Reviewerly/exHarmony huggingface dataset:
Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge

Neural-based context-aware models for slot tagging have achieved state-of-the-art performance.However, the presence of OOV(outof-vocab) words significantly degrades the performance of neural-based models, especially in a few-shot scenario.In this paper, we propose a novel knowledge-enhanced slot tagging model to integrate contextual representation of input text and the large-scale lexical background knowledge.Besides, we use multilevel graph attention to explicitly model lexical relations.The experiments show that our proposed knowledge integration mechanism achieves consistent improvements across settings with different sizes of training data on two public benchmark datasets.
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach

The recently proposed unlabeled-unlabeled (UU) classification method allows us to train a binary classifier only from two unlabeled datasets with different class priors. Since this method is based on the empirical risk minimization, it works as if it is a supervised classification method, compatible with any model and optimizer. However, this method sometimes suffers from severe overfitting, which we would like to prevent in this paper. Our empirical finding in applying the original UU method is that overfitting often co-occurs with the empirical risk going negative, which is not legitimate. Therefore, we propose to wrap the terms that cause a negative empirical risk by certain correction functions. Then, we prove the consistency of the corrected risk estimator and derive an estimation error bound for the corrected risk minimizer. Experiments show that our proposal can successfully mitigate overfitting of the UU method and significantly improve the classification accuracy.
BioMRC: A Dataset for Biomedical Machine Reading Comprehension

We introduce BIOMRC, a large-scale clozestyle biomedical MRC dataset.Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018).Experiments show that simple heuristics do not perform well on the new dataset, and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible.Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better.We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments.We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.
Computing Tight Differential Privacy Guarantees Using FFT.

Differentially private (DP) machine learning has recently become popular. The privacy loss of DP algorithms is commonly reported using $(\varepsilon,\delta)$-DP. In this paper, we propose a numerical accountant for evaluating the privacy loss for algorithms with continuous one dimensional output. This accountant can be applied to the subsampled multidimensional Gaussian mechanism which underlies the popular DP stochastic gradient descent. The proposed method is based on a numerical approximation of an integral formula which gives the exact $(\varepsilon,\delta)$-values. The approximation is carried out by discretising the integral and by evaluating discrete convolutions using the fast Fourier transform algorithm. We give both theoretical error bounds and numerical error estimates for the approximation. Experimental comparisons with state-of-the-art techniques demonstrate significant improvements in bound tightness and/or computation time. Python code for the method can be found in Github (this https URL).
An TPM Based Approach for Generation of Secret Key

As the world becoming so much internet dependent and near about all the communications are done via internet, so the security of the communicating data is to be enhanced accordingly.For these purpose many encryption-decryption algorithms are available and many neural network based keys are also available which is used in these algorithms.Neural Network is a technique which is designed to work like a human brain.It has the ability to perform complex calculations with ease.To generate a secret key using neural networks many techniques are available like Tree Parity Machine (TPM) and many others.In TPM there are some flaws like less randomness, less time efficient.There are already three rules available i.e.Hebbian Rule, Anti Hebbian Rule and Random Walk, with same problems.So to overcome these issues, we propose a new approach based on the same concept(TPM, as Tree-structured Neural Network's execution time is comparatively less than that of the other Neural Networks) which generate random and timeefficient secret key.
Image Recommendation Algorithm Based on Deep Learning

With the rapid development of network technology, the number of digital images is growing at an alarming rate, people's demand for information gradually shift from text into images.However, it is very difficult for users to quickly find the images they are interested in from the large number of image libraries.The purpose of this paper is to study the image recommendation algorithm based on deep learning.In this paper, image classification algorithm is firstly studied.LReLU -Softplus activation function is formed by combining LReLU function and Softplus function, and CNN is improved.Then, an image retrieval model based on local sensitive hash algorithm is proposed in this paper.This model calculates the distance in hamming space for the binary hash code generated by mapping.Euclidean distance is calculated inside the result set after similarity measurement to improve the accuracy, and the image retrieval model is constructed.Finally, an image recommendation model based on implicit support vector machine (SVM) is proposed in this paper.This image recommendation method combines image text information and image content information.The experimental results show that the proposed image recommendation model can meet the practical needs.In this paper, the overlap rate between the CNN-based recommendation model and the human recommendation algorithm was tested, and the coincidence degree of the two recommended images reached 88%.
Image Denoising Using Superpixel-Based PCA

Denoising is a fundamental task in image processing, aimed at estimating an unknown image from its noisy observation. In this paper, we develop a computationally simple paradigm for image denoising using superpixel grouping and principal component analysis (PCA) of similar patches within the superpixels. Our method comprises three steps. First, we perform a superpixel segmentation on the noisy images. Next, similar patches within the superpixels are grouped in order to preserve the local image structures. Finally, each group of similar patches is factorized by PCA transform and estimated by performing coefficient shrinkage in the PCA domain to remove the noise. The proposed method exploits the optimal energy compaction property of PCA on groups of similar patches in the least squares sense. The performance of our approach is experimentally verified on a variety of synthetic images at various noise levels, and on real world noisy images. Our proposed method achieves very competitive denoising performance, especially in preserving the fine image structures, compared with many existing denoising algorithms with respect to both objective measurement and visual evaluation. We also show that our proposed method is computationally more efficient than other local PCA based methods.
Location-Aware Box Reasoning for Anchor-Based Single-Shot Object Detection

In the majority of object detection frameworks, the confidence of instance classification is used as the quality criterion of predicted bounding boxes, like the confidence-based ranking in non-maximum suppression (NMS). However, the quality of bounding boxes, indicating the spatial relations, is not only correlated with the classification scores. Compared with the region proposal network (RPN) based detectors, single-shot object detectors suffer the box quality as there is a lack of pre-selection of box proposals. In this paper, we aim at single-shot object detectors and propose a location-aware anchor-based reasoning (LAAR) for the bounding boxes. LAAR takes both the location and classification confidences into consideration for the quality evaluation of bounding boxes. We introduce a novel network block to learn the relative location between the anchors and the ground truths, denoted as a localization score, which acts as a location reference during the inference stage. The proposed localization score leads to an independent regression branch and calibrates the bounding box quality by scoring the predicted localization score so that the best-qualified bounding boxes can be picked up in NMS. Experiments on MS COCO and PASCAL VOC benchmarks demonstrate that the proposed location-aware framework enhances the performances of current anchor-based single-shot object detection frameworks and yields consistent and robust detection results.
Inference attacks against differentially private query results from genomic datasets including dependent tuples

Abstract Motivation The rapid decrease in the sequencing technology costs leads to a revolution in medical research and clinical care. Today, researchers have access to large genomic datasets to study associations between variants and complex traits. However, availability of such genomic datasets also results in new privacy concerns about personal information of the participants in genomic studies. Differential privacy (DP) is one of the rigorous privacy concepts, which received widespread interest for sharing summary statistics from genomic datasets while protecting the privacy of participants against inference attacks. However, DP has a known drawback as it does not consider the correlation between dataset tuples. Therefore, privacy guarantees of DP-based mechanisms may degrade if the dataset includes dependent tuples, which is a common situation for genomic datasets due to the inherent correlations between genomes of family members. Results In this article, using two real-life genomic datasets, we show that exploiting the correlation between the dataset participants results in significant information leak from differentially private results of complex queries. We formulate this as an attribute inference attack and show the privacy loss in minor allele frequency (MAF) and chi-square queries. Our results show that using the results of differentially private MAF queries and utilizing the dependency between tuples, an adversary can reveal up to 50% more sensitive information about the genome of a target (compared to original privacy guarantees of standard DP-based mechanisms), while differentially privacy chi-square queries can reveal up to 40% more sensitive information. Furthermore, we show that the adversary can use the inferred genomic data obtained from the attribute inference attack to infer the membership of a target in another genomic dataset (e.g. associated with a sensitive trait). Using a log-likelihood-ratio test, our results also show that the inference power of the adversary can be significantly high in such an attack even using inferred (and hence partially incorrect) genomes. Availability and implementation https://github.com/nourmadhoun/Inference-Attacks-Differential-Privacy
Development of tunnel intelligent monitoring and early warning system based on micro-service architecture: the case of AnPing tunnel

Along with the development of computer science and communication techniques, tunnel monitoring has been digital and intellectualized gradually. In recent years, traditional tunnel monitoring systems have been becoming powerless to deal with huge amounts of data as the number of tunnels and monitoring sensors surge. In this study, an innovative and comprehensive intelligent tunnel monitoring system was designed based on micro-service architecture. Various function modules were distributed to multiple service instances to get the computing pressure decentralized. The system offered powerful massive data processing support, flexibility, and extensibility. For a certain tunnel, the system could acquire the monitoring data in real-time using wireless sensors deployed on its surface. When data reached the early warning threshold, the system would immediately transmit the warning message to users. Besides, an app was developed to offer support for wireless communication between the monitoring apparatus and watchers. The application of the Anping case, one of the monitoring targets, proved the high efficiency of data transmitting and effective emergency response of the developed system. The results indicated that the microservice architecture could facilitate algorithm development, decentralize calculation pressure, and promote the service capacity by decoupling components and distributing loads.
An Objective-Based Entropy Approach for Interpretable Decision Tree Models in Support of Human Resource Management: The Case of Absenteeism at Work

The negative impact of absenteeism on organizations’ productivity and profitability is well established. To decrease absenteeism, it is imperative to understand its underlying causes and to identify susceptible employee subgroups. Most research studies apply hypotheses testing and regression models to identify features that are correlated with absenteeism—typically, these models are limited to finding simple correlations. We illustrate the use of interpretable classification algorithms for uncovering subgroups of employees with common characteristics and a similar level of absenteeism. This process may assist human resource managers in understanding the underlying reasons for absenteeism, which, in turn, could stimulate measures to decrease it. Our proposed methodology makes use of an objective-based information gain measure in conjunction with an ordinal CART model. Our results indicate that the ordinal CART model outperforms conventional classifiers and, more importantly, identifies patterns in the data that have not been revealed by other models. We demonstrate the importance of interpretability for human resource management through three examples. The main contributions of this research are (1) the development of an information-based ordinal classifier for a published absenteeism dataset and (2) the illustration of an interpretable approach that could be of considerable value in supporting human resource management decision-making.
Towards Understanding ASR Error Correction for Medical Conversations

Domain Adaptation for Automatic Speech Recognition (ASR) error correction via machine translation is a useful technique for improving out-of-domain outputs of pre-trained ASR systems to obtain optimal results for specific in-domain tasks.We use this technique on our dataset of Doctor-Patient conversations using two off-the-shelf ASR systems: Google ASR (commercial) and the ASPIRE model (open-source).We train a Sequenceto-Sequence Machine Translation model and evaluate it on seven specific UMLS Semantic types, including Pharmacological Substance, Sign or Symptom, and Diagnostic Procedure to name a few.Lastly, we breakdown, analyze and discuss the 7% overall improvement in word error rate in view of each Semantic type.
Object Detection using Template and HOG Feature Matching

In the present era, the applications of computer vision is increasing day by day.Computer vision is related to the automatic recognition, exploration and extraction of the necessary information from a particular image or a group of image sets.This paper addresses the method to detect the desired object from an image.Usually, a template of the desired object is used in detection through a matching technique named Template Matching.But it works well when the template image is cropped from the original one, which is not always invariant due to various transformations in the test images.To cope with this difficulty and to develop a generalized approach, we investigate in detail another technique which is known as HOG (Histogram of Oriented Gradient) approach.In HOG, the image is divided into overlapping blocks of template size and then compare each block's normalized HOG with the normalized HOG of the template to find the best match of the object.We perform experiments with a large number of images and have found satisfactory performance.
An end-to-end deep source recording device identification system for Web media forensics

Purpose Most source recording device identification models for Web media forensics are based on a single feature to complete the identification task and often have the disadvantages of long time and poor accuracy. The purpose of this paper is to propose a new method for end-to-end network source identification of multi-feature fusion devices. Design/methodology/approach This paper proposes an efficient multi-feature fusion source recording device identification method based on end-to-end and attention mechanism, so as to achieve efficient and convenient identification of recording devices of Web media forensics. Findings The authors conducted sufficient experiments to prove the effectiveness of the models that they have proposed. The experiments show that the end-to-end system is improved by 7.1% compared to the baseline i-vector system, compared to the authors’ previous system, the accuracy is improved by 0.4%, and the training time is reduced by 50%. Research limitations/implications With the development of Web media forensics and internet technology, the use of Web media as evidence is increasing. Among them, it is particularly important to study the authenticity and accuracy of Web media audio. Originality/value This paper aims to promote the development of source recording device identification and provide effective technology for Web media forensics and judicial record evidence that need to apply device source identification technology.
Cloud Computing and Energy Efficiency: Mapping the Thematic Structure of Research

The dynamic growth in the use of cloud computing systems results in increasing energy consumption. Consequently, more and more attention is given to energy efficiency issues both in research and theory development as well as the business practice of cloud computing systems. In spite of the rapid development of research, the field has not been mapped from the bibliometric perspective yet. This study aims at publication profiling and mapping the thematic structure of the cloud computing energy efficiency research field. Detailed research objectives include: (1) profiling scientific publications in the field, (2) identifying and exploring thematic research areas, (3) identifying emerging topics and discussing their potential as future research lines. The aforementioned objectives are translated into the following study questions: (1) What are the most productive nations, institutions, source titles, and scholars contributing to research on energy efficiency in cloud computing? (2) What does the thematic structure of the research field look like? (3) What are the “hot” research topics attracting scholars’ attention? The research methodology toolbox includes a combination of bibliometric descriptive studies (research profiling), science mapping (keyword co-occurrence analysis), and literature reviews (systematic literature review). Bibliometric data for analysis were elicited from the Scopus database. The VOSviewer software supported bibliometric analysis and data visualization.
Deep Bi-LSTM Networks for Sequential Recommendation

Recent years have seen a surge in approaches that combine deep learning and recommendation systems to capture user preference or item interaction evolution over time. However, the most related work only consider the sequential similarity between the items and neglects the item content feature information and the impact difference of interacted items on the next items. This paper introduces the deep bidirectional long short-term memory (LSTM) and self-attention mechanism into the sequential recommender while fusing the information of item sequences and contents. Specifically, we deal with the issues in a three-pronged attack: the improved item embedding, weight update, and the deep bidirectional LSTM preference learning. First, the user-item sequences are embedded into a low-dimensional item vector space representation via Item2vec, and the class label vectors are concatenated for each embedded item vector. Second, the embedded item vectors learn different impact weights of each item to achieve item awareness via self-attention mechanism; the embedded item vectors and corresponding weights are then fed into the bidirectional LSTM model to learn the user preference vectors. Finally, the top similar items in the preference vector space are evaluated to generate the recommendation list for users. By conducting comprehensive experiments, we demonstrate that our model outperforms the traditional recommendation algorithms on Recall@20 and Mean Reciprocal Rank (MRR@20).
Visual Interpretability in Computer-Assisted Diagnosis of Thyroid Nodules Using Ultrasound Images

BACKGROUND:The number of studies on deep learning in artificial intelligence (AI)-assisted diagnosis of thyroid nodules is increasing. However, it is difficult to explain what the models actually learn in artificial intelligence-assisted medical research. Our aim is to investigate the visual interpretability of the computer-assisted diagnosis of malignant and benign thyroid nodules using ultrasound images. MATERIAL AND METHODS:We designed and implemented 2 experiments to test whether our proposed model learned to interpret the ultrasound features used by ultrasound experts to diagnose thyroid nodules. First, in an anteroposterior/transverse (A/T) ratio experiment, multiple models were trained by changing the A/T ratio of the original nodules, and their classification, accuracy, sensitivity, and specificity were tested. Second, in a visualization experiment, class activation mapping used global average pooling and a fully connected layer to visualize the neural network to show the most important features. We also examined the importance of data preprocessing. RESULTS:The A/T ratio experiment showed that after changing the A/T ratio of the nodules, the accuracy of the neural network model was reduced by 9.24–30.45%, indicating that our neural network model learned the A/T ratio information of the nodules. The visual experiment results showed that the nodule margins had a strong influence on the prediction of the neural network. CONCLUSIONS:This study was an active exploration of interpretability in the deep learning classification of thyroid nodules. It demonstrated the neural network-visualized model focused on irregular nodule margins and the A/T ratio to classify thyroid nodules.