Reviewerly/exHarmony huggingface dataset:
Secure training of decision trees with continuous attributes

Abstract We apply multiparty computation (MPC) techniques to show, given a database that is secret-shared among multiple mutually distrustful parties, how the parties may obliviously construct a decision tree based on the secret data. We consider data with continuous attributes (i.e., coming from a large domain), and develop a secure version of a learning algorithm similar to the C4.5 or CART algorithms. Previous MPC-based work only focused on decision tree learning with discrete attributes (De Hoogh et al. 2014). Our starting point is to apply an existing generic MPC protocol to a standard decision tree learning algorithm, which we then optimize in several ways. We exploit the fact that even if we allow the data to have continuous values, which a priori might require fixed or floating point representations, the output of the tree learning algorithm only depends on the relative ordering of the data. By obliviously sorting the data we reduce the number of comparisons needed per node to O ( N log 2 N ) from the naive O ( N 2 ), where N is the number of training records in the dataset, thus making the algorithm feasible for larger datasets. This does however introduce a problem when duplicate values occur in the dataset, but we manage to overcome this problem with a relatively cheap subprotocol. We show a procedure to convert a sorting network into a permutation network of smaller complexity, resulting in a round complexity of O (log N ) per layer in the tree. We implement our algorithm in the MP-SPDZ framework and benchmark our implementation for both passive and active three-party computation using arithmetic modulo 2 64 . We apply our implementation to a large scale medical dataset of ≈ 290 000 rows using random forests, and thus demonstrate practical feasibility of using MPC for privacy-preserving machine learning based on decision trees for large datasets.
Document-level Relation Extraction with Dual-tier Heterogeneous Graph

Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result.In this paper, we propose a novel graphbased model with Dual-tier Heterogeneous Graph (DHG) for document-level RE.In particular, DHG is composed of a structure modeling layer followed by a relation reasoning layer.The major advantage is that it is capable of not only capturing both the sequential and structural information of documents but also mixing them together to benefit for multi-hop reasoning and final decisionmaking.Furthermore, we employ Graph Neural Networks (GNNs) based message propagation strategy to accumulate information on DHG.Experimental results demonstrate that the proposed method achieves state-of-the-art performance on two widely used datasets, and further analyses suggest that all the modules in our model are indispensable for document-level RE.
Simulating Many-Body Systems with a Projective Quantum Eigensolver

We present a new hybrid quantum-classical algorithm for optimizing unitary coupled-cluster (UCC) wave functions deemed the projective quantum eigensolver (PQE), amenable to near-term noisy quantum hardware. Contrary to variational quantum algorithms, PQE optimizes a trial state using residuals (projections of the Schr\"{o}dinger equation) rather than energy gradients. We show that the residuals may be evaluated by simply measuring two energy expectation values per element. We also introduce a selected variant of PQE (SPQE) that uses an adaptive ansatz built from arbitrary-order particle-hole operators, offering an alternative to gradient-based selection procedures. PQE and SPQE are tested on a set of molecular systems covering both the weak and strong correlation regimes, including hydrogen clusters with 4-10 atoms and the BeH$_2$ molecule. When employing a fixed ansatz, we find that PQE can converge disentangled (factorized) UCC wave functions to essentially identical energies as variational optimization while requiring fewer computational resources. A comparison of SPQE and adaptive variational quantum algorithms shows that - for ans\"{a}tze containing the same number of parameters - the two methods yield results of comparable accuracy. Finally, we show that SPQE performs similar to, and in some cases, better than selected configuration interaction and the density matrix renormalization group on 1-3 dimensional strongly correlated H$_{10}$ systems in terms of energy accuracy for a given number of variational parameters.
A Robust and Fast Image Encryption Scheme Based on a Mixing Technique

This paper introduces a new image encryption scheme using a mixing technique as a way to encrypt one or multiple images of different types and sizes. The mixing model follows a nonlinear mathematical expression based on Cramer’s rule. Two 1D systems already developed in the literature, namely, the May-Gompertz map and the piecewise linear chaotic map, were used in the mixing process as pseudo-random number generators for their good chaotic properties. The image to be encrypted was first of all partitioned into N subimages of the same size. The subimages underwent a block permutation using the May-Gompertz map. This was followed by a pixel-based permutation using the piecewise linear chaotic map. The result of the two previous permutations was divided into 4 subimages, which were then mixed using pseudo-random matrices generated from the two maps mentioned above. Tests carried out on the cryptosystem designed proved that it was fast due to the 1D maps used, robust in terms of noise and data loss, exhibited a large key space, and resisted all common attacks. A very interesting feature of the proposed cryptosystem is that it works well for simultaneous multiple-image encryption.
Enhancing object detection for autonomous driving by optimizing anchor generation and addressing class imbalance

Object detection has been one of the most active topics in computer vision for the past years. Recent works have mainly focused on pushing the state-of-the-art in the general-purpose COCO benchmark. However, the use of such detection frameworks in specific applications such as autonomous driving is yet an area to be addressed. This study presents an enhanced 2D object detector based on Faster R-CNN that is better suited for the context of autonomous vehicles. Two main aspects are improved: the anchor generation procedure and the performance drop in minority classes. The default uniform anchor configuration is not suitable in this scenario due to the perspective projection of the vehicle cameras. Therefore, we propose a perspective-aware methodology that divides the image into key regions via clustering and uses evolutionary algorithms to optimize the base anchors for each of them. Furthermore, we add a module that enhances the precision of the second-stage header network by including the spatial information of the candidate regions proposed in the first stage. We also explore different re-weighting strategies to address the foreground-foreground class imbalance, showing that the use of a reduced version of focal loss can significantly improve the detection of difficult and underrepresented objects in two-stage detectors. Finally, we design an ensemble model to combine the strengths of the different learning strategies. Our proposal is evaluated with the Waymo Open Dataset, which is the most extensive and diverse up to date. The results demonstrate an average accuracy improvement of 6.13% mAP when using the best single model, and of 9.69% mAP with the ensemble. The proposed modifications over the Faster R-CNN do not increase computational cost and can easily be extended to optimize other anchor-based detection frameworks.
Annealing Knowledge Distillation

Significant memory and computational requirements of large deep neural networks restrict their application on edge devices.Knowledge distillation (KD) is a prominent model compression technique for deep neural networks in which the knowledge of a trained large teacher model is transferred to a smaller student model.The success of knowledge distillation is mainly attributed to its training objective function, which exploits the softtarget information (also known as "dark knowledge") besides the given regular hard labels in a training set.However, it is shown in the literature that the larger the gap between the teacher and the student networks, the more difficult is their training using knowledge distillation.To address this shortcoming, we propose an improved knowledge distillation method (called Annealing-KD) by feeding the rich information provided by the teacher's softtargets incrementally and more efficiently.Our Annealing-KD technique is based on a gradual transition over annealed soft-targets generated by the teacher at different temperatures in an iterative process, and therefore, the student is trained to follow the annealed teacher output in a step-by-step manner.This paper includes theoretical and empirical evidence as well as practical experiments to support the effectiveness of our Annealing-KD method.We did a comprehensive set of experiments on different tasks such as image classification (CIFAR-10 and 100) and NLP language inference with BERT-based models on the GLUE benchmark and consistently got superior results.
Harnessing the digital potential of the next generation of health professionals

Abstract Digital technologies are rapidly being integrated into a wide range of health fields. This new domain, often termed ‘digital health’, has the potential to significantly improve healthcare outcomes and global health equity more broadly. However, its effective implementation and responsible use are contingent on building a health workforce with a sufficient level of knowledge and skills to effectively navigate the digital transformations in health. More specifically, the next generation of health professionals—namely youth—must be adequately prepared to maximise the potential of these digital transformations. In this commentary, we highlight three priority areas which should be prioritised in digital education to realise the benefits of digital health: capacity building, opportunities for youth, and an ethics-driven approach. Firstly, capacity building requires educational frameworks and curricula to not only be updated, but to also place an emphasis on interdisciplinary learning. Secondly, opportunities are important for youth to meaningfully participate in decision-making processes and gain invaluable practical experiences. Thirdly, training in digital ethics and the responsible use of data as a standard component of education will help to safeguard against potential future inequities resulting from the implementation and use of digital health technologies.
Physical Education Teaching in Colleges and Universities Assisted by Virtual Reality Technology Based on Artificial Intelligence

Virtual reality technology has promoted the reform of education. This research mainly discusses college physical education teaching assisted by artificial intelligence-based virtual reality technology. According to the position change of the virtual human’s center of gravity, the spline keyframe interpolation method is used for interpolation, and the model pose obtained in each frame is rendered to obtain the virtual human’s animation. After synthesizing a virtual human animation with three-dimensional human motion data, the animation can have functions such as video storage, fast playback, slow playback, and freeze. At the same time, the system can also display and play the virtual human animation and the video shot by the camera on the same screen, in order to make an intuitive comparison of the athletes’ movements. Coaches can edit by hand or shoot the sports of outstanding domestic and foreign athletes on the spot and then use VC++6.0 as a development tool to analyze and get the simulation video of the 3D virtual human body. The virtual human animation technology in the motion analysis system is to relocate the three-dimensional motion data extracted from the video captured by the camera to the three-dimensional virtual human model we have established, and the three-dimensional virtual human will then simulate the technical actions of the athletes, which indirectly reflects that the three-dimensional movement information of the athletes enables coaches and athletes to observe the athletes’ technical movements in a three-dimensional space in real time, repeatedly, and from multiple angles so that the coach can accurately guide the athletes’ technical movements. Finally, a neural network based on artificial intelligence technology is used to evaluate the teaching effect. In the comparative experiment, 35% of the people in the virtual teaching experiment group were excellent, while the control group had only 10% in this excellent range (90–100). This research contributes to the smooth progress of VR technology teaching in colleges and universities.
A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images

Abstract The differentiation between major histological types of lung cancer, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small-cell lung cancer (SCLC) is of crucial importance for determining optimum cancer treatment. Hematoxylin and Eosin (H&amp;E)-stained slides of small transbronchial lung biopsy (TBLB) are one of the primary sources for making a diagnosis; however, a subset of cases present a challenge for pathologists to diagnose from H&amp;E-stained slides alone, and these either require further immunohistochemistry or are deferred to surgical resection for definitive diagnosis. We trained a deep learning model to classify H&amp;E-stained Whole Slide Images of TBLB specimens into ADC, SCC, SCLC, and non-neoplastic using a training set of 579 WSIs. The trained model was capable of classifying an independent test set of 83 challenging indeterminate cases with a receiver operator curve area under the curve (AUC) of 0.99. We further evaluated the model on four independent test sets—one TBLB and three surgical, with combined total of 2407 WSIs—demonstrating highly promising results with AUCs ranging from 0.94 to 0.99.
Spatio-Temporal Analysis of Heavy Metals in Arid Soils at the Catchment Scale Using Digital Soil Assessment and a Random Forest Model

Predicting the spatio-temporal distribution of absorbable heavy metals in soil is needed to identify the potential contaminant sources and develop appropriate management plans to control these hazardous pollutants. Therefore, our aim was to develop a model to predict soil adsorbable heavy metals in arid regions of Iran from 1986 to 2016. Soil adsorbable heavy metals were measured in 201 samples from locations selected using the Latin hypercube sampling method in 2016. A random forest (RF) model was used to determine the relationship between a suite of geospatial predictors derived from remote sensing and digital elevation model data with georeferenced measurements of soil absorbable heavy metals. The trained RF model from 2016 was used to reconstruct the spatial distribution of soil absorbable heavy metals at three historical timesteps (1986, 1999, and 2010). Results indicated that the RF model was effective at predicting the distribution of heavy metals with coefficients of determination of 0.53, 0.59, 0.41, 0.45, and 0.60 for Fe, Mn, Ni, Pb, and Zn, respectively. The predicted maps showed high spatio-temporal variability; for example, there were substantial increases in Pb (the 1.5–2 mg/kg−1 class) where its distribution increased by ~25% from 1988 to 2016—similar trends were observed for the other heavy metals. This study provides insights into the spatio-temporal trends and the potential causes of soil heavy metal contamination to facilitate appropriate planning and management strategies to prevent, control, and reduce the impact of heavy metal contamination in soils.
Employing Blockchain Technology to Strengthen Security of Wireless Sensor Networks

The proposed approach uses blockchain-based technology to strengthen the data security of wireless sensor networks (WSNs). This paper integrates blockchain-based technology with data transfer to establish an extremely secure WSNs structure. The present wireless network is built on the architecture of the Internet of Things (IoT) and employs a blockchain-based method to make the reliability of data transmission strong. In this proposed research, many small-area wireless sensor networks establish the entire WSNs structure, and every small-area wireless sensor network has a primary data collection node called a “mobile database.” The “mobile database” node of this study uses embedded microcontrollers with an operating system, such as Raspberry Pi and Arduino Yun. This block contains the sensor data collected by itself and the hash value of the previous block. Then the hash value of its own block, which is also part of the hash calculation of the next block, was calculated through the mining calculation program. Any block in the proposed method includes the encrypted hash-value of the previous block, the current timestamp, and the transaction data. In our research content, the transaction data is represented as wireless network sensing data. Basically, the system employs the hash function for calculation using the Merkel-tree algorithm. Such programming makes the block with blockchain-based technology difficult to tamper with content. This study approach revises the blockchain-based transaction ledger to become a sensor data record. Therefore, the proposed system gathers and analyzes sensor data for more reliability in the wireless sensing network structure. Furthermore, the innovative system with blockchain-based technology can treat a private cloud-end. This paper also carries on to visualize the uploaded sensing data by the sensors and draws corresponding charts based on big data analysis. The wireless network architecture proposed in this paper is built on embedded devices, making it easy for the system to build a web server. Using Python or JavaScript programming language in the web environment is relatively more convenient for data visualization and data analysis. Finally, this study uses traditional methods and innovative methods to compare data transmission. When the system uses innovative methods with blockchain-based technology, it is almost impossible for any operator to tamper with the data transmitted by the sensor.
Multi-Modal MRI Image Synthesis via GAN With Multi-Scale Gate Mergence

Multi-modal magnetic resonance imaging (MRI) plays a critical role in clinical diagnosis and treatment nowadays. Each modality of MRI presents its own specific anatomical features which serve as complementary information to other modalities and can provide rich diagnostic information. However, due to the limitations of time consuming and expensive cost, some image sequences of patients may be lost or corrupted, posing an obstacle for accurate diagnosis. Although current multi-modal image synthesis approaches are able to alleviate the issues to some extent, they are still far short of fusing modalities effectively. In light of this, we propose a multi-scale gate mergence based generative adversarial network model, namely MGM-GAN, to synthesize one modality of MRI from others. Notably, we have multiple down-sampling branches corresponding to input modalities to specifically extract their unique features. In contrast to the generic multi-modal fusion approach of averaging or maximizing operations, we introduce a gate mergence (GM) mechanism to automatically learn the weights of different modalities across locations, enhancing the task-related information while suppressing the irrelative information. As such, the feature maps of all the input modalities at each down-sampling level, i.e., multi-scale levels, are integrated via GM module. In addition, both the adversarial loss and the pixel-wise loss, as well as gradient difference loss (GDL) are applied to train the network to produce the desired modality accurately. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art multi-modal image synthesis methods.
Comparative Analysis Review of Pioneering DBSCAN and Successive Density-Based Clustering Algorithms

The density-based spatial clustering of applications with noise (DBSCAN) is regarded as a pioneering algorithm of the density-based clustering technique. It provides the ability to handle outlier objects, detect clusters of different shapes, and disregard the need for prior knowledge about existing clusters in a dataset. These features along with its simplistic approach helped it become widely applicable in many areas of science. However, for all its accolades, the DBSCAN still has limitations in terms of performance, its ability to detect clusters of varying densities, and its dependence on user input parameters. Multiple DBSCAN-inspired algorithms have been subsequently proposed to alleviate these and more problems of the algorithm. In this paper, the implementation, features, strengths, and drawbacks of the DBSCAN are thoroughly examined. The successive algorithms proposed to provide improvement on the original DBSCAN are classified based on their motivations and are discussed. Experimental tests were conducted to understand and compare the changes presented by a C++ implementation of these algorithms along with the original DBSCAN algorithm. Finally, the analytical evaluation is presented based on the results found.
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks

This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks.Although some CL techniques have been proposed for document sentiment classification, we are not aware of any CL work on ASC.A CL system that incrementally learns a sequence of ASC tasks should address the following two issues: (1) transfer knowledge learned from previous tasks to the new task to help it learn a better model, and (2) maintain the performance of the models for previous tasks so that they are not forgotten.This paper proposes a novel capsule network based model called B-CL to address these issues.B-CL markedly improves the ASC performance on both the new task and the old tasks via forward and backward knowledge transfer.The effectiveness of B-CL is demonstrated through extensive experiments.
TableSense: Spreadsheet Table Detection with Convolutional Neural Networks

Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layouts on the spreadsheet. Considering the analogy between a cell matrix as spreadsheet and a pixel matrix as image, and encouraged by the successful application of Convolutional Neural Networks (CNN) in computer vision, we have developed TableSense, a novel end-to-end framework for spreadsheet table detection. First, we devise an effective cell featurization scheme to better leverage the rich information in each cell; second, we develop an enhanced convolutional neural network model for table detection to meet the domain-specific requirement on precise table boundary detection; third, we propose an effective uncertainty metric to guide an active learning based smart sampling algorithm, which enables the efficient build-up of a training dataset with 22,176 tables on 10,220 sheets with broad coverage of diverse table structures and layouts. Our evaluation shows that TableSense is highly effective with 91.3% recall and 86.5% precision in EoB-2 metric, a significant improvement over both the current detection algorithm that are used in commodity spreadsheet tools and state-of-the-art convolutional neural networks in computer vision.