Reviewerly/exHarmony huggingface dataset:
Revisiting Negation in Neural Machine Translation

In this paper, we evaluate the translation of negation both automatically and manually, in English–German (EN–DE) and English– Chinese (EN–ZH). We show that the ability of neural machine translation (NMT) models to translate negation has improved with deeper and more advanced networks, although the performance varies between language pairs and translation directions. The accuracy of manual evaluation in EN→DE, DE→EN, EN→ZH, and ZH→EN is 95.7%, 94.8%, 93.4%, and 91.7%, respectively. In addition, we show that under-translation is the most significant error type in NMT, which contrasts with the more diverse error profile previously observed for statistical machine translation. To better understand the root of the under-translation of negation, we study the model’s information flow and training data. While our information flow analysis does not reveal any deficiencies that could be used to detect or fix the under-translation of negation, we find that negation is often rephrased during training, which could make it more difficult for the model to learn a reliable link between source and target negation. We finally conduct intrinsic analysis and extrinsic probing tasks on negation, showing that NMT models can distinguish negation and non-negation tokens very well and encode a lot of information about negation in hidden states but nevertheless leave room for improvement.
Efficient Inter-Cloud Authentication and Micropayment Protocol for IoT Edge Computing

In this paper, we present a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {S}$ </tex-math></inline-formula> ymmetric <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {K}$ </tex-math></inline-formula> ey <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {I}$ </tex-math></inline-formula> nter- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {C}$ </tex-math></inline-formula> loud <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {A}$ </tex-math></inline-formula> uthentication and redeemable micropayment <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {P}$ </tex-math></inline-formula> rotocol ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {SKICAP}$ </tex-math></inline-formula> ) for IoT-Edge computing applications. The proposed protocol takes into account the mobile and limited resource nature of IoT devices by enabling them to register with their home cloud admin for computation offloading service provided by foreign Edge-Cloud instances. More precisely, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {SKICAP}$ </tex-math></inline-formula> ensures that mobile IoT devices can be authenticated and serviced by edge nodes outside of their home cloud coverage, while guaranteeing the associated charge redemption. Additionally, since our protocol considers the mobile and anywhere deployable requirements of IoT devices, we utilize physical unclonable function and cryptographic hash function to make it privacy-preserving and resilient to hardware compromise. Under the assumption of an indistinguishably secure physical unclonable function, we provide a formal security analysis of our protocol. Finally, we report <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {SKICAP}$ </tex-math></inline-formula> performance on an ARM Cortex-A72 processor, and compare it with other closely related protocols.
Towards a secure and trustworthy imaging with non-fungible tokens

Non fungible tokens (NFTs) are used to define the ownership of digital assets. More recently, there has been a surge of platforms to auction digital art as well as other digital assets in form of image, video, and audio content of all sorts. Although NFTs have the potential of revolutionizing the foundations of ownership, they also face various challenges notably in terms of trust and security. This paper starts by identifying the challenges in current NFTs and proposes a solution in order to remedy to their current shortcomings.
Deep Learning, WordNet, and spaCy based Hybrid Method for Detection of Implicit Aspects for Sentiment Analysis

In the past decade, sentiment analysis/opinion mining has become a very enticing research domain. Sentiment analysis is the task of identification, extraction, and aggregation of sentiments present in the given text. Sentiment analysis may be performed at different granularity. Aspect-level sentiment analysis being the most fine-grained, and sentiments are aggregated for different aspects of an entity. Most of the current research work focuses on detecting explicit aspects, and a handful of attempts were made to detect implicit aspects that the context may imply.Most of the existing work for implicit aspect detection is based on calculating collocation from a corpus. The emergence of deep learning and its suitability for tasks similar to aspect detection has motivated us to use it for implicit aspect detection. Based on the study of existing research, we have also noticed that using an existing linguistic knowledge base like WordNet, results in significant improvement in efficiency. Hence, we have proposed a hybrid method incorporating a Recurrent Neural Network(RNN), which is trained on a dataset prepared by us and similarity calculations based on WordNet and similarity function from spaCy to detect implicit aspects. When tested on our dataset, it gives us reasonable results.
Hand Gesture Recognition for Deaf and Dumb Using CNN Technique

The communication through Sign language is a successful way of communication for speech and hearing impaired humans. A Hand gesture recognition system to aid deaf and mute is developed using convolutional neural networks to identify the static signs of ISL (Indian Sign Language). In order to have sufficient amount of dataset, a total of 3500 static sign images of 10 (Indian sign language) static signs are gathered from various impaired humans. A total of 4 layers and 16 filters were used in Convolution Neutral Network (CNN) Architecture based on Deep learning technique. Adam optimizer has been used as the optimizer to tweak the weighs of the model is useful for reducing the loss and improving the accuracy. Model is trained in total of 15 epochs. The optimizer used to train and validate process is Stochastic Gradient Descent (SGD). The proposed model gives the maximum possible training accuracy of about 99.76%.
Historical Data Trend Analysis in Extended Reality Education Field

The arrival of the digital age brings Virtual Reality, Augmented Reality, and Mixed Reality technologies into our daily life. It provides a brand-new user experience to composite with real environments. Due to the development of related devices in recent years, the highly interactive connections between users and devices have gradually evolved. The paper starts from a literature review to discuss Virtual Reality, Augmented Reality, and Mixed Reality's history and social impact. The review reveals not only the traditional historical review but also contains a data research study. The research focuses on the case study paper, which proposed a bright, interactive future with technology in educational field. We compared the proposed future view and the current development. This paper collected 269 citations from 2005 to 2020 and analyzed them, assessing whether they belonged to technical or theoretical paper. The paper uses the collected data to discuss industrial developing trends and indicates the possible future view based on the data study result.
Adaptive graph guided concept factorization on Grassmann manifold

Motivated by applications in clustering and optimization, there has been significant interest in the variant for which the database models are high-dimensional data (such as imagesets or videos). Matrix factorization approaches, particularly Nonnegative Matrix Factorization (NMF) have achieved favorable results for data clustering tasks. However, these methods suffer from two major drawbacks. First, matrix factorization approaches only partially explore the inter- or intra- relationships between vector-valued data and are unable to handle high-dimensional data. Second, the clustering results largely rely on the predefined similarity matrix, which usually contain large amounts of noise. In this paper, we propose a novel Adaptive Graph Guided Concept Factorization model on Grassmann manifold (AG3CF) for imageset clustering in an unsupervised fashion, which imposes concept factorization over the space of linear subspaces. To further explore the structure of data and assign ideal neighbors, an adaptive graph regularization constraint is designed to automatically capture the local relationships of data samples and integrated well into concept factorization framework. An efficient algorithm is also derived to tackle the resulting optimization problem. Experimental results on several public datasets validate that the proposed approach achieves competitive performance in high-dimensional data clustering.
Multi-UAV Cooperative Target Tracking Based on Swarm Intelligence

In recent years, unmanned aerial vehicles (UAV) have been widely adopted to support complex target tracking tasks for military and civilian applications, especially in open and unknown environments. In practical cases, the moving trajectory of the target cannot be known to the UAVs in advance, which brings great challenges to UAVs to realize real-time and effective tracking. In addition, the limited tracking ability of a single UAV can hardly meet the requirements of a high tracking success rate. To deal with these problems above, this paper establishes a multi-UAV cooperative target tracking system. Besides, a deep reinforcement learning (DRL) based algorithm is designed to enable UAVs to make flight action decisions intelligently to track the moving air target, according to the past and current position information of the target only. To further increase the detection coverage of the UAV network when tracking, spatial information entropy is introduced to the reward designing in this algorithm. Simulation results validate that the proposed algorithm yields impressive target tracking performances, and significantly outperforms several common DRL baselines in terms of the tracking success rate. The convergence of the algorithm is also verified by the simulations.
MIG-Net: Multi-Scale Network Alternatively Guided by Intensity and Gradient Features for Depth Map Super-Resolution

The studies of previous decades have shown that the quality of depth maps can be significantly lifted by introducing the guidance from intensity images describing the same scenes. With the rising of deep convolutional neural network, the performance of guided depth map super-resolution is further improved. The variants always consider deep structure, optimized gradient flow and feature reusing. Nevertheless, it is difficult to obtain sufficient and appropriate guidance from intensity features without any prior. In fact, features in the gradient domain, e.g., edges, present strong correlations between the intensity image and the corresponding depth map. Therefore, the guidance in the gradient domain can be more efficiently explored. In this paper, the depth features are iteratively upsampled by 2&#x00D7;. In each upsampling stage, the low-quality depth features and the corresponding gradient features are iteratively refined by the guidance from the intensity features via two parallel streams. Then, to make full use of depth features in the image and gradient domains, the depth features and gradient features are alternatively complemented with each other. Compared with state-of-the-art counterparts, the sufficient experimental results show improvements according to the objective and subjective assessments. The code is available at <uri>https://github.com/Yifan-Zuo/MIG-net-gradient_guided_depth_enhancement</uri>.
Fine-Grained Visual Attribute Extraction from Fashion Wear

Automatically extracting visual attributes for ecommerce data has widespread applications in cataloging, catalogue qualification and enrichment, visual search, etc. Here, we address the task of visual attribute extraction for a highly challenging real-world fashion data from Flipkart catalogue (an Indian e-commerce platform), which is collected from seller uploaded product images. This data not only contains widely varying categories (e.g., shirt, sari, shoes), but also has both coarse-grained (e.g., occasion, top type, sari type) and fine-grained (e.g., neck type, print type) attributes. Training examples available for different attributes are highly imbalanced, making this task even more challenging. To this end, we propose an end-to-end framework which integrates multi-task learning with transformer as an attention module, in addition to handling the data imbalance. The proposed architecture supports multiple attributes across various product categories in a scalable manner. Extensive experiments on the in-house dataset shows effectiveness of the proposed framework in improving performance of the fine-grained attributes by 13% on the baseline across the attributes.
An overview of complex data stream ensemble classification

With the rapid development of information technology, data streams in various fields are showing the characteristics of rapid arrival, complex structure and timely processing. Complex types of data streams make the classification performance worse. However, ensemble classification has become one of the main methods of processing data streams. Ensemble classification performance is better than traditional single classifiers. This article introduces the ensemble classification algorithms of complex data streams for the first time. Then overview analyzes the advantages and disadvantages of these algorithms for steady-state, concept drift, imbalanced, multi-label and multi-instance data streams. At the same time, the application fields of data streams are also introduced which summarizes the ensemble algorithms processing text, graph and big data streams. Moreover, it comprehensively summarizes the verification technology, evaluation indicators and open source platforms of complex data streams mining algorithms. Finally, the challenges and future research directions of ensemble learning algorithms dealing with uncertain, multi-type, delayed, multi-type concept drift data streams are given.
A Novel Privacy-Preserving Location-Based Services Search Scheme in Outsourced Cloud

With the development of wireless communications and the pervasiveness of location-aware mobile electronic devices, location-based services (LBS) which can provide a convenient lifestyle for people have attracted considerable interest recently. However, there still exists the privacy disclosure problem of LBS today. To solve this problem, in this article, we present a novel privacy-preserving LBS search scheme in outsourced cloud. In the proposed LBS search scheme, the LBS providers data are first outsourced to the cloud server in an encrypted method. Then, a registered user constructs a query model to obtain accurate LBS query results without divulging his/her location information and query attribute to the LBS provider and the cloud server. Specifically, based on the designed matrix encryption technology, the LBS search scheme can achieve privacy preservation of users query and confidentiality of LBS data in the outsourced cloud server. Through security analysis, we show that our scheme can resist various known security threats. The experimental results further show that our LBS search scheme greatly reduces the communication overhead and provides convenient search experience to the users.
MHz2k: MPC from HE over $$\mathbb {Z}_{2^k}$$ with New Packing, Simpler Reshare, and Better ZKP

We propose a multi-party computation (MPC) protocol over $$\mathbb {Z}_{2^k}$$ secure against actively corrupted majority from somewhat homomorphic encryption. The main technical contributions are: (i) a new efficient packing method for $$\mathbb {Z}_{2^k}$$ -messages in lattice-based somewhat homomorphic encryption schemes, (ii) a simpler reshare protocol for level-dependent packings, (iii) a more efficient zero-knowledge proof of plaintext knowledge on cyclotomic rings $${\mathbb Z}[X]/\varPhi _M(X)$$ with M being a prime. Integrating them, our protocol shows from 2.2x upto 4.8x improvements in amortized communication costs compared to the previous best results. Our techniques not only improve the efficiency of MPC over $$\mathbb {Z}_{2^k}$$ considerably, but also provide a toolkit that can be leveraged when designing other cryptographic primitives over $$\mathbb {Z}_{2^k}$$ .
Private‐encoder: Enforcing privacy in latent space for human face images

Summary The explosive growth of various computer vision technologies generates a tremendous amount of visual data online every day. In addition to bringing convenience and revolutionizing our daily life, image data also reveal a wide range of sensitive information and pose unprecedented privacy leakage risks. Particularly, in the case of photos contain human faces, people can easily access those face images on social media without any consent, and the misuse of personal information could cause serious privacy violation to individuals. Therefore, it is essential to consider sanitizing people's identity information when using images containing human faces. As a result, there has been rapid development in the area of facial anonymization, also called image de‐identification. However, due to the emergence of numerous deep‐learning based attacks, traditional anonymization methods such as blurring and mosaic are weak and ineffective to protect individual's privacy in face images. To respond to this challenge, this article proposes a novel de‐identification method that utilizes a deep neural network. The proposed framework encompasses two modules: encoder network and generator network. The encoder transforms a face image into a high‐semantic latent vector of codes, which will be de‐identified according to the differential privacy criterion. The generator leverages the unconditional generative adversarial network to synthesize high‐quality images based on the modified latent codes from the encoder. Extensive experimental results indicate that our proposed model can protect image privacy while keeping the processed image visual realistic.
CERL: A Unified Optimization Framework for Light Enhancement With Realistic Noise

Low-light images captured in the real world are inevitably corrupted by sensor noise. Such noise is spatially variant and highly dependent on the underlying pixel intensity, deviating from the oversimplified assumptions in conventional denoising. Existing light enhancement methods either overlook the important impact of real-world noise during enhancement, or treat noise removal as a separate pre- or post-processing step. We present \underline{C}oordinated \underline{E}nhancement for \underline{R}eal-world \underline{L}ow-light Noisy Images (CERL), that seamlessly integrates light enhancement and noise suppression parts into a unified and physics-grounded optimization framework. For the real low-light noise removal part, we customize a self-supervised denoising model that can easily be adapted without referring to clean ground-truth images. For the light enhancement part, we also improve the design of a state-of-the-art backbone. The two parts are then joint formulated into one principled plug-and-play optimization. Our approach is compared against state-of-the-art low-light enhancement methods both qualitatively and quantitatively. Besides standard benchmarks, we further collect and test on a new realistic low-light mobile photography dataset (RLMP), whose mobile-captured photos display heavier realistic noise than those taken by high-quality cameras. CERL consistently produces the most visually pleasing and artifact-free results across all experiments. Our RLMP dataset and codes are available at: https://github.com/VITA-Group/CERL.
More practical differentially private publication of key statistics in GWAS

Motivation: Analyses of datasets that contain personal genomic information are very important for revealing associations between diseases and genomes. Genome-wide association studies, which are large-scale genetic statistical analyses, often involve tests with contingency tables. However, if the statistics obtained by these tests are made public as they are, sensitive information of individuals could be leaked. Existing studies have proposed privacy-preserving methods for statistics in the χ2 test with a 3 × 2 contingency table, but they do not cover all the tests used in association studies. In addition, existing methods for releasing differentially private P-values are not practical. Results: In this work, we propose methods for releasing statistics in the χ2 test, the Fisher's exact test and the Cochran-Armitage's trend test while preserving both personal privacy and utility. Our methods for releasing P-values are the first to achieve practicality under the concept of differential privacy by considering their base 10 logarithms. We make theoretical guarantees by showing the sensitivity of the above statistics. From our experimental results, we evaluate the utility of the proposed methods and show appropriate thresholds with high accuracy for using the private statistics in actual tests.A python implementation of our experiments is available at https://github.com/ay0408/DP-statistics-GWAS.Supplementary data are available at Bioinformatics Advances online.