Reviewerly/exHarmony huggingface dataset:
Entropy property testing with finitely many errors

Let P be a class of distributions over natural numbers, and A be a subset of ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> . We study the problem of deciding, using i.i.d. samples X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ,... from an unknown p ∈ P, whether the entropy H(p) is in A or not. The decision is updated based on every new observation X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> -we are interested in decision rules that make only finitely many errors no matter what the underlying source is. We give necessary and sufficient conditions on the class P and A that can be decided with only finitely many errors. We show for example that such rules exist for testing the rationality of entropy within a given interval, for testing if the entropy falls in an interval of form (a,b], but no such decision rule exists to determine if the entropy is finite or if the entropy falls in an interval of form [a,b]. In the process, we also highlight the conceptual foundation this framework shares with regularization.
Other Key Concepts in Artificial Intelligence

While machine and deep learning are garnering most of the attention in the artificial intelligence (AI) domain at the present time, other essential tools are also important in this portfolio of AI resources. Cognitive computing, as exemplified by IBM Watson, is a framework of machine learning, pattern recognition, and natural language processing (NLP) to mimic the human brain. The DeepQA technology of Watson (a massively parallel probabilistic evidence-based architecture) can effectively deconstruct a question and result in a hypothesis with evidence and confidence scoring. There are key differences between characteristics of AI and cognitive computing even though cognitive computing is considered within the realm of AI. NLP, the machine’s capability to understand language and is the intersection of AI, computer science, and linguistics. NLP consists of natural language understanding and generation and is used in chatbots. Autonomous systems, including robotics, is an AI domain that has a significant role in health care, with surgery and rehabilitation being leading areas. Other key areas of AI technology for health care include robotic process automation, extended reality (augmented, virtual, and mixed reality), blockchain, cloud computing, and internet of things.
Collector’s Note: Balmat Mining District Update, St. Lawrence County, New York

Prepared by William Besse.When our book Collector’s Guide to the Balmat Mining District, St. Lawrence County, New York (Chamberlain et al. 2018) went to press, talc mining had ceased entirely, and ...
A Comparative Analysis on Online Handwritten Strokes Classification Using Online Learning

The online handwriting recognition is recognition of handwritten data through the machine using a digital pen. The online learning includes training of the classifier with test data and the test data becomes part of a training model for next test data. We have done a novel study first in this direction to experiment online learning with online handwritten strokes. The experimentation carried out with benchmarked datasets as unipen and online handwritten Gurmukhi script strokes including 12,477 and 26,572 samples, respectively. The tool used in experimentation is Libol which includes all the state of art algorithms for online learning. The results indicate that online learning could be a suitable choice for online handwriting recognition. The online learning is popular today for its use with large data and less computation time. The present study could be benefited for online handwriting recognition like applications in online learning environments.
Inversion of Smoke Black Concentration Field in a Tangentially Fired Furnace Based on Super-Resolution Reconstruction

The tangentially fired furnace has the advantages of sufficient combustion and low NOx emission, and the smoke black concentration in the furnace reflects the combustion situation of combustion equipment. The flame field and smoke black concentration field in the furnace are complex, so it is meaningful to study the flame field and the smoke black concentration field of the tangential furnace. In this article, the visual method was used to reconstruct the flame and smoke black concentration fields in the furnace. Due to the low resolution of industrial cameras, the final reconstruction effect is limited. Given this situation, this article proposes a strengthen edge characteristic super-resolution network (SECSR) algorithm suitable for the tangentially fired furnace flame images. The flame edge processing is added to the depth neural network, which enhances the ability of flame edge feature extraction. It constructs a generative adversarial network which can greatly improve the resolution of furnace flame image, to obtain high-resolution tangential furnace flame image. Secondly, based on the high-quality flame image, this article proposes an inversion algorithm of the black concentration field of the tangentially fired furnace smoke. The algorithm obtains the high-precision tangentially furnace flame temperature field through an inversion calculation to calculate the soot concentration field. In the future work, we will predict the variation trend of smoke black concentration in the furnace through the results of the inversion calculation model, to understand the combustion situation in the furnace and control the fuel consumption to reduce smoke black emission, which has important guiding significance for protecting the environment and saving resources.
A Non-Iterative Quantile Change Detection Method in Mixture Model with Heavy-Tailed Components

Estimating parameters of mixture model has wide applications ranging from classification problems to estimating of complex distributions. Most of the current literature on estimating the parameters of the mixture densities are based on iterative Expectation Maximization (EM) type algorithms which require the use of either taking expectations over the latent label variables or generating samples from the conditional distribution of such latent labels using the Bayes rule. Moreover, when the number of components is unknown, the problem becomes computationally more demanding due to well-known label switching issues [28]. In this paper, we propose a robust and quick approach based on change-point methods to determine the number of mixture components that works for almost any location-scale families even when the components are heavy tailed (e.g., Cauchy). We present several numerical illustrations by comparing our method with some of popular methods available in the literature using simulated data and real case studies. The proposed method is shown be as much as 500 times faster than some of the competing methods and are also shown to be more accurate in estimating the mixture distributions by goodness-of-fit tests.
The Class Overlap Model for System Log Anomaly Detection Based on Ensemble Learning

Using machine learning to detect system log data is essential. It is prone to the phenomenon of class overlap because of too many similar system log data. The occurrence of this phenomenon will have a serious impact on the anomaly detection of the system logs. In order to solve the problem of class overlap in system logs, this paper proposes an anomaly detection model for class overlap on system logs. We first calculate the relationship between the sample data and the membership of different classes, normal or anomaly, and use the fuzziness to separate the sample data of the overlapping parts of the classes from the data of the other parts. AdaBoost, an ensemble learning approach, is used to detect overlapping data. Compared with machine learning algorithms, ensemble learning can better classify the data of the overlapping parts, so as to achieve the purpose of detecting the anomalies of the system logs. Experimental results show that our model can be effectively applied in a variety of basic algorithms, and the results of each measure have been improved.
Extracting Entities and Relations by an End-to-End Neural Model with Multi-Layer Encoder

Extracting entities and relations in unstructured texts is an important task in the field of information extraction. However, the relations in sentences are often very complicated. Most existing methods mainly focus on the extraction of a single relation, and cannot accurately extract overlapping relations. In this paper, we propose an end-to-end neural model with multi-layer encoders to extract jointly overlapping relations and entities in sentences. In our model, we use character sequence of each word as supplementary information for word embedding, and adopt two bidirectional LSTMs to encode words and their character sequences to extract more of the semantic information in the text. Furthermore, we adopt multi-label classification to extract multiple relations. The difference from the major existing methods is that our model extracts the relations in sentences and combine each relation with encoding information to further extract entities. Finally, we conducted several experiments in the public dataset produced by distant supervision method to illustrate the performance of our method. The experimental results show that our model is superior to the previous methods, and our method has higher precision and F1 score.
Beyond Experiments: Considering the Range of Investigative and Data-Collection Methods in Science

This chapter provides an overview of the variety of methods available that scientists have developed to explore the natural and material world. Each method is accompanied by discussion of a rich set of procedures that extend beyond just experiments that many believe to be the way that science works. This limited view of scientific research is frequently found in classroom discussions, textbooks, lab manuals, professional development plans, online resources, classroom posters, state science standards, and even implied by the requirements found in many science competitions. To counter this pervasive misconception, this chapter offers a more complete illustration of the ways that investigations may occur in science by providing a detailed overview of all scientific methodology in a useful tool we call the “Modes of Scientific Inquiry” (MSI) flowchart. This flowchart provides examples of qualitative and quantitative methods, offers distinctions between descriptive, correlational, comparative and experimental designs, observational attributes, useful analytical approaches, and graphical representations of results from such investigations. Science teachers, other formal and informal science educators and students will find this overview useful in discussions about how science works and, more importantly, in conducting authentic scientific research.
Intrusion Detection of Abnormal Objects for Railway Scenes Using Infrared Images

Intrusion detection of abnormal objects is critical to avoid traffic accidents and ensure the safety of train operations. Computer-vision based approaches using RGB images have been intensively investigated for intrusion detection at daytime. However, the abnormal object detection using infrared images at nighttime remains more challenging because training samples of infrared images are limited to address this issue, we propose a data augmentation strategy motivated by image style transfer using CycleGAN. First, the synthetic images are generated which conditioned on railway scene images at daytime and non-railway scene images at nighttime. Then, a SSD object detection model is trained using the generated synthetic samples. Finally, the trained SSD model is used to detect abnormal objects for infrared images at nighttime. Experimental results demonstrate that the proposed data augmentation strategy and the object detection method for nighttime scene is effective.
Why Subspace Clustering Works on Compressed Data?

Subspace clustering (SC) refers to the problem of clustering unlabeled high-dimensional data into a union of low-dimensional subspaces. In many practical scenarios, one may only have access to the compressed data due to constraints of measurement or computation. In this paper, we introduce a general framework for analyzing the performance of various subspace clustering algorithms when applied to the compressed data. Our framework captures the connection between the problems of compressed subspace clustering (CSC) and noisy subspace clustering. With the existing study of noisy SC, our framework makes it possible to easily extend the results in noisy SC to CSC. In this paper, we apply the framework to a most commonly used sparse subspace clustering (SSC) algorithm and obtain its performance. Finally, the practicability and efficiency of CSC are verified by numerical experiments.
Design of Mach-Zehnder modulator based optical reversible gate for high speed data transmission

The limitation of conventional electronics is reduced by optical integrated circuits because of its high speed information and processing. Reversible logic gates are favorable in the processing of optical signals in optical domain. In all reversible gates inputs and outputs are correlated that is advantageous to collect the information from inputs and outputs. Reversible gates are useful in high speed data transmission with low power dissipation. Reversible gates are also used in quantum computing with low loss of information. In the current work, Reversible Feynman and Fredkin optical gates are designed using Mach Zehnder Modulator for high speed information processing. A Mach Zehnder Modulator has capability to switch the source light according biased electrical signal. The amount of biased electrical signal modulates the output. The reversible optical gates are used in optical switching, optical modulator and as protection switch. The proposed gates are explained with mathematical formulation and the truth table of the reversible gates is verified using Lumerical Interconnect tool.
An Interacting Multiple Model Smoothing Algorithm for Nonlinear Maneuvering Target Tracking

In order to improve the accuracy of nonlinear maneuvering target tracking system, this paper proposes a novel IMM smoothing algorithm, which can be used in two types of IMM smoothing by changing the delay time of IMM smoother, for nonlinear maneuvering target tracking. The IMM method combined with smoothing algorithm provides more accurate maneuvering target state estimation. The novel IMM smoothing is consisted of forward IMM filtering and backward IMM smoothing. The forward IMM filtering is a standard IMM filter, while the backward IMM smoothing utilizes the updated value and predicted value of the forward IMM filtering to get the smoothing state of the maneuvering target. Simulation results show that the novel IMM smoothing algorithm has better performance compared with the other algorithms.
Incorporating Entity Type Information into Knowledge Representation Learning

Knowledge Representation Learning (KRL), which is also known as Knowledge Embedding, is a very useful method to represent complex relations in knowledge graphs. The low-dimensional representation learned by KRL models makes a contribution to many tasks like recommender system and question answering. Recently, many KRL models are trained using square loss or cross entropy loss based on Closed World Assumption (CWA). Although CWA is an easy way for training, it violates the link prediction task which exploits KRL. To overcome the drawback, in this paper, we introduce a new method, Type-based Prior Possibility Assumption (TPPA). TPPA calculates type based prior possibilities for missing triplets instead of zeros in the training process of KRL to weaken the bad influence of CWA. We compare TPPA with the baseline method CWA in ConvE and TuckER, two common frameworks for knowledge representation learning. The experiment results on FB15k-237 dataset show that TPPA based training method outperforms CWA based training method in link prediction task.
Using Robust Networks to Inform Lightweight Models in Semi-Supervised Learning for Object Detection

A common trade-off among object detection algorithms is accuracy-for-speed (or vice versa). To meet our application's real-time requirement, we use a Single Shot MultiBox Detector (SSD) model. This architecture meets our latency requirements; however, a large amount of training data is required to achieve an acceptable accuracy level. While unusable for our end application, more robust network architectures, such as Regions with CNN features (R-CNN), provide an important advantage over SSD models-they can be more reliably trained on small datasets. By fine-tuning R-CNN models on a small number of hand-labeled examples, we create new, larger training datasets by running inference on the remaining unlabeled data. We show that these new, inferenced labels are beneficial to the training of lightweight models. These inferenced datasets are imperfect, and we explore various methods of dealing with the errors, including hand-labeling mislabeled data, discarding poor examples, and simply ignoring errors. Further, we explore the total cost, measured in human and computer time, required to execute this workflow compared to a hand-labeling baseline.
Modified Rider Optimization-Based V Channel Magnification for Enhanced Video Super Resolution

In video Super Resolution (SR), the problem of cost expense concerning the attainment of enhanced spatial resolution, computational complexity and difficulties in motion blur makes video SR a complex task. Moreover, maintaining temporal consistency is crucial to achieving an efficient and robust video SR model. This paper plans to develop an intelligent SR model for video frames. Initially, the video frames in RGB format will be transformed into HSV. In general, the improvement in video frames is done in V-channel to achieve High-Resolution (HR) videos. In order to enhance the RGB pixels, the current window size is enhanced to high-dimensional window size. As a novelty, this paper intends to formulate a high-dimensional matrix with enriched pixel intensity in V-channel to produce enhanced HR video frames. Estimating the enriched pixels in the high-dimensional matrix is complex, however in this paper, it is dealt in a significant way by means of a certain process: (i) motion estimation (ii) cubic spline interpolation and deblurring or sharpening. As the main contribution, the cubic spline interpolation process is enhanced via optimization in terms of selecting the optimal resolution factor and different cubic spline parameters. For optimal tuning, this paper introduces a new modified algorithm, which is the modification of the Rider Optimization Algorithm (ROA) named Mean Fitness-ROA (MF-ROA). Once the HR image is attained, it combines the HSV and converts to RGB, which obtains the enhanced output RGB video frame. Finally, the performance of the proposed work is compared over other state-of-the-art models with respect to BRISQUE, SDME and ESSIM measures, and proves its superiority over other models.
Decentralized backup and recovery of TOTP secrets

This work proposes a set of security, privacy, and usability design requirements for the backup and recovery systems of apps implementing the Time-based One-Time Password (TOTP) algorithm, a widely deployed method of two-factor authentication (2FA). We explain how several prevalent apps fail to satisfy these requirements and outline how our scheme leverages decentralized security techniques to satisfy the majority of these requirements and provide stronger security and privacy guarantees.
A Novel EEMD-Based Privacy Preserving Approach for Top-k SNPs Query in Genome-Wide Association Studies

Genome-wide association studies (GWAS) have been a popular method for querying Top-k most significant sets of singe-nucleotide polymorphism locations (SNPs) to discover the genetic factors of diseases. Doctors have been increasing interest to in querying SNPs that significantly associated to diseases. However, the queries and GWAS data share service bring great privacy breach risk to the patients. In traditional Laplace privacy preserving mechanism, privacy budget ε is different to be determined, the Laplace noise is added to original data only once, which causes randomness and potential security risk to privacy protection. A novel approach is proposed to implement the differential privacy preserving for Top-k SNPs query in GWAS. Ensemble Empirical Mode Decomposition (EEMD) is a noise-assisted data analysis method, it is firstly proposed to be used as noise-adding approach in privacy protection mechanism. In the EEMD approach, random white noise will be added into the SNPs querying results for many times. Moreover, the parameters of added white noise can be precisely controlled to achieve better effect. PriSTRAT software tool was redeveloped to evaluate EEMD-based privacy preserving approach, the dataset was generated by PLINK tools. Experimental results show that EEMD-based privacy preserving approach has achieved higher accuracy than Laplace mechanism under the same noise-adding strength. The novel approach could be used to realize differential privacy preserving for Top-k SNPs query in GWAS to achieve the balance between privacy preserving and query accuracy.
Efficient Optimized Strategy of Big Data Retrieval

With the development of new information technologies, the accumulation of data volume has been exploding, and big data retrieval has played an increasingly important role in big data technology. The challenge of data retrieval are the improvement of retrieval accuracy and retrieval speed. Aiming at the demand of big data platform for efficient data retrieval, an efficient optimized strategy is proposed. We found when the primary key query is used, the query response can be quick. However, when using a non-primary key query, the cache table needs to be comprehensively scanned and the longer response delay may be induced. This paper proposes a secondary index based on Solr to increase the accuracy of information retrieval and the quality of user experience. Then a cache-heat evaluation algorithm categorizes data according to access frequency to reduce query latency. Moreover, an index optimization method based on memory cache updates the cache to save space and enhance utilization. The experiments and simulation demonstrate that the proposed strategy can effectively improves the big data retrieval.