Reviewerly/exHarmony huggingface dataset:
A stacked convolutional sparse denoising autoencoder model for underwater heterogeneous information data

Deep learning denoising models can automatically extract underwater heterogeneous information data features to improve denoising performance through an internal network structure. In the present study, a novel stacked convolutional sparse denoising autoencoder (SCSDA) model was proposed in this paper to complete the blind denoising task of underwater heterogeneous information data. Specifically, for the first time, the stacked sparse denoising autoencoder (SSDA) was constructed by three sparse denoising autoencoders (SDA) to extract overcomplete sparse features. Then, the output of the last encoding layer of the SSDA was used as the input of the convolutional neural network (CNN) to further extract the deep features. Finally, in order to solve the lack of the pure underwater heterogenous information data during the acquisition and transmission process, the unrelated dataset was developed to simulate the underwater heterogeneous information data as the training set in proposed SCSDA model. Compared with the existing other algorithms, the experiment results demonstrate that the proposed SCSDA model combines the advantages of SSDA and CNN, which has great blind denoising ability. It can process faster and preserves more edge features of underwater heterogeneous information data. Also, it has a certain degree of robustness and effectiveness.
Extracting Robust and Accurate Features via a Robust Information Bottleneck

We propose a novel strategy for extracting features in supervised learning that can be used to construct a classifier which is more robust to small perturbations in the input space. Our method builds upon the idea of the information bottleneck, by introducing an additional penalty term that encourages the Fisher information of the extracted features to be small when parametrized by the inputs. We present two formulations where the relevance of the features to output labels is measured using either mutual information or MMSE. By tuning the regularization parameter, we can explicitly trade off the opposing desiderata of robustness and accuracy when constructing a classifier. We derive optimal solutions to both robust information bottleneck formulations when the inputs and outputs are jointly Gaussian, proving that the optimally robust features are also jointly Gaussian in this setting. We also propose methods for optimizing variational bounds on the robust information bottleneck objectives in general settings using stochastic gradient descent, which may be implemented efficiently in neural networks. Our experimental results for synthetic and real data sets show that the proposed feature extraction methods indeed produce classifiers with increased robustness to perturbations.
Lattice LSTM for Chinese Sentence Representation

Words provide a useful source of information for Chinese NLP, and word segmentation has been taken as a pre-processing step for most downstream tasks. For many NLP tasks, however, word segmentation can introduce noise and lead to error propagation. The rise of neural representation learning models allows sentence-level semantic information to be collected from characters directly. As a result, it is an empirical question whether a fully character-based model should be used instead of first performing word segmentation. We investigate a neural representation that simultaneously encodes character and word information without the need for segmentation. In particular, candidate words are found in a sentence by matching with a pre-defined lexicon. A lattice structured LSTM is used to encode the resulting word-character lattice, where gate vectors are used to control information flow through words, so that the more useful words can be automatically identified by end-to-end training. We compare the performance of the resulting lattice LSTM and baseline sequence LSTM structures over both character sequences and automatically segmented word sequences. Results on NER show that the character-word lattice model can significantly improve the performance. In addition, as a general sentence representation architecture, character-word lattice LSTM can also be used for learning contextualized representations. To this end, we compare lattice LSTM structure with its sequential LSTM counterpart, namely ELMo. Results show that our lattice version of ELMo gives better language modeling performances. On Chinese POS-tagging, chunking and syntactic parsing tasks, the resulting contextualized Chinese embeddings also give better performance than ELMo trained on the same data.
Fiber optic gyro noise reduction based on hybrid CEEMDAN-LWT method

The noise involved in drift signal of fiber optic gyroscope (FOG) mostly comes from electronic component, detection circuit and variable working environment. Gaussian white noise and fractional noise submerged in FOG output is difficult to be eliminated by conventional methods because of the non-stationary characteristics. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is a novel nonlinear and non-stationary signal processing method, which is exploited in FOG denoising. Lifting wavelet transform (LWT) technology is employed to combine with CEEMDAN method to expedite the computing efficiency and improve filtering accuracy, so that a hybrid CEEMDAN-LWT-based model is achieved. Comparison analysis with other filtering methods based on empirical mode decomposition (EMD) and its improved version is done. And ensemble empirical mode decomposition (EEMD) method combined with LWT considered as EEMD-LWT method is also applied as compared. Experimental analysis results show that new hybrid method outperforms other EMD-based filtering methods. The new method requires only 11.3% sifting iterations of the EEMD-LWT method. Meanwhile, the rate white noise, bias instability and quantization noise buried in FOG output signal decreases from 0.0031°/h, 0.0352°/h and 0.5412° to 0.0005°/h, 0.0056°/h and 0.0231°, respectively. Furthermore, de-trended fluctuation analysis (DFA) algorithm is employed to evaluate the effectiveness of hybrid method for the FOG signal filtering.
Endpoint-Flexible Coflow Scheduling Across Geo-Distributed Datacenters

Over the last decade, we have witnessed growing data volumes generated and stored across geographically distributed datacenters. Processing such geo-distributed datasets may suffer from significant slowdown as the underlying network flows have to go through the inter-datacenter networks with relatively low and highly heterogeneous available link bandwidth. Thus, optimizing the transmissions of inter-datacenter flows, especially coflows that capture application-level semantics, is important for improving the communication performance of such geo-distributed applications. However, prior solutions on coflow scheduling have significant limitations: they schedule coflows with already-fixed endpoints of flows, making them insufficient to optimize the coflow completion time (CCT). In this article, we focus on the problem of jointly considering endpoint placement and coflow scheduling to minimize the average CCT of coflows across geo-distributed datacenters. To solve this problem without any prior knowledge of coflow arrivals, we present a coflow-aware optimization framework called SmartCoflow. In SmartCoflow, we first apply an approximate algorithm to obtain the endpoint placement and scheduling decisions for a single coflow. Based on the single-coflow solution, we then develop an efficient online algorithm to handle the dynamically arrived coflows. Through rigorous theoretical analysis, we prove that SmartCoflow has a non-trivial competitive ratio. We also extend SmartCoflow to incorporate various design choices or requirements of applications and operators, such as enforcing an inter-datacenter bandwidth usage budget and considering coflow deadline. Through experimental results from testbed implementation and trace-driven simulations, we demonstrate that SmartCoflow can reduce the average CCT, lower bandwidth usage, and improve coflow deadline meet rate, when compared to the state-of-the-art scheduling-only method.
An Efficient System Framework for Managing Identity in Educational System based on Blockchain Technology

In recent years, there have been many difficulties occurred in the field of education from school to universities while conducting exams. Fraudulent activities can be identified in this field and it leads to reduce trust in the quality of the education system. Most of the scandals are based on the abuse of personal information systems and the user's data breaches occur and affect the security as well as privacy. In this research work, we propose a high-level framework of the block chain-based identity management system. In Hyperledger Indy using plenum based RBFT protocol the framework will be created. RBFT algorithm in block chain mechanism provides trust and security and complete transparency in the whole system. In this paper, we proposed the secured identity management system and a human intervention could not affect the user's data system and makes the security even with better results.
CSI

Mobile video streaming services have widely adopted Adaptive Bitrate (ABR) streaming to dynamically adapt the streaming quality to variable network conditions. A wide range of third-party entities such as network providers and testing services need to understand such adaptation behavior for purposes such as QoE monitoring and network management. The traditional approach involved conducting test runs and analyzing the HTTP-level information from the associated network traffic to understand the adaptation behavior under different network conditions. However, end-to-end traffic encryption protocols such as HTTPS and QUIC are being increasingly used by streaming services, hindering such traditional traffic analysis approaches.
Learning Graph Neural Networks with Deep Graph Library

Learning from graph and relational data plays a major role in many applications including social network analysis, marketing, e-commerce, information retrieval, knowledge modeling, medical and biological sciences, engineering, and others. In the last few years, Graph Neural Networks (GNNs) have emerged as a promising new supervised learning framework capable of bringing the power of deep representation learning to graph and relational data. This ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations.
Automatic Detection of Satire in Bangla Documents: A CNN Approach Based on Hybrid Feature Extraction Model

Wide spread of satirical news in online communities is an ongoing trend. The nature of satires are so inherently ambiguous that sometimes it’s too hard even for humans to understand whether it’s actually satire or not. So, research interest has grown in this field. The purpose of this research is to detect Bangla satirical news spread in online news portals as well as social media. In this paper we propose a hybrid technique for extracting feature from text documents combining Word2Vec and TF-IDF. Using our proposed feature extraction technique, with standard CNN architecture we could detect whether a Bangla text document is satire or not with an accuracy of more than 96%.
Imputation of Missing Data in Electronic Health Records Based on Patients’ Similarities

Using electronic health records (EHR) as the source of data for mining and analysis of different health conditions has become an increasingly common approach. However, due to irregular observation times and other uncertainties inherent in medical settings, the EHR data sets suffer from a large number of missing values. Most of the traditional data mining and machine learning approaches are designed to operate on complete data. In this paper, we propose a novel imputation method for missing data to facilitate using these approaches for the analysis of EHR data. The imputation is based on a set of interpatient, multivariate similarities among patients. For a missing data point in a patient's lab results during his/her intensive care unit stay, the method ranks other patients based on their similarities with the ego patient in terms of lab values, then the missing value is estimated as a weighted average of the known values of the same laboratory test from other patients, considering their similarities as weights. A comparison of the estimated values by the proposed method with values estimated by several common and state-of-the-are methods, such as MICE and 3D-MICE, shows that the proposed method outperforms them and produces promising results.
Analysis of adult disease characteristics and mortality on MIMIC-III

To deeply analyze the basic information and disease information of adult patients in the MIMIC-III (Medical Information Mart for Intensive Care III) database, and provide data reference for clinicians and researchers.Tableau2019.1.0 and Navicat12.0.29 were used for data analysis and extraction of disease distribution of adult patients in the MIMIC-III database.A total of 38,163 adult patients were included in the MIMIC-III database. Only 38,156 patients with the first diagnosis were selected. Among them, 21,598 were males accounting for 56.6% the median age was 66 years (Q1-Q3: 53-78), the median length of a hospital stay was 7 days (Q1-Q3: 4-12), and the median length of an ICU stay was 2.1 days (Q1-Q3: 1.2-4.1). Septicemia was the disease with the highest mortality rate among patients and the total mortality rate was 48.9%. The disease with the largest number of patients at the last time was other forms of chronic ischemic heart disease.By analyzing the patients' basic information, the admission spectrum and the disease morbidity and mortality can help more researchers understand the MIMIC-III database and facilitate further research.
Institutional Management Elaboration through Cognitive Modeling of the Balanced Sustainable Development of Regional Innovation Systems

The main aim of this research was to search for relevant indicators and effective instruments for modeling the impact and institutional management of the regional innovation system for its balanced development. The objective of the study was to justify approaches for institutional management elaboration for balanced sustainable development of regional innovation systems regarding related factors and the needs of the region. The methodology of cognitive modeling and scenario impulse modeling are used for the analysis of the interconnection between the regional innovation system and higher education institutions and developing an instrument to diagnose the problems of no-congruence and improving the institutional management elaboration in the regional innovation policy. The analysis of system indicators of the cognitive map allowed to define the basic patterns in the regional system, determine the most significant factors and relationships for the economic system of the region and visualize them in the form of a cognitive map, identify the influence of the innovation environment elements on the target indicators, quantify its positive and negative impact, forecast and determine the directions of its improvement and enhancing the interaction of regional actors. The results of the study have practical value for use in improving institutional management in planning reforms and transformations of regional innovation systems.
Identifiability and estimation of recursive max‐linear models

Abstract We address the identifiability and estimation of recursive max‐linear structural equation models represented by an edge‐weighted directed acyclic graph (DAG). Such models are generally unidentifiable and we identify the whole class of DAG s and edge weights corresponding to a given observational distribution. For estimation, standard likelihood theory cannot be applied because the corresponding families of distributions are not dominated. Given the underlying DAG, we present an estimator for the class of edge weights and show that it can be considered a generalized maximum likelihood estimator. In addition, we develop a simple method for identifying the structure of the DAG. With probability tending to one at an exponential rate with the number of observations, this method correctly identifies the class of DAGs and, similarly, exactly identifies the possible edge weights.
Ensemble-learning based neural networks for novelty detection in multi-class systems

In most real-world systems or processes, determining the complete set of classes during the training phase is generally impossible. There is a high chance that novelties or abnormal data can appear in future phases which might severely affect the performance of the machine learning system. Novelty detection is of great importance in many critical systems and domains, such as business intelligence, process monitoring, information security, clinical decision support etc. Most of the available methods for novelty detection use a one-class classification (OCC) criterion, i.e. treating multiple known classes as a single ”Normal” class, whose aim is to distinguish data samples between “Normal” and “Not Normal” classes. In this paper, the problem of novelty detection in multi-class systems is addressed through ensemble based learning of neural networks (EBNN), capable of both detecting novelties and classifying the known normal samples in future datasets. Moreover, the model is analogous to the semi-supervised learning system as it is trained using only the available normal classes. Evaluation of the proposed model (EBNN) on UCI machine learning datasets showed that the model not only outperforms other models in detecting novelties but also has a better multi-class classification accuracy for known normal classes. The proposed model implements a novel activation function in its framework and differs from the commonly available novelty detection models in three aspects. First, the model is much simpler to implement and does not need any initial assumptions about the model. Second, the model does not require any novel or abnormal data during training phase (semi-supervised learning). Third, it can be used as a two in one system to detect novelties and at the same time to classify data based on known classes.
Proactive Forensics: Keystroke Logging from the Cloud as Potential Digital Evidence for Forensic Readiness Purposes

The relationship between negative and positive connotations with regard to malware in the cloud is rarely investigated according to the prevailing literature. However, there is a significant relationship between the use of positive and negative connotations. A clear distinction between the two emanates when we use the originally considered malicious code, for positive connotation like in the case of capturing keystrokes in a proactive forensic purpose. This is done during the collection of digital evidence for Digital Forensic Readiness (DFR) purposes, in preparation of a Digital Forensic Investigation (DFI) process. The paper explores the problem of having to use the keystrokes for positive reasons as a piece of potential evidence through extraction and digitally preserving it as highlighted in ISO/IEC 27037: 2012 (security approaches) and ISO/IEC 27043: 2015 (legal connotations). In this paper, therefore, the authors present a technique of how DFR can be achieved through the collection of digital information from the originally considered malicious code. This is achieved without modifying the cloud operations or the infrastructure thereof, while preserving the integrity of digital information and possibly maintain the chain of custody at the same time. The paper proposes that the threshold of malicious code intrusion in the cloud can be transformed to an efficacious process of DFR through logical acquisition and digitally preserving keystrokes. The experiment-tested keystrokes have shown a significant approach that could achieve proactive forensics.
A new Pseudo random number generator based on generalized Newton complex map with dynamic key

In recent years, the chaotic systems in engineering applications attracted many researchers. One of the most important applications of chaos theory is the production of pseudo random numbers that can be used in different areas of cryptography, signal processing, and a variety of other fields. In production of pseudo random numbers generators the chaotic maps are the source of entropy for producing randomness. Most chaotic map based techniques have been easily attacked in recent years by using non-linear prediction as well as phase space analyze on the map. In this paper a new generalized map based on Newton complex map is proposed. The proposed map has appropriate statistical characteristics and the map phase space is completely random, which makes it impossible to predict in practice. The proposed map is capable of generating pseudo random numbers in both integer and complex domain, and it can be used in hardware implementations due to its low dimension. The generalized Newton complex map has dynamic key length, which is the main feature of the map. This feature is due to the existence of an additional Order parameter in map structure. This parameter controls the polynomial length and the β control parameter of map is dependent on it. Generating hexadecimal pseudo random numbers, large key space, high randomness and unpredictability are the main features of the proposed pseudo random number generator. To evaluate the pseudo random number generator, a set of statistical tests include NIST, DIEHARD, ENT, and TESTU01 as well as various performance and security measures, have been used to show the high performance of the proposed generator.
Online anomaly detection with sparse Gaussian processes

Online anomaly detection of time-series data is an important and challenging task in machine learning. Gaussian processes (GPs) are powerful and flexible models for modeling time-series data. However, the high time complexity of GPs limits their applications in online anomaly detection. Attributed to some internal or external changes, concept drift usually occurs in time-series data, where the characteristics of data and meanings of abnormal behaviors alter over time. Online anomaly detection methods should have the ability to adapt to concept drift. Motivated by the above facts, this paper proposes the method of sparse Gaussian processes with Q-function (SGP-Q). The SGP-Q employs sparse Gaussian processes (SGPs) whose time complexity is lower than that of GPs, thus significantly speeding up online anomaly detection. By using Q-function properly, the SGP-Q can adapt to concept drift well. Moreover, the SGP-Q makes use of few abnormal data in the training data by its strategy of updating training data, resulting in more accurate sparse Gaussian process regression models and better anomaly detection results. We evaluate the SGP-Q on various artificial and real-world datasets. Experimental results validate the effectiveness of the SGP-Q.