Reviewerly/exHarmony huggingface dataset:
Improving Transformer with Sequential Context Representations for Abstractive Text Summarization

Recent dominant approaches for abstractive text summarization are mainly RNN-based encoder-decoder framework, these methods usually suffer from the poor semantic representations for long sequences. In this paper, we propose a new abstractive summarization model, called RC-Transformer (RCT). The model is not only capable of learning long-term dependencies, but also addresses the inherent shortcoming of Transformer on insensitivity to word order information. We extend the Transformer with an additional RNN-based encoder to capture the sequential context representations. In order to extract salient information effectively, we further construct a convolution module to filter the sequential context with local importance. The experimental results on Gigaword and DUC-2004 datasets show that our proposed model achieves the state-of-the-art performance, even without introducing external information. In addition, our model also owns an advantage in speed over the RNN-based models.
A Minimal Core Calculus for Solidity Contracts

The Ethereum platform supports the decentralized execution of smart contracts, i.e. computer programs that transfer digital assets between users. The most common language used to develop these contracts is Solidity, a Javascript-like language which compiles into EVM bytecode, the language actually executed by Ethereum nodes. While much research has addressed the formalisation of the semantics of EVM bytecode, relatively little attention has been devoted to that of Solidity. In this paper we propose a minimal calculus for Solidity contracts, which extends an imperative core with a single primitive to transfer currency and invoke contract procedures. We build upon this formalisation to give semantics to the Ethereum blockchain. We show our calculus expressive enough to reason about some typical quirks of Solidity, like e.g. re-entrancy.
Multi-objective Neural Architecture Search via Predictive Network Performance Optimization

Neural Architecture Search (NAS) has shown great potentials in finding better neural network designs. Sample-based NAS is the most reliable approach which aims at exploring the search space and evaluating the most promising architectures. However, it is computationally very costly. As a remedy, the one-shot approach has emerged as a popular technique for accelerating NAS using weight-sharing. However, due to the weight-sharing of vastly different networks, the one-shot approach is less reliable than the sample-based approach. In this work, we propose BONAS (Bayesian Optimized Neural Architecture Search), a sample-based NAS framework which is accelerated using weight-sharing to evaluate multiple related architectures simultaneously. Specifically, we apply Graph Convolutional Network predictor as a surrogate model for Bayesian Optimization to select multiple related candidate models in each iteration. We then apply weight-sharing to train multiple candidate models simultaneously. This approach not only accelerates the traditional sample-based approach significantly, but also keeps its reliability. This is because weight-sharing among related architectures are more reliable than those in the one-shot approach. Extensive experiments are conducted to verify the effectiveness of our method over many competing algorithms.
Image Encryption Algorithm Based on Adaptive Wavelet Chaos

Images, as one of the important carriers for information exchange, play an important role in work and daily life. Image encryption technology has received a lot of attention, and varieties of encryption technologies for images have emerged. Early image encryption technologies have shortcomings such as simple algorithm structure, small key space, and poor resistance to plaintext attacks. These algorithms have been unable to meet the needs of information security at this stage. Based on the wavelet algorithm, chaos algorithm, and cyclic encryption algorithm, combined with frequency domain encryption and spatial domain encryption, a digital image encryption algorithm based on adaptive wavelet is proposed in this paper. In terms of frequency, this paper enhances the adaptive ability of the wavelet algorithm by convex optimization-particle swarm optimization (PSO). At the same time, the chaotic algorithm is used to scramble the low-frequency coefficients. In the aspect of airspace, this paper uses the block-encryption adaptive encryption algorithm to rescramble the wavelet-reconstructed image. And the SHA-1 algorithm is introduced into the plaintext image generation key sequence as a cyclic index value for cyclic encryption. The key composition of the encryption algorithm proposed in this paper is complex, and the idea of a one-time pad is used, which makes the improved algorithm increase the key space and improve the ability to resist the choice of plaintext attack. In each cyclic encryption of the airspace, mutual encryption is performed by image subblocks, and the algorithm adaptability is improved. Through the experimental test of statistical characteristics, key space, and key sensitivity, the encryption performance of the algorithm is verified, and it has strong antiattack and interference ability. It is a relatively secure encryption algorithm.
Deep Gesture Video Generation With Learning on Regions of Interest

Generating videos with semantic meaning, such as gestures in sign language, is a challenging problem. The model should not only learn to generate videos with realistic appearance, but also take notice of crucial details in frames to convey precise information. In this paper, we focus on the problem of generating long-term gesture videos containing precise and complete semantic meanings. We develop a novel architecture to learn the temporal and spatial transforms in regions of interest, i.e., gesticulating hands or face in our case. We adopt a hierarchical approach for generating gesture videos, by first making predictions on future pose configurations, and then using the encoder-decoder architecture to synthesize future frames based on the predicted pose structures. We develop the scheme of action progress in our architecture to represent how far the action has been performed during its expected execution, and to instruct our model to synthesize actions with various paces. Our approach is evaluated on two challenging datasets for the task of gesture video generation. Experimental results show that our method can produce gesture videos with more realistic appearance and precise meaning than the state-of-the-art video generation approaches.
Kinematic-Structure-Preserved Representation for Unsupervised 3D Human Pose Estimation

Estimation of 3D human pose from monocular image has gained considerable attention, as a key step to several human-centric applications. However, generalizability of human pose estimation models developed using supervision on large-scale in-studio datasets remains questionable, as these models often perform unsatisfactorily on unseen in-the-wild environments. Though weakly-supervised models have been proposed to address this shortcoming, performance of such models relies on availability of paired supervision on some related task, such as 2D pose or multi-view image pairs. In contrast, we propose a novel kinematic-structure-preserved unsupervised 3D pose estimation framework, which is not restrained by any paired or unpaired weak supervisions. Our pose estimation framework relies on a minimal set of prior knowledge that defines the underlying kinematic 3D structure, such as skeletal joint connectivity information with bone-length ratios in a fixed canonical scale. The proposed model employs three consecutive differentiable transformations namely forward-kinematics, camera-projection and spatial-map transformation. This design not only acts as a suitable bottleneck stimulating effective pose disentanglement, but also yields interpretable latent pose representations avoiding training of an explicit latent embedding to pose mapper. Furthermore, devoid of unstable adversarial setup, we re-utilize the decoder to formalize an energy-based loss, which enables us to learn from in-the-wild videos, beyond laboratory settings. Comprehensive experiments demonstrate our state-of-the-art unsupervised and weakly-supervised pose estimation performance on both Human3.6M and MPI-INF-3DHP datasets. Qualitative results on unseen environments further establish our superior generalization ability.
Face Super-Resolution Reconstruction Based on Self-Attention Residual Network

Aiming at the problems of the face image super-resolution reconstruction method based on convolutional neural network, such as single feature extraction scale, low utilization rate of features and blurred face images texture, a model combining convolutional neural network with self-attention mechanism is proposed.Firstly, the shallow features of the image are extracted by the cascaded 3 × 3 convolutional kernels, and then self-attention mechanism is combined with the residual blocks in depth residual network to extract the deep detail features of faces.Finally, the extracted features are fused globally by skip connections, which provide more high-frequency details for face reconstruction.Experiments on Helen, CelebA face datasets and real-world images showed that the proposed method could make full use of facial feature information, and its peak signal to noise ratio (PSNR) and structural similarity (SSIM) were both higher than the comparison methods with better subjective visual effects.
User perspectives in the design of interactive everyday objects for sustainable behaviour

Addressing efficient management of energy has become a central objective due to the scarcity of traditional energy sources and global warming. To cope with this overarching issue, some technological solutions such as Smart Grids, Internet of Things or Demand response are proposed. However, the majority of them overlooks the role of human beings in the equation. Moreover, the very nascent body of research combining human and machine intelligence proposes methods, frameworks, and guidelines which vary depending on the application scenario complicating the selection of gold-standards to ensure seamless cooperation between smart devices and people. Hence, the purpose of this paper is to provide a set of design-hypotheses to devise augmented objects that ally with their users to reduce energy consumption. We expect designers, engineers, makers or even hobbyists in the intersection between technology-enablers (through IoT) and behavioural scientists to benefit from them. To this aim, we describe the results of a long-term study in office-based workplaces, where participants were randomly assigned to different experimental conditions (persuasion, dashboard, and automation) to increase their energy-efficient behaviour. Grounded Theory analysis was applied over qualitative data collected during focus group sessions obtaining five themes around a central category. The resulting themes were linked to design-hypotheses for IoT devices which were then tested through the implementation of a new IoT object also conceived for the workplace.
Blockchain Competition Between Miners: A Game Theoretic Perspective

We model the competition over mining resources and over several cryptocurrencies as a non-cooperative game. Leveraging results about congestion games, we establish conditions for the existence of pure Nash equilibria and provide efficient algorithms for finding such equilibria. We account for multiple system models, varying according to the way that mining resources are allocated and shared and according to the granularity at which mining puzzle complexity is adjusted. When constraints on resources are included, the resulting game is a constrained resource allocation game for which we characterize a normalized Nash equilibrium. Under the proposed models, we provide structural properties of the corresponding types of equilibrium, e.g., establishing conditions under which at most two mining infrastructures will be active or under which no miners will have incentives to mine a given cryptocurrency.
Getting There Together: Group Navigation in Distributed Virtual Environments

We analyzed the design space of group navigation tasks in distributed virtual environments and present a framework consisting of techniques to form groups, distribute responsibilities, navigate together, and eventually split up again. To improve joint navigation, our work focused on an extension of the Multi-Ray Jumping technique that allows adjusting the spatial formation of two distributed users as part of the target specification process. The results of a quantitative user study showed that these adjustments lead to significant improvements in joint two-user travel, which is evidenced by more efficient travel sequences and lower task loads imposed on the navigator and the passenger. In a qualitative expert review involving all four stages of group navigation, we confirmed the effective and efficient use of our technique in a more realistic use-case scenario and concluded that remote collaboration benefits from fluent transitions between individual and group navigation.
Adaptively Secure ABE for DFA from k-Lin and More

In this work, we present: Our adaptively secure ABE for DFA relies on a new combinatorial mechanism avoiding the exponential security loss in the number of states when naively combining two recent techniques from CRYPTO'19 and EUROCRYPT'19. This requires us to design a selectively secure ABE for NFA; we give a construction which is sufficient for our purpose and of independent interest. Our ABE for branching programs leverages insights from our ABE for DFA.
Development of Vision Stabilizing System for a Large-Scale Flapping-Wing Robotic Bird

Bio-inspired flapping-wing flying robots have huge application potential in military reconnaissance, environment exploring, disaster rescue and so on. Vision sensors are usually mounted to provide image information of targets. However, the position and attitude of this kind of robot changes drastically during flapping flight, resulting in more challenges for stable imaging. In this paper, a vision stabilizing system (also called stabilizer or camera gimble) is developed to supply stable imaging condition for a large-scale flapping wing robotic bird with 2.3m wingspan and 650g weight. Based on theoretical analyses and experiments, flight characteristics of the robot are first obtained. Then a 3-DOF stabilizer is designed to supply 2-DOF rotation and 1-DOF translation movement. The kinematics and dynamics equations are then derived to analyze the disturbance on the fuselage caused by the stabilizer. Furthermore, the key parameters of the stabilizer are determined according to the constraints on the flight capability. To deal with the interaction between the stabilizer and the fuselage, a coordinated control strategy is proposed and the onboard controller is developed to control the compounded system. Finally, the prototype of the stabilizer is assembled and integrated to a real robotic bird. Practical flight experiments are carried out and the results show that the developed stabilizer has good working performance.
Fractional Distance Regularized Level Set Evolution With Its Application to Image Segmentation

To avoid the irregularities during the level set evolution, a fractional distance regularized variational model is proposed for image segmentation.We first define a fractional distance regularization term which punishes the deviation of the level set function (LSF) and the signed distance function.Since the fractional derivative of the constant value function outside the starting point is nonzero, the fractional gradient modular of the LSF does not approach infinity where the integer order gradient modular is close to 0. This prevents the sharp reverse diffusion of LSF in flat areas and ensures the smooth evolution of LSF.Then, we use the Grünwald-Letnikov (G-L) fractional derivative to derive the discrete forms of the conjugate of fractional derivatives and fractional divergence.To facilitate the calculation of fractional derivatives and their conjugates, we designed their covering templates.Finally, a numerical solution to the minimization of the energy functional is obtained from these discrete forms and covering templates.Numerical experiments of medical images with different modalities show that the model in this paper can well segment weak images and intensity inhomogeneity images.
Multi-information Source HIN for Medical Concept Embedding

Learning low-dimensional representations for medical concepts is of great importance in improving public healthcare applications such as computer-aided diagnosis systems. Existing methods rely on Electronic Health Records (EHR) as their only information source and do not make use of abundant available external medical knowledge, and therefore they ignore the correlations between medical concepts. To address this issue, we propose a novel multi-information source Heterogeneous Information Network (HIN) to model EHR while incorporating external medical knowledge including ICD-9-CM and MeSH for an enriched network schema. Our model is well aware of the structure of EHR as well as the correlations between medical concepts it refers to, and learns semantically reflective medical concept embeddings. In experiments, our model outperforms unsupervised baselines in a variety of medical data mining tasks.
An attention-based row-column encoder-decoder model for text recognition in Japanese historical documents

This paper presents an attention-based row-column encoder-decoder (ARCED) model for recognizing an input image of multiple text lines from Japanese historical documents without explicit segmentation of lines. The recognition system has three main parts: a feature extractor, a row-column encoder, and a decoder. We introduce a row-column BLSTM in the encoder and a residual LSTM network in the decoder. The whole system is trained end-to-end by a standard cross-entropy loss function, requiring only document images and their ground-truth text. We experimentally evaluate the performance of ARCED on the dataset of Japanese historical documents: Kana-PRMU. The results of the experiments show that ARCED outperforms the state-of-the-art recognition methods on the dataset. Furthermore, we demonstrate that the row-column BLSTM in the encoder and the residual LSTM in the decoder improves the performance of the encoder-decoder model for the recognition of Japanese historical document.
A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain

Supply Chain Finance (SCF) is important for improving the effectiveness of supply chain capital operations and reducing the overall management cost of a supply chain. In recent years, with the deep integration of supply chain and Internet, Big Data, Artificial Intelligence, Internet of Things, Blockchain, etc., the efficiency of supply chain financial services can be greatly promoted through building more customized risk pricing models and conducting more rigorous investment decision-making processes. However, with the rapid development of new technologies, the SCF data has been massively increased and new financial fraud behaviors or patterns are becoming more covertly scattered among normal ones. The lack of enough capability to handle the big data volumes and mitigate the financial frauds may lead to huge losses in supply chains. In this article, a distributed approach of big data mining is proposed for financial fraud detection in a supply chain, which implements the distributed deep learning model of Convolutional Neural Network (CNN) on big data infrastructure of Apache Spark and Hadoop to speed up the processing of the large dataset in parallel and reduce the processing time significantly. By training and testing on the continually updated SCF dataset, the approach can intelligently and automatically classify the massive data samples and discover the fraudulent financing behaviors, so as to enhance the financial fraud detection with high precision and recall rates, and reduce the losses of frauds in a supply chain.
Exploiting Personal Characteristics of Debaters for Predicting Persuasiveness

Predicting the persuasiveness of arguments has applications as diverse as writing assistance, essay scoring, and advertising. While clearly relevant to the task, the personal characteristics of an argument’s source and audience have not yet been fully exploited toward automated persuasiveness prediction. In this paper, we model debaters’ prior beliefs, interests, and personality traits based on their previous activity, without dependence on explicit user profiles or questionnaires. Using a dataset of over 60,000 argumentative discussions, comprising more than three million individual posts collected from the subreddit r/ChangeMyView, we demonstrate that our modeling of debater’s characteristics enhances the prediction of argument persuasiveness as well as of debaters’ resistance to persuasion.
Hsi Road: A Hyper Spectral Image Dataset For Road Segmentation

Road segmentation is a challenging task in the field of self-driving research. This paper present a road dataset built by hyper spectral imaging (HSI) cameras instead of the widely-used RGB cameras. HSI image is informative in spectrums and full of potential for natural environment perception. In this article, a first-of-its-kind HSI road segmentation dataset is built with careful annotation in both urban and rural scenes. It contains 3799 scenes with RGB and NIR bands as well as their respective masks. Unlike many existing datasets that provide urban scenes in RGB images only, our dataset expands the sensing spectrum to 28 bands and includes various kinds of road surfaces, such as asphalt, cement, dirt and sand, under rural and natural scenes. We also provide benchmark performances based on the recently popular segmentation algorithms on this dataset. The dataset is released at github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">‡</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">‡</sup> https://github.com/NUST-Machine-Intelligence-Laboratory/hsi_road
Efficient Constant-Round MPC with Identifiable Abort and Public Verifiability

Recent years have seen a tremendous growth in the interest in secure multiparty computation (MPC) and its applications. While much progress has been made concerning its efficiency, many current, state-of-the-art protocols are vulnerable to Denial of Service attacks, where a cheating party may prevent the honest parties from learning the output of the computation, whilst remaining anonymous. The security model of identifiable abort aims to prevent these attacks, by allowing honest parties to agree upon the identity of a cheating party, who can then be excluded in the future. Several existing MPC protocols offer security with identifiable abort against a dishonest majority of corrupted parties. However, all of these protocols have a round complexity that scales linearly with the depth of the circuit (and are therefore unsuitable for use in high latency networks) or use cryptographic primitives or techniques that have a high computational overhead. In this work, we present the first efficient MPC protocols with identifiable abort in the dishonest majority setting, which run in a constant number of rounds and make only black-box use of cryptographic primitives. Our main construction is built from highly efficient primitives in a careful way to achieve identifiability at a low cost. In particular, we avoid the use of public-key operations outside of a setup phase, incurring a relatively low overhead on top of the fastest currently known constant-round MPC protocols based on garbled circuits. Our construction also avoids the use of adaptively secure primitives and heavy zero-knowledge machinery, which was inherent in previous works. In addition, we show how to upgrade our protocol to achieve public verifiability using a public bulletin board, allowing any external party to verify correctness of the computation or identify a cheating party.