Reviewerly/exHarmony huggingface dataset:
Wireless Channel Scenario Recognition Based on Neural Networks

Wireless channel scenario recognition plays a key role in improving the performance of mobile communication systems. This paper combines wireless channel characteristics extracted using expert experience and neural networks, and proposes a wireless channel scenario recognition framework based on neural networks. Firstly, the wireless propagation environment is analyzed, and some wireless channel characteristics are extracted, such as the frequency domain fading factor, multipath power delay distribution, time domain energy peak response ratio and time correlation characteristics. Secondly, the combined algorithm model using the wireless channel characteristics and neural networks are proposed. Finally, after simulation verification, the new method has a greater improvement in the recognition accuracy than the traditional threshold algorithm.
Satellite On-2rbit Anomaly Detection and Adaptive Model Updating Method

This paper proposes an on-orbit fault monitoring and adaptive model updating method, which is based on on-orbit telemetry to achieve quasi real-time fault monitoring. This method is based on condition recognition, mining the relationship between multiple telemetry and the change pattern of telemetry time sequence characteristics under different conditions, combining expert experience knowledge and data-driven method to build an anomaly detection model, realizing high-precision telemetry interpretation. It has the characteristics of small demand for computing resources, fast computing speed and strong interpretability. An adaptive model updating method is used to solve the problem that the monitoring model is not applicable at the end of satellite life due to the performance degradation of satellite components during long-term in orbit operation.
Multi-Temporal Abstraction with Time-Aware Deep Q-Learning for Septic Shock Prevention

Sepsis is a life-threatening organ dysfunction and a disease of astronomical burden. Septic shock, the most severe complication of sepsis, leads to a mortality rate as high as 50%. However, septic shock prevention is extremely challenging because individual patients often have very different disease progression, and thus the timings of medical interventions can play a key role in their effectiveness. Recently, reinforcement learning (RL) methods like deep Q-learning networks (DQN) have shown great promise in developing effective treatments for preventing septic shock. In this work, we propose MTA-TQN, a Multi-view -Temporal Abstraction mechanism within a Time-aware deep Q-learning Network framework for this task. More specifically, 1) MTA-TQN leverages irregular time intervals to discount expected return which would prevent systemic overestimations caused by temporal discount errors; 2) it learns both short and long-range dependencies with multi-view temporal abstractions which would reduce bias to a specific series of observations for a single state. The effectiveness of MTA-TQN is validated on two hard exploration Atari games and the septic shock prevention task using real-world EHRs. Our results demonstrate that both time-awareness and multi-view temporal abstraction are essential to induce effective policies, particularly with irregular time-series data. In the septic shock prevention task, while the top 10% of patients whose treatments agreed with DQN induced policy experienced a 17% septic shock rate, our MTA-TQN policies achieved a 5.7% septic shock rate.
Evaluation the influence of distance-based K-means method for detecting moving vehicles

Abstract Detecting moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detecting moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE, PSNR, SSIM and PCQI values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages.
Separation of Overlapping Chirp Signals Using STFRFT

In signal processing, separation of signals overlapped in either time or frequency domain is one of the crucial phenomenon wherever analysis is to be performed on an individual signal. However, neither time domain windowing nor frequency domain filtering is able to separate the signals if they are overlapped in both the domains. This article presents an alternate method using short time fractional Fourier transform (STFRFT) for the separation of chirp signals which have got overlapped in both the domains of time and frequency. Efficacy of the proposed method has been tested on the composite ultrasound signal of an earlier reported work, generated by the addition of three overlapping chirp signals. The simulated results of proposed work demonstrate that all three components of this composite ultrasound signal are separated successfully and recovered with smaller error in comparison to previous work.
Global Horizontal Irradiance Modeling from Sky Images Using ResNet Architectures

Large scale integration of photovoltaic (PV) energy into power grids has seen an expressive growth. But PV power output variability hinders the large scale deployment of PV. Images captured from ground based sky imaging systems are commonly used in irradiance forecasting to address the intermittent energy production from solar panels. Facing that scenario, this study presents a novel approach to model the Global Horizontal Irradiance (GHI) based on contemporaneous hemispherical sky images (nowcast). A physics-based nonparametric classification model based on threshold of fractional cloudiness of sky images was utilized to classify the images into 3 sky conditions: sunny, partially cloudy and overcast. Several ResNet architectures were first examined as end-to-end models and then they were tailored to each sky-condition to create sky-condition based sub models. For each ResNet architecture, results of the end-to-end model and sky-condition specific sub models were compared. These sky-condition specific sub models were then combined to create a classification-nowcast framework. The best performing model yielded a MAE of 23.86 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , nRMSE of 10.80% and reached 98.88% in Pearson’s correlation on the test set. The errors were comparable to or lower than other studies despite the high fluctuations in the GHI.
ECNN

Despite been extensively explored, current techniques in sequential data modeling and prediction are generally designed for solving regression tasks in a batch learning setting, making them not only computationally inefficient but also poorly scalable in real-world applications, especially for real-time intelligent ocean data quality control (QC), where the data arrives sequentially and the QC should be conducted in real time. This paper investigates the online learning for ocean data streams by resolving two main challenges: (i) how to develop a deep learning model to capture the complex ocean data distribution that could evolve dynamically, namely tackling the 'concept drift' problem for non-stationary time series; (ii) how to develop a deep learning model that can dynamically adapt its structure from shallow to deep with the inflow of the data to overcome under-fitting problem, namely tackling the 'model selection' problem. To tackle these challenges, we propose one Evolutive Convolutional Neural Network (ECNN) that dynamically re-weighting the sub-structure of the model from data streams in a sequential or online learning fashion, by which the capacity scalability and sustainability are introduced into the model. The experiments on real ocean observation data verify the effectiveness of our model. As far as we know, it is the first work that introduce online deep learning techniques into ocean data prediction research.
NER in Archival Finding Aids: Extended

The amount of information preserved in Portuguese archives has increased over the years. These documents represent a national heritage of high importance, as they portray the country’s history. Currently, most Portuguese archives have made their finding aids available to the public in digital format, however, these data do not have any annotation, so it is not always easy to analyze their content. In this work, Named Entity Recognition solutions were created that allow the identification and classification of several named entities from the archival finding aids. These named entities translate into crucial information about their context and, with high confidence results, they can be used for several purposes, for example, the creation of smart browsing tools by using entity linking and record linking techniques. In order to achieve high result scores, we annotated several corpora to train our own Machine Learning algorithms in this context domain. We also used different architectures, such as CNNs, LSTMs, and Maximum Entropy models. Finally, all the created datasets and ML models were made available to the public with a developed web platform, NER@DI.
MAPS: Joint Multimodal Attention and POS Sequence Generation for Video Captioning

Video captioning is considered to be challenging due to the combination of video understanding and text generation. Recent progress in video captioning has been made mainly using methods of visual feature extraction and sequential learning. However, the syntax structure and semantic consistency of generated captions are not fully explored. Thus, in our work, we propose a novel multimodal attention based framework with Part-of-Speech (POS) sequence guidance to generate more accu-rate video captions. In general, the word sequence generation and POS sequence prediction are hierarchically jointly modeled in the framework. Specifically, different modalities including visual, motion, object and syntactic features are adaptively weighted and fused with the POS guided attention mechanism when computing the probability distributions of prediction words. Experimental results on two benchmark datasets, i.e. MSVD and MSR-VTT, demonstrate that the proposed method can not only fully exploit the information from video and text content, but also focus on the decisive feature modality when generating a word with a certain POS type. Thus, our approach boosts the video captioning performance as well as generating idiomatic captions.
Uniform and Completely Nonequilibrium Invariant States for Weak Coupling Limit Type Quantum Markov Semigroups Associated with Eulerian Cycles

We define the uniform and completely nonequilibrium invariant states, which are associated with Eulerian cycles; once we did this, we use the Hierholzer’s algorithm to obtain a canonical Euler-Hierholzer cycle, and for it, characterize the invariant state. For the simplest case of nonequilibrium, we give sufficient conditions for these states to be invariant and write its eigenvalues explicitly.
A Survey of Single Image De-raining in 2020

Fernando, HasalAyoob, MohamedPoravi, GuhanathanImpact of rain streaks is often considered a disturbance which causes performance reductions in many computer vision systems. To get rid of this problem, various rain removal systems have been introduced. The process of removing rain is called “de-raining”. There are two main categories of de-raining. Those are single image de-raining and video de-raining. Performing single image de-raining is more challenging than video de-raining because it has no temporal information to be used in the process. This paper undertakes a comparative analysis of existing novel and credible single image de-raining works, and it is followed by an empirical study defining research gaps and possible future works.
User Behavior Identification and Personalized Recommendation Based on Web Data Mining

A good understanding of user behavior and consumption preferences can provide support for website operators to improve their service quality. However, the existing personalized recommendation systems generally have problems such as low Web data mining efficiency, low degree of automated recommendation, and low durability. Targeting at these unsolved issues, this paper mainly carries out the following works: Firstly, the authors established a user behavior identification and personalized recommendation model based on Web data mining, it gave the user behavior analysis process based on Web data mining, improved the traditional k-means algorithm, and gave the detailed execution steps of the improved algorithm; moreover, it also elaborated on the K nearest neighbor model based on user scoring information, the score matrix decomposition method, and the personalized recommendation method for network users. At last, experimental results verified the effectiveness of the constructed model.
Pooling Modalities and Pointwise Intersection: Semantics, Expressivity, and Dynamics

Abstract We study classical modal logics with pooling modalities , i.e. unary modal operators that allow one to express properties of sets obtained by the pointwise intersection of neighbourhoods. We discuss salient properties of these modalities, situate the logics in the broader area of modal logics (with a particular focus on relational semantics), establish key properties concerning their expressive power, discuss dynamic extensions of these logics and provide reduction axioms for the latter.
Graph Structure-Aware Bi-directional Graph Convolution Model for Semantic Role Labeling

Dependency tree contains rich syntactic structure information, which is very effective for semantic role labeling. Semantic role labeling is a task with strong syntactic dependence. How to effectively use subgraphs to predict surrounding graph structures for context of dependency tree and encode the dependency relationships between predicates and arguments in the sentence which to be annotated is still a challenging problem. In this paper, we propose a bidirectional graph convolution model based on distribution of graph structure and the attention mechanism, which is used to make better use of all syntactic information in the sentence dependency tree and the dependencies between predicates and arguments, so as to improve the accuracy of semantic role labeling. Without heavy feature engineering and extra knowledge of a particular language, our model obtained 88.9% F1 value on the WSJ test set of public dataset CoNLL2005, and 0.7% improvement compared with the existing best model.
Analytical prediction of delayed Hopf bifurcations in a simplified stochastic model of reed musical instruments

This paper investigates the dynamic behavior of a simplified single reed instrument model subject to a stochastic forcing of white noise type when one of its bifurcation parameters (the dimensionless blowing pressure) increases linearly over time and crosses the Hopf bifurcation point of its trivial equilibrium position. The stochastic slow dynamics of the model is first obtained by means of the stochastic averaging method. The resulting averaged system reduces to a non-autonomous one-dimensional Itô stochastic differential equation governing the time evolution of the mouthpiece pressure amplitude. Under relevant approximations the latter is solved analytically treating separately cases where noise can be ignored and cases where it cannot. From that, two analytical expressions of the bifurcation parameter value for which the mouthpiece pressure amplitude gets its initial value back are deduced. These special values of the bifurcation parameter characterize the effective appearance of sound in the instrument and are called deterministic dynamic bifurcation point if the noise can be neglected and stochastic dynamic bifurcation point otherwise. Finally, for illustration and validation purposes, the analytical results are compared with direct numerical integration of the model in both deterministic and stochastic situations. In each considered case, a good agreement is observed between theoretical results and numerical simulations, which validates the proposed analysis.
Interactive Attention Network for Chinese Address Element Recognition

Existing Named Entity Recognition (NER) models have achieved good performance, but they have low accuracy in Chinese address element recognition tasks. After analysis, we believe that the boundary information of the address text is more sensitive than the general text, and the sentences are independent of each other, unlike the general paragraph-style text with contextual connections. On the other hand, the previous NER models rarely consider the use of interaction between subtasks to enhance the performance of the NER task.This paper proposes an Interactive Attention Network (IAN) model, which uses boundary-based information and type-based information to improve NER task performance and introduces an interaction mechanism to share information between each subtask. In addition, a boundary auxiliary module is added to obtain explicit boundary information. The experimental results show that the proposed IAN model can solve the address element recognition task more effectively.
MIME: Mutual Information Minimizer for Selection of Categorical Features

MIME is a feature selection method that can be used to reduce the feature set before clustering of categorical features. The algorithm uses Mutual Information as a decision variable to subset the feature set. This feature set can then be used as an input to KModes clustering algorithm. Since this is a filter based algorithm, it is faster than wrapper or embedded algorithms. The primary objective of this algorithm is to reduce the computation time taken to cluster datasets consisting of only categorical features.
Monitoring and analysis of seismic data during the 2018 sunda strait tsunami

The tsunami of Sunda Strait occurred on December 22, 2018, at 21:03 West Indonesia Time (zone). An eruption of Mount Anak Krakatau caused an eruption that triggered a landslide on the slopes of Mount Anak Krakatau covering an area of 64 hectares that hit the coastal area of western Banten and southern Lampung and resulted in 437 deaths, 14.059 people were injured, and 33.721 people were displaced. Before the tsunami, signal transmissions (gaps) at the Lava seismograph station installed on the body of Mount Anak Krakatau experienced broken so that Mount Anak Krakatau Observation Post could not record volcanic earthquake signals since December 22, 2018, at 21.03 West Indonesia Time (zone). Given these facts, proper monitoring and analysis were required to monitor and analyze the source of ground vibrations originating from the eruption of Mount Anak Krakatau. Therefore, this study aims to confirm the eruptive activity of Mount Anak Krakatau based on seismic monitoring and analysis sourced from the BMKG's seismic sensor network. The method the author uses is by monitoring the seismic signal recorded by the seismometer and analyzing the seismic signal using the Seiscomp3 software. By the results of monitoring and analysis of seismic data, it was found that the location of the center of the ground shaking was on Mount Anak Krakatau with a magnitude of 3.4, and a depth of 1 km. To anticipate similar tsunami events in the future, it is very necessary to have a tsunami early warning system originating from volcanic activity and volcanic body avalanches.
Applying a Consumer-centric Framework for Trust Assessment of Cloud Computing Service Providers

Cloud computing services consumers do not have enough reliable information about critical characteristics of their providers, such as performance, security, trust and privacy, compliance with laws and regulations, among others. Our proposal addresses these problems presenting a trust assessment framework that integrates three domains: Governance, Transparency, and Security Information. Our approach is consumer-centric and deals with trust aspects from the end-user's perspective. We use Indicators to communicate the outcomes, which aim to represent the expression of cybersecurity, manageability, and transparency of services under assessment. This paper includes an implementation proposal, prototype, and proof of concept, in which the framework was applied in a real scenario and executed over a long-term (18 months) usage simulation to verify its applicability, sensitivity, and robustness. Our study is intended for use by consumers of cloud computing who seek to know and measure levels of cybersecurity, protection of privacy, transparency of security, and high levels of quality in their services and infrastructure.
Deep conditional image generation

Learning visual patterns is a key and fundamental building block for visual perception towards machine intelligence. By definition, a visual pattern is a discernable visual regularity in the world, whose compositional elements as a whole, repeat in a predictable manner. Correspondingly, there are three fundamental tasks in pattern analysis, i.e., pattern recognition, pattern discovery, and pattern synthesis. In this chapter, we devote our discussions to leveraging recent advancement of deep generative models, such as variational auto-encoders (VAEs) and generative adversarial networks (GANs), to achieve more controllable (besides random sampling) synthesis of visual patterns. Compared to traditional statistical models such as Markov Random Fields, these deep learning-based methods target end-to-end pattern modeling by formulating it as a conditional image generation problem. We argue that the key to achieve controllable pattern modeling is to learn disentangled representations for the visual patterns, where various controlling factors can be explicitly pin-pointed in the embedded visual representation. We will start with the basics of generative modeling, followed by a detailed introduction of modern deep generative models for learning visual patterns. Then, we will use several case studies, in style transfer, vision-language generation and face synthesis, to illustrate how we may achieve disentangled representations for controllable visual modeling and synthesis in a weakly supervised fashion. In the end, we conclude with some discussions on how this may guide us to build more explainable deep learning-based models for visual perception.
Permissioned Blockchain Platform to Enhance Scalability, Security and Performance Issues in Livestock Farms in Sri Lanka

The blockchain is being applied in the food supply chain, livestock, and farm management to enhance food quality, management activities, and animal health and build trustworthy transactions. The present paper provides an overview of blockchain in livestock farming. This paper identifies the current research topics, their contribution, and the benefits of applying blockchain in livestock farming. Blockchain applications in the livestock sector are increasing worldwide, and most of them are proposed for security and trust issues. This research study implements a permission-based blockchain platform to enhance the livestock sector’s scalability, security, and performance issues. The designated Platform aims to provide distributed digital data storage with animal disease tracking service and access control security mechanism that cannot be tampered with by an unauthorized person. The performance of the proposed blockchain is evaluated through a series of experiments using a different kinds of metrics.