Reviewerly/exHarmony huggingface dataset:
Grus

Today’s GPU graph processing frameworks face scalability and efficiency issues as the graph size exceeds GPU-dedicated memory limit. Although recent GPUs can over-subscribe memory with Unified Memory (UM), they incur significant overhead when handling graph-structured data. In addition, many popular processing frameworks suffer sub-optimal efficiency due to heavy atomic operations when tracking the active vertices. This article presents Grus, a novel system framework that allows GPU graph processing to stay competitive with the ever-growing graph complexity. Grus improves space efficiency through a UM trimming scheme tailored to the data access behaviors of graph workloads. It also uses a lightweight frontier structure to further reduce atomic operations. With easy-to-use interface that abstracts the above details, Grus shows up to 6.4× average speedup over the state-of-the-art in-memory GPU graph processing framework. It allows one to process large graphs of 5.5 billion edges in seconds with a single GPU.
Clustering based Contrastive Learning for Improving Face Representations

A good clustering algorithm can discover natural groupings in data. These groupings, if used wisely, provide a form of weak supervision for learning representations. In this work, we present Clustering-based Contrastive Learning (CCL), a new clustering-based representation learning approach that uses labels obtained from clustering along with video constraints to learn discriminative face features. We demonstrate our method on the challenging task of learning representations for video face clustering. Through several ablation studies, we analyze the impact of creating pair-wise positive and negative labels from different sources. Experiments on three challenging video face clustering datasets: BBT-0101, BF-0502, and ACCIO show that CCL achieves a new state-of-the-art on all datasets.
Understanding and Improving Lexical Choice in Non-Autoregressive Translation

Knowledge distillation (KD) is essential for training non-autoregressive translation (NAT) models by reducing the complexity of the raw data with an autoregressive teacher model. In this study, we empirically show that as a side effect of this training, the lexical choice errors on low-frequency words are propagated to the NAT model from the teacher model. To alleviate this problem, we propose to expose the raw data to NAT models to restore the useful information of low-frequency words, which are missed in the distilled data. To this end, we introduce an extra Kullback-Leibler divergence term derived by comparing the lexical choice of NAT model and that embedded in the raw data. Experimental results across language pairs and model architectures demonstrate the effectiveness and universality of the proposed approach. Extensive analyses confirm our claim that our approach improves performance by reducing the lexical choice errors on low-frequency words. Encouragingly, our approach pushes the SOTA NAT performance on the WMT14 English-German and WMT16 Romanian-English datasets up to 27.8 and 33.8 BLEU points, respectively. The codes and models will be released.
Clinical notes as prognostic markers of mortality associated with diabetes mellitus following critical care: A retrospective cohort analysis using machine learning and unstructured big data

Clinical notes are ubiquitous resources offering potential value in optimizing critical care via data mining technologies. To determine the predictive value of clinical notes as prognostic markers of 1-year all-cause mortality among people with diabetes following critical care. Mortality of diabetes patients were predicted using three cohorts of clinical text in a critical care database, written by physicians (n = 45253), nurses (159027), and both (n = 204280). Natural language processing was used to pre-process text documents and LASSO-regularized logistic regression models were trained and tested. Confusion matrix metrics of each model were calculated and AUROC estimates between models were compared. All predictive words and corresponding coefficients were extracted. Outcome probability associated with each text document was estimated. Models built on clinical text of physicians, nurses, and the combined cohort predicted mortality with AUROC of 0.996, 0.893, and 0.922, respectively. Predictive performance of the models significantly differed from one another whereas inter-rater reliability ranged from substantial to almost perfect across them. Number of predictive words with non-zero coefficients were 3994, 8159, and 10579, respectively, in the models of physicians, nurses, and the combined cohort. Physicians’ and nursing notes, both individually and when combined, strongly predicted 1-year all-cause mortality among people with diabetes following critical care. Clinical notes of physicians and nurses are strong and novel prognostic markers of diabetes-associated mortality in critical care, offering potentially generalizable and scalable applications. Clinical text-derived personalized risk estimates of prognostic outcomes such as mortality could be used to optimize patient care.
Maximum Response Deep Learning Using Markov, Retinal &amp; Primitive Patch Binding With GoogLeNet &amp; VGG-19 for Large Image Retrieval

Smart and productive image retrieval from flexible image datasets is an unavoidable necessity of the current period. Crude picture marks are imperative to mirror the visual ascribes for content-based image retrieval (CBIR). Algorithmically enlightened and recognized visual substance structure image marks to accurately file and recover comparative outcomes. Consequently, highlighted vectors ought to contain adequate image data with color, shape, objects, spatial data viewpoints to recognize image class as a qualifying applicant. This article presents the maximum response of visual features of an image over profound convolutional neural networks in blend with an innovative content-based image retrieval plan to recover phenomenally precise outcomes. For this determination, a serial fusion of GoogLeNet and VGG-19 based generated signatures are formulated with visual features including texture, color and shape. Initially, the maximum response is calculated for texture pattern by using Markov Random Field (MRF) classifier. Thereafter, cascaded samples are passed through a human retinal system like descriptor named Fast Retina Keypoint (FREAK) for corresponding fundamental points through the image. GoogLeNet and VGG-19 are applied to extract deep features of an image; hence color components are obtained using a correlogram. Finally, all the image signatures are combined and passed through the BoW scheme. The proposed method is applied experimentally to challenging datasets, including Caltech-256, ALOT (250), Corel 1000, Cifar-100, and Cifar 10. Remarkable precision,Recall and F-score results obtained.The texture dataset ALOT (250) with the uppermost precision rate 0.99 for a maximum of its categories, whereas Caltech-256 gives 0.66 precision, and Corel 1000 0.99 for VGG-19 and 0.95 for GoogLeNet. Recall, F-score, ARR and ARP rates shows the significant rates in most of the image categories.
Broad learning system for semi-supervised learning

As an emerging technique for supervised learning, broad learning system (BLS) has been proved to have many advantages such as fast learning speed, good generalization, etc. However, it is difficult for BLS to perform well on the dataset which only contains few labeled samples. Because the focus of semi-supervised learning is utilizing the information between labeled and unlabeled samples while the original BLS apparently ignores this significant information. In BLS, the mapped features are obtained by sparse autoencoder which is an unsupervised learning method and the enhancement nodes are formed based on the mapped features. But sparse autoencoder will definitely lose the information of labeled instances. Therefore, we propose a novel semi-supervised BLS (S2-BLS) in this paper. In S2-BLS, inspired by the effectiveness of extreme learning machine based autoencoder (ELM-AE), we first propose semi-supervised ELM-AE (SS-ELM-AE) to obtain the mapped features. Then, the discriminative projecting weights between the ground truths and the transformed features (including the mapping features and the enhancement nodes) are calculated directly. Finally, we adopt some public datasets to verify the learning ability of S2-BLS. And the experimental results illustrate that S2-BLS has an obvious advantage to learn labeled and unlabeled data, comparing with related algorithms.
Detecting Outlier Machine Instances Through Gaussian Mixture Variational Autoencoder With One Dimensional CNN

Today&#x0027;s large datacenters house a massive number of machines, each of which is being closely monitored with multivariate time series (e.g., CPU idle, memory utilization) to ensure service quality. Detecting outlier machine instances with multivariate time series is crucial for service management. However, it is a challenging task due to the multiple classes and various shapes, high dimensionality, and lack of labels of multivariate time series. In this article, we propose DOMI, a novel unsupervised model that combines Gaussian mixture VAE with 1D-CNN, to <b>d</b>etect <b>o</b>utlier <b>m</b>achine <b>i</b>nstances. Its core idea is to capture the normal patterns of machine instances by learning their latent representations that consider the shape characteristics, reconstruct input data by the learned representations, and apply reconstruction probabilities to determine outliers. Moreover, DOMI interprets the detected outlier instance based on the reconstruction probability changes of univariate time series. Extensive experiments have been conducted on the dataset collected from 1821 machines with a 1.5-month-period, which are deployed in ByteDance, a top global content service provider. DOMI achieves the best F1-Score of 0.94 and AUC score of 0.99, significantly outperforming the best performing baseline method by 0.08 and 0.03, respectively. Moreover, its interpretation accuracy is up to 0.93.
Container scheduling techniques: A Survey and assessment

Containers have emerged as the most promising lightweight virtualization technology in providing cloud services due to its flexible deployment, portability, and scalability especially in micro-services, smart vehicles, IoTs, and fog/edge computing. An important and vital role in cloud container services is played by the scheduler’s component to optimize performance and reduce cost due to the diverse nature of the workload and cloud resources. Despite the immense traction of containers in cloud computing, there is no comprehensive survey that covers container scheduling techniques. In this timely survey, we investigate the landscape of the state-of-the-art container scheduling techniques aiming to inspire more research work in this active area of research. The survey is structured around classifying the scheduling techniques into four categories based on the type of optimization algorithm employed to generate the schedule namely mathematical modeling, heuristics, meta-heuristics and machine learning. Then for each class of scheduling algorithms, we analyze and identify key benefits and pitfalls, together with key challenges of the available techniques based on the performance metrics. Finally, this paper highlights fertile future research opportunities to realize the full potential of the emergent container technology.
Vote-Based 3D Object Detection with Context Modeling and SOB-3DNMS

Most existing 3D object detection methods recognize objects individually, without giving any consideration on contextual information between these objects. However, objects in indoor scenes are usually related to each other and the scene, forming the contextual information. Based on this observation, we propose a novel 3D object detection network, which is built on the state-of-the-art VoteNet but takes into consideration of the contextual information at multiple levels for detection and recognition of 3D objects. To encode relationships between elements at different levels, we introduce three contextual sub-modules, capturing contextual information at patch, object, and scene levels respectively, and build them into the voting and classification stages of VoteNet. In addition, at the post-processing stage, we also consider the spatial diversity of detected objects and propose an improved 3D NMS (non-maximum suppression) method, namely Survival-Of-the-Best 3DNMS (SOB-3DNMS), to reduce false detections. Experiments demonstrate that our method is an effective way to promote detection accuracy, and has achieved new state-of-the-art detection performance on challenging 3D object detection datasets, i.e., SUN RGBD and ScanNet, when only taking point cloud data as input.
An Ontology-Based Information Extraction System for Organic Farming

In the existing farming system, information is obtained manually, and most times, farmers act based on their discretion. Sometimes, farmers rely on information from experts and extension officers for decision making. In recent times, a lot of information systems are available with relevant information on organic farming practices; however, such information is scattered in different context, form, and media all over the internet, making their retrieval difficult. The use of ontology with the aid of a conceptual scheme makes the comprehensive and detailed formalization of any subject domain possible. This study is aimed at acquiring, storing, and providing organic farming-based information available to current and intending software developer who may wish to develop applications for farmers. It employs information extraction (IE) and ontology development techniques to develop an ontology-based information extraction (OBIE) system called ontology-based information extraction system for organic farming (OBIESOF). The knowledge base was built using protégé editor; Java was used for the implementation of the ontology knowledge base with the aid of the high-level application programming language for working web ontology language application program interface (OWL API). In contrast, HermiT was used to checking the consistencies of the ontology and for submitting queries in order to verify their validity. The queries were expressed in description logic (DL) query language. The authors tested the capability of the ontology to respond to user queries by posing instances of the competency questions from DL query interface. The answers generated by the ontology were promising and serve as positive pointers to its usefulness as a knowledge repository.
A Method for Radar Model Identification Using Time-Domain Transient Signals

Radar specific emitter identification (SEI) is the process of uniquely identifying an individual emitter from the same class of radars by their individual properties that arise from hardware imperfections. However, it is challenging or perhaps impossible to generate globally unique emitter identifiers by using SEI techniques alone, due to the increasing number of radars and the subtle differences in their signal properties. We therefore introduce a multitier radar emitter identification concept that includes radar function identification, model identification, and SEI. The combination of function identifiers, model identifiers, and specific emitter identifiers could generate globally unique radar emitter identifiers. In this article, we propose to use the radio frequency (RF) features extracted from time domain transient signals for radar model identification. The RF features include the duration, maximum derivative, skewness, kurtosis, mean, variance, fractal dimension, Shannon entropy, and polynomial coefficients of the normalized energy trajectory of a transient signal, as well as the area under the trajectory curve. We propose three RF fingerprints for radar model identification, each consisting of a predetermined subset of the features. The performance of the RF fingerprints was evaluated by using five classification algorithms with two radar datasets. Our results show attractive performance with respect to hetero-model radar identification. In particular, for the hetero-model radar dataset, the pair of the all-features (AF) fingerprint and gradient boosting algorithm achieved 91.9, 90.25, and 91.1% classification accuracy for three, four, and five emitters, respectively. On this basis, we conclude that the proposed AF fingerprint could be applied directly to radar model identification by training the gradient boosting algorithm using a dataset with many radars per model.
Learning Deep Lucas-Kanade Siamese Network for Visual Tracking

In most recent years, Siamese trackers have drawn great attention because of their well-balanced accuracy and efficiency. Although these approaches have achieved great success, the discriminative power of the conventional Siamese trackers is still limited by the insufficient template-candidate representation. Most of the existing approaches take non-aligned features to learn a similarity function for template-candidate matching, while the target object's geometrical transformation is seldom explored. To address this problem, we propose a novel Siamese tracking framework, which enables to dynamically transform the template-candidate features to a more discriminative viewpoint for similarity matching. Specifically, we reformulate the template-candidate matching problem of the conventional Siamese tracker from the perspective of Lucas-Kanade (LK) image alignment approach. A Lucas-Kanade network (LKNet) is proposed and incorporated to the Siamese architecture to learn aligned feature representations in data-driven trainable manner, which is able to enhance the model adaptability in challenging scenarios. Within this framework, we propose two Siamese trackers named LK-Siam and LK-SiamRPN to validate the effectiveness. Extensive experiments conducted on the prevalent datasets show that the proposed method is more competitive over a number of state-of-the-art methods.
Subtractive Sets over Cyclotomic Rings

We study when (dual) Vandermonde systems of the form $$\mathbf {V} _T^{{(\intercal )}} \cdot \mathbf {z} = s\cdot \mathbf {w}$$ admit a solution $$\mathbf {z}$$ over a ring $$\mathcal {R}$$ , where $$\mathbf {V} _T$$ is the Vandermonde matrix defined by a set T and where the “slack” s is a measure of the quality of solutions. To this end, we propose the notion of (s, t)-subtractive sets over a ring $$\mathcal {R}$$ , with the property that if S is (s, t)-subtractive then the above (dual) Vandermonde systems defined by any t-subset $$T \subseteq S$$ are solvable over $$\mathcal {R}$$ . The challenge is then to find large sets S while minimising (the norm of) s when given a ring $$\mathcal {R}$$ . By constructing families of (s, t)-subtractive sets S of size $$n = \textsf {poly}(\lambda )$$ over cyclotomic rings $$\mathcal {R}= \mathbb {Z}[\zeta _{p^\ell }]$$ for prime p, we construct Schnorr-like lattice-based proofs of knowledge for the SIS relation $$\mathbf {A} \cdot \mathbf {x} = s \cdot \mathbf {y} \bmod q$$ with O(1/n) knowledge error, and $$s = 1$$ in case $$p = \textsf {poly}(\lambda )$$ . Our technique slots naturally into the lattice Bulletproof framework from Crypto’20, producing lattice-based succinct arguments for NP with better parameters. We then give matching impossibility results constraining n relative to s, which suggest that our Bulletproof-compatible protocols are optimal unless fundamentally new techniques are discovered. Noting that the knowledge error of lattice Bulletproofs is $$\varOmega (\log k/n)$$ for witnesses in $$\mathcal {R}^k$$ and subtractive set size $$n$$ , our result represents a barrier to practically efficient lattice-based succinct arguments in the Bulletproof framework. Beyond these main results, the concept of (s, t)-subtractive sets bridges group-based threshold cryptography to lattice settings, which we demonstrate by relating it to distributed pseudorandom functions.
Machine learning to identify geologic factors associated with production in geothermal fields: a case-study using 3D geologic data, Brady geothermal field, Nevada

Abstract In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fluid-flow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fluid-flow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k -means clustering (NMF k ), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.
A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulse-coupled neural networks

<abstract> Radiology experts often face difficulties in mammography mass lesion labeling, which may lead to conclusive yet unnecessary and expensive breast biopsies. This paper focuses on building an automated diagnosis tool that supports radiologists in identifying and classifying mammography mass lesions. The paper's main contribution is to design a hybrid model based on Pulse-Coupled Neural Networks (PCNN) and Deep Convolutional Neural Networks (CNN). Due to the need for large datasets to train and tune CNNs, which are not available for medical images, Transfer Learning (TL) was exploited in this research. TL can be an effective approach when working with small-sized datasets. The paper's implementation was tested on three public benchmark datasets: DDMS, INbreast, and BCDR datasets for training and testing and MIAS for testing only. The results indicated the enhancement that PCNN provides when combined with CNN compared to other methods for the same public datasets. The hybrid model achieved 98.72% accuracy for DDMS, 97.5% for INbreast, and 96.94% for BCDR. To avoid overfitting, the proposed hybrid model was tested on an unseen MIAS dataset, achieving 98.77% accuracy. Other evaluation metrics are reported in the results section. </abstract>