Reviewerly/exHarmony huggingface dataset:
Dataset-aware multi-task learning approaches for biomedical named entity recognition

Abstract Motivation Named entity recognition is a critical and fundamental task for biomedical text mining. Recently, researchers have focused on exploiting deep neural networks for biomedical named entity recognition (Bio-NER). The performance of deep neural networks on a single dataset mostly depends on data quality and quantity while high-quality data tends to be limited in size. To alleviate task-specific data limitation, some studies explored the multi-task learning (MTL) for Bio-NER and achieved state-of-the-art performance. However, these MTL methods did not make full use of information from various datasets of Bio-NER. The performance of state-of-the-art MTL method was significantly limited by the number of training datasets. Results We propose two dataset-aware MTL approaches for Bio-NER which jointly train all models for numerous Bio-NER datasets, thus each of these models could discriminatively exploit information from all of related training datasets. Both of our two approaches achieve substantially better performance compared with the state-of-the-art MTL method on 14 out of 15 Bio-NER datasets. Furthermore, we implemented our approaches by incorporating Bio-NER and biomedical part-of-speech (POS) tagging datasets. The results verify Bio-NER and POS can significantly enhance one another. Availability and implementation Our source code is available at https://github.com/zmmzGitHub/MTL-BC-LBC-BioNER and all datasets are publicly available at https://github.com/cambridgeltl/MTL-Bioinformatics-2016. Supplementary information Supplementary data are available at Bioinformatics online.
REDN: A Recursive Encoder-Decoder Network for Edge Detection

In this paper, we introduce REDN: A Recursive Encoder-Decoder Network with Skip-Connections for edge detection in natural images. The proposed network is a novel integration of a Recursive Neural Network with an Encoder-Decoder architecture. The recursive network enables iterative refinement of the edges using a single network model. Adding skip-connections between encoder and decoder helps the gradients reach all the layers of a network more easily and allows information related to finer details in the early stage of the encoder to be fully utilized in the decoder. Based on our extensive experiments on popular boundary detection datasets including BSDS500 [1], NYUD [2] and Pascal Context [3], REDN significantly advances the state-of-the-art on edge detection regarding standard evaluation metrics such as Optimal Dataset Scale (ODS) F-measure, Optimal Image Scale (OIS) F-measure, and Average Precision (AP).
The Quantum Analysis of Nonlinear Optical Parametric Processes with Thermal Reservoirs

In this paper, employing the stochastic differential equations associated with the normal ordering, the quantum properties of a nondegenerate three-level cascade laser with a parametric amplifier and coupled to a two-mode thermal reservoir are thoroughly analyzed. Particularly, the enhancement of squeezing and the amplification of photon entanglement of the two-mode cavity light are investigated. It is found that the two cavity modes are strongly entangled and the degree of entanglement is directly related to the two-mode squeezing. Despite the fact that the entanglement and squeezing decrease with the increment of the mean photon number of the thermal reservoir, strong amount of these nonclassical properties can be generated for a considerable amount of thermal noise with the help of the nonlinear crystal introduced into the laser cavity. Moreover, the squeezing and entanglement of the cavity radiation enhance with the rate of atomic injection.
Evaluation of patient perception towards dynamic health data sharing using blockchain based digital consent with the Dovetail digital consent application: A cross sectional exploratory study

New patient-centric integrated care models are enabled by the capability to exchange the patient's data amongst stakeholders, who each specialise in different aspects of the patient's care. This requires a robust, trusted and flexible mechanism for patients to offer consent to share their data. Furthermore, new IT technologies make it easier to give patients more control over their data, including the right to revoke consent. These characteristics challenge the traditional paper-based, single-organisation-led consent process. The Dovetail digital consent application uses a mobile application and blockchain based infrastructure to offer this capability, as part of a pilot allowing patients to have their data shared amongst digital tools, empowering patients to manage their condition within an integrated care setting.To evaluate patient perceptions towards existing consent processes, and the Dovetail blockchain based digital consent application as a means to manage data sharing in the context of diabetes care.Patients with diabetes at a General Practitioner practice were recruited. Data were collected using focus groups and questionnaires. Thematic analysis of the focus group transcripts and descriptive statistics of the questionnaires was performed.There was a lack of understanding of existing consent processes in place, and many patients did not have any recollection of having previously given consent. The digital consent application received favourable feedback, with patients recognising the value of the capability offered by the application. Patients overwhelmingly favoured the digital consent application over existing practice.Digital consent was received favourably, with patients recognising that it addresses the main limitations of the current process. Feedback on potential improvements was received. Future work includes confirmation of results in a broader demographic sample and across multiple conditions.
Removing Arbitrary-Scale Rain Streaks via Fractal Band Learning With Self-Supervision

Data-driven rain streak removal methods, most of which rely on synthesized paired data, usually come across the generalization problem when being applied in real scenarios. In this paper, we propose a novel deep-learning based rain streak removal method injected with self-supervision to obtain the capacity of removing more varied-scale rain streaks in practical applications. To this end, in this work, efforts are made from two perspectives. First, considering that rain streak removal is highly correlated with texture characteristics, we create a fractal band learning (FBL) network based on frequency band recovery. It integrates commonly seen band feature operations as neural forms and effectively improves the capacity to capture discriminative features for deraining. Second, to further improve the generalization ability of FBL to remove rain streaks of varied scales, we incorporate scale-robust self-supervision to regularize the network training. The constraint forces the extracted features of an input rain image at different scales to be equivalent after rescaling operations. Therefore, our method can offer similar responses based on solely image content without the interference of scale change and is capable to remove varied-scale rain streaks. Extensive experiments in quantitative and qualitative evaluations demonstrate the superiority of our method for rain streak removal, especially for the real cases where very large rain streaks exist, and prove the effectiveness of each component.
The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment

Mounting privacy legislation calls for the preservation of privacy in speech technology, though solutions are gravely lacking.While evaluation campaigns are long-proven tools to drive progress, the need to consider a privacy adversary implies that traditional approaches to evaluation must be adapted to the assessment of privacy and privacy preservation solutions.This paper presents the first step in this direction: metrics.We introduce the zero evidence biometric recognition assessment (ZEBRA) framework and propose two new privacy metrics.They measure the average level of privacy preservation afforded by a given safeguard for a population and the worst-case privacy disclosure for an individual.The paper demonstrates their application to privacy preservation assessment within the scope of the VoicePrivacy challenge.While the ZEBRA framework is designed with speech applications in mind, it is a candidate for incorporation into biometric information protection standards and is readily extendable to the study of privacy in applications even beyond speech and biometrics.
Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging

The written forms of Semitic languages are both highly ambiguous and morphologically rich: a word can have multiple interpretations and is one of many inflected forms of the same concept or lemma.This is further exacerbated for dialectal content, which is more prone to noise and lacks a standard orthography.The morphological features can be lexicalized, like lemmas and diacritized forms, or non-lexicalized, like gender, number, and partof-speech tags, among others.Joint modeling of the lexicalized and non-lexicalized features can identify more intricate morphological patterns, which provide better context modeling, and further disambiguate ambiguous lexical choices.However, the different modeling granularity can make joint modeling more difficult.Our approach models the different features jointly, whether lexicalized (on the characterlevel), or non-lexicalized (on the word-level).We use Arabic as a test case, and achieve stateof-the-art results for Modern Standard Arabic with 20% relative error reduction, and Egyptian Arabic with 11% relative error reduction.
Effective Opinion Spam Detection: A Study on Review Metadata Versus Content

Abstract Purpose This paper aims to analyze the effectiveness of two major types of features—metadata-based (behavioral) and content-based (textual)—in opinion spam detection. Design/methodology/approach Based on spam-detection perspectives, our approach works in three settings: review-centric (spam detection), reviewer-centric (spammer detection) and product-centric (spam-targeted product detection). Besides this, to negate any kind of classifier-bias, we employ four classifiers to get a better and unbiased reflection of the obtained results. In addition, we have proposed a new set of features which are compared against some well-known related works. The experiments performed on two real-world datasets show the effectiveness of different features in opinion spam detection. Findings Our findings indicate that behavioral features are more efficient as well as effective than the textual to detect opinion spam across all three settings. In addition, models trained on hybrid features produce results quite similar to those trained on behavioral features than on the textual, further establishing the superiority of behavioral features as dominating indicators of opinion spam. The features used in this work provide improvement over existing features utilized in other related works. Furthermore, the computation time analysis for feature extraction phase shows the better cost efficiency of behavioral features over the textual. Research limitations The analyses conducted in this paper are solely limited to two well-known datasets, viz., YelpZip and YelpNYC of Yelp.com. Practical implications The results obtained in this paper can be used to improve the detection of opinion spam, wherein the researchers may work on improving and developing feature engineering and selection techniques focused more on metadata information. Originality/value To the best of our knowledge, this study is the first of its kind which considers three perspectives (review, reviewer and product-centric) and four classifiers to analyze the effectiveness of opinion spam detection using two major types of features. This study also introduces some novel features, which help to improve the performance of opinion spam detection methods.
Highly Efficient Implementation of Block Ciphers on Graphic Processing Units for Massively Large Data

With the advent of IoT and Cloud computing service technology, the size of user data to be managed and file data to be transmitted has been significantly increased. To protect users’ personal information, it is necessary to encrypt it in secure and efficient way. Since servers handling a number of clients or IoT devices have to encrypt a large amount of data without compromising service capabilities in real-time, Graphic Processing Units (GPUs) have been considered as a proper candidate for a crypto accelerator for processing a huge amount of data in this situation. In this paper, we present highly efficient implementations of block ciphers on NVIDIA GPUs (especially, Maxwell, Pascal, and Turing architectures) for environments using massively large data in IoT and Cloud computing applications. As block cipher algorithms, we choose AES, a representative standard block cipher algorithm; LEA, which was recently added in ISO/IEC 29192-2:2019 standard; and CHAM, a recently developed lightweight block cipher algorithm. To maximize the parallelism in the encryption process, we utilize Counter (CTR) mode of operation and customize it by using GPU’s characteristics. We applied several optimization techniques with respect to the characteristics of GPU architecture such as kernel parallelism, memory optimization, and CUDA stream. Furthermore, we optimized each target cipher by considering the algorithmic characteristics of each cipher by implementing the core part of each cipher with handcrafted inline PTX (Parallel Thread eXecution) codes, which are virtual assembly codes in CUDA platforms. With the application of our optimization techniques, in our implementation on RTX 2070 GPU, AES and LEA show up to 310 Gbps and 2.47 Tbps of throughput, respectively, which are 10.7% and 67% improved compared with the 279.86 Gbps and 1.47 Tbps of the previous best result. In the case of CHAM, this is the first optimized implementation on GPUs and it achieves 3.03 Tbps of throughput on RTX 2070 GPU.
Validating the CorrectWearing of Protection Mask by Taking a Selfie: Design of a Mobile Application “CheckYourMask” to Limit the Spread of COVID-19

In a context of a virus that is transmissive by sputtering, wearing masks appear necessary to protect the wearer and to limit the propagation of the disease. Currently, we are facing the 2019–2020 coronavirus pandemic. Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. The symptom of COVID-19 was reported first in China and very quickly spreads to the rest of the world. The COVID-19 contagiousness is known to be high by comparison with the flu. In this paper, we propose a design of a mobile application for permitting everyone having a smartphone and being able to take a picture to verify that his/her protection mask is correctly positioned on his/her face. Such application can be particularly useful for people using face protection mask for the first time and notably for children and old people. The designed method exploits Haar-like feature descriptors to detect key features of the face and a decision-making algorithm is applied. Experimental results show the potential of this method in the validation of the correct mask wearing. To the best of our knowledge, our work is the only one that currently proposes a mobile application design “CheckYourMask” for validating the correct wearing of protection mask.
Understanding employees' adoption of the Bring-Your-Own-Device (BYOD): the roles of information security-related conflict and fatigue

Purpose While the bring-your-own-device (BYOD) trend provides benefits for employees, it also poses security risks to organizations. This study explores whether and how employees decide to adopt BYOD practices when they encounter information security–related conflict. Design/methodology/approach Using survey data from 235 employees of Chinese enterprises and applying partial least squares based structural equation modeling (PLS-SEM), we test a series of hypotheses. Findings The results suggest that information security–related conflict elicits information security fatigue among employees. As their information security fatigue increases, employees become less likely to adopt BYOD practices. In addition, information security–related conflict has an indirect effect on employee's BYOD adoption through the full mediation of information security fatigue. Practical implications This study provides practical implications to adopt BYOD in the workplace through conflict management measures and emotion management strategies. Conflict management measures focused on the reducing of four facets of information security–related conflict, such as improve organization's privacy policies and help employees to build security habits. Emotion management strategies highlighted the solutions to reduce fatigue through easing conflict, such as involving employees in the development or update of information security policies to voice their demands of privacy and other rights. Originality/value Our study extends knowledge by focusing on the barriers to employees' BYOD adoption when considering information security in the workplace. Specifically, this study takes a conflict perspective and builds a multi-faceted construct of information security–related conflict. Our study also extends information security behavior research by revealing an emotion-based mediation effect, that of information security fatigue, to explore the mechanism underlying the influence of information security–related conflict on employee behavior.
Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks

This research addresses the challenge of recognizing human daily activities using surface electromyography (sEMG) and wearable inertial sensors. Effective and efficient recognition in this context has emerged as a cornerstone in robust remote health monitoring systems, among other applications. We propose a novel pipeline that can attain state-of-the-art recognition accuracies on a recent-and-standard dataset—the Human Gait Database (HuGaDB). Using wearable gyroscopes, accelerometers, and electromyography sensors placed on the thigh, shin, and foot, we developed an approach that jointly performs sensor fusion and feature selection. Being done jointly, the proposed pipeline empowers the learned model to benefit from the interaction of features that might have been dropped otherwise. Using statistical and time-based features from heterogeneous signals of the aforementioned sensor types, our approach attains a mean accuracy of 99.8%, which is the highest accuracy on HuGaDB in the literature. This research underlines the potential of incorporating EMG signals especially when fusion and selection are done simultaneously. Meanwhile, it is valid even with simple off-the-shelf feature selection methods such the Sequential Feature Selection family of algorithms. Moreover, through extensive simulations, we show that the left thigh is a key placement for attaining high accuracies. With one inertial sensor on that single placement alone, we were able to achieve a mean accuracy of 98.4%. The presented in-depth comparative analysis shows the influence that every sensor type, position, and placement can have on the attained recognition accuracies—a tool that can facilitate the development of robust systems, customized to specific scenarios and real-life applications.
Automatic Stitching for Hyperspectral Images Using Robust Feature Matching and Elastic Warp

Hyperspectral images, which contain not only spatial information but also rich spectral information, have been extensively applied to the fields of agriculture, urban planning, etc. However, it is difficult for a single image to cover a large area. Therefore, it requires to take photos of various parts and apply image stitching technology to obtain a panoramic hyperspectral image. When the viewpoint of the scene changes a lot, the ghost issue will occur with traditional methods. In order to get the high-precision resultant panoramas, this article proposes an automatic image stitching algorithm for hyperspectral images using robust feature matching and elastic warp. Our method contains two stages. The first stage is to choose one band as reference band and obtain the panorama in a single band. In particular, we extract feature points by scale-invariant feature transform. Then, we propose an efficient algorithm called multiscale top K rank preservation algorithm, for establishing robust point correspondences between two sets of points. Next, we adopt robust elastic warp to obtain the panorama of each band. The second stage is to stitch all remaining bands based on the transformation obtained in the first stage and fuse the information of all bands together to get the final panoramic hyperspectral image. Extensive experiments have demonstrated the effectiveness of our proposed method.
&lt;p&gt;Localization of Nuclei in Breast Cancer Using Whole Slide Imaging System Supported by Morphological Features and Shape Formulas&lt;/p&gt;

Cancer rates are exponentially increasing worldwide and over 15 million new cases are expected in the year 2020 according to the World Cancer Report. To support the clinical diagnosis of the disease, recent technical advancements in digital microscopy have been achieved to reduce the cost and increase the efficiency of the process. Food and Drug Administration (FDA or Agency) has issued the guidelines, in particular, the development of digital whole slide image scanning system. It is very helpful to the computer-aided diagnosis of breast cancer.Whole slide imaging supported by fluorescence, immunohistochemistry, and multispectral imaging concepts. Due to the high dimension of WSI images and computation, it is a challenging task to find the region of interest (ROI) on a malignant sample image. The unsupervised machine learning and quantitative analysis of malignant sample images are supported by morphological features and shape formulas to find the correct region of interest. Due to computational limitations, it starts to work on small patches, integrate the results, and automated localize or detect the ROI. It is also compared to the handcrafted and automated region of interest provided in the ICIAR2018 dataset.A total of 10 hematoxylins and eosin (H&E) stained malignant breast histology microscopy whole slide image samples are labeled and annotated by two medical experts who are team members of the ICIAR 2018 challenge. After applying the proposed methodology, it is successfully able to localize the malignant patches of WSI sample images and getting the ROI with an average accuracy of 85.5%.With the help of the k-means clustering algorithm, morphological features, and shape formula, it is possible to recognize the region of interest using the whole slide imaging concept.
More Diverse Dialogue Datasets via Diversity-Informed Data Collection

Automated generation of conversational dialogue using modern neural architectures has made notable advances. However, these models are known to have a drawback of often producing uninteresting, predictable responses; this is known as the diversity problem. We introduce a new strategy to address this problem, called Diversity-Informed Data Collection. Unlike prior approaches, which modify model architectures to solve the problem, this method uses dynamically computed corpus-level statistics to determine which conversational participants to collect data from. Diversity-Informed Data Collection produces significantly more diverse data than baseline data collection methods, and better results on two downstream tasks: emotion classification and dialogue generation. This method is generalizable and can be used with other corpus-level metrics.