Reviewerly/exHarmony huggingface dataset:
TET-GAN: Text Effects Transfer via Stylization and Destylization

Text effects transfer technology automatically makes the text dramatically more impressive. However, previous style transfer methods either study the model for general style, which cannot handle the highly-structured text effects along the glyph, or require manual design of subtle matching criteria for text effects. In this paper, we focus on the use of the powerful representation abilities of deep neural features for text effects transfer. For this purpose, we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a stylization subnetwork and a destylization subnetwork. The key idea is to train our network to accomplish both the objective of style transfer and style removal, so that it can learn to disentangle and recombine the content and style features of text effects images. To support the training of our network, we propose a new text effects dataset with as much as 64 professionally designed styles on 837 characters. We show that the disentangled feature representations enable us to transfer or remove all these styles on arbitrary glyphs using one network. Furthermore, the flexible network design empowers TET-GAN to efficiently extend to a new text style via oneshot learning where only one example is required. We demonstrate the superiority of the proposed method in generating high-quality stylized text over the state-of-the-art methods.
Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation

Recent years have witnessed the great success of deep learning models in semantic segmentation. Nevertheless, these models may not generalize well to unseen image domains due to the phenomenon of domain shift. Since pixel-level annotations are laborious to collect, developing algorithms which can adapt labeled data from source domain to target domain is of great significance. To this end, we propose self-ensembling attention networks to reduce the domain gap between different datasets. To the best of our knowledge, the proposed method is the first attempt to introduce selfensembling model to domain adaptation for semantic segmentation, which provides a different view on how to learn domain-invariant features. Besides, since different regions in the image usually correspond to different levels of domain gap, we introduce the attention mechanism into the proposed framework to generate attention-aware features, which are further utilized to guide the calculation of consistency loss in the target domain. Experiments on two benchmark datasets demonstrate that the proposed framework can yield competitive performance compared with the state of the art methods.
Privacy-Preserving Traffic Monitoring with False Report Filtering via Fog-Assisted Vehicular Crowdsensing

Traffic monitoring system empowers cloud server and drivers to collect real-time driving information and acquire traffic conditions. However, drivers are more interested in local traffic, and sending driving reports to a faraway cloud server wastes a lot of bandwidth and incurs a long response delay. Recently, fog computing is introduced to provide location-sensitive and latency-aware local data management in vehicular crowdsensing, but it incurs new privacy concerns since drivers’ information could be disclosed. Although these messages are encrypted before transmission, malicious drivers can upload false reports to sabotage the systems, and filtering out false encrypted reports remains a challenging issue. To address the problems, we define a new security model and propose a privacy preserving traffic monitoring scheme. Specifically, we utilize short group signature to authenticate drivers in a conditionally anonymous way, adopt a range query technique to acquire driving information in a privacy-preserving way, and integrate it to the construction of a weighted proximity graph at each fog node through a WiFi challenge handshake to filter out false reports. Moreover, we use variant Bloom filters to achieve fast traffic conditions storage and retrieval. Finally, we prove security and privacy, evaluate performance with real-world cloud servers.
Context-aware emotion cause analysis with multi-attention-based neural network

Emotion cause analysis has elicited wide interest in both academia and industry, and aims to identify the reasons behind certain emotions expressed in text. Most of the current studies on emotion cause analysis do not consider two types of information: i) the context of the emotional word, which can provide rich emotional details, and ii) the interaction between the candidate clause and the emotional clause (containing the emotional word). The above information is able to provide important clues in emotion cause analysis. In this paper, we propose a multi-attention-based neural network model to address this issue. First, our model encodes the clause via bidirectional long short-term memory, which can incorporate the contextual information of the word. Second, a multi-attention mechanism is designed to capture the mutual influences between the emotion clause and each candidate clause, and then generate the representations for the above two clauses separately. With this design, our model creates better-distributed representations of the emotion expressions and clauses. Finally, these representations are fed into a convolutional neural network to model the emotion cause clause. The experimental results show that our proposed approach outperforms the state-of-the-art baseline methods by a significant margin.
Internet-of-Things and Information Fusion: Trust Perspective Survey

The advent of Internet-of-Things (IoT) is creating an ecosystem of smart applications and services enabled by a multitude of sensors. The real value of these IoT smart applications comes from analyzing the information provided by these sensors. Information fusion improves information completeness/quality and, hence, enhances estimation about the state of things. Lack of trust and therefore, malicious activities renders the information fusion process and hence, IoT smart applications unreliable. Behavior-related issues associated with the data sources, such as trustworthiness, honesty, and accuracy, must be addressed before fully utilizing these smart applications. In this article, we argue that behavior trust modeling is indispensable to the success of information fusion and, hence, to smart applications. Unfortunately, the area is still in its infancy and needs further research to enhance information fusion. The aim of this article is to raise the awareness and the need of behavior trust modelling and its effect on information fusion. Moreover, this survey describes IoT architectures for modelling trust as well as classification of current IoT trust models. Finally, we discuss future directions towards trustworthy reliable fusion techniques.
Confusionset-guided Pointer Networks for Chinese Spelling Check

This paper proposes Confusionset-guided Pointer Networks for Chinese Spell Check (CSC) task.More concretely, our approach utilizes the off-the-shelf confusionset for guiding the character generation.To this end, our novel Seq2Seq model jointly learns to copy a correct character from an input sentence through a pointer network, or generate a character from the confusionset rather than the entire vocabulary.We conduct experiments on three human-annotated datasets, and results demonstrate that our proposed generative model outperforms all competitor models by a large margin of up to 20% F1 score, achieving state-of-the-art performance on three datasets.
Bitcoin futures: An effective tool for hedging cryptocurrencies

In December 2017, the CBOE and CME launched bitcoin futures, arguing that, similar to other futures, these contracts would provide more price transparency, price discovery, and a risk management tool for bitcoin. Using daily data from several sources, this paper investigates the hedging properties of CBOE Bitcoin futures during the initial months of trading. The results point out that bitcoin futures are effective hedging instruments not only for bitcoin, but also for other major cryptocurrencies. Bitcoin futures can even cope with bitcoin tail risk, however they may leverage the existence of extreme losses for other currencies.
A Novel Chaos-Based Symmetric Image Encryption Using Bit-Pair Level Process

Nowadays, chaos-based image encryption is recognized as an effective choice in secure communications.This paper proposes a novel approach, which uses bit-pair level XOR, add, and rotation from top to bottom and enhances diffusion with pixel level XOR operator from the lower right corner to the upper left corner.Due to the bit-pair level encryption, the proposed scheme has ensured great security and high encryption speed.The chaotic series employed for encryption are obtained from the modified pulsecoupled spiking neurons circuit map.It is suitable for use encryption because of its abundant parameters: wide chaotic range, ergodicity, complexity, and high sensitivity.Furthermore, the results show the superiority of this scheme compared to the other four methods in terms of robustness to differential attack.
ZigZagNet: Fusing Top-Down and Bottom-Up Context for Object Segmentation

Multi-scale context information has proven to be essential for object segmentation tasks. Recent works construct the multi-scale context by aggregating convolutional feature maps extracted by different levels of a deep neural network. This is typically done by propagating and fusing features in a one-directional, top-down and bottom-up, manner. In this work, we introduce ZigZagNet, which aggregates a richer multi-context feature map by using not only dense top-down and bottom-up propagation, but also by introducing pathways crossing between different levels of the top-down and the bottom-up hierarchies, in a zig-zag fashion. Furthermore, the context information is exchanged and aggregated over multiple stages, where the fused feature maps from one stage are fed into the next one, yielding a more comprehensive context for improved segmentation performance. Our extensive evaluation on the public benchmarks demonstrates that ZigZagNet surpasses the state-of-the-art accuracy for both semantic segmentation and instance segmentation tasks.
Depth with nonlinearity creates no bad local minima in ResNets

In this paper, we prove that depth with nonlinearity creates no bad local minima in a type of arbitrarily deep ResNets with arbitrary nonlinear activation functions, in the sense that the values of all local minima are no worse than the global minimum value of corresponding classical machine-learning models, and are guaranteed to further improve via residual representations. As a result, this paper provides an affirmative answer to an open question stated in a paper in the conference on Neural Information Processing Systems 2018. This paper advances the optimization theory of deep learning only for ResNets and not for other network architectures.
Advanced 3D Motion Prediction for Video-Based Dynamic Point Cloud Compression

Point cloud based immersive media representation format has provided many opportunities for extended reality applications and has become widely used in volumetric content capturing scenarios. The high data rate of the point cloud is one of the key problems preventing the adoption of this media format. MPEG Immersive media working group (MPEG-I) aims to create a point cloud compression methodology relying on the existing video coding hardware implementations to solve this problem. However, in the scope of the state-of-the-art video-based dynamic point cloud compression (V-PCC) standard under MPEG-I, the intrinsic 3D object's motion continuity is destroyed by the 2D projections resulting in a significant loss of inter prediction coding efficiency. In this paper, we first propose a general model utilizing the 3D motion and 3D to 2D correspondence to calculate the 2D motion vector (MV). Then under the V-PCC, we propose a geometry-based method using the accurate 3D reconstructed geometry from the 2D geometry video to estimate the 2D MV in the 2D attribute video. In addition, we propose an auxiliary-information-based method using the coarse 3D reconstructed geometry provided by the auxiliary information to estimate the 2D MV in both the 2D geometry and attribute videos. Furthermore, we provide the following two ways to use the estimated 2D MV to improve the coding efficiency. The first one is normative. We propose adding the estimated MV into the advanced motion vector candidate list and find a better motion vector predictor for each prediction unit (PU). The second one is non-normative. We propose applying the estimated MV as an additional candidate of the centers for motion estimation. We implement the proposed algorithms in the V-PCC reference software. The experimental results show that the proposed methods present significant coding gains compared with the current state-of-the-art motion prediction algorithm.
Question Answering for Privacy Policies: Combining Computational and Legal Perspectives

Privacy policies are long and complex documents that are difficult for users to read and understand, and yet, they have legal effects on how user data is collected, managed and used.Ideally, we would like to empower users to inform themselves about issues that matter to them, and enable them to selective explore those issues.We present PRIVA-CYQA, a corpus consisting of 1750 questions about the privacy policies of mobile applications, and over 3500 expert annotations of relevant answers.We observe that a strong neural baseline underperforms human performance by almost 0.3 F1 on PRIVACYQA, suggesting considerable room for improvement for future systems.Further, we use this dataset to shed light on challenges to question answerability, with domain-general implications for any question answering system.The PRIVACYQA corpus offers a challenging corpus for question answering, with genuine real-world utility.
Tree-structured Decoding for Solving Math Word Problems

Automatically solving math word problems is an interesting research topic that needs to bridge natural language descriptions and formal math equations.Previous studies introduced end-to-end neural network methods, but these approaches did not efficiently consider an important characteristic of the equation, i.e., an abstract syntax tree.To address this problem, we propose a tree-structured decoding method that generates the abstract syntax tree of the equation in a top-down manner.In addition, our approach can automatically stop during decoding without a redundant stop token.The experimental results show that our method achieves single model state-of-the-art performance on Math23K, which is the largest dataset on this task.* This denotes equal contribution. Problem The distance between the two placesA and B is 660 km, the car starting from A drives 32 km/h, and the car starting from B drives 34 km/h.The two cars are starting from the two places at the same time in inverse direction.How many hours later would the two cars meet?Equation x = 660 / ( 32 + 34 ) Prefix
Edge AIBench: Towards Comprehensive End-to-End Edge Computing Benchmarking

In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues. So for edge computing benchmarking, we must take an end-to-end view, considering all three layers: client-side devices, edge computing layer, and cloud servers. Unfortunately, the previous work ignores this most important point. This paper presents the BenchCouncil's coordinated effort on edge AI benchmarks, named Edge AIBench. In total, Edge AIBench models four typical application scenarios: ICU Patient Monitor, Surveillance Camera, Smart Home, and Autonomous Vehicle with the focus on data distribution and workload collaboration on three layers. Edge AIBench is publicly available from http://www.benchcouncil.org/EdgeAIBench/index.html . We also build an edge computing testbed with a federated learning framework to resolve performance, privacy, and security issues.
Learning to Rank Proposals for Object Detection

Non-Maximum Suppression (NMS) is an essential step of modern object detection models for removing duplicated candidates. The efficacy of NMS heavily affects the final detection results. Prior works exploit suppression criterions relying on either the objectiveness derived from classification or the overlapness produced by regression, both of which are heuristically designed and fail to explicitly link with the suppression rank. To address this issue, in this paper, we propose a novel Learning-to-Rank (LTR) model to produce the suppression rank via a learning procedure, thus facilitating the candidate generation and lifting the detection performance. In particular, we define a ranking score based on IoU to indicate the ranks of candidates during the NMS step, where candidates with high ranking score will be reserved and the ones with low ranking score will be eliminated. We design a lightweight network to predict the ranking score. We introduce a ranking loss to supervise the generation of these ranking scores, which encourages candidates with IoU to the ground-truth to rank higher. To facilitate the training procedure, we design a novel sampling strategy via dividing candidates into different levels and select hard pairs to adopt in the training. During the inference phase, this module can be exploited as a plugin to the current object detector. The training and inference of the overall framework is end-to-end. Comprehensive experiments on benchmarks PASCAL VOC and MS COCO demonstrate the generality and effectiveness of our model for facilitating existing object detectors to state-of-the-art accuracy.
Aspect Based Sentiment Analysis With Feature Enhanced Attention CNN-BiLSTM

Previous work has recognized the importance of using the attention mechanism to obtain the interaction between aspect words and contexts for sentiment analysis. However, for the most attention mechanisms, it is unrigorous to use the average vector of the aspect words to calculate the context attention. Besides, the feature extraction ability of the model is also essential for effective analysis, the combination of CNN and LSTM can enhance the feature extraction ability and semantic expression ability of the model, which is also a popular research trend. This paper introduces an aspect level neural network for sentiment analysis named Feature Enhanced Attention CNN-BiLSTM (FEA-NN). Our method is to extract a higher-level phrase representation sequence from the embedding layer by using CNN, which provides effective support for subsequent coding tasks. In order to improve the quality of context encoding and preserve semantic information, we use BiLSTM to capture both local features of phrases as well as global and temporal sentence semantics. Besides, we add an attention mechanism to model interaction relationships between aspect words and sentences to focus on those keywords of targets to learn more effective context representation. We evaluate the proposed model on three datasets: Restaurant, Laptop, and Twitter. Extensive experiments show that the effectivess of FEA-NN.
A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking

Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web.In order to achieve satisfactory performance, machine learning methods require a large corpus with reliable annotations for the different tasks in the fact-checking process.Having analyzed existing fact-checking corpora, we found that none of them meets these criteria in full.They are either too small in size, do not provide detailed annotations, or are limited to a single domain.Motivated by this gap, we present a new substantially sized mixed-domain corpus with annotations of good quality for the core fact-checking tasks: document retrieval, evidence extraction, stance detection, and claim validation.To aid future corpus construction, we describe our methodology for corpus creation and annotation, and demonstrate that it results in substantial inter-annotator agreement.As baselines for future research, we perform experiments on our corpus with a number of model architectures that reach high performance in similar problem settings.Finally, to support the development of future models, we provide a detailed error analysis for each of the tasks.Our results show that the realistic, multi-domain setting defined by our data poses new challenges for the existing models, providing opportunities for considerable improvement by future systems.claims docs.evid.stance
A discrete tree-seed algorithm for solving symmetric traveling salesman problem

Tree-Seed algorithm (TSA) is a recently developed nature inspired population-based iterative search algorithm. TSA is proposed for solving continuous optimization problems by inspiring the relations between trees and their seeds. The constrained and binary versions of TSA are present in the literature but there is no discrete version of TSA which decision variables represented as integer values. In the present work, the basic TSA is redesigned by integrating the swap, shift, and symmetry transformation operators in order to solve the permutation-coded optimization problems and it is called as DTSA. In the basic TSA, the solution update rules can be used for the decision variables whose are defined in continuous solution space, this rules are replaced with the transformation operators in the proposed DTSA. In order to investigate the performance of DTSA, well-known symmetric traveling salesman problems are considered in the experiments. The obtained results are compared with well-known metaheuristic algorithms and their variants, such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Simulated Annealing (SA), State Transition Algorithm (STA), Artificial Bee Colony (ABC), Black Hole (BH), and Particle Swarm Optimization (PSO). Experimental results show that DTSA is another qualified and competitive solver on discrete optimization.
A Closed-Form Solution to Universal Style Transfer

Universal style transfer tries to explicitly minimize the losses in feature space, thus it does not require training on any pre-defined styles. It usually uses different layers of VGG network as the encoders and trains several decoders to invert the features into images. Therefore, the effect of style transfer is achieved by feature transform. Although plenty of methods have been proposed, a theoretical analysis of feature transform is still missing. In this paper, we first propose a novel interpretation by treating it as the optimal transport problem. Then, we demonstrate the relations of our formulation with former works like Adaptive Instance Normalization (AdaIN) and Whitening and Coloring Transform (WCT). Finally, we derive a closed-form solution named Optimal Style Transfer (OST) under our formulation by additionally considering the content loss of Gatys. Comparatively, our solution can preserve better structure and achieve visually pleasing results. It is simple yet effective and we demonstrate its advantages both quantitatively and qualitatively. Besides, we hope our theoretical analysis can inspire future works in neural style transfer.