Reviewerly/exHarmony huggingface dataset:
Query Performance Prediction Through Retrieval Coherency

Post-retrieval Query Performance Prediction (QPP) methods benefit from the characteristics of the retrieved set of documents to determine query difficulty. While existing works have investigated the relation between query and retrieved document spaces, as well as retrieved document scores, the association between the retrieved documents themselves, referred to as coherency, has not been extensively investigated for QPP. We propose that the coherence of the retrieved documents can be formalized as a function of the characteristics of a network that represents the associations between these documents. Based on experiments on three corpora, namely Robust04, Gov2 and ClueWeb09 and their TREC topics, we show that our coherence measures outperform existing metrics in the literature and are able to significantly improve the performance of state of the art QPP methods.
Score-Oriented Loss (SOL) functions

Loss functions engineering and the assessment of prediction performances are two crucial and intertwined aspects of supervised machine learning. This paper focuses on binary classification to introduce a class of loss functions that are defined on probabilistic confusion matrices and that allow an automatic and a priori maximization of the skill scores. These loss functions are tested in various classification experiments, which show that the probability distribution function associated with the confusion matrices significantly impacts the outcome of the score maximization process, and that the proposed functions are competitive with other state-of-the-art probabilistic losses.
2D Laser and 3D Camera Data Integration and Filtering for Human Trajectory Tracking

This paper addresses a robust human trajectory tracking method through the data integration of 2D laser scanner and 3D camera. Mapping the deep learning-based 3D camera human detection onto the point cloud of the depth information to build up the 3D bounding box-represented human and using the state-of-the-art 2D laser-based leg detection are the main data streams of the human tracking system. The human-oriented global nearest neighbour (HOGNN) data association, inspired from the Hall's proxemics, was developed to improve both the 3D camera-based and 2D laser-based human detection techniques. The dual Kalman filters are employed to track the human trajectory in parallel. The integration of the 3D camera-based and 2D laser-based human tracking is the key function of the system providing the real-time feedback for both the HOGNN to reduce false-positives of the camera-based and laser-based human detection and the Kalman filter to enhance the quality of the human trajectory tracking under uncertain environmental conditions. We implemented the sensor integration on ROS and validated it through real-world experiments.
Determining the acceptability of testing contacts of confirmed COVID-19 cases to improve secondary case ascertainment

ABSTRACT Background UK asymptomatic contacts of confirmed COVID-19 cases are not routinely tested for SARS-CoV-2. Testing contacts may improve case ascertainment and reduce onward transmission. This study investigated the acceptability of SARS-CoV-2 testing among contacts of confirmed cases as an integral part of the contact-tracing process. Methods A cross-sectional descriptive survey of case contacts was conducted in the UK. All contacts who completed a telephone call with the NHS Test and Trace Agile Lighthouse team were eligible for inclusion and were offered a molecular test. Consenting participants were sent a self-swab kit. Results Of the 1523 individuals contacted, 602 (39.5%) accepted the test offer. Of the 240 (39.9%) samples returned for testing, 16.3% tested polymerase chain reaction-positive for SARS-CoV-2. Most individuals who declined with a reason (638/905; 70.5%) reported they had already taken or booked a SARS-CoV-2 test, or were part of a testing programme. Matched laboratory records confirmed 73.1% of those who declined were tested by another route. Conclusions Most case contacts were tested, either through arranging a test by themselves or by accepting the study offer. Results demonstrate high acceptability, with substantial test positivity, indicating that there is public health benefit in offering tests to contacts as a routine part of the contact-tracing process.
D‐BERT: Incorporating dependency‐based attention into BERT for relation extraction

Relation extraction between entity pairs is an increasingly critical area in natural language processing. Recently, the pre-trained bidirectional encoder representation from transformer (BERT) performs excellently on the text classification or sequence labelling tasks. Here, the high-level syntactic features that consider the dependency between each word and the target entities into the pre-trained language models are incorporated. Our model also utilizes the intermediate layers of BERT to acquire different levels of semantic information and designs multi-granularity features for final relation classification. Our model offers a momentous improvement over the published methods for the relation extraction on the widely used data sets.
Detecting Associations Based on the Multi-Variable Maximum Information Coefficient

The maximum information coefficient (MIC) is a novel and widely-using measure of association detection in large datasets. The most outstanding feature of MIC is that it has both generality and equability. However, MIC can only deal with two variables and cannot precisely estimate coupling associations of multiple variables. In this paper, we propose an extension of MIC to deal with multi-variable datasets, called the multi-variable maximum information coefficient (MMIC). Some inherited and novel properties of MMIC are proved, including generality, equability, monotonicity, and subadditivity. We design an algorithm based on greedy stepwise strategy and upper confidence bound (UCB) for an approximate calculation of MMIC. The tests of MMIC on generated datasets and examples on real datasets are carried out to detect known and novel associations.
Single image haze removal via attention-based transmission estimation and classification fusion network

With the rapid development of deep learning theory, many excellent convolutional neural network (CNN) based dehazing methods have been proposed for single image dehazing. However, the slow training convergence rate and haze residual are still two serious flaws of these existing dehazing networks. To tackle these issues, we propose a novel end-to-end CNN-based dehazing framework called attention-based transmission estimation and classification fusion network (ATECFN). The ATECFN framework consists of three submodules: attention-based transmission-airlight estimation network (ATAEN), multi-scale Auto-Encoders (MAE), and patch-based classification fusion network (PCFN). First, the transmission similarity, that is the similarity of neighboring pixels in transmission map, is introduced to significantly increase the capability of CNN to fit transmission map. Second, the ATAEN is exploited to estimate airlight map and transmission map and then they are used to obtain a rough dehazed result according to the atmospheric scattering model. Third, we present the MAE to further refine the rough result, where the multi-scale structure can effectively capture local details over a wide range of scales. Finally, PCFN, a new fusion strategy, is employed to integrate the two results generated by ATAEN and MAE, in which a probability map is derived from a binary classification network and viewed as the fusion coefficient map. Extensive experiments demonstrate that the proposed algorithm outperforms the state-of-the-art methods on both synthetic and real-world images, which can not only improve the training convergence rate but also remove the residual haze effectively.
Concept pluralism in conceptual engineering

In this paper, I argue that an adequate meta-semantic framework capable of accommodating the range of projects currently identified as projects in conceptual engineering must be sensitive to the fact that concepts (and hence projects relating to them) fall into distinct kinds. Concepts can vary, I will argue, with respect to their direction of determination, their modal range, and their temporal range. Acknowledging such variations yields a preliminary taxonomy of concepts and generates a meta-semantic framework that allows us both to accommodate the full range of cases and to identify a proper subset of concepts for special ameliorative consideration. Ignoring such variations, in contrast, leads to a restricted meta-semantic framework that accommodates only a subset of the particular projects while generating implausible accounts of others.
Deep node clustering based on mutual information maximization

Variational Graph Autoencoders (VGAs) are generative models for unsupervised learning of node representations within graph data. While VGAs have been achieved state-of-the-art results for different predictive tasks on graph-structured data, they are susceptible to the over-pruning problem where only a small subset of the stochastic latent units are active. This can limit their modeling capacity and their ability to learn meaningful representations. In this paper, we present SOLI (Stacked auto-encoder for nOde cLusterIng), an information maximization approach for learning graph representations by leveraging maximal cliques. SOLI relies on aggregating useful representations by assigning clique-based weights to various edges in a neighborhood while maximizing mutual information. The learned representations are mindful of graph patches centered around each node, and can be used for a range of downstream tasks, and thus encouraging more active units. We demonstrate strong performance across three graph benchmark datasets.(Code is available at https://github.com/SoheilaMolaei/SOLI.)
Intelligent Medicine Identification System Using a Combination of Image Recognition and Optical Character Recognition

This research aims to develop an automatic verification system with deep learning techniques to verify prescription dispensing accuracy. The proposed method will be able to help pharmacies to reduce errors that lead to patients receiving the wrong medicine to patients. The system consists of two models: image classification and text classification. The image classification model uses raw medicine blister pack images, then removes the background to interpret the features based on the pattern recognition for Histograms of Oriented Gradients (HOG) of the model. It is composed of Convolution Neural Network (CNN), Linear Regression, and Logistic Regression. The text classification model uses text extraction to obtain imprints appearing on the blister package then matches the words to a bag of word. The dataset collected two-hundred types of medicine blister packs images inside plastic zip bags as a dataset. It includes 300 high-quality images of front-side medicine blister packages for each type of package in light-controlled conditions with a black background, which are used for training the model. The automatic verification system uses the majority vote based on the confidence of the two models. Experimental results, indicate that the image classification model of CNN with HOG feature extraction has the highest accuracy at 95.83 percent. In-text classification results show that the method using Character Region Awareness For Text detection (CRAFT), Keras-OCR, and text correction gave the highest accuracy at 92 percent. Overall accuracy was 94.23 percent.
Topic prediction and knowledge discovery based on integrated topic modeling and deep neural networks approaches

Understanding the real-world short texts become an essential task in the recent research area. The document deduction analysis and latent coherent topic named as the important aspect of this process. Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) are suggested to model huge information and documents. This type of contexts’ main problem is the information limitation, words relationship, sparsity, and knowledge extraction. The knowledge discovery and machine learning techniques integrated with topic modeling were proposed to overcome this issue. The knowledge discovery was applied based on the hidden information extraction to increase the suitable dataset for further analysis. The integration of machine learning techniques, Artificial Neural Network (ANN) and Long Short-Term (LSTM) are applied to anticipate topic movements. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. We demonstrate general information from different techniques applied in short text topic modeling. We proposed three categories based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation using representative design and analysis of all categories’ performance in different tasks. Finally, the proposed system evaluates with state-of-art methods on real-world datasets, comprises them with long document topic modeling algorithms, and creates a classification framework that considers further knowledge and represents it in the machine learning pipeline.
Unsupervised Neural Machine Translation for Similar and Distant Language Pairs

Unsupervised neural machine translation (UNMT) has achieved remarkable results for several language pairs, such as French–English and German–English. Most previous studies have focused on modeling UNMT systems; few studies have investigated the effect of UNMT on specific languages. In this article, we first empirically investigate UNMT for four diverse language pairs (French/German/Chinese/Japanese–English). We confirm that the performance of UNMT in translation tasks for similar language pairs (French/German–English) is dramatically better than for distant language pairs (Chinese/Japanese–English). We empirically show that the lack of shared words and different word orderings are the main reasons that lead UNMT to underperform in Chinese/Japanese–English. Based on these findings, we propose several methods, including artificial shared words and pre-ordering, to improve the performance of UNMT for distant language pairs. Moreover, we propose a simple general method to improve translation performance for all these four language pairs. The existing UNMT model can generate a translation of a reasonable quality after a few training epochs owing to a denoising mechanism and shared latent representations. However, learning shared latent representations restricts the performance of translation in both directions, particularly for distant language pairs, while denoising dramatically delays convergence by continuously modifying the training data. To avoid these problems, we propose a simple, yet effective and efficient, approach that (like UNMT) relies solely on monolingual corpora: pseudo-data-based unsupervised neural machine translation. Experimental results for these four language pairs show that our proposed methods significantly outperform UNMT baselines.
Rotation Forest for Big Data

The Rotation Forest classifier is a successful ensemble method for a wide variety of data mining applications. However, the way in which Rotation Forest transforms the feature space through PCA, although powerful, penalizes training and prediction times, making it unfeasible for Big Data. In this paper, a MapReduce Rotation Forest and its implementation under the Spark framework are presented. The proposed MapReduce Rotation Forest behaves in the same way as the standard Rotation Forest, training the base classifiers on a rotated space, but using a functional implementation of the rotation that enables its execution in Big Data frameworks. Experimental results are obtained using different cloud-based cluster configurations. Bayesian tests are used to validate the method against two ensembles for Big Data: Random Forest and PCARDE classifiers. Our proposal incorporates the parallelization of both the PCA calculation and the tree training, providing a scalable solution that retains the performance of the original Rotation Forest and achieves a competitive execution time (in average, at training, more than 3 times faster than other PCA-based alternatives). In addition, extensive experimentation shows that by setting some parameters of the classifier (i.e., bootstrap sample size, number of trees, and number of rotations), the execution time is reduced with no significant loss of performance using a small ensemble.
Insulator Anomaly Detection Method Based on Few-Shot Learning

Due to the advantages of safety and economy, it has become a trend to use unmanned aerial vehicles (UAVs) instead of humans to inspect high-voltage transmission lines. Considering the manual inspection process and the few-shot learning, a two-stage method for insulator anomaly detection is proposed. In the first stage, a positioning-restoration-cropping method is discussed for insulator string detection and processing. In the second stage, an insulator anomaly detection model called a multi-scale feature reweighting (MFR) network is built. With the help of few-shot object detection, the detection of five kinds of anomaly insulator caps, such as falling off, breakage and ablation is realized. The mean average precision (mAP) of the proposed method is 88.76%.
Graded Hoare Logic and its Categorical Semantics

Abstract Deductive verification techniques based on program logics (i.e., the family of Floyd-Hoare logics) are a powerful approach for program reasoning. Recently, there has been a trend of increasing the expressive power of such logics by augmenting their rules with additional information to reason about program side-effects. For example, general program logics have been augmented with cost analyses, logics for probabilistic computations have been augmented with estimate measures, and logics for differential privacy with indistinguishability bounds. In this work, we unify these various approaches via the paradigm of grading , adapted from the world of functional calculi and semantics. We propose Graded Hoare Logic (GHL), a parameterisable framework for augmenting program logics with a preordered monoidal analysis. We develop a semantic framework for modelling GHL such that grading, logical assertions (pre- and post-conditions) and the underlying effectful semantics of an imperative language can be integrated together. Central to our framework is the notion of a graded category which we extend here, introducing graded Freyd categories which provide a semantics that can interpret many examples of augmented program logics from the literature. We leverage coherent fibrations to model the base assertion language, and thus the overall setting is also fibrational.
A machine learning approach to tracking crustal thickness variations in the eastern North China Craton

The variation of crustal thickness is a critical index to reveal how the continental crust evolved over its four billion years. Generally, ratios of whole-rock trace elements, such as Sr/Y, (La/Yb)n and Ce/Y, are used to characterize crustal thicknesses. However, sometimes confusing results are obtained since there is no enough filtered data. Here, a state-of-the-art approach, based on a machine-learning algorithm, is proposed to predict crustal thickness using global major- and trace-element geochemical data of intermediate arc rocks and intraplate basalts, and their corresponding crustal thicknesses. After the validation processes, the root-mean-square error (RMSE) and the coefficient of determination (R2) score were used to evaluate the performance of the machine learning algorithm based on the learning dataset which has never been used during the training phase. The results demonstrate that the machine learning algorithm is more reliable in predicting crustal thickness than the conventional methods. The trained model predicts that the crustal thickness of the eastern North China Craton (ENCC) was ~45 km from the Late Triassic to the Early Cretaceous, but ~35 km from the Early Cretaceous, which corresponds to the paleo-elevation of 3.0 ± 1.5 km at Early Mesozoic, and decease to the present-day elevation in the ENCC. The estimates are generally consistent with the previous studies on xenoliths from the lower crust and on the paleoenvironment of the coastal mountain of the ENCC, which indicates that the lower crust of the ENCC was delaminated abruptly at the Early Cretaceous.
Whispered and Lombard Neural Speech Synthesis

It is desirable for a text-to-speech system to take into account the environment where synthetic speech is presented, and provide appropriate context-dependent output to the user. In this paper, we present and compare various approaches for generating different speaking styles, namely, normal, Lombard, and whisper speech, using only limited data. The following systems are proposed and assessed: 1) Pre-training and fine-tuning a model for each style. 2) Lombard and whisper speech conversion through a signal processing based approach. 3) Multi-style generation using a single model based on a speaker verification model. Our mean opinion score and AB preference listening tests show that 1) we can generate high quality speech through the pre-training/fine-tuning approach for all speaking styles. 2) Although our speaker verification (SV) model is not explicitly trained to discriminate different speaking styles, and no Lombard and whisper voice is used for pretrain this system, SV model can be used as style encoder for generating different style embeddings as input for Tacotron system. We also show that the resulting synthetic Lombard speech has a significant positive impact on intelligibility gain.
Design of Power-Efficient Training Accelerator for Convolution Neural Networks

To realize deep learning techniques, a type of deep neural network (DNN) called a convolutional neural networks (CNN) is among the most widely used models aimed at image recognition applications. However, there is growing demand for light-weight and low-power neural network accelerators, not only for inference but also for training process. In this paper, we propose a training accelerator that provides low power and compact chip size targeted for mobile and edge computing applications. It accelerates to achieve the real-time processing of both inference and training using concurrent floating-point data paths. The proposed accelerator can be externally controlled and employs resource sharing and an integrated convolution-pooling block to achieve low area and low energy consumption. We implemented the proposed training accelerator in an FPGA (Field Programmable Gate Array) and evaluated its training performance using an MNIST CNN example in comparison with a PC with GPU (Graphics Processing Unit). While both methods achieved a similar training accuracy of 95.1%, the proposed accelerator, when implemented in a silicon chip, reduced the energy consumption by 480 times compared to the counterpart. Additionally, when implemented on an FPGA, an energy reduction of over 4.5 times was achieved compared to the existing FPGA training accelerator for the MNIST dataset. Therefore, the proposed accelerator is more suitable for deployment in mobile/edge nodes compared to the existing software and hardware accelerators.
Simplified TinyBERT: Knowledge Distillation for Document Retrieval

Despite the effectiveness of utilizing the BERT model for document ranking, the high computational cost of such approaches limits their uses. To this end, this paper first empirically investigates the effectiveness of two knowledge distillation models on the document ranking task. In addition, on top of the recently proposed TinyBERT model, two simplifications are proposed. Evaluations on two different and widely-used benchmarks demonstrate that Simplified TinyBERT with the proposed simplifications not only boosts TinyBERT, but also significantly outperforms BERT-Base when providing 15 $$\times $$ speedup.