Reviewerly/exHarmony huggingface dataset:
Research on the construction of social co-governance system of ecological environment protection

Abstract Ecological environment is an important issue of people's livelihood. Good ecological environment is the fairest public product, which is related to people's livelihood, national ecological security and social sustainable development. The boundary of eco-environmental protection capacity is fuzzy, and the effect of social co- governance of eco-environmental protection needs to be improved. This study precisely defines the role of "five subjects" in eco-environmental protection, and puts forward the policy suggestions of establishing the social co-governance behavior integral system of eco-environmental protection, determining the list of clues of social co- governance of eco-environmental protection, determining the list of awards for social co-governance of eco-environmental protection, and building an information platform for social co-governance of eco-environmental protection, so as to promote social co- governance of eco-environmental protection System construction.
A multivariate Poisson regression model for count data

We propose a new technique for the study of multivariate count data. The proposed model is applied to the study of the number of individuals several fossil species found in a set of geographical observation points. First, we are proposing a multivariate model based on the Poisson distributions, which allows positive and negative correlations between the components. We are extending the log-linear Poisson model in the multivariate case through the conditional distributions. For this model, we obtain the maximum likelihood estimates and compute several goodness of fit statistics. Finally we illustrate the application of the proposed method over data sets: various simulated data sets and a count data set of various fossil species.
Automated Speech to Sign language Conversion using Google API and NLP

Sign Language is a visual language that requires manual modality to express what a person wants to convey. Sign Language has its own grammar and lexicon. It is used by dumb and deaf people to por- tray what they want to say. It acts as a basic mode of communication for deaf and dumb people, without which communication between them and normal people might be difficult. Thus, this paper is focused to develop an automated software system to convert speech to sign language so that the words of a normal person can be conveyed to a deaf/dumb person. This automated system first recognizes the speech, the second converts it to text, third matches tokenized text with the visual sign word library (videos of sign language), fourth concatenates all the matched videos ac- cording to the text recognized and finally display the merged video to the deaf/dumb person. System accuracy is compared with the state-of- the-art approaches and found to be the best.
CT-Net: Channel Tensorization Network for Video Classification

3D convolution is powerful for video classification but often computationally expensive, recent studies mainly focus on decomposing it on spatial-temporal and/or channel dimensions. Unfortunately, most approaches fail to achieve a preferable balance between convolutional efficiency and feature-interaction sufficiency. For this reason, we propose a concise and novel Channel Tensorization Network (CT-Net), by treating the channel dimension of input feature as a multiplication of K sub-dimensions. On one hand, it naturally factorizes convolution in a multiple dimension way, leading to a light computation burden. On the other hand, it can effectively enhance feature interaction from different channels, and progressively enlarge the 3D receptive field of such interaction to boost classification accuracy. Furthermore, we equip our CT-Module with a Tensor Excitation (TE) mechanism. It can learn to exploit spatial, temporal and channel attention in a high-dimensional manner, to improve the cooperative power of all the feature dimensions in our CT-Module. Finally, we flexibly adapt ResNet as our CT-Net. Extensive experiments are conducted on several challenging video benchmarks, e.g., Kinetics-400, Something-Something V1 and V2. Our CT-Net outperforms a number of recent SOTA approaches, in terms of accuracy and/or efficiency.
Using Reflectance Transformation Imaging (RTI) to document ancient amphora stamps from Tanais, Russia. Reflections on first approach to their digitalisation

The presented paper discusses the use of Reflectance Transformation Imaging (RTI) in the process of digitalisation of stamps on Greek amphorae. The 2019 season of Polish excavations at ancient Tanais, Russia yielded twenty eight stamps. Authors of this paper documented each stamp by means of traditional photographic documentation, drawing, stamp rubbing and two RTI models. The aim of this paper is to discuss the possible applications of RTI technology with respect to documentation of stamps, as well as its use in the field of epigraphic analysis and information transfer.
SOLAR: Sparse Orthogonal Learned and Random Embeddings

Dense embedding models are commonly deployed in commercial search engines, wherein all the document vectors are pre-computed, and near-neighbor search (NNS) is performed with the query vector to find relevant documents. However, the bottleneck of indexing a large number of dense vectors and performing an NNS hurts the query time and accuracy of these models. In this paper, we argue that high-dimensional and ultra-sparse embedding is a significantly superior alternative to dense low-dimensional embedding for both query efficiency and accuracy. Extreme sparsity eliminates the need for NNS by replacing them with simple lookups, while its high dimensionality ensures that the embeddings are informative even when sparse. However, learning extremely high dimensional embeddings leads to blow up in the model size. To make the training feasible, we propose a partitioning algorithm that learns such high dimensional embeddings across multiple GPUs without any communication. This is facilitated by our novel asymmetric mixture of Sparse, Orthogonal, Learned and Random (SOLAR) Embeddings. The label vectors are random, sparse, and near-orthogonal by design, while the query vectors are learned and sparse. We theoretically prove that our way of one-sided learning is equivalent to learning both query and label embeddings. With these unique properties, we can successfully train 500K dimensional SOLAR embeddings for the tasks of searching through 1.6M books and multi-label classification on the three largest public datasets. We achieve superior precision and recall compared to the respective state-of-the-art baselines for each task with up to 10 times faster speed.
Text Simplification for Scientific Information Access

Modern information access systems hold the promise to give users direct access to key information from authoritative primary sources such as scientific literature, but non-experts tend to avoid these sources due to their complex language, internal vernacular, or lacking prior background knowledge. Text simplification approaches can remove some of these barriers, thereby avoiding that users rely on shallow information in sources prioritizing commercial or political incentives rather than the correctness and informational value. The CLEF 2021 SimpleText track will address the opportunities and challenges of text simplification approaches to improve scientific information access head-on. We aim to provide appropriate data and benchmarks, starting with pilot tasks in 2021, and create a community of NLP and IR researchers working together to resolve one of the greatest challenges of today.
A Framework for Personalized Location Privacy

Location privacy has been one of the most important research areas over recent years, and many location Privacy Preserving Mechanisms (PPMs) have been proposed. Each PPM typically achieves certain tradeoffs between privacy protection and resource consumption, and no PPM performs perfectly in all cases. Instead of designing one PPM that works for all cases, this paper studies how to make the best use of multiple single PPMs for location privacy protection in different scenarios. In particular, we propose a general framework called SmartGuard, which dynamically selects the best privacy preservation strategy for a user based on her preferences and the current status of her mobile device. SmartGuard quantifies user privacy under various scenarios, models the effects of different PPMs on several key factors such as the remaining battery level and network bandwidth, and then recommends the best privacy strategy for the user. To illustrate how our SmartGuard works, we apply it to a specific scenario of LBSs and implement it on Android based phones. Evaluation results show that our solution outperforms existing PPMs under various scenarios.
Sparse Longitudinal Representations of Electronic Health Record Data for the Early Detection of Chronic Kidney Disease in Diabetic Patients

Chronic kidney disease (CKD) is a gradual loss of renal function over time, and it increases the risk of mortality, decreased quality of life, as well as serious complications. The prevalence of CKD has been increasing in the last couple of decades, which is partly due to the increased prevalence of diabetes and hypertension. To accurately detect CKD in diabetic patients, we propose a novel framework to learn sparse longitudinal representations of patients' medical records. The proposed method is also compared with widely used baselines such as Aggregated Frequency Vector and Bag-of-Pattern in Sequences on real EHR data, and the experimental results indicate that the proposed model achieves higher predictive performance. Additionally, the learned representations are interpreted and visualized to bring clinical insights.
Intrinsic Autoencoders for Joint Deferred Neural Rendering and Intrinsic Image Decomposition

Neural rendering techniques promise efficient photorealistic image synthesis while providing rich control over scene parameters by learning the physical image formation process. While several supervised methods have been proposed for this task, acquiring a dataset of images with accurately aligned 3D models is very difficult. The main contribution of this work is to lift this restriction by training a neural rendering algorithm from unpaired data. We propose an autoencoder for joint generation of realistic images from synthetic 3D models while simultaneously decomposing real images into their intrinsic shape and appearance properties. In contrast to a traditional graphics pipeline, our approach does not require to specify all scene properties, such as material parameters and lighting by hand. Instead, we learn photo-realistic deferred rendering from a small set of 3D models and a larger set of unaligned real images, both of which are easy to acquire in practice. Simultaneously, we obtain accurate intrinsic decompositions of real images while not requiring paired ground truth. Our experiments confirm that a joint treatment of rendering and decomposition is indeed beneficial and that our approach outperforms state-of-the-art image-to-image translation baselines both qualitatively and quantitatively.
IEEE Access Special Section Editorial: Utility-Pattern Mining: Theoretical Analytics and Applications

Utility-pattern mining in data has received a lot of attention from the knowledge discovery in database (KDD) community due to its high potential for many applications such as finance, biomedicine, manufacturing, e-commerce, and social media. Current research in utility-pattern mining primarily focuses on discovering patterns of high value (e.g., high profit) in large databases and analyzing/learning important factors (e.g., economic factors) in a data mining process. One of the most popular applications of utility mining is the analysis of large transactional databases to discover high-utility itemsets, which are sets of items that yield a high profit when purchased together.
Extracting biomedical relations via a multi-head attention based graph convolutional network

Automatic extraction of biomedical relations is important for many tasks, such as drug discovery, protein prediction and knowledge graph construction. However, due to the complex and noisy expressions in biomedical texts, existing traditional neural networks, such as recurrent neural networks and convolutional neural networks, fail to capture syntactic information effectively. In this paper, we introduce a multi-head attention mechanism into graph convolutional networks to extract biomedical relations. In our method, the graph convolutional network is exploited to encode the dependency structure of an input sentence and the multi-head attention mechanism is utilized to alleviate the influence of noisy words. We evaluated our method on the ChemProt corpus and the protein-protein interaction corpus which includes five separate sub-datasets and it achieves F-scores of 67.37% and 84.8% on ChemProt corpus and PPI corpora, respectively. The experimental results suggest that Our model can not only alleviate the influence of noisy words, but also obtain more semantic and syntactic information from dependency graph than previous proposed models.
A Robust and Effective Text Detector Supervised by Contrastive Learning

Scene text detection is a task that detects the position of text in natural scenes.Due to the different sizes, arbitrary orientations, different colors of texts, as well as low contrast and resolution in the complex background, text detection in natural scene images is very challenging.So far, the detection results for text instances in motion blur, low-resolution images are still not satisfactory.In this paper, in order to solve the above problems, we propose an effective and robust text detection network that combines a state-of-the-art contrastive learning method SimCLR.Before being input to the feature extractor, the data is augmented in different methods, and then we calculate the similarity of the extracted corresponding feature pairs.This can significantly improve the performance of the detector in difficult conditions.We conduct a series of experiments on the public dataset ICDAR2013, ICDAR2015 and MSRA-TD500.On the ICDAR 2015 dataset, our method achieves F-measure of 0.840 and runs at 9.1 FPS at 720p resolution, demonstrating that the proposed method is effective and efficient.
Toward Trustworthy Blockchain-as-a-Service with Auditing

Many Blockchain-as-a-Service (BaaS) providers have emerged with the growing interest in BaaS among enterprises. However, current BaaS providers can pose a potential security threat in the context of a centralized service provider and for clients that depend on the provider. In this study, we first consider the problem of auditing BaaS and develop an Enforcer architecture for trustworthy BaaS.
Novel superpixel‐based algorithm for segmenting lung images via convolutional neural network and random forest

Accurately segmenting lungs from CT images is a fundamental step for quantitative analysis of lung diseases. However, it is still a challenging task because of some interferential factors, such as juxta-pleural nodules, pulmonary inflammation, as well as individual anatomical varieties. In this study, with the combination of a superpixel approach and a hybrid model composed of convolutional neural network and random forest (CNN-RF), the authors propose a novel algorithm to segment lungs from CT images in an automatic and accurate fashion. The authors' lung segmentation covers three main stages: image preprocessing, lung segmenting and segmentation refining. A lung CT image denoised with a fractional-order grey similarity approach is first segmented to a set of superpixels, and the CNN-RF model is then employed to classify the superpixels and identify lungs from the CT image. The segmentation result is further refined by separating the left and right lungs, eliminating trachea, and correcting lung contours. Experiments show that their algorithm can generate more accurate lung segmentation results with 94.98% Jaccard's index and 97.99% Dice similarity coefficient, compared with ground truths, and it achieved better results compared with several feature-based machine learning techniques and current methods on lung segmentation.
Deformable MRI-CT liver image registration using convolutional neural network with modality independent neighborhood descriptors

In radiotherapy, liver CT images are routinely acquired for treatment planning since CT images can provide important electron density information for dose calculation in treatment planning system. Besides the CT images, MRI is often acquired for diagnosis since it has superior soft tissue contrast. Therefore, for accurate contouring of the target, liver CT and MRI image registration plays an important role. It is very challenging to register the CT and MRI liver images due to the distinct image intensities and liver motion. In this study, a deep learning-based network was proposed to directly predict the DVF for MRI-CT liver image registration. To overcome the challenge of multimodal registration, we propose to incorporate a modality independent descriptor into the deep learning network to explore the correlations between the MRI and CT images. Our results show that the proposed network could accurately align MRI to CT liver images. The average Dice increased from 0.89±0.04 before registration to 0.93±0.02 after registration.
General Convergence Analysis of Stochastic First-Order Methods for Composite Optimization

In this paper, we consider stochastic composite convex optimization problems with the objective function satisfying a stochastic bounded gradient condition, with or without a quadratic functional growth property. These models include the most well-known classes of objective functions analyzed in the literature: nonsmooth Lipschitz functions and composition of a (potentially) nonsmooth function and a smooth function, with or without strong convexity. Based on the flexibility offered by our optimization model, we consider several variants of stochastic first-order methods, such as the stochastic proximal gradient and the stochastic proximal point algorithms. Usually, the convergence theory for these methods has been derived for simple stochastic optimization models satisfying restrictive assumptions, and the rates are in general sublinear and hold only for specific decreasing stepsizes. Hence, we analyze the convergence rates of stochastic first-order methods with constant or variable stepsize under general assumptions covering a large class of objective functions. For constant stepsize, we show that these methods can achieve linear convergence rate up to a constant proportional to the stepsize and under some strong stochastic bounded gradient condition even pure linear convergence. Moreover, when a variable stepsize is chosen we derive sublinear convergence rates for these stochastic first-order methods. Finally, the stochastic gradient mapping and the Moreau smoothing mapping introduced in the present paper lead to simple and intuitive proofs.
Distributed Deep Learning of ResNet50 and VGG16 with Pipeline Parallelism

Data parallel distributed deep learning has been used to accelerate the learning speed. The communication is becoming a bottleneck as the computation time is accelerated. Therefore, the communication time is needed to be reduced. As a new acceleration method, research activity for pipeline parallelism has increased since 2018. However, the existing methods generally combine data parallelism and pipeline parallelism, and there has been a little analysis of speed improvement in pipeline parallelism alone. This paper analyzes the effect of speed improvement by pipeline parallelism. We compared the execution time of data parallelism and pipeline parallelism with VGG16 and ResNet50, respectively. VGG16 includes more parameters comparing to ResNet50 when the size of input data is the same. A comparison of the execution time shows that data parallelism is 3.3 times faster than pipeline parallelism for ResNet50. Pipeline parallelism is 2.45 times faster than data parallelism for VGG16. The speed-up of pipeline parallelism was significant in a model with a large number of parameters. Therefore, pipeline parallelism is suitable for large-scale models that have a large number of parameters.
Robust Curriculum Learning: from clean label detection to noisy label self-correction

Neural network training can easily overfit noisy labels resulting in poor generalization performance. Existing methods address this problem by (1) filtering out the noisy data and only using the clean data for training or (2) relabeling the noisy data by the model during training or by another model trained only on a clean dataset. However, the former does not leverage the features' information of wrongly-labeled data, while the latter may produce wrong pseudo-labels for some data and introduce extra noises. In this paper, we propose a smooth transition and interplay between these two strategies as a curriculum that selects training samples dynamically. In particular, we start with learning from clean data and then gradually move to learn noisy-labeled data with pseudo labels produced by a time-ensemble of the model and data augmentations. Instead of using the instantaneous loss computed at the current step, our data selection is based on the dynamics of both the loss and output consistency for each sample across historical steps and different data augmentations, resulting in more precise detection of both clean labels and correct pseudo labels. On multiple benchmarks of noisy labels, we show that our curriculum learning strategy can significantly improve the test accuracy without any auxiliary model or extra clean data.
Iris: Allocation Banking and Identity and Access Management for the Exascale Era

Without a reliable and scalable system for managing authorized users and ensuring they receive their allocated share of computational and storage resources, modern HPC centers would not be able to function. Exascale will amplify these demands with greater machine scale, more users, higher job throughput, and ever-increasing need for management insight and automation throughout the HPC environment. When our legacy system reached retirement age, NERSC took the opportunity to design and build Iris not only to meet our current needs, with 8,000 users and tens of thousands of jobs per day, but also to scale well into the exascale era. In this paper, we describe how we have designed Iris to meet these needs and discuss its key features as well as our implementation experience.