Reviewerly/exHarmony huggingface dataset:
Secure‐aware and privacy‐preserving electronic health record searching in cloud environment

Summary Cloud storage has become a trend of storage in modern age. The cloud‐based electronic health record (EHR) system has brought great convenience for health care. When a user visits a doctor for a treatment, the doctor may be necessary to access the history health records generated at other medical institutions. Thus, we present a secure EHR searching scheme based on conjunctive keyword search with proxy re‐encryption to realize data sharing between different medical institutions. Firstly, we propose a framework for health data sharing among multiple medical institutions based on cloud storage. We explore the public key encryption with conjunctive keyword search to encrypt the original data and store it in the cloud. It ensures data security with searchability. Furthermore, we adopt the identity‐based access control mechanism and proxy re‐encryption scheme to guarantee the legitimacy of access and the privacy of the original data. Generally speaking, our work can achieve authentication, keyword privacy, and privacy preservation. Moreover, the performance evaluation shows that the scheme can achieve high computational efficiency.
Factorising

Kris Cao, Stephen Clark. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
A clustering framework based on FCM and texture features for denoising ESPI fringe patterns with variable density

Denoising of electronic speckle pattern interferometry (ESPI) fringe patterns with variable density is challenging. In this paper, we present a method to denoise ESPI fringe patterns with variable density by constructing a clustering framework. The framework first clusters the variable density fringes into low density fringes and high density fringes according to the texture features of fringes by using fuzzy c-means (FCM) clustering algorithm. The texture features include energy, contrast, homogeneity and correlation, which can characterize the densities of fringes. Then, the effective existing methods are adopted to denoise the low and high density fringes individually. Finally, the filtered low and high density fringes are combined to obtain the final denoising results of the fringe patterns with variable density. With the help of proposed framework, variable density fringes can be clustered into low and high density fringes without calculating the density map of fringe pattern. Furthermore, the filtering strength applied in high density fringes can be different from that in low density fringes in a fringe pattern, which can improve the performance of any filtering method in the denosing of fringe patterns with variable density. Experiments on computer-simulated and experimentally obtained ESPI fringe patterns tested with several filtering methods show that the proposed framework provides consistently better results than denoising the image directly, in terms of signal-to-noise ratio, equivalent number of looks and edge preservation metrics.
A High-Performance Parallel Computation Hardware Architecture in ASIC of SHA-256 Hash

The SHA-256 is playing an important role in various applications, such as e-transactions and bitcoins. To achieve more profits, the SHA-256 computation capacity is a main research direction of Hashing Algorithm. In this paper, a high-performance hardware architecture of SHA-256 hash is proposed. The computation of SHA-256 is rescheduled based on hardware characterises. Three pipelines are used to replace the critical path in the round functions which can shorten the long critical path, and divide the computation chain into independent parts. Multi-computation of SHA-256 is working in parallel pipelines, indicating that the computation capacity can be 3 times of standard SHA-256 implementation. The proposed SHA-256 hardware architecture has been implemented and synthesized with Intel 14nm technology. Simulation and synthesis results show the proposed SHA-256 hashing throughput can be improved by 3 times with 50.7% power reduction, at an area cost of 2.9 times compared to the standard implementation.
Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data from Landsat

Mapping of hydrothermally altered areas, which are usually associated with mineralization, is essential in mineral exploration.In this research, open source reflectance spectroscopy data from the multispectral moderate-resolution Landsat 8 satellite was used to map altered rocks in the Gauteng and Mpumalanga provinces of South Africa.The unique spectral reflectance and absorption characteristics of remotely sensed Landsat data in the visible, near-infrared (NIR), shortwave-infrared (SWIR) and thermal infrared (TIR) regions of the electromagnetic spectrum were used in different digital image processing techniques.The band ratios (red/blue, SWIR 2/NIR, SWIR 1/NIR), spectral band combinations (Kaufmann ratio, Sabins ratio) and principal component analysis (Crosta technique) were applied to efficiently and successfully map hydrothermal alteration minerals.The results showed that the combination of spectral bands and the principal component analysis method is effective in delineating mineral alteration through remotely sensed satellite data.The validation of results by using the published mineral maps of the Council for Geoscience South Africa showed a good relationship with the identified zones of mineralization.The methodology developed in this study is cost-effective and time-saving, and can be applied to inaccessible and/or new areas with limited ground-based knowledge to obtain reliable and up-to-date mineral information.
Implementation of generalized measurements on a qudit via quantum walks

Quantum measurements play a fundamental role in quantum mechanics and quantum information processing, but it is not easy to implement generalized measurements, the most powerful measurements allowed by quantum mechanics. Here, we propose a simple recipe for implementing generalized measurements on a qudit via quantum walks. With this recipe, any discrete quantum measurement can be implemented via a one-dimensional discrete quantum walk; the number of steps is only two times the number of measurement outcomes. As an illustration, we present a unified solution for implementing arbitrary symmetric informationally complete measurements in dimension 3.
Multi-robot informative path planning in unknown environments through continuous region partitioning

We consider the NP-hard problem of multirobot informative path planning in the presence of communication constraints, where the objective is to collect higher amounts of information of an ambient phenomenon. We propose a novel approach that uses continuous region partitioning into Voronoi components to efficiently divide an initially unknown environment among the robots based on newly discovered obstacles enabling improved load balancing between robots. Simulation results show that our proposed approach is successful in reducing the initial imbalance of the robots’ allocated free regions while ensuring close-to-reality spatial modeling within a reasonable amount of time.
Adaptive and Dynamic Adjustment of Fault Detection Cycles in Cloud Computing

In past decades, we witnessed many applications and fast development of cloud computing technologies. Cloud faults are encountered in a cloud computing environment. They badly impact users and cause serious economic losses in business. As a vital technology, fault detection can guarantee a high reliability cloud environment. However, fault detection with a fixed detection cycle has defects and shortcomings. On the one hand, for the service with good performance, if a small cycle is set, it may need a lot of system overhead due to unnecessary over detection; on the other hand, for the service with poor performance, if a large cycle is set, it may result in the omission of faults which should be detected. To address these issues, in this paper, a fault detection model is proposed to improve the detection accuracy based on support vector machine and a decision tree. For abnormal samples, their abnormality is calculated by using the model. We design algorithms to adaptively and dynamically adjust cycles for fault detection. The cycle is shortened if a system experiences many faults, thus increasing fault detection success rate; it is lengthened if the system runs without any problem, thereby reducing much computational overhead. Experimental results show that the proposed method outperforms two classical methods, i.e., one based on self-organizing competitive neutral network and the other based on a probabilistic neural network.
SemEval-2019 Task 10: Math Question Answering

We report on the SemEval 2019 task on math question answering. We provided a question set derived from Math SAT practice exams, including 2778 training questions and 1082 test questions. For a significant subset of these questions, we also provided SMT-LIB logical form annotations and an interpreter that could solve these logical forms. Systems were evaluated based on the percentage of correctly answered questions. The top system correctly answered 45% of the test questions, a considerable improvement over the 17% random guessing baseline.
BNU-HKBU UIC NLP Team 2 at SemEval-2019 Task 6: Detecting Offensive Language Using BERT model

In this study we deal with the problem of identifying and categorizing offensive language in social media. Our group, BNU-HKBU UIC NLP Team2, use supervised classification along with multiple version of data generated by different ways of pre-processing the data. We then use the state-of-the-art model Bidirectional Encoder Representations from Transformers, or BERT (Devlin et al, 2018), to capture linguistic, syntactic and semantic features. Long range dependencies between each part of a sentence can be captured by BERT’s bidirectional encoder representations. Our results show 85.12% accuracy and 80.57% F1 scores in Subtask A (offensive language identification), 87.92% accuracy and 50% F1 scores in Subtask B (categorization of offense types), and 69.95% accuracy and 50.47% F1 score in Subtask C (offense target identification). Analysis of the results shows that distinguishing between targeted and untargeted offensive language is not a simple task. More work needs to be done on the unbalance data problem in Subtasks B and C. Some future work is also discussed.
Cox Proportional Hazard Regression Versus a Deep Learning Algorithm in the Prediction of Dementia: An Analysis Based on Periodic Health Examination

Background With the increase in the world’s aging population, there is a growing need to prevent and predict dementia among the general population. The availability of national time-series health examination data in South Korea provides an opportunity to use deep learning algorithm, an artificial intelligence technology, to expedite the analysis of mass and sequential data. Objective This study aimed to compare the discriminative accuracy between a time-series deep learning algorithm and conventional statistical methods to predict all-cause dementia and Alzheimer dementia using periodic health examination data. Methods Diagnostic codes in medical claims data from a South Korean national health examination cohort were used to identify individuals who developed dementia or Alzheimer dementia over a 10-year period. As a result, 479,845 and 465,081 individuals, who were aged 40 to 79 years and without all-cause dementia and Alzheimer dementia, respectively, were identified at baseline. The performance of the following 3 models was compared with predictions of which individuals would develop either type of dementia: Cox proportional hazards model using only baseline data (HR-B), Cox proportional hazards model using repeated measurements (HR-R), and deep learning model using repeated measurements (DL-R). Results The discrimination indices (95% CI) for the HR-B, HR-R, and DL-R models to predict all-cause dementia were 0.84 (0.83-0.85), 0.87 (0.86-0.88), and 0.90 (0.90-0.90), respectively, and those to predict Alzheimer dementia were 0.87 (0.86-0.88), 0.90 (0.88-0.91), and 0.91 (0.91-0.91), respectively. The DL-R model showed the best performance, followed by the HR-R model, in predicting both types of dementia. The DL-R model was superior to the HR-R model in all validation groups tested. Conclusions A deep learning algorithm using time-series data can be an accurate and cost-effective method to predict dementia. A combination of deep learning and proportional hazards models might help to enhance prevention strategies for dementia.
SDSRS: A Novel White-Box Cryptography Scheme for Securing Embedded Devices in IIoT

In this article, with the rapid development of industrial Internet of Things, a large number of embedded devices, such as sensors and tag readers, have been widely deployed for gathering and sending data. These devices are commonly unreliable and vulnerable to many threats, because they are located in unattended areas which are vulnerable to device capture attacks. Such environments can be regarded as white-box attack contexts, in which the adversary has total visibility and full control of the implementations. White-box cryptography (WBC) aims to protect implementations of symmetric encryption algorithms in white-box attack contexts. Unfortunately, existing WBC schemes are vulnerable to various attacks, and most of them are insufficiently secure in strict white-box attack contexts. Based on the investigation of existing designs and the corresponding cryptanalysis, we propose a novel design approach for securing WBC schemes, which is named state-dependent selectable random substitutions (SDSRS). It uses SDSRSs to defeat various related white-box cryptanalytic approaches. With special considerations for IIoT systems, such as high performance for supporting real-time applications and small block size for fitting industrial protocols, a concrete WBC scheme designed with the proposed approach has been provided. Our theoretical analysis shows that the proposed scheme is secure. Additionally, experimental results indicate that the scheme performs well in practice, and it is significantly efficient in time and energy consumptions compared with existing secure white-box cryptographic schemes.
Joint Event Detection and Description in Continuous Video Streams

Dense video captioning involves first localizing events in a video and then generating captions for the identified events. We present the Joint Event Detection and Description Network (JEDDi-Net) for solving this task in an end-to-end fashion, which encodes the input video stream with three-dimensional convolutional layers, proposes variable-length temporal events based on pooled features, and then uses a two-level hierarchical LSTM module with context modeling to transcribe the event proposals into captions. We show the effectiveness of our proposed JEDDi-Net on the large-scale ActivityNet Captions dataset.
Single-Label Multi-Class Image Classification by Deep Logistic Regression

The objective learning formulation is essential for the success of convolutional neural networks. In this work, we analyse thoroughly the standard learning objective functions for multiclass classification CNNs: softmax regression (SR) for singlelabel scenario and logistic regression (LR) for multi-label scenario. Our analyses lead to an inspiration of exploiting LR for single-label classification learning, and then the disclosing of the negative class distraction problem in LR. To address this problem, we develop two novel LR based objective functions that not only generalise the conventional LR but importantly turn out to be competitive alternatives to SR in single label classification. Extensive comparative evaluations demonstrate the model learning advantages of the proposed LR functions over the commonly adopted SR in single-label coarse-grained object categorisation and cross-class fine-grained person instance identification tasks. We also show the performance superiority of our method on clothing attribute classification in comparison to the vanilla LR function. The code had been made publicly available.
New signal detection theory-based framework for eyewitness performance in lineups.

Eyewitness research has adapted signal detection theory (SDT) to investigate eyewitness performance. SDT-based measures in yes/no tasks fit well for the measurement of eyewitness performance in show-ups, but not in lineups, because the application of the measures to eyewitness identifications neglects the role of fillers. In the present study, we introduce a SDT-based framework for eyewitness performance in lineups-Multi-d' Model.The Multi-d' model provides multiple discriminability measures which can be used as parameters to investigate eyewitness performance. We apply the Multi-d' model to issues in eyewitness research, such as the comparison of eyewitness discriminability between show-ups and lineups; the influence of lineup bias on eyewitness performance; filler selection methods (match-to-description vs. match-to-suspect); eyewitness confidence; and lineup presentation modes (simultaneous vs. sequential lineups).The Multi-d' model demonstrates that the discriminability of a guilty suspect from an innocent suspect is a function of discriminability involving fillers; and underscores that the decisions that eyewitnesses make in lineups can be regarded from two perspective-detection and identification.We propose that the Multi-d' model is a useful tool to understand decisionmakers' performance in a variety of compound decision tasks, as well as eyewitness identifications in lineups. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Multilevel similarity model for high-resolution remote sensing image registration

Image registration is a prerequisite and the basis for many important applications of remote sensing images. Compared with medium-/low-resolution images, high-resolution (HR) remote sensing images exhibit considerable resolution differences, complex distortions and repeatable textures. Most of the existing registration methods are designed for images with medium/low resolutions. However, these methods typically suffer from many false matches of keypoints when working with HR images. This problem often causes automatic registration to fail in applications. To address these problem, we propose a multilevel similarity model for HR remote sensing image registration. The multilevel similarity model includes three progressive levels of elements: the similarity of keypoint physical size (i.e., point-like similarity), the similarity of textures between two keypoints (i.e., line-like similarity) and the similarity of keypoint space relationship (i.e., plane-like similarity). First, a candidate match set of keypoints is identified depending upon the physical sizes of the keypoint blob-like structures, so that many useless keypoints can be significantly excluded. Then, a minimum spanning tree is developed to discover the false matches, where the weights of the tree are estimated based on the similarities of image windows created between two target keypoints and their candidate homologous keypoints. Finally, a spatial relationship matrix is constructed to further refine the matches between images by efficiently coding the relative spatial locations among keypoints. Experiments were conducted on various HR remote sensing images with different resolutions and distortions, and the experimental results demonstrate the effectiveness of our method.
Authorized Keyword Search over Outsourced Encrypted Data in Cloud Environment

For better data availability and accessibility while ensuring data secrecy, end-users often tend to outsource their data to the cloud servers in an encrypted form. However, this brings a major challenge to perform the search for some keywords over encrypted content without disclosing any information to unintended entities. This paper proposes a novel expressive authorized keyword search scheme relying on the concept of ciphertext-policy attribute-based encryption. The originality of the proposed scheme is multifold. First, it supports the generic and convenient multi-owner and multi-user scenario, where the encrypted data are outsourced by several data owners and searchable by multiple users. Second, the formal security analysis proves that the proposed scheme is semantically secure against chosen keyword and outsiders keyword guessing attacks. Third, an interactive protocol is introduced which avoids the need of any secure-channels between users and service provider. Fourth, due to the concept of bilinear-map accumulator, the system can efficiently revoke users and/or their attributes, and authenticate them prior to launching any expensive search operations. Fifth, conjunctive keyword search is provided thus enabling to search for multiple keywords simultaneously, with minimal cost. Sixth, the performance analysis shows that the proposed scheme outperforms closely-related works.
Review of image low-level feature extraction methods for content-based image retrieval

Purpose In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years. Design/methodology/approach First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared. Findings The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR. Originality/value A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.