Reviewerly/exHarmony huggingface dataset:
Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space

Traditional Learning to Rank (LTR) models in E-commerce are usually trained on logged data from a single domain. However, data may come from multiple domains, such as hundreds of countries in international E-commerce platforms. Learning a single ranking function obscures domain differences, while learning multiple functions for each domain may also be inferior due to ignoring the correlations between domains. It can be formulated as a multi-task learning problem where multiple tasks share the same feature and label space. To solve the above problem, which we name Multi-Scenario Learning to Rank, we propose the Hybrid of implicit and explicit Mixture-of-Experts (HMoE) approach. Our proposed solution takes advantage of Multi-task Mixture-of-Experts to implicitly identify distinctions and commonalities between tasks in the feature space, and improves the performance with a stacked model learning task relationships in the label space explicitly. Furthermore, to enhance the flexibility, we propose an end-to-end optimization method with a task-constrained back-propagation strategy. We empirically verify that the optimization method is more effective than two-stage optimization required by the stacked approach. Experiments on real-world industrial datasets demonstrate that HMoE significantly outperforms the popular multi-task learning methods. HMoE is in-use in the search system of AliExpress and achieved 1.92% revenue gain in the period of one-week online A/B testing. We also release a sampled version of our dataset to facilitate future research.
A Lightweight Feature Fusion Single Shot Multibox Detector for Garbage Detection

To solve the problems of a poor manual garbage sorting environment, including heavy tasks and low sorting efficiency, we propose the Lightweight Feature Fusion Single Shot Multibox Detector (L-SSD) algorithm to realize intelligent trash classification and recognition. Since waste has a small volume and the image resolution of garbage is always low, the algorithm that we propose is an enhanced single shot multibox detector (SSD) with a lightweight and novel feature fusion module. This SSD can significantly improve the performance of rubbish detection. In this feature fusion module, features from different layers with different scales are connected in series. A new feature pyramid was generated by using downsampling blocks, which will be fed to appointed multibox detectors to predict the final detection results. Due to the extremely unbalanced ratio of positive samples to negative samples, which leads to a low accuracy of SSD, Focal Loss using balanced cross-entropy is employed, which is provided by easy examples that corresponds to difficult samples with a decline in the loss weight. Thus, the training is biased towards meaningful samples. We have replaced the backbone network of VGG16 with ResNet-101 to achieve more accurate detection. We analyzed the performance of a nonmaximum suppression (NMS) algorithm and discovered that Soft-NMS was more suitable for learning better image representations. The strategy of Soft-NMS is to suppress the undesirable detection box rather than remove it completely. The experimental results show that the L-SSD exceeds a large number of state-of-the-art object detection algorithms in both accuracy and speed.
Controller-Free Hand Tracking for Grab-and-Place Tasks in Immersive Virtual Reality: Design Elements and Their Empirical Study

Hand tracking enables controller-free interaction with virtual environments, which can, compared to traditional handheld controllers, make virtual reality (VR) experiences more natural and immersive. As naturalness hinges on both technological and user-based features, fine-tuning the former while assessing the latter can be used to increase usability. For a grab-and-place use case in immersive VR, we compared a prototype of a camera-based hand tracking interface (Leap Motion) with customized design elements to the standard Leap Motion application programming interface (API) and a traditional controller solution (Oculus Touch). Usability was tested in 32 young healthy participants, whose performance was analyzed in terms of accuracy, speed and errors as well as subjective experience. We found higher performance and overall usability as well as overall preference for the handheld controller compared to both controller-free solutions. While most measures did not differ between the two controller-free solutions, the modifications made to the Leap API to form our prototype led to a significant decrease in accidental drops. Our results do not support the assumption of higher naturalness for hand tracking but suggest design elements to improve the robustness of controller-free object interaction in a grab-and-place scenario.
An Infinite Family of Linear Codes Supporting 4-Designs

The question as to whether there exists an infinite family of near MDS codes holding an infinite family of t-designs for t ≥ 2 was answered in the recent paper [Infinite families of near MDS codes holding t-designs, IEEE Trans. Inf. Theory 66(9) (2020)], where an infinite family of near MDS codes holding an infinite family of 3-designs and an infinite family of near MDS codes holding an infinite family of 2-designs were presented, but no infinite family of linear codes holding an infinite family of 4-designs was presented. Hence, the question as to whether there is an infinite family of linear codes holding an infinite family of 4-designs remains open for 71 years. This paper settles this longstanding problem by presenting an infinite family of BCH codes of length 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m+1</sup> +1 over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2m+1</sup> ) holding an infinite family of 4-(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2m+1</sup> + 1, 6, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2m</sup> - 4) designs. This paper also provides another solution to the first question, as some of the BCH codes presented in this paper are also near MDS. Moreover, an infinite family of linear codes holding the spherical geometry design S(3, 5, 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> + 1) is presented. The new direction of searching for t-designs with elementary symmetric polynomials will be further advanced.
The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview

Background Semantic textual similarity is a common task in the general English domain to assess the degree to which the underlying semantics of 2 text segments are equivalent to each other. Clinical Semantic Textual Similarity (ClinicalSTS) is the semantic textual similarity task in the clinical domain that attempts to measure the degree of semantic equivalence between 2 snippets of clinical text. Due to the frequent use of templates in the Electronic Health Record system, a large amount of redundant text exists in clinical notes, making ClinicalSTS crucial for the secondary use of clinical text in downstream clinical natural language processing applications, such as clinical text summarization, clinical semantics extraction, and clinical information retrieval. Objective Our objective was to release ClinicalSTS data sets and to motivate natural language processing and biomedical informatics communities to tackle semantic text similarity tasks in the clinical domain. Methods We organized the first BioCreative/OHNLP ClinicalSTS shared task in 2018 by making available a real-world ClinicalSTS data set. We continued the shared task in 2019 in collaboration with National NLP Clinical Challenges (n2c2) and the Open Health Natural Language Processing (OHNLP) consortium and organized the 2019 n2c2/OHNLP ClinicalSTS track. We released a larger ClinicalSTS data set comprising 1642 clinical sentence pairs, including 1068 pairs from the 2018 shared task and 1006 new pairs from 2 electronic health record systems, GE and Epic. We released 80% (1642/2054) of the data to participating teams to develop and fine-tune the semantic textual similarity systems and used the remaining 20% (412/2054) as blind testing to evaluate their systems. The workshop was held in conjunction with the American Medical Informatics Association 2019 Annual Symposium. Results Of the 78 international teams that signed on to the n2c2/OHNLP ClinicalSTS shared task, 33 produced a total of 87 valid system submissions. The top 3 systems were generated by IBM Research, the National Center for Biotechnology Information, and the University of Florida, with Pearson correlations of r=.9010, r=.8967, and r=.8864, respectively. Most top-performing systems used state-of-the-art neural language models, such as BERT and XLNet, and state-of-the-art training schemas in deep learning, such as pretraining and fine-tuning schema, and multitask learning. Overall, the participating systems performed better on the Epic sentence pairs than on the GE sentence pairs, despite a much larger portion of the training data being GE sentence pairs. Conclusions The 2019 n2c2/OHNLP ClinicalSTS shared task focused on computing semantic similarity for clinical text sentences generated from clinical notes in the real world. It attracted a large number of international teams. The ClinicalSTS shared task could continue to serve as a venue for researchers in natural language processing and medical informatics communities to develop and improve semantic textual similarity techniques for clinical text.
Dual Path Interaction Network for Video Moment Localization

Video moment localization aims to localize a specific moment in a video by a natural language query. Previous works either use alignment information to find out the best-matching candidate (i.e., top-down approach) or use discrimination information to predict the temporal boundaries of the match (i.e., bottom-up approach). Little research has taken both the candidate-level alignment information and frame-level boundary information together and considers the complementarity between them. In this paper, we propose a unified top-down and bottom-up approach called Dual Path Interaction Network (DPIN), where the alignment and discrimination information are closely connected to jointly make the prediction. Our model includes a boundary prediction pathway encoding the frame-level representation and an alignment pathway extracting the candidate-level representation. The two branches of our network predict two complementary but different representations for moment localization. To enforce the consistency and strengthen the connection between the two representations, we propose a semantically conditioned interaction module. The experimental results on three popular benchmarks (i.e., TACoS, Charades-STA, and Activity-Caption) demonstrate that the proposed approach effectively localizes the relevant moment and outperforms the state-of-the-art approaches.
Adversarial Privacy-Preserving Graph Embedding Against Inference Attack

Recently, the surge in popularity of the Internet of Things (IoT), mobile devices, social media, etc., has opened up a large source for graph data. Graph embedding has been proved extremely useful to learn low-dimensional feature representations from graph-structured data. These feature representations can be used for a variety of prediction tasks from node classification to link prediction. However, the existing graph embedding methods do not consider users' privacy to prevent inference attacks. That is, adversaries can infer users' sensitive information by analyzing node representations learned from graph embedding algorithms. In this article, we propose adversarial privacy graph embedding (APGE), a graph adversarial training framework that integrates the disentangling and purging mechanisms to remove users' private information from learned node representations. The proposed method preserves the structural information and utility attributes of a graph while concealing users' private attributes from inference attacks. Extensive experiments on real-world graph data sets demonstrate the superior performance of APGE compared to the state-of-the-arts. Our source code can be found at https://github.com/KaiyangLi1992/Privacy-Preserving-Social-Network-Embedding.
Physical Zero-Knowledge Proof for Numberlink Puzzle and k Vertex-Disjoint Paths Problem

Numberlink is a logic puzzle with an objective to connect all pairs of cells with the same number by non-crossing paths in a rectangular grid. In this paper, we propose a physical protocol of zero-knowledge proof for Numberlink using a deck of cards, which allows a prover to convince a verifier that he/she knows a solution without revealing it. In particular, the protocol shows how to physically count the number of elements in a list that are equal to a given secret value without revealing that value, the positions of elements in the list that are equal to it, or the value of any other element in the list. Finally, we show that our protocol can be modified to verify a solution of the well-known k vertex-disjoint paths problem, both the undirected and directed settings.
RETRACTED: Sports and health big data system based on 5G network and Internet of Things system

Technology, sports and healthcare industry have been deeply intertwined for a long time. However, new opportunities are now rapidly expanding on the Internet (population), and the result is a huge data. Also, people around the world will be able to wear biosensors for personalizing things, as well as new applications have started using 5G technology. Therefore, the design and development of the 5G network Internet games and healthcare applications of (Internet of Things) IoT and 5G have taken place. 5G-assisted smart medical networks are the convergence of IoT devices that require improved network performance and enhanced mobile phone radio waves. Today's connectivity solutions face the challenges of the Internet of Things, such as equipment, standardization, energy efficiency, device density, and the vast number of security supports. In this article, we will give a comprehensive review of IoT-assisted 5G-assisted smart sports and health care solutions. It must be categorized, and presented in the structure of 5G smart medicine through existing literature. Its smart sports and healthcare system also have key requirements for a successful 5G deployment in some cases. Finally, to discuss the challenges of researching IoT smart 5G sports and healthcare solutions with some open issues.
An Attention-Driven Two-Stage Clustering Method for Unsupervised Person Re-identification

The progressive clustering method and its variants, which iteratively generate pseudo labels for unlabeled data and per form feature learning, have shown great process in unsupervised person re-identification (re-id). However, they have an intrinsic problem of modeling the in-camera variability of images successfully, that is, pedestrian features extracted from the same camera tend to be clustered into the same class. This often results in a non-convergent model in the real world application of clustering based re-id models, leading to degenerated performance. In the present study, we propose an attention-driven two-stage clustering (ADTC) method to solve this problem. Specifically, our method consists of two strategies. Firstly, we use an unsupervised attention kernel to shift the learned features from the image background to the pedestrian foreground, which results in more informative clusters. Secondly, to aid the learning of the attention driven clustering model, we separate the clustering process into two stages. We first use kmeans to generate the centroids of clusters (stage 1) and then apply the k-reciprocal Jaccard distance (KRJD) metric to re-assign data points to each cluster (stage 2). By iteratively learning with the two strategies, the attentive regions are gradually shifted from the background to the foreground and the features become more discriminative. Using two benchmark datasets Market1501 and DukeMTMC, we demonstrate that our model outperforms other state-of-the-art unsupervised approaches for person re-id.
LadderLeak: Breaking ECDSA with Less than One Bit of Nonce Leakage

Although it is one of the most popular signature schemes today, ECDSA presents a number of implementation pitfalls, in particular due to the very sensitive nature of the random value (known as the nonce) generated as part of the signing algorithm. It is known that any small amount of nonce exposure or nonce bias can in principle lead to a full key recovery: the key recovery is then a particular instance of Boneh and Venkatesan's hidden number problem (HNP). That observation has been practically exploited in many attacks in the literature, taking advantage of implementation defects or side-channel vulnerabilities in various concrete ECDSA implementations. However, most of the attacks so far have relied on at least 2 bits of nonce bias (except for the special case of curves at the 80-bit security level, for which attacks against 1-bit biases are known, albeit with a very high number of required signatures). In this paper, we uncover LadderLeak, a novel class of side-channel vulnerabilities in implementations of the Montgomery ladder used in ECDSA scalar multiplication. The vulnerability is in particular present in several recent versions of OpenSSL. However, it leaks less than 1 bit of information about the nonce, in the sense that it reveals the most significant bit of the nonce, but with probability <1. Exploiting such a mild leakage would be intractable using techniques present in the literature so far. However, we present a number of theoretical improvements of the Fourier analysis approach to solving the HNP (an approach originally due to Bleichenbacher), and this lets us practically break LadderLeak-vulnerable ECDSA implementations instantiated over the sect163r1 and NIST P-192 elliptic curves. In so doing, we achieve several significant computational records in practical attacks against the HNP.
Deep face clustering using residual graph convolutional network

Face clustering has important applications in image retrieval and criminal investigation. Face images can be seen as the nodes of a graph and the possibility of links between the nodes will help us find clusters. Graph Convolutional Networks (GCNs) are powerful tools to infer the possibility of linkage between a given node and its neighbors. However, existing face clustering methods use shallow GCNs and have limited learning capabilities. We propose a deep face clustering method using Residual Graph Convolutional Network (RGCN), which contains more hidden layers. For each node, k-Nearest Neighbor (kNN) algorithm is used to construct its sub-graphs. Then we apply the idea of ResNet into GCNs and construct RGCN to learn the possibility of linkage between two nodes. Compared with other popular face clustering approaches, our method is more efficient and has better clustering results in the experiments. In addition, the proposed RGCN clustering approach is able to detect the quantity of clusters automatically and can be extended to large datasets.
Label distribution feature selection for multi-label classification with rough set

Multi-label learning deals with cases where every instance corresponds to multiple labels. The objective is to learn mapping from an instance to a relevant label set. Existing multi-label learning approaches assume that the significance for all related labels is same for every instance. Several problems of label ambiguity can be dealt with using multi-label learning, but some practical applications with significance among related labels for every instance cannot be effectively processed. To achieve superior results by conducting different significance of labels, label distribution learning is used for such applications. First, the probability model and rough set are embedded in the labeling significance, thus more supervised information can be obtained from original multi-label data. Subsequently, to resolve the feature selection problem of label distribution data, according to the feature dependency and the rough set, a novel feature selection algorithm for multi-label classification is designed. Finally, to verify the effectiveness of the proposed algorithms, an extensive experiment is conducted on 15 real-world multiple label data sets. The performance of the proposed algorithm through the multi-label classifier is compared with seven state-of-the-art approaches, thereby indicating the applicability and effectiveness of label distribution feature selection.
Kappa

Serverless computing has recently emerged as a new paradigm for running software on the cloud. In this paradigm, programs need to be expressed as a set of short-lived tasks, each of which can complete within a short bounded time (e.g., 15 minutes on AWS Lambda). Serverless computing is beneficial to cloud providers---by allowing them to better utilize resources---and to users---by simplifying management and enabling greater elasticity. However, developing applications to run in this environment is challenging, requiring users to appropriately partition their code, develop new coordination mechanisms, and deal with failure recovery. In this paper, we propose Kappa, a framework that simplifies serverless development. It uses checkpointing to handle lambda function timeouts, and provides concurrency mechanisms that enable parallel computation and coordination.
Quantum Fisher information with coherence

In recent proposals for achieving optical super-resolution, variants of the Quantum Fisher Information (QFI) quantify the attainable precision. We find that claims about a strong enhancement of the resolution resulting from coherence effects are questionable because they refer to very small subsets of the data without proper normalization. When the QFI is normalized, accounting for the strength of the signal, there is no advantage of coherent sources over incoherent ones. Our findings have a bearing on further studies of the achievable precision of optical instruments.
Universal SAR and optical image registration via a novel SIFT framework based on nonlinear diffusion and a polar spatial-frequency descriptor

Due to severe speckle noise in synthetic aperture radar (SAR) images and the large nonlinear intensity differences between SAR and optical images, the registration of SAR and optical images is a challenging problem that remains to be solved. In this paper, an improved nonlinear scale-invariant feature transform (SIFT)-framework-based algorithm that combines spatial feature detection with local frequency-domain description for the registration of SAR and optical images is proposed. First, multiscale representations of the SAR and optical images are constructed based on nonlinear diffusion to better preserve edges and obtain consistent edge information. The ratio of exponentially weighted averages (ROEWA) operator and the Sobel operator are utilized in the process of scale space construction to calculate consistent gradient information. Then, a new feature detection strategy based on the Harris–Laplace ROEWA and Harris–Laplace Sobel techniques is proposed to detect stable and repeatable keypoints in the scale space. Finally, a novel descriptor, called the rotation-invariant amplitudes of log-Gabor orientation histograms (RI-ALGH), and a simplified version, ALGH, are proposed. The proposed descriptors are built based on the amplitudes of multiscale and multiorientation log-Gabor responses and utilize an improved spatial structure of the gradient location and orientation histogram (GLOH) descriptor, which is robust to local distortions. The experimental results on both simulated and real images demonstrate that the proposed method can achieve better results than other state-of-the-art methods in terms of registration accuracy.