Reviewerly/exHarmony huggingface dataset:
CNAK: Cluster number assisted K-means

Determining the number of clusters present in a dataset is an important problem in cluster analysis. Conventional clustering techniques generally assume this parameter to be provided up front. %user supplied. %Recently, robustness of any given clustering algorithm is analyzed to measure cluster stability/instability which in turn determines the cluster number. In this paper, we propose a method which analyzes cluster stability for predicting the cluster number. Under the same computational framework, the technique also finds representatives of the clusters. The method is apt for handling big data, as we design the algorithm using \emph{Monte-Carlo} simulation. Also, we explore a few pertinent issues found to be of also clustering. Experiments reveal that the proposed method is capable of identifying a single cluster. It is robust in handling high dimensional dataset and performs reasonably well over datasets having cluster imbalance. Moreover, it can indicate cluster hierarchy, if present. Overall we have observed significant improvement in speed and quality for predicting cluster numbers as well as the composition of clusters in a large dataset.
FASTER Recurrent Networks for Efficient Video Classification

Typical video classification methods often divide a video into short clips, do inference on each clip independently, then aggregate the clip-level predictions to generate the video-level results. However, processing visually similar clips independently ignores the temporal structure of the video sequence, and increases the computational cost at inference time. In this paper, we propose a novel framework named FASTER, i.e., Feature Aggregation for Spatio-TEmporal Redundancy. FASTER aims to leverage the redundancy between neighboring clips and reduce the computational cost by learning to aggregate the predictions from models of different complexities. The FASTER framework can integrate high quality representations from expensive models to capture subtle motion information and lightweight representations from cheap models to cover scene changes in the video. A new recurrent network (i.e., FAST-GRU) is designed to aggregate the mixture of different representations. Compared with existing approaches, FASTER can reduce the FLOPs by over 10× while maintaining the state-of-the-art accuracy across popular datasets, such as Kinetics, UCF-101 and HMDB-51.
List Decodable Learning via Sum of Squares

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)List Decodable Learning via Sum of SquaresPrasad Raghavendra and Morris YauPrasad Raghavendra and Morris Yaupp.161 - 180Chapter DOI:https://doi.org/10.1137/1.9781611975994.10PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In the list-decodable learning setup, an overwhelming majority (say a 1 – β-fraction) of the input data consists of outliers and the goal of an algorithm is to output a small list of hypotheses such that one of them agrees with inliers. We devise list decodable learning algorithms for the problem of linear regression using the sum of squares SDP hierarchy. In the list-decodable linear regression problem, we are given labelled examples {(Xi, yi)}iϵ[n] containing a subset S of βN inliers {Xi}iϵs that are drawn i.i.d. from standard Gaussian distribution N(0, I) in ℝd, where the corresponding labels yi are well-approximated by a linear function . We devise an algorithm that outputs a list of linear functions such that there exists some ϵ that is close to . This yields the first algorithm for linear regression in a list-decodable setting. Our results hold for a general distribution of examples whose concentration and anti-concentration properties can be certified by low degree sum-of-squares proofs. In an independent and concurrent work, Karmalkar et al. [KKK19] also obtain an algorithm for list-decodable linear regression using the Sum-of-Squares SDP hierarchy. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Mapping RDF Databases to Property Graph Databases

RDF triplestores and property graph databases are two approaches for data management which are based on modeling, storing, and querying graph-like data. In spite of such common principles, they present special features that complicate the task of database interoperability. While there exist some methods to transform RDF graphs into property graphs, and vice versa, they lack compatibility and a solid formal foundation. This paper presents three direct mappings (schema-dependent and schema-independent) for transforming an RDF database into a property graph database, including data and schema. We show that two of the proposed mappings satisfy the properties of semantics preservation and information preservation. The existence of both mappings allows us to conclude that the property graph data model subsumes the information capacity of the RDF data model.
USGS “Did You Feel It?”—Science and Lessons From 20 Years of Citizen Science-Based Macroseismology

The U.S. Geological Survey (USGS) “Did You Feel It?” (DYFI) system is an automatic method for rapidly collecting macroseismic intensity data from Internet users’ shaking and damage reports and for generating intensity maps immediately following felt earthquakes. DYFI has been in operation for nearly two decades (1999-2019) in the United States, and for nearly 15 years globally. During that period the amount of data collected is astounding: Over 5 million individual DYFI intensity reports—spanning all magnitude and distance ranges—have been amassed and archived. DYFI allows for macroseismic data collection at rates and quantities never before imagined, and thus high-quality macroseismic intensity maps can be made almost immediately, and with more complete coverage at higher resolution than in the past. DYFI also allows for valuable positive interactions of the citizenry with a Federal science agency. In essence, the widespread adoption of DYFI—along with ShakeMap—has facilitated the general acceptance of the very concept of shaking intensity, fundamentally improving our agency’s ability to communicate both hazard and risk to the population. DYFI effectively confirms the importance of reporting and inculcating the public’s understanding of intensity—in addition to magnitude—for a proper perspective of earthquake risk-related decision-making. Furthermore, the vast amount of DYFI data allow for data-rich analyses of otherwise intractable seismological, sociological, and earthquake impact studies that range from quantifying the shaking due to induced earthquakes, to human response and risk perception, to relating recorded shaking metrics to macroseismic effects, and the attenuation of intensity with magnitude and distance. Naturally, web-based data collection also poses challenges. After two decades of experience acquiring data with the DYFI system, we address some of these challenges by documenting refinements to our algorithmic and operational procedures that have evolved over that time. Lastly, we outline new opportune research and development directions for our DYFI approach to citizen seismology.
High-Fidelity Software-Defined Quantum Logic on a Superconducting Qudit

We present an efficient approach to achieving arbitrary, high-fidelity control of a multilevel quantum system using optimal control techniques. As an demonstration, we implement a continuous, software-defined microwave pulse to realize a $0\ensuremath{\leftrightarrow}2$ SWAP gate that achieves an average gate fidelity of 99.4%. We describe our procedure for extracting the system Hamiltonian, calibrating the quantum and classical hardware chain, and evaluating the gate fidelity. Our work represents an alternative, fully generalizable route towards achieving universal quantum control by leveraging optimal control techniques.
An overview of internet of things

The internet of things is an emerging technology that is currently present in most processes and devices, allowing to improve the quality of life of people and facilitating the access to specific information and services. The main purpose of the present article is to offer a general overview of internet of things, based on the analysis of recently published work. The added value of this article lies in the analysis of the main recent publications and the diversity of applications of internet of things technology. As a result of the analysis of the current literature, internet of things technology stands out as a facilitator in business and industrial performance but above all in improving the quality of life. As a conclusion to this document, the internet of things is a technology that can overcome the challenges in terms of security, processing capacity and data mobility, as long as the development related to other technologies follows its expected course.
Ethics and informatics in the age of COVID-19: challenges and recommendations for public health organization and public policy

The COVID-19 pandemic response in the United States has exposed significant gaps in information systems and processes that prevent timely clinical and public health decision-making. Specifically, the use of informatics to mitigate the spread of SARS-CoV-2, support COVID-19 care delivery, and accelerate knowledge discovery bring to the forefront issues of privacy, surveillance, limits of state powers, and interoperability between public health and clinical information systems. Using a consensus-building process, we critically analyze informatics-related ethical issues in light of the pandemic across 3 themes: (1) public health reporting and data sharing, (2) contact tracing and tracking, and (3) clinical scoring tools for critical care. We provide context and rationale for ethical considerations and recommendations that are actionable during the pandemic and conclude with recommendations calling for longer-term, broader change (beyond the pandemic) for public health organization and policy reform.
Colorectal Cancer Detection Based on Deep Learning

The initial point in the diagnostic workup of solid tumors remains manual, with the assessment of hematoxylin and eosin (H&E)-stained tissue sections by microscopy. This is a labor-intensive step that requires attention to detail. In addition, diagnoses are influenced by an individual pathologist's knowledge and experience and may not always be reproducible between pathologists.We introduce a deep learning-based method in colorectal cancer detection and segmentation from digitized H&E-stained histology slides.In this study, we demonstrate that this neural network approach produces median accuracy of 99.9% for normal slides and 94.8% for cancer slides compared to pathologist-based diagnosis on H&E-stained slides digitized from clinical samples.Given that our approach has very high accuracy on normal slides, use of neural network algorithms may provide a screening approach to save pathologist time in identifying tumor regions. We suggest that this new method may be a powerful assistant for colorectal cancer diagnostics.
Improving CT Image Tumor Segmentation Through Deep Supervision and Attentional Gates

Computer Tomography (CT) is an imaging procedure that combines many X-ray measurements taken from different angles. The segmentation of areas in the CT images provides a valuable aid to physicians and radiologists in order to better provide a patient diagnose. The CT scans of a body torso usually include different neighboring internal body organs. Deep learning has become the state-of-the-art in medical image segmentation. For such techniques, in order to perform a successful segmentation, it is of great importance that the network learns to focus on the organ of interest and surrounding structures and also that the network can detect target regions of different sizes. In this paper, we propose the extension of a popular deep learning methodology, Convolutional Neural Networks (CNN), by including deep supervision and attention gates. Our experimental evaluation shows that the inclusion of attention and deep supervision results in consistent improvement of the tumor prediction accuracy across the different datasets and training sizes while adding minimal computational overhead.
TADOC: Text analytics directly on compression

This article provides a comprehensive description of Text Analytics Directly on Compression (TADOC), which enables direct document analytics on compressed textual data. The article explains the concept of TADOC and the challenges to its effective realizations. Additionally, a series of guidelines and technical solutions that effectively address those challenges, including the adoption of a hierarchical compression method and a set of novel algorithms and data structure designs, are presented. Experiments on six data analytics tasks of various complexities show that TADOC can save 90.8% storage space and 87.9% memory usage, while halving data processing times.
Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation

We propose a new efficient architecture for semantic segmentation, based on a “Waterfall” Atrous Spatial Pooling architecture, that achieves a considerable accuracy increase while decreasing the number of network parameters and memory footprint. The proposed Waterfall architecture leverages the efficiency of progressive filtering in the cascade architecture while maintaining multiscale fields-of-view comparable to spatial pyramid configurations. Additionally, our method does not rely on a postprocessing stage with Conditional Random Fields, which further reduces complexity and required training time. We demonstrate that the Waterfall approach with a ResNet backbone is a robust and efficient architecture for semantic segmentation obtaining state-of-the-art results with significant reduction in the number of parameters for the Pascal VOC dataset and the Cityscapes dataset.
Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks

Abstract Rapid and accurate hazard forecasting is important for prompt evacuations and reducing casualties during natural disasters. In the decade since the 2011 Tohoku tsunami, various tsunami forecasting methods using real-time data have been proposed. However, rapid and accurate tsunami inundation forecasting in coastal areas remains challenging. Here, we propose a tsunami forecasting approach using convolutional neural networks (CNNs) for early warning. Numerical tsunami forecasting experiments for Tohoku demonstrated excellent performance with average maximum tsunami amplitude and tsunami arrival time forecasting errors of ~0.4 m and ~48 s, respectively, for 1,000 unknown synthetic tsunami scenarios. Our forecasting approach required only 0.004 s on average using a single CPU node. Moreover, the CNN trained on only synthetic tsunami scenarios provided reasonable inundation forecasts using actual observation data from the 2011 event, even with noisy inputs. These results verify the feasibility of AI-enabled tsunami forecasting for providing rapid and accurate early warnings.
Anomaly detection using improved deep SVDD model with data structure preservation

Support vector data description (SVDD) is a classical anomaly detection algorithm. How to develop a deep version of SVDD is one valuable problem in the anomaly detection field. Aiming at this problem, an improved SVDD model called deep structure preservation SVDD (DSPSVDD) is proposed by integrating the deep feature extraction with the data structure preservation. Firstly, the typical SVDD methods are revisited in view of model depth profiles and the limitations of the present deep SVDD model are analyzed. Then in order to extract the deep data features more effectively, an enhanced comprehensive optimization objective is designed for the deep SVDD model by considering both the hypersphere volume minimization and the network reconstruction error minimization simultaneously. The experimental results on the MNIST, Fashion-MNIST, and MVTec AD image benchmark datasets show that the proposed DSPSVDD method achieves the better anomaly detection performance compared with the traditional deep SVDD method.
A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting

The COVID-19 pandemic has disrupted the economy and businesses and impacted all facets of people's lives. It is critical to forecast the number of infected cases to make accurate decisions on the necessary measures to control the outbreak. While deep learning models have proved to be effective in this context, time series augmentation can improve their performance. In this paper, we use time series augmentation techniques to create new time series that take into account the characteristics of the original series, which we then use to generate enough samples to fit deep learning models properly. The proposed method is applied in the context of COVID-19 time series forecasting using three deep learning techniques, (1) the long short-term memory, (2) gated recurrent units, and (3) convolutional neural network. In terms of symmetric mean absolute percentage error and root mean square error measures, the proposed method significantly improves the performance of long short-term memory and convolutional neural networks. Also, the improvement is average for the gated recurrent units. Finally, we present a summary of the top augmentation model as well as a visual representation of the actual and forecasted data for each country.
Human Activity Classification Using the 3DCNN Architecture

Interest in utilizing neural networks in a variety of scientific and academic studies and in industrial applications is increasing. In addition to the growing interest in neural networks, there is also a rising interest in video classification. Object detection from an image is used as a tool for various applications and is the basis for video classification. Identifying objects in videos is more difficult than for single images, as the information in videos has a time continuity constraint. Common neural networks such as ConvLSTM (Convolutional Long Short-Term Memory) and 3DCNN (3D Convolutional Neural Network), as well as many others, have been used to detect objects from video. Here, we propose a 3DCNN for the detection of human activity from video data. The experimental results show that the optimized proposed 3DCNN provides better results than neural network architectures for motion, static and hybrid features. The proposed 3DCNN obtains the highest recognition precision of the methods considered, 87.4%. In contrast, the neural network architectures for motion, static and hybrid features achieve precisions of 65.4%, 63.1% and 71.2%, respectively. We also compare results with previous research. Previous 3DCNN architecture on database UCF Youtube Action worked worse than the architecture we proposed in this article, where the achieved result was 29%. The experimental results on the UCF YouTube Action dataset demonstrate the effectiveness of the proposed 3DCNN for recognition of human activity. For a more complex comparison of the proposed neural network, the modified UCF101 dataset, full UCF50 dataset and full UCF101 dataset were compared. An overall precision of 82.7% using modified UCF101 dataset was obtained. On the other hand, the precision using full UCF50 dataset and full UCF101 dataset was 80.6% and 78.5%, respectively.
EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network

Recently, it has been demonstrated that the performance of a deep convolutional neural network can be effectively improved by embedding an attention module into it. In this work, a novel lightweight and effective attention method named Pyramid Squeeze Attention (PSA) module is proposed. By replacing the 3 $$\,\times \,$$ 3 convolution with the PSA module in the bottleneck blocks of the ResNet, a novel representational block named Efficient Pyramid Squeeze Attention (EPSA) is obtained. The EPSA block can be easily added as a plug-and-play component into a well-established backbone network, and significant improvements on model performance can be achieved. Hence, a simple and efficient backbone architecture named EPSANet is developed in this work by stacking these ResNet-style EPSA blocks. Correspondingly, a stronger multi-scale representation ability can be offered by the proposed EPSANet for various computer vision tasks including but not limited to, image classification, object detection, instance segmentation, etc. Without bells and whistles, the performance of the proposed EPSANet outperforms most of the state-of-the-art channel attention methods. As compared to the SENet-50, the Top-1 accuracy is improved by 1.93 $$\%$$ on ImageNet dataset, a larger margin of +2.7 box AP for object detection and an improvement of +1.7 mask AP for instance segmentation by using the Mask-RCNN on MS-COCO dataset are obtained.
HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering

Minimum sum-of-squares clustering (MSSC) is a widely used clustering model, of which the popular K-means algorithm constitutes a local minimizer. It is well known that the solutions of K-means can be arbitrarily distant from the true MSSC global optimum, and dozens of alternative heuristics have been proposed for this problem. However, no other algorithm has been predominantly adopted in the literature. This may be related to differences of computational effort, or to the assumption that a near-optimal solution of the MSSC has only a marginal impact on clustering validity. In this article, we dispute this belief. We introduce an efficient population-based metaheuristic that uses K-means as a local search in combination with problem-tailored crossover, mutation, and diversification operators. This algorithm can be interpreted as a multi-start K-means, in which the initial center positions are carefully sampled based on the search history. The approach is scalable and accurate, outperforming all recent state-of-the-art algorithms for MSSC in terms of solution quality, measured by the depth of local minima. This enhanced accuracy leads to clusters which are significantly closer to the ground truth than those of other algorithms, for overlapping Gaussian-mixture datasets with a large number of features. Therefore, improved global optimization methods appear to be essential to better exploit the MSSC model in high dimension.
See No Evil, Hear No Evil? Dissecting the Impact of Online Hacker Forums

Online hacker forums offer a prominent avenue for sharing hacking knowledge. Using a field dataset culled from multiple sources, we find that online discussion of distributed denial of service (DDOS) attacks in hackforums.net decreases the number of DDOS
Mitigating the Attraction Effect with Visualizations

Human decisions are prone to biases, and this is no less true for decisions made within data visualizations. Bias mitigation strategies often focus on the person, by educating people about their biases, typically with little success. We focus instead on the system, presenting the first evidence that altering the design of an interactive visualization tool can mitigate a strong bias - the attraction effect. Participants viewed 2D scatterplots where choices between superior alternatives were affected by the placement of other suboptimal points. We found that highlighting the superior alternatives weakened the bias, but did not eliminate it. We then tested an interactive approach where participants completely removed locally dominated points from the view, inspired by the elimination by aspects strategy in the decision-making literature. This approach strongly decreased the bias, leading to a counterintuitive suggestion: tools that allow removing inappropriately salient or distracting data from a view may help lead users to make more rational decisions.