpixel searching range. The proposed deep-learning assisted method is evaluated on the Middlebury dataset and high-resolution stereo images collected by our customized binocular stereo camera. The combination of learning and non-learning methods achieves better performance on 12 out of 15 cases of the Middlebury dataset. In our infrastructure inspection experiments, the average 3D reconstruction error is less than 0.004m.
Robust UAV Thermal Infrared Remote Sensing Images Stitching Via Overlap-Prior-Based Global Similarity Prior Model

The main problem of stitching unmanned aerial vehicle (UAV) thermal infrared remote sensing (TIRS) images lies in that the cumulative error caused by the inaccurate alignment of image matching pairs easily leads to deformation and even failure. Few studies in the literature are reported in stitching TIRS images. For the first time, we propose a simple and robust TIRS image stitching method by exploring prior information during flight. First, according to the position and orientation system information and parameter of camera, the overlap ratio of adjacent images is estimated, and the image pairs with high overlap ratio and high matching confidence in different directions are selected. Then, they are added into the alignment term of global similarity prior (GSP) model. Therefore, each image has more matching pairs constraints compared to the traditional construction method of matching pairs, which greatly improves the local registration capability of GSP and then prevents it from converging to the local optimal solution. The proposed method was extensively evaluated on a dataset including to 24 groups of large-scale farmland TIRS images collected in four experimental areas under different crop growth periods and meteorological conditions. Compared with two commercial tools and two representative stitching algorithms, the proposed method significantly improves the local alignment ability and overall stitching quality on both qualitative and quantitative evaluation. Besides, when the front overlap ratio is reduced from 85% to 70%, the proposed method still shows obvious advantages over the related methods and commercial tools, which improves the acquisition efficiency of UAV TIRS images.
Offline Evaluation by Maximum Similarity to an Ideal Ranking

NDCG and similar measures remain standard for the offline evaluation of search, recommendation, question answering and similar systems. These measures require definitions for two or more relevance levels, which human assessors then apply to judge individual documents. Due to this dependence on a definition of relevance, it can be difficult to extend these measures to account for factors beyond relevance. Rather than propose extensions to these measures, we instead propose a radical simplification to replace them. For each query, we define a set of ideal rankings and compute the maximum rank similarity between members of this set and an actual ranking generated by a system. This maximum similarity to an ideal ranking becomes our effectiveness measure, replacing NDCG and similar measures. We propose rank biased overlap (RBO) to compute this rank similarity, since it was specifically created to address the requirements of rank similarity between search results. As examples, we explore ideal rankings that account for document length, diversity, and correctness.
Hybridchain: A Novel Architecture for Confidentiality-Preserving and Performant Permissioned Blockchain Using Trusted Execution Environment

Blockchain is making headlines due to it promises to provide a decentralized, transparent, tamper-resistant, traceable and verifiable historical transaction records that can resist faults of any single node. According to the latest data from State of the Dapps, developers have currently released 3,717 Decentralized Applications (DApps), only three have an average of more than 10,000 daily active users. Most of the real-world DApps exercise little of their potential power. The key reason is that the current permissioned blockchain systems suffer from poor performance and lack of confidentiality. To address this issue, we present Hybridchain, a system that combines blockchain with Trusted Execution Environment (TEE). Hybridchain decouples computation from consensus and adopts hierarchical network to minimize the computational burden and latency of on-chain execution by performing most of the heavy-weight computation off-chain. Hybridchain leverages secure communication protocols to enable each participant to share transaction data in a secure way. To mitigate the small enclave memory restriction of TEE, Hybridchain extends the enclave memory that allows blockchain applications running in TEE to securely store transaction records to the whole key-value storage codes placed outside of TEE. Analysis and experiments of sealed-bid auction show that Hybridchain can support confidentiality-preserving along with high performance.
DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation

In previous works, only parameter weights of ASR models are optimized under fixed-topology architecture. However, the design of successful model architecture has always relied on human experience and intuition. Besides, many hyperparameters related to model architecture need to be manually tuned. Therefore in this paper, we propose an ASR approach with efficient gradient-based architecture search, DARTS-ASR. In order to examine the generalizability of DARTS-ASR, we apply our approach not only on many languages to perform monolingual ASR, but also on a multilingual ASR setting. Following previous works, we conducted experiments on a multilingual dataset, IARPA BABEL. The experiment results show that our approach outperformed the baseline fixed-topology architecture by 10.2% and 10.0% relative reduction on character error rates under monolingual and multilingual ASR settings respectively. Furthermore, we perform some analysis on the searched architectures by DARTS-ASR.
IoT security system with modified Zero Knowledge Proof algorithm for authentication

It has been predicted that more devices will be connected to the internet network along with the development of IoT, and that will increase the complexity in the security system. The most important thing in security systems is the process of encryption and authentication. Various encryption techniques are developed to overcome security problems of the data. One of the methods used for authentication of data is Zero Knowledge Proof. This method works to identify the authenticity of someones statement to proof without showing any knowledge of the statement mentioned. This research will mainly discuss about the security of data transmission system by combining data encryption and data authentication. The proposed data encryption is using Advanced Encryption System and method for authentication of data using the Zero Knowledge Proof. This research will conduct the development methods of authentication Zero Knowledge Proof from previous research and the result was compared with the proposed method based on the simulation results transmission system client and server. Experiments will be conducted using thirty-text data, each of data will be measured on the performance of both encryption and authentication process between the previous method and proposed method. Experimental results show the performance of the proposed method has better speed to process application for security of data transmission systems, with performance to authenticate approximately 5 ms from the client side and server side.
Scalable readout interface for superconducting nanowire single-photon detectors using AQFP and RSFQ logic families

We propose a scalable readout interface for superconducting nanowire single-photon detector (SSPD) arrays, which we call the AQFP/RSFQ interface. This interface is composed of adiabatic quantum-flux-parametron (AQFP) and rapid single-flux-quantum (RSFQ) logic families. The AQFP part reads out the spatial information of an SSPD array via a single cable, and the RSFQ part reads out the temporal information via a single cable. The hybrid interface has high temporal resolution owing to low timing jitter in the operation of the RSFQ part. In addition, the hybrid interface achieves high circuit scalability because of low supply current in the operation of the AQFP part. Therefore, the hybrid interface is suitable for handling many-pixel SSPD arrays. We demonstrate a four-pixel SSPD array using the hybrid interface as proof of concept. The measurement results show that the hybrid interface can read out all of the pixels with a low error rate and low timing jitter.
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy

We provide a detailed asymptotic study of gradient flow trajectories and their implicit optimization bias when minimizing the exponential loss over diagonal linear networks. This is the simplest model displaying a transition between kernel and non-kernel (rich or active) regimes. We show how the transition is controlled by the relationship between the initialization scale and how accurately we minimize the training loss. Our results indicate that some limit behaviors of gradient descent only kick in at ridiculous training accuracies (well beyond $10^{-100}$). Moreover, the implicit bias at reasonable initialization scales and training accuracies is more complex and not captured by these limits.
Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks

Convolutional neural networks excel in a number of computer vision tasks. One of their most crucial architectural elements is the effective receptive field size, which has to be manually set to accommodate a specific task. Standard solutions involve large kernels, down/up-sampling and dilated convolutions. These require testing a variety of dilation and down/up-sampling factors and result in non-compact networks and large number of parameters. We address this issue by proposing a new convolution filter composed of displaced aggregation units (DAU). DAUs learn spatial displacements and adapt the receptive field sizes of individual convolution filters to a given problem, thus reducing the need for hand-crafted modifications. DAUs provide a seamless substitution of convolutional filters in existing state-of-the-art architectures, which we demonstrate on AlexNet, ResNet50, ResNet101, DeepLab and SRN-DeblurNet. The benefits of this design are demonstrated on a variety of computer vision tasks and datasets, such as image classification (ILSVRC 2012), semantic segmentation (PASCAL VOC 2011, Cityscape) and blind image de-blurring (GOPRO). Results show that DAUs efficiently allocate parameters resulting in up to 4 $$\times $$ more compact networks in terms of the number of parameters at similar or better performance.
Incremental Event Detection via Knowledge Consolidation Networks

Conventional approaches to event detection usually require a fixed set of pre-defined event types. Such a requirement is often challenged in real-world applications, as new events continually occur. Due to huge computation cost and storage budge, it is infeasible to store all previous data and re-train the model with all previous data and new data, every time new events arrive. We formulate such challenging scenarios as incremental event detection, which requires a model to learn new classes incrementally without performance degradation on previous classes. However, existing incremental learning methods cannot handle semantic ambiguity and training data imbalance problems between old and new classes in the task of incremental event detection. In this paper, we propose a Knowledge Consolidation Network (KCN) to address the above issues. Specifically, we devise two components, prototype enhanced retrospection and hierarchical distillation, to mitigate the adverse effects of semantic ambiguity and class imbalance, respectively. Experimental results demonstrate the effectiveness of the proposed method, outperforming the state-of-the-art model by 19% and 13.4% of whole F1 score on ACE benchmark and TAC KBP benchmark, respectively.
Calibrating Deep Neural Networks using Focal Loss

Miscalibration - a mismatch between a model's confidence and its correctness - of Deep Neural Networks (DNNs) makes their predictions hard to rely on. Ideally, we want networks to be accurate, calibrated and confident. We show that, as opposed to the standard cross-entropy loss, focal loss [Lin et. al., 2017] allows us to learn models that are already very well calibrated. When combined with temperature scaling, whilst preserving accuracy, it yields state-of-the-art calibrated models. We provide a thorough analysis of the factors causing miscalibration, and use the insights we glean from this to justify the empirically excellent performance of focal loss. To facilitate the use of focal loss in practice, we also provide a principled approach to automatically select the hyperparameter involved in the loss function. We perform extensive experiments on a variety of computer vision and NLP datasets, and with a wide variety of network architectures, and show that our approach achieves state-of-the-art calibration without compromising on accuracy in almost all cases. Code is available at this https URL.