Reviewerly/exHarmony huggingface dataset:
HyperMinHash: MinHash in LogLog space

In this extended abstract, we describe and analyze a lossy compression of MinHash from buckets of size <inline-formula><tex-math notation="LaTeX">$O(\log n)$</tex-math></inline-formula> to buckets of size <inline-formula><tex-math notation="LaTeX">$O(\log \log n)$</tex-math></inline-formula> by encoding using floating-point notation. This new compressed sketch, which we call HyperMinHash, as we build off a HyperLogLog scaffold, can be used as a drop-in replacement of MinHash. Unlike comparable Jaccard index fingerprinting algorithms in sub-logarithmic space (such as b-bit MinHash), HyperMinHash retains MinHash's features of streaming updates, unions, and cardinality estimation. For a additive approximation error <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> on a Jaccard index <inline-formula><tex-math notation="LaTeX">$ t$</tex-math></inline-formula> , given a random oracle, HyperMinHash needs <inline-formula><tex-math notation="LaTeX">$O\left(\epsilon ^{-2} \left(\log \log n + \log \frac{1}{ \epsilon } \right)\right)$</tex-math></inline-formula> space. HyperMinHash allows estimating Jaccard indices of 0.01 for set cardinalities on the order of <inline-formula><tex-math notation="LaTeX">$10^{19}$</tex-math></inline-formula> with relative error of around 10 percent using 2MiB of memory; MinHash can only estimate Jaccard indices for cardinalities of <inline-formula><tex-math notation="LaTeX">$10^{10}$</tex-math></inline-formula> with the same memory consumption.
Estimating the Number of Hidden Nodes of the Single-Hidden-Layer Feedforward Neural Networks

In order to solve the problem that there is no effective means to find the optimal number of hidden nodes of single-hidden-layer feedforward neural network, in this paper, a method will be introduced to solve it effectively by using singular value decomposition. First, the training data need to be normalized strictly by attribute-based data normalization and sample-based data normalization. Then, the normalized data is decomposed based on the singular value decomposition, and the number of hidden nodes is determined according to main eigenvalues. The experimental results of MNIST data set and APS data set show that the feedforward neural network can attain satisfactory performance in the classification task.
Detecting Safety and Security Faults in PLC Systems with Data Provenance

Programmable Logic Controllers are an integral component for managing many different industrial processes (e.g., smart building management, power generation, water and wastewater management, and traffic control systems), and manufacturing and control industries (e.g., oil and natural gas, chemical, pharmaceutical, pulp and paper, food and beverage, automotive, and aerospace). Despite being used widely in many critical infrastructures, PLCs use protocols which make these control systems vulnerable to many common attacks, including man-in-the-middle attacks, denial of service attacks, and memory corruption attacks (e.g., array, stack, and heap overflows, integer overflows, and pointer corruption). In this paper, we propose PLC-PROV, a system for tracking the inputs and outputs of the control system to detect violations in the safety and security policies of the system. We consider a smart building as an example of a PLC-based system and show how PLC-PROV can be applied to ensure that the inputs and outputs are consistent with the intended safety and security policies.
Data Association for Tracking Extended Targets

The sum-product algorithm for data association (SPADA) provides a highly effective, efficient, and scalable solution to the probabilistic data association problem, a major challenge in multitarget tracking. Similar to most existing data association algorithms, the SPADA is also based on the conventional data association assumption: Targets either produce no measurements or produce a single measurement at each time step and sensor. This paper presents the results of experiments with an extension of SPADA that is suitable for the case where targets can generate multiple measurements. This extension is general in the sense that the number of measurements generated by a target is modeled by an arbitrary truncated probability mass function (PMF) and enables extended target tracking (ETT) by performing probabilistic multiple-measurement to target associations. ETT is especially suitable for inexpensive high-resolution millimeter-wave radar sensors. We demonstrate the favorable performance-complexity tradeoff of the proposed method in a challenging tracking problem involving three closely-spaced targets that produce multiple measurements.
GORFLM: Globally Optimal Robust Fitting for Linear Model

Fitting a model to data contaminated by noise and outliers is a common task in computer vision, and it is often solved by maximizing inlier set. Most existing methods cannot guarantee global optimality, due to the two techniques widely utilized in inlier maximization: randomized sampling to generate candidate models and a hard threshold on residuals to classify inliers and outliers. In this paper, we propose a deterministic globally optimal linear model fitting method, in which we use the negative Gaussian function as a soft loss function over the residual and formulate model fitting as minimizing the sum of the Gaussian functions. We derive a convex quadratic function as the lower bound function of the objective function so that it can be globally minimized by a branch-and-bound algorithm. Experiments showed that the proposed method outperformed the state-of-the-art methods on several typical CV problems, especially when there are multiple models with different noise levels and large number of data points.
Block Chain Technology towards Identity Management in Health Care Application

Blockchain is decentralized architecture, where data are stored in the form of blocks for processing. The data has to be transferred from one person to another with safety and security and updated with smart contract in the blockchain. But there are some challenges such as data spoofing, integrity, authentication of the data. In the health sector the privacy of the patients' data has to be maintained. The proposed system, “Insurance Management in Healthcare Sector” uses blockchain combined with identity management to access the identity of a person when authorized by the person. After verifying the details, the insured amount will be transferred to the policy holder or the hospital with the help of matching smart contracts in the blockchain of the Ethereum platform. As a result, the insurance claim can reach the policy holder who has initiated the claim process with proof of work. The other use cases such as health care industries, social media networks are also discussed and the analysis of how the blockchain can be used in various fields.
Preprocessing of YouTube Myanmar Music Comments for Sentiment Analysis

Over the years, social media sites have become an important role of communication and a source of the tremendous amount of real-time data of images, videos, etc. YouTube is likely the most popular of them, with millions of uploaded videos and billions of comments for all these videos. This paper presents a new Music dataset, which has the YouTube comments written in Myanmar language, to be applied in sentiment analysis. Data preprocessing is very important for our language because it allows improving the quality of the raw data and converting the raw data into a clean dataset. The preprocessing of music comments is followed by basic phase, removing phase, segmentation phase, replacement phase and translation phase. The outcome of YouTube comment preprocessing will aid in better sentiment analysis. Results show that the preprocessing approaches give a significant effect on the musical opinion extraction process using information gain.
Learning to represent healthcare providers knowledge of neonatal emergency care

Modelling healthcare providers' knowledge while they are gaining new concepts is an important step towards supporting self-regulated personalised learning at scale. This is especially important if we are to address health workforce skills development and enhance the subsequent quality of care patients receive in the Global South, where a huge skills gap exists. Rich data about healthcare providers' learning can be captured by their responses to close-ended problems within conjunctive solution space -such as clinical training scenarios for emergency care delivery- on smartphone-based learning interventions which are being proposed as a solution for reducing the healthcare skills gap in this context. Together with sequential data detailing a learner's progress while they are solving a learning task, this provides useful insights into their learning behaviour. Predicting learning or forgetting curves from representations of healthcare providers knowledge is a difficult task, but recent promising machine learning advances have produced techniques capable of learning knowledge representations and overcoming this challenge. In this study, we train a Long Short-Term Memory neural network for predicting learners' future performance and forgetting curves by feeding it sequence embeddings of learning task attempts from healthcare providers from Global South. From this training, the model captures nuanced representations of a healthcare provider's clinical knowledge and their patterns of learning behaviours, predicting their future performance with high accuracy. More significantly, by differentiating reduced performance based on spaced learning, the model can help provide timely warning that helps support healthcare providers to reinforce their self-regulated learning while providing a basis for personalised instructional support to aid improved clinical outcomes from their professional practices.
Two-Hierarchical Scheme of Statistical Coding of Information Resource Data with Quantitative Clustering

The two-hierarchical scheme of statistical coding is being developed to improve the efficiency of statistical coding of information resource data from the point of view of increasing the protection of information resource and reducing the length of the presentation of information. The tool for clustering is data restructuring. A fundamentally new approach to the restructuring of information resource data has been proposed-internal restructuring on a quantitative basis. The quantitative indicator is the number of series of units identified in the internal structure of the message element. The two-hierarchical scheme of statistical coding with clustering allows to solve the actual scientific-applied problem connected with increase of efficiency of statistical coding of data of an information resource from the position of increase of protection of an information resource and reduction of length on information representation.
Web navigation prediction based on dynamic threshold heuristics

Web Navigation Prediction is a popular area of research as users on the web are continuously growing. Capturing users' behavior provides insights about the user demands, accessibility patterns, and inconsistencies in the website design. Several navigation models have been developed in the past. Most of the models rely upon fixed threshold mechanism for prediction, which is inadequate as they require multiple computational steps which not necessarily give the best possible value. To combat this issue, we propose two models based on dynamic thresholds: All-Kth Modified Markov Model based on Geometric threshold (KMMMG) and (b) All-Kth Modified Markov Model based on Branching Factor Threshold (KMMMBF). Our experiments show that longer navigations are rare and are highly co-related. However, smaller navigations are more and are less co-related. Dynamic threshold models produce more optimum predictions as compare to fixed threshold models. KMMMBF performs the best and achieves the highest prediction accuracy, 78.53%, 91.32%, and 61.37% on CTI, MSWEB, and BMS dataset, respectively.
Using Personas as Curricular Design Tools: Engaging the Boundaries of Engineering Culture

This innovative practice paper introduces the use of personas as a design technique to guide the large-scale curriculum reform of an Electrical and Computer Engineering (ECE) program at [University X], a large Mid-Atlantic research-intensive institution. The team has created five personas to explore the potential breadth of the curriculum and expand the faculty perspective on who could be a successful ECE student in the department. The concept of personas originated in the field of human-computer interaction to represent subsets of potential users as abstracted sets of characteristics. Drawing from qualitative data including observations of team meetings and document analysis, this work describes the ethnographic inquiry designers used to develop profiles not solely based on their biases. Responses to personas during implementation show that they can be used to engage the boundaries of culture, bringing to light not only characteristics of students who are not typically present in the program, but also faculty members' assumptions about characteristics needed for success.
Improving deep neural network performance by integrating kernelized Min-Max objective

Deep neural networks (DNN), such as convolutional neural networks (CNN) have been widely used for object recognition. However, they are usually unable to ensure the required intra-class compactness and inter-class separability in the kernel space. These are known to be important in pattern recognition for achieving both robustness and accuracy. In this paper, we propose to integrate a kernelized Min-Max objective in the DNN training in order to explicitly enforce both kernelized within-class compactness and between-class margin. The involved kernel space is implicitly mapped from the feature space associated with a certain upper layer of DNN by exploiting a kernel trick, while the Min-Max objective in this space is interpolated with the original DNN loss function and finally optimized in the training phase. With a very small additional computation cost, the proposed strategy can be easily integrated in different DNN models without changing any other part of the original model. The comparative recognition accuracy of the proposed method is evaluated with multiple DNN models (including shallow CNN, deep CNN and deep residual neural network models) on two benchmark datasets: CIFAR-10 and CIFAR-100. Extensive experimental results demonstrate that the integration of kernelized Min-Max objective in the training of DNN models can achieve better results compared to state-of-the-art models, without incurring additional model complexity.
Front-End IoT Application for the Bitcoin based on Proof of Elapsed Time (PoET)

There are some registry agreements that may be appropriate for the Internet of Things (IoT), including Bitcoin, Hyperledger Fabric and IOTA. This article presents quickly and examines them in terms of the progress of Internet applications. Block-dependent IoT applications can consolidate the chain's rationale (smart contracts) and front-end, portable or front-end web applications. We present three possible designs for BC IoT front-end applications. They vary depending on the Bitcoin block chain customer (neighborhood gadget, remote server) and the key location needed to manage active exchanges. The vital requirements of these projects, which use Bitcoin to organize constructive exchanges, are the volumes of information, the area and time of the complete block and block block, and the entry of the Bitcoin store. The implications of these surveys show that it is unlikely that a full Bitcoin distributor will continue to operate reliably with a mandatory IoT gadget. Then, designing with remote Bitcoin customers is, in all respects, a suitable methodology in which there are two minor alternatives and vary in key storage / management. Similarly, we recommend using the design with a unique match between the IoT gadget and the remote blockchain client to reduce system activity and improve security. We hope you also have the ability to operate with versatile verses with low control and low productivity. Our review eliminates the contradictions between synthesis methodologies, but the final choice for a particular registration agreement and the original technique completely depends on the proposed use case.
Human Action Recognition Based on Skeleton and Convolutional Neural Network

Automatic human action recognition plays an important role in many real applications, such as video surveillance, virtual reality and human-computer intelligent interaction, etc. The spatial complexity and time variability are the main challenges to be addressed. Most of the traditional methods are based on handcrafting video features, which leads to limited expressive power and difficulties of generalization. In recent years, with the rise of deep network, the deep learning method is applied in automatic human action recognition and achieves better performanceIn this paper we present a novel Convolutional Neural Network (CNN) based automatic human action recognition method, which automatically learns the spatial and temporal characteristics of the data to improve the recognition performance. Specifically, we preprocess the dataset to extract keyframes by using interframe difference method to reduce data redundancy and preserve the spatiotemporal characteristics of the data simultaneously; then we utilize the real-time key point recognition system Openpose to get the skeleton information. It consists of human joint points which are the input features of our recognition model. For model training, we use the large data set of UCF-101 which is the common benchmark in this filed. For model evaluation, we compare our method with the state-of-the-art methods. The experimental results show that our method achieves significant performance improvement on the dataset of UCF-101. Finally, based on the model we implement a system by using a Kinect V2 to record human action in real environment. Our system can automatically mark the range of human action and output the corresponding action labels in real time.
INOR—An Intelligent noise reduction method to defend against adversarial audio examples

Recently, Automatic Speech Recognition(ASR) systems are seriously threatened by adversarial audio examples. The defense against adversarial audio examples has become an urgent issue. Different from adversarial image examples whose target is limited in the finite categories, the target of adversarial audio examples can be any combination of the words in a language. Adversarial audio examples aim to change the semantic of the audio. The semantic is explicitly represented in transcription distance, which affects the adversarial perturbation. This paper analyzes the relationship between semantic difference and adversarial perturbation. Quantization and local smoothing are calibrated to evaluate their performance. We observe that, for adversarial audio examples with different transcription distance levels, the capability of different denoising strategies varies. Therefore, we first introduce the wavelet filter, which denoises the signal in the transformed domain. Then we explore the defense capability of combined filters. Finally, a new intelligent noise reduction method–INOR is proposed to improve the denoising performance of audios under different levels of transcription distance. Experimental results show that INOR is effective in mitigating the adversarial perturbations for adversarial examples with different transcription distance levels. The average CER and WER is reduced by 33% and 55%.
Research on Pre-Modern Earthquakes Based on Fusion of Humanities and Sciences

The Great East Japan Earthquake and Tsunami has reawakened people to the reality of large-scale earthquakes that recur in cycles of several hundred to a thousand years. The historical resources and archeology research group, which was established in 2014 within the Coordinating Committee of Earthquake and Volcanic Eruption Prediction Researches, is collaborating with researchers of seismology, history, archeology, and information science to investigate infrequent earthquakes using historical documents that record earthquakes and traces of disasters at archeological sites. To this end, we are creating a database of published historical sources of earthquakes to make the data readily accessible, and reexamining these sources and uncovering new historical material to investigate earthquakes that occurred in pre-modern times. We are also engaged in research on relief efforts for victims of past earthquakes and the post-disaster reconstruction process.
Novel recommendation system based on long‐term composition for adaptive web services

ABSTRACT In the era of digital web services, composition of features on the fly is inevitable. The Long‐term Composed Service (LCS) entertains the composition of features to any extent, since it has an open‐ended lifetime. In the proposed research work, we have intended to provide service support to run the business toward a long time commitment. Structure‐based recommended system for LCSs (RS‐LCSs) is proposed, where user queries and recent updation/requirements are considered for exhibiting the response through the system. In the proposed system, business has been regulated according to the time constraints. We have tested our proposed system on the standard benchmark dataset and quantitative metrics show our proposed method has performed well against the compared methods. The forecasting of business has been done through our model to address the recent queries and new requirements issues to provide an adaptive web service for the business development.
Distributed Automatic Modulation Classification with Compressed Data

This work presents an approach for performing automatic modulation classification (AMC) in a distributed environment using a novel multi-input averaging Convolutional Neural Network (CNN) which ingests one instance of raw received data, in Inphase/Quadrature (IQ) format, and compressed realizations of the same signal from neighboring nodes. Further, this work examines the use of undercomplete autoencoders (AEs) as a means to compress raw received IQ data for transmission to neighboring nodes while retaining the signal features most pertinent to performing AMC. The accuracy of the developed approach is evaluated using simulated BPSK, QPSK, and 16QAM signals, with a noise-only class, and the impact of the compression ratio, number of nodes, and SNR are considered. While results show that the implemented AE is not an effective means of compressing raw IQ data, results did show that by combining data realizations from neighboring nodes using the proposed approach, classification accuracy increases by as much as 7% per node.
Feature-Aware Uniform Tessellations on Video Manifold for Content-Sensitive Supervoxels

alysis a n d M a c hi n e In t ellig e n c e 4 3 (9) , p p. 3 1 8 3-3 1 9 5. 1 0.