Reviewerly/exHarmony huggingface dataset:
Safe Continuous Control with Constrained Model-Based Policy Optimization

The applicability of reinforcement learning (RL) algorithms in real-world domains often requires adherence to safety constraints, a need difficult to address given the asymptotic nature of the classic RL optimization objective. In contrast to the traditional RL objective, safe exploration considers the maximization of expected returns under safety constraints expressed in expected cost returns. We introduce a model-based safe exploration algorithm for constrained high-dimensional control to address the often prohibitively high sample complexity of model-free safe exploration algorithms. Further, we provide theoretical and empirical analyses regarding the implications of model-usage on constrained policy optimization problems and introduce a practical algorithm that accelerates policy search with model-generated data. The need for accurate estimates of a policy’s constraint satisfaction is in conflict with accumulating model-errors. We address this issue by quantifying model-uncertainty as the expected Kullback-Leibler divergence between predictions of an ensemble of probabilistic dynamics models and constrain this error-measure, resulting in an adaptive resampling scheme and dynamically limited rollout horizons. We evaluate this approach on several simulated constrained robot locomotion tasks with high-dimensional action- and state-spaces. Our empirical studies find that our algorithm reaches model-free performances with a 10-20 fold reduction of training samples while maintaining approximate constraint satisfaction levels of model-free methods.
A Comparative Analysis on Denoising Techniques in Brain MRI and Cardiac Echo

Preprocessing plays a vital role in image for reducing noise and other unwanted data. There is a need for preprocessing in many fields, including medical imaging. Many of the medical images are contaminated with noise. In this paper, works related to denoising for brain magnetic resonance imaging (MRI) and cardiac echo have been studied and implemented. Numerous types of traditional filters were compared for this purpose using mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM).
Usability assessment of virtual reality as a training tool for oral presentation

Abstract One of the widely used teaching-learning strategies in the educational sector is the delivery of oral presentation by students. They are tasked to present their outputs-such as research paper, project, case study findings—to a group of audience. The problem experienced by most students in this type of task is the fear of public speaking combined with the pressure of relaying information effectively. In practice, this problem is resolved by giving more opportunity to students to rehearse repeatedly in front of peers. Organizing such setup, however, becomes almost impossible during times of pandemic when face-to-face interaction is limited, if not prohibited. Hence, this study ventured on assessing the usability of an alternative solution. This is with the use of virtual reality technology that can simulate the traditional training scenario and render it in a virtual environment. Three categories of participants were involved in the rating of its usability - IT students, non-IT students and Trainers. They were given ample time to navigate a public speaking app using a VR headset. Afterwards, they accomplished a survey questionnaire where they assessed the usability of the VR tool as a mode of training for oral presentation. Findings show that the usability rating of virtual reality as a training tool for oral presentation can vary depending on the technological background of users. This is due to the lack of on-screen assistant that would guide users in navigating the system. Despite these minor problems, the use of virtual reality as a training tool for oral presentation is highly recommended. This is supported by the unanimous positive response of participants in the SUS scale measuring their interest in using the VR tool for oral presentation.
From Component-Based Architectures to Microservices: A 25-years-long Journey in Designing and Realizing Service-Based Systems

Distributed information systems and applications are generally described in terms of components and interfaces among them. How these component-based architectures have been designed and implemented evolved over the years, giving rise to the so-called paradigm of Service-Oriented Computing (SOC). In this chapter, we will follow a 25-years-long journey on how design methodologies and supporting technologies influenced one each other, and we discuss how already back in the late 90s the ancestors of the SOC paradigm were there, already paving the way for the technological evolution recently leading to microservice architectures and serverless computing.
Probability, Markov Chain, and their applications

Abstract Probability is a branch of mathematics that focuses on the possibility of an event or the possibility of a position. Probability is significant to the field of politics since it could help people analyze and predict election results. Specifically, the Markov chain plays an important role in this because it focuses on transition of states. With election, reelection, retirement, and other elements involved, the Markov chain is helpful for predicting election results.
The space of models in machine learning: using Markov chains to model transitions

Abstract Machine and statistical learning is about constructing models from data. Data is usually understood as a set of records, a database. Nevertheless, databases are not static but change over time. We can understand this as follows: there is a space of possible databases and a database during its lifetime transits this space. Therefore, we may consider transitions between databases, and the database space. NoSQL databases also fit with this representation. In addition, when we learn models from databases, we can also consider the space of models. Naturally, there are relationships between the space of data and the space of models. Any transition in the space of data may correspond to a transition in the space of models. We argue that a better understanding of the space of data and the space of models, as well as the relationships between these two spaces is basic for machine and statistical learning. The relationship between these two spaces can be exploited in several contexts as, e.g., in model selection and data privacy. We consider that this relationship between spaces is also fundamental to understand generalization and overfitting. In this paper, we develop these ideas. Then, we consider a distance on the space of models based on a distance on the space of data. More particularly, we consider distance distribution functions and probabilistic metric spaces on the space of data and the space of models. Our modelization of changes in databases is based on Markov chains and transition matrices. This modelization is used in the definition of distances. We provide examples of our definitions.
Coverage Path Planning for Mapping of Underwater Structures

This paper addresses the problem of the coverage path planning in a 3D environment for surveying underwater structures. We propose to use the navigation strategy that a human diver will execute when circumnavigating around a region of interest, in particular when collecting data from a shipwreck. In contrast to the previous methods in the literature, we are aiming to perform coverage in completely unknown environment with some initial prior information. Our proposed method uses convolutional neural networks to learn the control commands based on the visual input. Preliminary results and a detailed overview of the proposed method are discussed.
Political Ideology Prediction from Bengali Text Using Word Embedding Models

The universal espousal of social media for political communication makes easy and extraordinary chances to keep an eye on the opinions of a large number of politically active individuals in real-time by providing an overall idea on the ideologies of those individuals regarding governmental issues. Nowadays, websites and android applications like Facebook, YouTube, and Instagram are the most embraced and popular means of information, communication, and entertainment to the people of Bangladesh. Hence, these social sites are a great source for collecting data related to the political views of the users from the perspective of this country. In this study, we have trained the data by an unsupervised machine learning and deep neural network model named word2vec to predict ideology from Bengali text. We have experimented with the word embedding model by utilizing CBOW and Skip-gram algorithms. The results of both of the algorithms were analyzed and compared with each other and as well as with the previous works related to this. Between them, CBOW provides higher accuracy which is 76.22% than the Skip-gram model in predicting political ideology.
Evolutionary Computing based Analysis of Diversity in Grammatical Evolution

Diversity is a much sought after aspect of any evolutionary system. More diversity means a cornucopia of diverse behaviors and traits among the individuals of a population. Lack of diversity, on the other hand, leads to a stagnant population whose individuals are more or less similar to each other. Subsequently, they fail to produce a variety of offspring. Grammatical Evolution (GE), being an Evolutionary Algorithm (EA), is also an aspirant of diversity. It allows a GE system to maintain a dynamic population over multiple generations.In this paper, we present our reflections about diversity estimates in a (large) number of experiments. We performed evolutionary experiments to estimate a bunch of well-known benchmark polynomials. We also employed hybrid optimization in our experiments. Our results are insightful. In this paper, we also test the effect of hybrid optimization algorithms integrated with GE on the diversity of the population.
Properties and computation of continuous-time solutions to linear systems

• A novel method for finding the A T , S ( 2 ) -inverse solution to a given linear system under some constraints is defined. • Various dynamical systems for computing A T , S ( 2 ) -inverse solution are considered. • Main properties of the A T , S ( 2 ) -inverse solution as well as the convergence results are claimed. • Properties of proposed Zhang neural network (ZNN) models for solving time-varying linear systems are considered. • Numerical examples are presented. We investigate solutions to the system of linear equations (SoLE) in both the time-varying and time-invariant cases, using both gradient neural network (GNN) and Zhang neural network (ZNN) designs. Two major limitations should be overcome. The first limitation is the inapplicability of GNN models in time-varying environment, while the second constraint is the possibility of using the ZNN design only under the presence of invertible coefficient matrix. In this paper, by overcoming the possible limitations, we suggest, in all possible cases, a suitable solution for a consistent or inconsistent linear system. Convergence properties are investigated as well as exact solutions.
Blockchain driven IoT based Delish2Go Decentralized Food Delivery Application

The purpose of Delish2Go is to reduce the commission taken by food aggregators by creating peer to peer transaction among restaurant, customer and delivery person. Thereby creating a convenient and easy to use application for all types of users irrespective of their location. The system is based on Consensus Blockchain network with smart contract protected identity and functionalities. This research work has a database server supporting hundreds of major urban and suburban areas in India. Above all the proposed model provides a comfortable user experience along with the available best pricing.
Depth Guided Cross-modal Residual Adaptive Network for RGB-D Salient Object Detection

Abstract Depth modal features can provide complementary information for salient object detection (SOD). Most of the existing RGB-D SOD methods focus on fully combining RGB and Depth modal features without distinguishing them. In this paper, we propose a new depth guided cross-modal residual adaptive network for RGB-D SOD. We use two independent resnet-50 to extract the features of the two modes respectively. Then the cross-modal channel-wise refinement module is designed to obtain complementary modal information. We design a crossmodal guided module to make complementary modal information guide RGB image feature extraction. Finally, the residual adaptive selection module is used to enhance the spatial mutual concerns between the two modal features to achieve multimodal information fusion. Experimental results show that our method can achieve a more reasonable fusion state of RGB and Depth, and verify the effectiveness of our final saliency model.
Application of Graph Neural Network in Automatic Text Summarization

The core of automatic text summarization technology is to extract the main content of input text information, remove redundant information in the text, and retain its core information to improve people’s reading efficiency. However, a lot of research work on automatic text summarization still focus on modeling the explicit information of the text when extracting summaries, and the mining of hidden information is not strong enough. In previous work, researchers tried to introduce neural networks into automatic text summarization technology. The researchers found that automatic text summarization through neural networks can obtain hidden information in the text, but it is difficult for general neural networks to process long text information. In the past two years, graph neural networks (GNN) have begun to rise. Researchers have found that through the application of graph structure, long text input has been well processed, and can dig deeper into the hidden information in the text, so that the quality of the generated abstract has been significantly improved compared with previous studies. The introduction of GNN has also greatly promoted the transition of automatic text summarization from single-document summarization to multi-document summarization. And GNN has played an important role in promoting the development of automatic text summarization technology. In this article, we introduced the application of GNN in the field of automatic text summarization in recent years, summarized commonly used data sets and evaluation indicators, and summarized and prospected the development trend of automatic text summarization.
RDF : An alternative approach for representing, storing, and maintaining meta-knowledge in web of data

The Web of Data needs additional information, i.e., meta-knowledge to ensure quality and build the trust in data. For representing meta-knowledge, there exist various approaches in the literature, for example, RDFt, tRDF, RDF Reification, Singleton Property and Named Graph. There are various issues associated with these approaches in representing the meta-knowledge, for example, the increasing graph size, additional statement generation, the representation of multi-dimensional and/or nested meta-knowledge, among others. In this work, we propose an approach called RDFM to represent, store, and manage meta-knowledge. RDFM integrates attributes to the predicate to represent nested and/or multi-dimensional meta-knowledge with lesser statements generation. A query language called SPARQLM is developed to extract RDFM data. The study analyzes the performance of RDFM in terms of the number of edges, number of statements generation, data redundancy, storage, query response time, required query length, representation of meta-knowledge in nested and different dimensions. The results show that the RDFM model performs significantly and gives advantage over storage management and graph data management.
isiZulu Word Embeddings

Word embeddings are currently the most popular vector space model in Natural Language Processing. How we encode words is important because it affects the performance of many downstream tasks such as Machine Translation (MT), Information Retrieval (IR) and Automatic Speech Recognition (ASR). While much focus has been placed on constructing word embeddings for English, very little attention is paid to under-resourced languages, especially native African languages. In this paper we select four popular word embedding models (Word2Vec CBOW and Skip-Gram; FastText and GloVe) and train them on the 10 million token isiZulu National Corpus (INC) to create isiZulu word embeddings. To the best of our knowledge, this is the first time that word embeddings in isiZulu have been constructed and made available to the public. We create a semantic similarity data set analogous to WordSim353, which we also make publicly available. This data set is used to conduct an evaluation of the four models to determine which is the best for creating isiZulu word embeddings in a low-resource (small corpus) setting. We found that the Word2Vec Skip-Gram model produced the highest quality embeddings, as measured by this semantic similarity task. However, it was the GloVe model which performed best on the nearest neighbours task.
Removing Haze Influence from Remote Sensing Images Captured with Airborne Visible/ Infrared imaging Spectrometer by Cascaded Fusion of DCP, GF, LCC with AHE

Haze is one of the factors which deteriorate the quality of AVIRIS (Airborne Visible/ Infrared imaging Spectrometer) images [1]. It reduces clarity and interpretability of images. Removal or suppression of haze becomes essential to enhance quality of images for further applicability and uses. Present article exemplifies haze detection and removal of AVIRIS images by using Proposed Method of Fusion of Dark Channel Prior (DCP), Guided Filter (GF) and Local Color Correction (LCC) with Adaptive Histogram Equalization (AHE). Proposed method of removing haze influence from remote sensing images captured by Airborne Visible/ Infrared imaging Spectrometer produces output with better quality. Images which contain, shadows, flat and highly reflective surfaces results in limitations of proposed method. Proposed fusion based haze removal method gives highest entropy among the algorithms compared, as reflected from experimentation.
Explicit Sparse Self-Attentive Network for CTR Prediction

The click-through rate (CTR) prediction is an essential task in the recommendation system. Available raw category features and feature combinations are important for CTR prediction, but their sparsity and high-dimension lead to huge computation. In recent years, methods based on deep learning, factorization machines and attention mechanism are widely used to model feature interactions among features for effective sparse predictive analysis. However, these previous algorithms are affected by irrelevant information. In this paper, we propose a novel model ESAtInt to model high-dimensional and sparse features, while being able to explicitly select meaningful higher-order feature interactions and eliminate the impact of irrelevant information. Specifically, we further conduct automatic feature interaction learning through a concentrated attention mechanism with explicit selection. Our model can explicitly select the most relevant parts to emphasize the attention to global features. Experimental results on the real-world dataset show that our proposed method is superior to state-of-the-art approaches for CTR prediction.
MBA: A Multimodal Bilinear Attention Model with Residual Connection for Abstractive Multimodal Summarization

Abstract The combination of vision and natural language modalities has become an important topic in both computer vision and natural language processing research communities. Multimodal summarization has received unprecedented attention with the rapid growth of multimodal information. This paper proposes MBA which consists of pre-trained feature extractors, text encoder, image encoder, multimodal bilinear attention fusion module, and summary decoder to complete abstractive multimodal summarization task. A residual network is added to the model to enhance the textual modality information and alleviate the modality-bias problem. Experiments show that the model is better than the baseline models and performs better than text summarization methods that ignore visual modality.
Echo State Networks and Existing Paradigms for Stock Market Prediction

Stock market is essentially a chaotic and a highly unstable time-series. People have used approaches such as clipping gradient i.e., stopping the gradient from getting too small or too high, via gated recurrent units and LSTM units, to predict the stock prices. However, information is still lost and hence is not an ideal approach. This paper aims to compare the efficiency of standard approaches of stock prediction namely, regression, recurrent neural networks using long short-term memory units, with the echo state network (ESN) which uses reservoir computing to model chaotic non-linear systems such as daily closing stock prices in a stock market. My experiments on the NY Stocks dataset demonstrates as to how the novel echo state network outperforms the conventional regression and neural network approach.
Anomalous Subgraph Detection in Given Expected Degree Networks With Deep Learning

Anomalous subgraph detection within networks is an important issue in many emerging applications. Existing algorithms, such as graph structure methods and spectral feature methods, usually focus on the special stochastic model (such as the Erdős-Rényi random graph) or may not efficiently extract the anomalous behaviors of the networks, which result in detection performance degradation. To mitigate the limitations, in this paper, we first present an anomalous subgraph detection framework associated with deep neural networks (DNN) for detecting anomalous behaviors within the networks. Furthermore, based on the developed framework, we propose a residual matrix-based convolutional neural network (RM-CNN) algorithm with respect to the given expected degree models, which are more general networks than the Erdős-Rényi random graphs. In particular, the trained RM-CNN can efficiently capture the anomalous changes of the network and then achieve the detection performance improvement. Simulation experiments display that the proposed RM-CNN algorithm is superior to the compared algorithms in both detection performance and detection speed.
Modeling Energy Consumption of Virtual Machines in DVFS-Enabled Cloud Data Centers

To cut back energy consumption of virtual-machine-powered data centers, we build an optimization model for virtual machines running in DVFS-enabled cloud data centers. With the model in place, cloud computing systems are equipped to keep track of dynamic power and static power of processors in virtual machines. Unlike existing dynamic voltage and frequency scaling schemes, our solution orchestrates frequency requirements rather than task execution times. The model makes it possible to obtain an optimal frequency ratio, which minimizes energy consumption of virtual machines. As a result, a data center's energy efficiency is boosted by controlling CPU frequency to meet the optimal frequency ratio. We demonstrate a way of manipulating frequency ratios to pushing up energy efficiency without violating virtual machines' frequency requirements. The experimental results unveil that our modeling approach offers a practical way of conserving the energy consumption of virtual machines running in data centers.