Reviewerly/exHarmony huggingface dataset:
Combining Explicit Entity Graph with Implicit Text Information for News Recommendation

News recommendation is very crucial for online news services to improve user experience and alleviate information overload. Precisely learning representations of news and users is the core problem in news recommendation. Existing models usually focus on implicit text information to learn corresponding representations, which may be insufficient for modeling user interests. Even if entity information is considered from external knowledge, it may still not be used explicitly and effectively for user modeling. In this paper, we propose a novel news recommendation approach, which combine explicit entity graph with implicit text information. The entity graph consists of two types of nodes and three kinds of edges, which represent chronological order, related and affiliation relationship. Then graph neural network is utilized for reasoning on these nodes. Extensive experiments on a real-world dataset, Microsoft News Dataset (MIND), validate the effectiveness of our proposed approach.
Marketing Method and System Optimization Based on the Financial Blockchain of the Internet of Things

This article makes relevant research and analysis from theory and practice, respectively. At the same time, with reference to the current state of the Internet, relative analysis was used to focus on the state of commercial banks. The analysis mainly focuses on the problems encountered in the current Internet development in various forms such as the bank’s sales status and customer products. In addition, it really made a reasonable opinion about the relevant sales status of this bank. It systematically studied the development history of marketing products of financial blockchain in China’s financial blockchain and analyzed the process of gradual improvement of the functions and characteristics of marketing products of financial blockchain in China. The characteristics and shortcomings of the marketing products of the financial blockchain were discussed in detail, and from the perspective of technological innovation, the application of emerging technologies in the marketing of China’s financial blockchain was analyzed. This article analyzes the many challenges and opportunities faced by security firms in the development of the Internet. Under the new situation, what kind of development model to adopt and how to transform and upgrade is the strategic proposition that security firms must think about. Based on Internet finance, this article finds that the equity crowdfunding model can be selected in the future development of security companies. On the one hand, the use of equity crowdfunding by security firms can enhance their direct financing capabilities, which will benefit the security industry, especially Internet security firms. On the other hand, through equity crowdfunding, security companies can expand direct financing channels for small, medium, and micro enterprises to promote entrepreneurship. The paper promotes the development of Internet finance, thereby improving the ability of the capital market to serve the real economy.
STPNet: A Spatial-Temporal Propagation Network for Background Subtraction

In background subtraction tasks, spatial and temporal contexts are beneficial in detecting moving objects. The methods based on Deep Neural Networks in this task has explored different topologies, which are composed of the conventional operations of convolutional neural networks, such as Convolutional Long-short Term Memory layer (ConvLSTM), 2D convolutional layer, or 3D convolutional layer, to capture these contexts. In this work, we propose a new background subtraction algorithm named spatial–temporal propagation network. An end-to-end network with novel layers, whose process of operation is equivalent to that the feature maps multiply with affinity matrices, is proposed to capture the spatial–temporal correlation in video sequences and aggregate the deep features from the consecutive frames. Experimental results on CDnet-2014 and LASIESTA datasets show that this novel layer provides an alternative way for our network to aggregate multiscale spatial–temporal features. Meanwhile, the proposed network achieves state-of-the-art performance and is generalizable to unseen videos.
Enhanced Image Security Using Stenography and Cryptography

In this age of information technology, the most vital part of information and communication exchange is the Internet. With advances in information technology and the Internet, digital media has become one of the best-known tools for data transfer, but this is still facing many challenges, including issues of authentication, modification, ad copyright protection. Many techniques such as encryption, authentication, and steganography can be used to protect these digital data. To secure information, encryption and information hiding are used widely. In the proposed work, a combination of encryption and information hiding is used to provide additional security, which means that it provides two-layer protection. The security level of the hidden image is measured in the term of PSNR and MSE parameters. In stenography, low MSE value and high PSNR value are better. The proposing work improves the result of previous work in the term of PSNR and MSE. To make the image security more robust and more secure, also add the cryptographic process with steganography.
Efficient Online Label Consistent Hashing for Large-Scale Cross-Modal Retrieval

Existing cross-modal hashing still faces three challenges: (1) Most batch-based methods are unsuitable for processing large-scale and streaming data. (2) Current online methods often suffer from insufficient semantic association, while lacking flexibility to learn the hash functions for varying streaming data. (3) Existing supervised methods always require much computation time or accumulate large quantization loss to learn hash codes. To address above challenges, we present an efficient Online Label Consistent Hashing (OLCH) for cross-modal retrieval, which aims to incrementally learn hash codes for the current arriving data, while updating the hash functions at a streaming manner. To be specific, an on-line semantic representation learning framework is designed to adaptively preserve the semantic similarity across different modalities, and a mini-batch online gradient descent approach associated with forward-backward splitting is developed to optimize the hash functions. Accordingly, the hash codes are adaptively learned online with the high discriminative capability, while avoiding high computation complexity to process the streaming data. Experimental results show its outstanding performance in comparison with the-state-of-arts.
Energy and Network Aware Workload Management for Geographically Distributed Data Centers

Cloud service providers are distributing data centers geographically to minimize energy costs through intelligent workload distribution. With increasing data volumes in emerging cloud workloads, it is critical to factor in the network costs for transferring workloads across data centers. For geo-distributed data centers, many researchers have been exploring strategies for energy cost minimization and intelligent inter-data-center workload distribution separately. However, prior work does not comprehensively and simultaneously consider data center energy costs, data transfer costs, and data center queueing delay. In this paper, we propose a novel game theory-based workload management framework that takes a holistic approach to the cloud operating cost minimization problem by making intelligent scheduling decisions aware of data transfer costs and the data center queueing delay. Our framework performs intelligent workload management that considers heterogeneity in data center compute capability, cooling power, interference effects from task co-location in servers, time-of-use electricity pricing, renewable energy, net metering, peak demand pricing distribution, and network pricing. Our simulations show that the proposed game-theoretic technique can minimize the cloud operating cost more effectively than existing approaches.
Some Punctured Codes of Several Families of Binary Linear Codes

Two general constructions of linear codes with functions over finite fields have been extensively studied in the literature. The first one is given by C(f)={ Tr(af(x)+bx)x ∈ \mathbb Fqm*: a,b ∈ \mathbb Fqm }, where q is a prime power, \mathbb Fqm* = \mathbb Fqm \{0}, Tr is the trace function from \mathbb Fqm to \mathbb Fq, and f(x) is a function from \mathbb Fqm to \mathbb Fqm with f(0)=0. Almost bent functions, quadratic functions and some monomials on \mathbb F2m were used in the first construction, and many families of binary linear codes with few weights were obtained in the literature. This paper studies some punctured codes of these binary codes. Several families of binary linear codes with few weights and new parameters are obtained in this paper. Several families of distance-optimal binary linear codes with new parameters are also produced in this paper.
Single image super-resolution using feature adaptive learning and global structure sparsity

Due to the important application value of image super-resolution, many image super-resolution algorithms have been proposed in recent years. However, many single-image super-resolution algorithms usually have their own limitations and cannot achieve ideal results. To this end, this paper proposes a new single-image super-resolution method that uses non-local self-similarity, cross-resolution similarity, and global structure sparsity without relying on external instances. First, we obtain the initial high-resolution image through feature-constrained polynomial interpolation. Then, we use a database built by the input image to perform cross-resolution learning to predict the missing high-frequency information in the image. Finally, we use the residual filtering proposed in this paper to remove the noise introduced during interpolation and cross-resolution learning. Our method can be combined with other image super-resolution algorithms. Through extensive comparison experiments to verify, our method achieves higher numerical accuracy and pleasing visual effects.
Face Super-Resolution Through Dual-Identity Constraint

Recently, most existing face SR methods only focus on generating pleasant texture details, even artifacts. Identity information is an important high-level face attribute, which is often ignored in the low-level super-resolution (SR) task. In view of this, we propose a dual-identity constraint dual-loop network (DIDnet), which employs identity information to constrain the SR model. First, the proposed framework consists of two closed-loop networks: one of the networks is used for generating high resolution (HR) images for exploring identity-preserving in HR feature space and the other one can learn degradation process for utilizing low resolution (LR) identity information. Furthermore, we integrate dual-identity constraints together for rendering characteristic facial images. Extensive experimental results are conducted on face databases and real-world data, which confirmed that the proposed DIDnet consistently and significantly improves both objective and subjective facial image reconstruction performances.
Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition

Named entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants. Although deep neural networks greatly improve the performance of NER, due to the requirement of large amounts of training data, deep neural networks can hardly scale out to many languages in an industry setting. To tackle this challenge, cross-lingual NER transfers knowledge from a rich-resource language to languages with low resources through pre-trained multilingual language models. Instead of using training data in target languages, cross-lingual NER has to rely on only training data in source languages, and optionally adds the translated training data derived from source languages. However, the existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages, which is relatively easy to collect in industry applications. To address the opportunities and challenges, in this paper we describe our novel practice in Microsoft to leverage such large amounts of unlabeled data in target languages in real production settings. To effectively extract weak supervision signals from the unlabeled data, we develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning. The empirical study on three benchmark data sets verifies that our approach establishes the new state-of-the-art performance with clear edges. Now, the NER techniques reported in this paper are on their way to become a fundamental component for Web ranking, Entity Pane, Answers Triggering, and Question Answering in the Microsoft Bing search engine. Moreover, our techniques will also serve as part of the Spoken Language Understanding module for a commercial voice assistant. We plan to open source the code of the prototype framework after deployment.
Cali: Compiler-Assisted Library Isolation

Software libraries can freely access the program's entire address space, and also inherit its system-level privileges. This lack of separation regularly leads to security-critical incidents once libraries contain vulnerabilities or turn rogue. We present Cali, a compiler-assisted library isolation system that fully automatically shields a program from a given library. Cali is fully compatible with mainline Linux and does not require supervisor privileges to execute. We compartmentalize libraries into their own process with well-defined security policies. To preserve the functionality of the interactions between program and library, Cali uses a Program Dependence Graph to track data flow between the program and the library during link time. We evaluate our open-source prototype against three popular libraries: Ghostscript, OpenSSL, and SQLite. Cali successfully reduced the amount of memory that is shared between the program and library to 0.08% (ImageMagick) - 0.4% (Socat), while retaining an acceptable program performance.
Large-scale bound constrained optimization based on hybrid teaching learning optimization algorithm

Evolutionary computing is an exciting sub-field of soft computing. Many evolutionary algorithm based on the Darwinian principles of natural selection are developed under the umbrella of EC in the last two decades. EAs provide a set of optimal solutions in single simulation unlike traditional optimization techniques for dealing with large-scale global optimization and search problems. Teaching Learning based Optimization (TLBO) is one of the most recently developed EA. TLBO employs a group of learners or a class of learners to perform global optimization search process. The framework of the TLBO consists of two phases, including the Teacher Phase and Learner Phase. The Teacher Phase’ means learning from the teachers and the Learner Phase means learning through interaction among learners. In this paper, we have developed a hybrid TLBO (HTLBO) with aim at to further improve the exploration and exploitation abilities of the baseline TLBO algorithm. The performance of the proposed HTLBO algorithm examined upon using recently designed benchmark functions for the special session of the CEC2017 problems. The experimental results of the proposed algorithm are better than some well-known evolutionary algorithms in terms of proximity and diversity.
DAPC-Net: Deformable Alignment and Pyramid Context Completion Networks for Video Inpainting

Video inpainting aims to fill missing regions with plausible content in a video sequence. Deep learning-based video inpainting methods have made promising progress over the past few years. However, these methods tend to generate degraded completion content, such as missing textural details. To address this issue, we propose a novel Deformable Alignment and Pyramid-context Completion Network for video inpainting (DAPC-Net), which takes advantage of temporal redundancy information among video sequence. Specifically, we construct a deformable convolution alignment network (DANet) for aligning reference frame at the feature level. After alignment, we further devise a pyramid-context completion network (PCNet) to complete missing regions of the target frame. Particularly, the pyramid completion mechanism and cross-scale transference strategy are used to ensure the visual and semantic coherence of the completed target frame. Experimental results show that the proposed method not only achieves better quantitative and qualitative performance but also improves the inference speed by 35.4%.
An Augmented Deep Learning Network with Noise Suppression Feature for Efficient Segmentation of Magnetic Resonance Images

The segmentation of cardiac MR images requires extensive attention as it needs a high level of care and analysis for the diagnosis of affected part. The advent of deep learning technology has paved the way for efficient, automatic and reliable segmentation of medical images for proper diagnosis. This manuscript proposes an augmented end to end trainable deep learning architecture for efficient segmentation of various domains of medical images. The model incorporates the depth wise separable convolution and group normalization as basic building blocks. Moreover, a noise stifler block is also induced between the encoder and decoder to counter the noise in the medical images. This path helps in delineating the precise boundary contours as the noise often reduces the boundary segmentation capability of the segmentation network. The network trained once produces exceedingly good results for the images of other datasets. An improvement of above (5 ± 0.03) % and (3.5 ± 0.02) % was observed in the Jaccard index and Dice score for cardiac MR images. The results are statistically validated as p < 0.05. The automatic computer investigated approach can help in reducing the burden on the medical system by producing accurate and reliable results. The algorithmic results were clinically verified by the senior radiologists by comparison with the manually segmented images. The training time of the network was about 30% less than U-net.
Porcupine: a synthesizing compiler for vectorized homomorphic encryption

Homomorphic encryption (HE) is a privacy-preserving technique that enables computation directly on encrypted data. Despite its promise, HE has seen limited use due to performance overheads and compilation challenges. Recent work has made significant advances to address the performance overheads but automatic compilation of efficient HE kernels remains relatively unexplored.
Multi-Graph Based Hierarchical Semantic Fusion for Cross-Modal Representation

The main challenge of cross-modal retrieval is how to efficiently realize semantic alignment and reduce the heterogeneity gap. However, existing approaches ignore the multi-grained semantic knowledge learning from different modalities. To this end, this paper proposes a novel end-to-end cross-modal representation method, termed as Multi-Graph based Hierarchical Semantic Fusion (MG-HSF). This method is an integration of multi-graph hierarchical semantic fusion with cross-modal adversarial learning, which captures fine-grained and coarse-grained semantic knowledge from cross-modal samples, and generate modalities-invariant representations in a common subspace. To evaluate the performance, extensive experiments are conducted on three benchmarks. The experimental results show that our method is superior than the state-of-the-arts.
Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels

Extreme Classification (XC) refers to supervised learning where each training/test instance is labeled with small subset of relevant labels that are chosen from a large set of possible target labels. The framework of XC has been widely employed in web applications such as automatic labeling of web-encyclopedia, prediction of related searches, and recommendation systems.
A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents

Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of "Who expressed what opinions towards what" in one sentence.In this work, motivated by its spanbased representations of opinion expressions and roles, we propose a unified span-based approach for the end-to-end OM setting.Furthermore, inspired by the unified span-based formalism of OM and constituent parsing, we explore two different methods (multi-task learning and graph convolutional neural network) to integrate syntactic constituents into the proposed model to help OM.We conduct experiments on the commonly used MPQA 2.0 dataset.The experimental results show that our proposed unified span-based approach achieves significant improvements over previous works in the exact F1 score and reduces the number of wrongly-predicted opinion expressions and roles, showing the effectiveness of our method.In addition, incorporating the syntactic constituents achieves promising improvements over the strong baseline enhanced by contextualized word representations.
DESN: An unsupervised MR image denoising network with deep image prior

Magnetic Resonance Imaging (MRI) is a widely used medical diagnosis technique. However, the quality of MR image is affected by the noise which is caused by mechanical and environmental reasons during the MR image acquisition process. For decades, various kinds of methods including filtering approaches, transform domain approaches and statistical approaches have been applied to the MR image denoising problem, while there are also some drawbacks exiting in these methods such as arising undesirable change of texture and long computation time needed. In this paper, we proposed a novel MR image denoising method called DESN which is a neural network method and has a novel network architecture with well-designed loss function. In DESN, the convolutional neural networks itself is considered as a regularizer or image prior information for the inverse problems such as denoising. The network architecture of DESN is designed from the auto-encoder architecture, it has three main parts: encoder network for extracting low-resolution image features, decoder network for restoring high-resolution features and skip connections for transmitting abstract information from encoder network to decoder network. Besides, we also design a novel loss function which contains two main parts: data fidelity loss (Lfidelity), image quality penalty (Lq) and three loss terms: mean squared error term (LMSE), image structure similarity term (LS), image information entropy term (LIE). We compare the performance of DESN with DIP and some state of the art denoising methods, and the performance of our network with different loss terms are also compared in three MR modalities. The comparative results show that DESN have the superior performance in generating high-quality MR image with enough edge and texture information.