Reviewerly/exHarmony huggingface dataset:
Secure Computation to Hide Functions of Inputs

We consider a two-user secure computation problem in which Alice and Bob communicate interactively in order to compute some deterministic functions of the inputs. The privacy requirement is that each user should not learn any additional information about a function of the inputs other than what can be inferred from its own input and output. For the distribution-free setting, i.e., when the protocol must be correct and private for any joint input distribution, we completely characterize the set of all securely computable functions. When privacy is required only against Bob who computes a function based on a single transmission from Alice, we show that asymptotically secure computability is equivalent to perfectly secure computability. Separately, we consider an eavesdropper who has access to all the communication and should not learn any information about some function of the inputs (possibly different from the functions to be computed by the users) and show that interaction may be necessary for secure computation.
Quantum Proxy Signature Scheme with Discrete Time Quantum Walks and Quantum One-Time Pad CNOT Operation

The quantum proxy signature is one of the most significant formalisms in quantum signatures. We put forward a quantum proxy signature scheme using quantum walk-based teleportation and quantum one-time pad CNOT (QOTP-CNOT) operation, which includes four phases, i.e., initializing phase, authorizing phase, signing phase and verifying phase. The QOTP-CNOT is achieved by attaching the CNOT operation upon the QOTP and it is applied to produce the proxy signature state. The quantum walk-based teleportation is employed to transfer the encrypted message copy derived from the binary random sequence from the proxy signer to the verifier, in which the required entangled states do not need to be prepared ahead and they can be automatically generated during quantum walks. Security analysis demonstrates that the presented proxy signature scheme has impossibility of denial from the proxy and original signers, impossibility of forgery from the original signatory and the verifier, and impossibility of repudiation from the verifier. Notably, the discussion shows the complexity of the presented algorithm and that the scheme can be applied in many real scenarios, such as electronic payment and electronic commerce.
Drone Video Object Detection using Convolutional Neural Networks with Time Domain Motion Features

The drone video objection detection is challenging owing to the appearance deterioration, object occlusion and motion blur in video frames, which are caused by the object motion, the camera motion, and the mixture of the object motion and the camera motion in the drone video. One of the typical solutions is to use Convolutional Neural Networks (CNNs) to train detection model by taking single frame as input. The state-of-the-art method only uses the spatial feature of the single frame in the video, but makes no use of the motion features in the time domain. In this paper, we propose a method for detecting drone video object by using convolutional neural networks in combination with time domain motion features. The proposed method includes the steps of firstly extracting the motion information between the two neighbor frames, and then combining the extracted motion information with the baseline network. In the VisDrone2019 dataset, our proposed method is shown to be effective in detecting the objects, especially in significantly reducing the occurrence of the false and missed detection of objects.
Product Lagrange Coded Computing

This work considers the distributed multivariate polynomial evaluation (DMPE) problem using a master-worker framework, which was originally considered by Yu et al., where Lagrange Coded Computing (LCC) was proposed as a coded computation scheme to provide resilience against stragglers for the DMPE problem. In this work, we propose a variant of the LCC scheme, termed Product Lagrange Coded Computing (PLCC), by combining ideas from classical product codes and LCC. The main advantage of PLCC is that they are more numerically stable than LCC; however, their resilience to stragglers is sub-optimal.
Fine-Grained Entity Linking

The Entity Linking (EL) task involves linking mentions of entities in a text with their identifier in a Knowledge Base (KB) such as Wikipedia, BabelNet, DBpedia, Freebase, Wikidata, YAGO, etc. Numerous techniques have been proposed to address this task down through the years. However, not all works adopt the same convention regarding the entities that the EL task should target; for example, while some EL works target common entities like “interview” appearing in the KB, others only target named entities like “Michael Jackson”. The lack of consensus on this issue (and others) complicates research on the EL task; for example, how can the performance of EL systems be evaluated and compared when systems may target different types of entities? In this work, we first design a questionnaire to understand what kinds of mentions and links the EL research community believes should be targeted by the task. Based on these results we propose a fine-grained categorization scheme for EL that distinguishes different types of mentions and links. We propose a vocabulary extension that allows to express such categories in EL benchmark datasets. We then relabel (subsets of) three popular EL datasets according to our novel categorization scheme, where we additionally discuss a tool used to semi-automate the labeling process. We next present the performance results of five EL systems for individual categories. We further extend EL systems with Word Sense Disambiguation and Coreference Resolution components, creating initial versions of what we call Fine-Grained Entity Linking (FEL) systems, measuring the impact on performance per category. Finally, we propose a configurable performance measure based on fuzzy sets that can be adapted for different application scenarios Our results highlight a lack of consensus on the goals of the EL task, show that the evaluated systems do indeed target different entities, and further reveal some open challenges for the (F)EL task regarding more complex forms of reference for entities.
Effect of Text Color on Word Embeddings

In natural scenes and documents, we can find a correlation between text and its color. For instance, the word, "hot," is often printed in red, while "cold" is often in blue. This correlation can be thought of as a feature that represents the semantic difference between the words. Based on this observation, we propose the idea of using text color for word embeddings. While text-only word embeddings (e.g. word2vec) have been extremely successful, they often represent antonyms as similar since they are often interchangeable in sentences. In this paper, we try two tasks to verify the usefulness of text color in understanding the meanings of words, especially in identifying synonyms and antonyms. First, we quantify the color distribution of words from the book cover images and analyze the correlation between the color and meaning of the word. Second, we try to retrain word embeddings with the color distribution of words as a constraint. By observing the changes in the word embeddings of synonyms and antonyms before and after re-training, we aim to understand the kind of words that have positive or negative effects in their word embeddings when incorporating text color information.
Dual-View Oracle Bone Script Recognition System via Temporal-Spatial Psychovisual Modulation

Dual-view oracle bone script recognition is a useful technique for the research and application of the oracle bone script. This paper introduces a dual-view oracle bone script recognition system combining the character recognition algorithm and temporal-spatial psychovisual modulation (TPVM). There are two main parts in our system. In the oracle bone script recognition part, we have two types of recognition targets, the oracle bone inscription images and the handwriting oracle character images. The inscription images have much noise from the erosion and the scratch of tortoiseshell or the stele, which cannot be separated from the character. So the current recognition methods cannot achieve high accuracy on the oracle bone inscription recognition tasks. We adopt the SIFT feature points for the inscription recognition. The accuracy of this method is 95.6% in our experiments. Handwriting oracle character recognition is helpful to popularize the oracle bone script knowledge and ancient Chinese culture, but no research has been done on it. We build a handwriting oracle character database and use convolutional neural networks for handwriting oracle character recognition. Our methods can achieve a high recognition accuracy. In the dual-view display part, we adopt temporal and spatial psychovisual modulation for different application scenarios. Without specific glasses, people see the oracle bone inscription. When wearing specific glasses, viewers see the the Chinese character corresponding to the oracle bone inscription.
Multipath <scp>TCP</scp> security over different attacks

Abstract Due to the increasing demand for bandwidth and availability of multi‐homed devices, the Multipath TCP (MPTCP) is an emerging protocol that uses multiple paths simultaneously to transfer the data, seamlessly utilizing their bandwidth. The security is an important issue in a system especially the communication network system. Since there are multiple paths in MPTCP, it has many doors open to an adversary especially at the time of connection initiation. These attacks include the man‐in‐the‐middle attack (MM), denial‐of‐service (DOS) attack, and SYN flooding attack. This article proposes a new opportunistic security protocol, named as secure connection multipath TCP (SCMTCP), that uses the elliptic curve cryptography to generate the secret key. It also uses a third‐party certificate authority to ensure the authenticity of public keys exchanged between the communicating parties. The SCMTCP generates a session key at the time of initial handshake (with MP_CAPABLE option) and protects the MPTCP from the man‐in‐the‐middle attack. It generates a unique authentication key for each new subflow within the host using the session key to authenticate them and protects the MPTCP from DoS and SYN flooding attacks. This article analyzes the security complexity of the SCMTCP using the random oracle model and shows that it is not possible for an attacker to get any security key and breaks the MPTCP security.
An Improved ORB Feature Extraction Algorithm

Abstract In this paper, an improved ORB feature extraction algorithm is proposed to solve the insufficient detection effect of the traditional ORB algorithm on the image edges as well as the low actual extraction of feature points. First, the image pyramid layering was established, and then on basis of the improved oFAST12-20 corner point algorithm, the method with a setting dynamic threshold, according to the idea of adaptive threshold, was proposed to extract feature points, and the candidate set of feature points was obtained. Then, Harris algorithm was used to calculate the response value of each candidate point, and the response value was sorted from high to low. Finally, the top 80% of the feature points with high response value were extracted as the most important feature points. The experimental results show that the improved oFAST algorithm improved the extraction rate of feature points and reduces the algorithm time compared with the traditional ORB corner detection.
Pose Recognition of 3D Human Shapes via Multi-View CNN with Ordered View Feature Fusion

Rapid pose classification and pose retrieval in 3D human datasets are important problems in shape analysis. In this paper, we extend the Multi-View Convolutional Neural Network (MVCNN) with ordered view feature fusion for orientation-aware 3D human pose classification and retrieval. Firstly, we combine each learned view feature in an orderly manner to form a compact representation for orientation-aware pose classification. Secondly, for pose retrieval, the Siamese network is adopted to learn descriptor vectors, where their L2 distances are close for pairs of shapes with the same poses and are far away for pairs of shapes with different poses. Furthermore, we also construct a larger 3D Human Pose Recognition Dataset (HPRD) consisting of 100,000 shapes for the evaluation of pose classification and retrieval. Experiments and comparisons demonstrate that our method obtains better results than previous works of pose classification and retrieval on the 3D human datasets, such as SHREC’14, FAUST, and HPRD.
How Virtual Exhibition Presentation Affects Visitor Communication and Enjoyment: An Exploration of 2D versus 3D

As online virtual exhibitions become a central communication channel in museums, and both the communication and entertainment aspects of museums are being emphasized, various presentation methods are being adopted to attract visitors to such exhibitions. Therefore, this study examines the effect of virtual exhibition presentation methods on visitor communication and enjoyment, focusing on a comparison of 2D and 3D presentation. The findings indicate that 2D presentation is more effective for intuitive communication than 3D, and visitor enjoyment was aroused at different points according to the presentation methods. The 2D visitors showed their interest in information acquisition while focusing on exhibition content, whereas 3D visitors enjoyed exploring the virtual environment. Additional implications of this study for both further research and practice are discussed.
Analyzing gaze behavior for text-embellished narrative visualizations under different task scenarios

We conduct an eye tracking study to investigate perception text-embellished narrative visualizations under different task conditions. Study stimuli are data visualizations embellished with text-based elements: annotations, captions, labels, and descriptive text. We consider three common viewing tasks that occur when these types of graphics are viewed: (1) simple observation, (2) active search to answer a query, and (3) information memorization for later recall. The overarching goal is to understand, at a perceptual level, if and how task affects how these visualizations are interacted with. By analyzing collected gaze data and conducting advanced semantic scanpath analysis, we find, at a high level, diverse patterns of gaze behavior: simple observation and information memorization lead to similar optical viewing strategies, while active search significantly diverges, both in regards to which areas of the visualization are focused upon and how often embellishments are interacted with. We discuss study outcomes in the context of embellishing visualizations with text for various usage scenarios.
A family of parsimonious mixtures of multivariate Poisson‐lognormal distributions for clustering multivariate count data

Multivariate count data are commonly encountered through high‐throughput sequencing technologies in bioinformatics, text mining, or sports analytics. Although the Poisson distribution seems a natural fit to these count data, its multivariate extension is computationally expensive. In most cases, mutual independence among the variables is assumed; however, this fails to take into account the correlation among the variables usually observed in the data. Recently, mixtures of multivariate Poisson‐lognormal (MPLN) models have been used to analyze such multivariate count measurements with a dependence structure. In the MPLN model, each count is modeled using an independent Poisson distribution conditional on a latent multivariate Gaussian variable. Owing to this hierarchical structure, the MPLN model can account for over‐dispersion as opposed to the traditional Poisson distribution and allows for correlation between the variables. Rather than relying on a Monte Carlo‐based estimation framework, which is computationally inefficient, a fast variational expectation–maximization (EM)‐based framework is used here for parameter estimation. Further, a family of parsimonious mixtures of Poisson‐lognormal distributions is proposed by decomposing the covariance matrix and imposing constraints on these decompositions. Utility of such models is shown using simulated and benchmark datasets.
Recognition of Japanese Connected Cursive Characters Using Multiple Softmax Outputs

It is difficult to recognize cursive characters kuzushiji in classic Japanese literature because multiple characters are connected. In this study, we propose a method for correctly recognizing consecutive kuzushiji characters by using multiple candidate regions as input to a neural network even if character regions are misaligned. An evaluation using an image database of three consecutive kuzushiji characters demonstrated that the proposed method had a higher accuracy rate than a method in which the character images were cropped based on the detected boundary.
Improved Dark Channel Defogging Algorithm for Defect Detection in Underwater Structures

Underwater structures are crucial for national economic and social development. However, because of their complex environment, they are susceptible to damage during service. This damage should be prevented to minimize casualties and economic loss. Therefore, this study investigates the problems of disease identification and area statistics of underwater structures. To this end, the Dark-Retinex (DR) algorithm that can enhance the image of underwater structure defects is proposed. The algorithm consists of a combination of a dark channel algorithm and the Retinex algorithm. This study analyzes the current research status of underwater image processing technology, designs the overall framework of the DR algorithm, and uses the underwater structure disease image to verify the algorithm. Comparing the effect of the image with only the dark channel defogging and DR algorithm processing, the DR algorithm is observed to achieve “defogging” processing of underwater structural disease images to achieve better enhancement effects. Moreover, for accurate disease area statistics, the binary morphology and optimal threshold segmentation theories are combined to perform disease edge screening and remove interference information. Finally, accurate statistics of the proportion of diseased pixels are achieved, as well as the quantitative detection of surface diseases of underwater structures. After actual operational verification, the improved image dehazing and parallel boundary screening algorithms can achieve better application results to detect underwater structure defects and disease statistics. The objective evaluation shows that the DR algorithm facilitates image processing, can obtain relatively high-quality target images, and can solve the problems of time-consuming and labor-intensive detection of underwater structures, with significant risks and limitations. This helps pave the way for (1) the actual engineering of surface structure detection of underwater structures, (2) future storage in the database and assessment of hazard levels, and (3) a guide for engineering technicians to take corresponding maintenance measures.
Monitoring Browsing Behavior of Customers in Retail Stores via RFID Imaging

In this paper, we propose to use commercial off-the-shelf (COTS) <i>monostatic</i> RFID devices (i.e. which use a single antenna at a time for both transmitting and receiving RFID signals to and from the tags) to monitor browsing activity of customers in front of display items in places such as retail stores. To this end, we propose <i>TagSee</i> , a multi-person imaging system based on monostatic RFID imaging. TagSee is based on the insight that when customers are browsing the items on a shelf, they stand between the tags deployed along the boundaries of the shelf and the reader, which changes the multi-paths that the RFID signals travel along, and both the RSS and phase values of the RFID signals that the reader receives change. Based on these variations observed by the reader, TagSee constructs a coarse grained image of the customers. Afterwards, TagSee identifies the items that are being browsed by the customers by analyzing the constructed images. The key novelty of this paper is on achieving browsing behavior monitoring of multiple customers in front of display items by constructing coarse grained images via robust, analytical model-driven deep learning based, RFID imaging. To achieve this, we first mathematically formulate the problem of imaging humans using monostatic RFID devices and derive an approximate analytical imaging model that correlates the variations caused by human obstructions in the RFID signals. Based on this model, we then develop a deep learning framework to robustly image customers with high accuracy. We implement TagSee scheme using a Impinj Speedway R420 reader and SMARTRAC DogBone RFID tags. TagSee can achieve a TPR of more than <inline-formula><tex-math notation="LaTeX">${\sim }90\%$</tex-math></inline-formula> and a FPR of less than <inline-formula><tex-math notation="LaTeX">${\sim }10\%$</tex-math></inline-formula> in multi-person scenarios using training data from just 3-4 users.
Re-Ranking for Writer Identification and Writer Retrieval

Automatic writer identification is a common problem in document analysis. State-of-the-art methods typically focus on the feature extraction step with traditional or deep-learning-based techniques. In retrieval problems, re-ranking is a commonly used technique to improve the results. Re-ranking refines an initial ranking result by using the knowledge contained in the ranked result, e. g., by exploiting nearest neighbor relations. To the best of our knowledge, re-ranking has not been used for writer identification/retrieval. A possible reason might be that publicly available benchmark datasets contain only few samples per writer which makes a re-ranking less promising. We show that a re-ranking step based on k-reciprocal nearest neighbor relationships is advantageous for writer identification, even if only a few samples per writer are available. We use these reciprocal relationships in two ways: encode them into new vectors, as originally proposed, or integrate them in terms of query-expansion. We show that both techniques outperform the baseline results in terms of mAP on three writer identification datasets.
A Biologically Inspired Feature Enhancement Framework for Zero-Shot Learning

Most of the Zero-Shot Learning (ZSL) algorithms currently use pre-trained models as their feature extractors, which are usually trained on ImageNet by using deep neural networks. The richness of the feature information embedded in the pre-trained models can help the ZSL model extract more useful features from its limited training data. However, sometimes the difference between the training data of the current ZSL task and the ImageNet is too large, which may cause the use of pre-trained models has no obvious help or even negative impact on the model performance. To solve this problem, this paper proposes a biologically inspired feature enhancement framework for ZSL. Specifically, we design a dual-channel learning framework that uses auxiliary data sets to enhance the feature extractor of the ZSL model and propose a novel method to guide the selection of the auxiliary data sets based on the knowledge of biological taxonomy. Extensive experimental results show that our proposed method can effectively improve the generalization ability of the ZSL model and achieve state-of-the-art results on three benchmark ZSL tasks. We also explained the experimental phenomena through the way of feature visualization.