Reviewerly/exHarmony huggingface dataset:
Optimization of Text Feature Selection Process Based on Advanced Searching for News Classification

Nowadays, the culture for accessing news around the world is changed from paper format to electronic and the rate of publication for newspapers and magazines on websites have increased dramatically. Therefore, the feature selection process from high-dimensional text feature set for an automatic news classification model is becoming the top challenge because irrelevant features can degrade the accuracy with high cost computation time for classification model. In this article, six-advanced search policies based on evolutionary, swarm intelligence, nature-inspired intelligence are observed for achieving the global optimal feature subset for optimal accuracy in news classification problem. According to the experimental results, the advanced search schemes that can provide flexibility in integrating classifier in accordance with its objective function such as optimal classification performance by adjusting the rate of modification parameters for the testing data.
Classification of Breast Cancer Images Using Ensembles of Transfer Learning

It is a challenging task to estimate the cancerous cells and tissues via computer-aided diagnosis systems on high-resolution histopathological images. In this study, it is suggested to use transfer learning and ensemble learning methods together in order to reduce the difficulty of this task and better diagnose cancer patients. In the studies, histopathological images with 40× and 100× magnification factors are analyzed. In order to prove the success of the study with experimental studies, firstly, the results provided by pre-modeled deep learning architectures trained by histopathological image dataset, then the results acquired by different transfer learning approaches and the results obtained with the ensembles of deeply learned features using transfer learning methods are presented comparatively. Three different approaches are applied for transfer learning by fine-tuning the pre-trained convolution neural networks. In the experimental section, results of single classifiers (i.e., support vector machines, logistic regression, k-nearest neighbor and bagging) are presented by employing features of CNN models obtained by defined transfer learning approaches. Then, decisions of each classifier model are combined separately by weighted decision fusion (WDF) and stacking decision fusion (SDF) ensemble learning methods that have proven to improve the classification performance of the proposed classification system.
Neural machine translation for Tamil to English

The Tamil language is spoken by 80 million people around the world. The translation between Tamil and English leads to a signiﬁcant impact by helping in the understanding of Tamil scripts, which otherwise would be a tedious, costly, and time-consuming process. Thus, developing an automated system to perform Tamil to English translation would save human time and effort. We publicly release a new high-quality corpus for standard training, evaluation, and report results experiments with two different architectures based on Encoder-Decoder to translate Tamil to English. We further tried to improve it by experimenting with pre-trained word embeddings and tuning hyperparameters. Although Google-Translator also provides Tamil to English and vice versa, our implemented architectures, along with the new dataset, completely outperformed the Google Translator with a margin of 7.5 BLEU score. Moreover, our proposed model solves out of vocabulary and polysemy problems up to a greater extent. Our dataset and implementation are available at: https://github.com/Ishikahooda/Tamil-English-Dataset
Application of nonlinear autoregressive neural network to estimation of global solar radiation over Nigeria

In this paper, surface data meteorological were used as input variables to create, train and validate the network in which global solar radiation serves as a target. These surface data were obtained from the archives of the European centre for Medium-Range weather forecast for a span of 36 years (1980-2015) over Nigeria. The research aims to evaluate the predictive ability of the nonlinear autoregressive neural network with exogenous input (NARX) model compared with the multivariate linear regression (MLR) model using the statistical metrics. Model selection analysis using the index of agreement (dr) metric showed that the MLR and NARX models have values of 0.710 and 0.853 in the Sahel, 0.748 and 0.849 in the Guinea Savannah, 0.664 and 0.791 in the Derived Savannah, 0.634 and 0.824 in the Coastal regions, and 0.771 and 0.806 in entire Nigeria respectively. Meanwhile, error analyses of the models using root mean square errors (RMSE) showed the values of 1.720 W/m2 and 1.417 in the Sahel region, 2.329 W/m2 and 1.985 W/m2 in the Guinea Savannah region, 2.459 W/m2 and 2.272 W/m2 in the Derived Savannah region, 2.397 W/m2 and 2.261 W/m2 in the Coastal region and 1.691 W/m2 and 1.600 W/m2 in entire Nigeria for MLR and NARX models respectively. These showed that the NARX model has higher dr values and lower RMSE values over all the climatic regions and entire Nigeria than the MLR model. Finally, it can be inferred from these metrics that the NARX model gives a better prediction of global solar radiation than the traditional common MLR models in all the zones in Nigeria.
From When to Where: A Multi-task Learning Approach for Next Point-of-Interest Recommendation

Temporal information plays a crucial role in analyzing user behaviors in Location-Based Social Networks (LSBNs). Different from existing methods such as Matrix Factorization (MF) and Recurrent Neural Networks (RNNs) methodologies that only make use of historical temporal information, we try to explore the prediction of the user’s future check-in time to help POI recommendation in this paper. We propose a new multi-task neural network recommendation model, namely MTNR, which jointly learns when and where users are likely to go next. To learn the user’s next POI preference based on the next check-in time prediction, we introduce two kinds of time-decay POI transition tensors to calculate the user’s common and personal POI transition probability, respectively. By combining the POI preference learned in POI and time prediction tasks, MTNR can get better recommendation accuracy in POI recommendation. We conducted experiments on three real-world datasets. The result shows that our model significantly outperforms well-known methods.
Distributed PCFG Password Cracking

In digital forensics, investigators frequently face cryptographic protection that prevents access to potentially significant evidence. Since users prefer passwords that are easy to remember, they often unwittingly follow a series of common password-creation patterns. A probabilistic context-free grammar is a mathematical model that can describe such patterns and provide a smart alternative for traditional brute-force and dictionary password guessing methods. Because more complex tasks require dividing the workload among multiple nodes, in the paper, we propose a technique for distributed cracking with probabilistic grammars. The idea is to distribute partially-generated sentential forms, which reduces the amount of data necessary to transfer through the network. By performing a series of practical experiments, we compare the technique with a naive solution and show that the proposed method is superior in many use-cases.
IoT-enabled directed acyclic graph in spark cluster

Abstract Real-time data streaming fetches live sensory segments of the dataset in the heterogeneous distributed computing environment. This process assembles data chunks at a rapid encapsulation rate through a streaming technique that bundles sensor segments into multiple micro-batches and extracts into a repository, respectively. Recently, the acquisition process is enhanced with an additional feature of exchanging IoT devices’ dataset comprised of two components: (i) sensory data and (ii) metadata. The body of sensory data includes record information, and the metadata part consists of logs, heterogeneous events, and routing path tables to transmit micro-batch streams into the repository. Real-time acquisition procedure uses the Directed Acyclic Graph (DAG) to extract live query outcomes from in-place micro-batches through MapReduce stages and returns a result set. However, few bottlenecks affect the performance during the execution process, such as (i) homogeneous micro-batches formation only, (ii) complexity of dataset diversification, (iii) heterogeneous data tuples processing, and (iv) linear DAG workflow only. As a result, it produces huge processing latency and the additional cost of extracting event-enabled IoT datasets. Thus, the Spark cluster that processes Resilient Distributed Dataset (RDD) in a fast-pace using Random access memory (RAM) defies expected robustness in processing IoT streams in the distributed computing environment. This paper presents an IoT-enabled Directed Acyclic Graph (I-DAG) technique that labels micro-batches at the stage of building a stream event and arranges stream elements with event labels. In the next step, heterogeneous stream events are processed through the I-DAG workflow, which has non-linear DAG operation for extracting queries’ results in a Spark cluster. The performance evaluation shows that I-DAG resolves homogeneous IoT-enabled stream event issues and provides an effective stream event heterogeneous solution for IoT-enabled datasets in spark clusters.
Defeasible Reasoning in Description Logics: An Overview on DLN

DLN is a recent approach that extends description logics with defeasible reasoning capabilities. In this paper we provide an overview on DLN, illustrating the underlying knowledge engineering requirements as well as the characteristic features that preserve DLN from some recurrent semantic and computational drawbacks. We also compare DLN with some alternative nonmonotonic semantics, enlightening the relationships between the KLM postulates and DLN.
Online AUV Path Replanning Using Quantum-Behaved Particle Swarm Optimization with Selective Differential Evolution

This paper presents an online AUV (autonomous underwater vehicle) path planner that employs path replanning approach and the SDEQPSO (selective differential evolution-hybridized quantum-behaved particle swarm optimization) algorithm to optimize an AUV mission conducted in an unknown, dynamic and cluttered ocean environment. The proposed path replanner considered the effect of ocean currents in path optimization to generate a Pareto-optimal path that guides the AUV to its target within minimum time. The optimization was based on the onboard sensor data measured from the environment, which consists of a priori unknown dynamic obstacles and spatiotemporal currents. Different sensor arrangements for the forward-looking sonar and horizontal acoustic Doppler current profiler (H-ADCP) were considered in 2D and 3D simulations. Based on the simulation results, the SDEQPSO path replanner was found to be capable of generating a time-optimal path that offered up to 13% reduction in travel time compared to the situation where the vehicle simply followed a path with the shortest distance. The proposed replanning technique also showed consistently better performance over a reactive path planner in terms of solution quality, stability, and computational efficiency. Robustness of the replanner was verified under stochastic process using the Monte Carlo method. The generated path fulfilled the vehicle’s safety and physical constraints, while intelligently exploiting ocean currents to improve the vehicle’s efficiency.
Generating similes like a Pro: A Style Transfer Approach for Simile Generation.

Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language such as a simile go beyond plain expressions to give readers new insights and inspirations. In this paper, we tackle the problem of simile generation. Generating a simile requires proper understanding for effective mapping of properties between two concepts. To this end, we first propose a method to automatically construct a parallel corpus by transforming a large number of similes collected from Reddit to their literal counterpart using structured common sense knowledge. We then propose to fine-tune a pretrained sequence to sequence model, BART~\cite{lewis2019bart}, on the literal-simile pairs to gain generalizability, so that we can generate novel similes given a literal sentence. Experiments show that our approach generates $88\%$ novel similes that do not share properties with the training data. Human evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts \textit{37\%}\footnote{We average 32.6\% and 41.3\% for 2 humans.} of the times, and three baseline systems including a recent metaphor generation model \textit{71\%}\footnote{We average 82\% ,63\% and 68\% for three baselines.} of the times when compared pairwise.\footnote{The simile in the title is generated by our best model. Input: Generating similes effortlessly, output: Generating similes \textit{like a Pro}.} We also show how replacing literal sentences with similes from our best model in machine generated stories improves evocativeness and leads to better acceptance by human judges.
Feature Analysis for Imbalanced Learning

Based on the results of artificial samples generated in the minority class and through the label regulation of the neighbor samples of the majority class, the precision of the classification prediction for imbalanced learning has clearly been enhanced. This article presents a unified solution combining learning factors to improve the learning performance. The proposed method solves this imbalance through a feature selection incorporating the generation of artificial samples and label regulation. A probabilistic representation is used for all aspects of learning: class, sample, and feature. A Bayesian inference is applied to the learning model to interpret the imbalance occurring in the training data and to describe solutions for recovering the balance. We show that the generation of artificial samples is sample based approach and label regulation is class based approach. We discovered that feature selection achieves surprisingly good results when combined with a sample- or class-based solution.
America’s covid-19 preexisting vulnerability: a government of men, not laws

The legislative response of the United States of America to the covid-19 pandemic is a calamity. Incompetent leaders have turned a natural disaster into a national catastrophe. The catastrophe unmasks a weak rule of law that is closer to a reign of men than it is to a government of laws. Lousy legal methods predating covid-19 allowed tragedy to happen. This article summarises the legislative-like responses to covid-19 and identifies systemic failures, i.e. covid-19 preexisting vulnerabilities.
An Event Ontology Model Research for Environmental Pollution Emergencies

In the modeling process, event ontology design pattern (EODP) is a solution to recurring the same error scheme. It can speed up the construction of event ontology and avoid design ambiguity. A general event model structure is proposed in this paper. In the absence of domain-specific vocabulary, the model structure can represent the most general event information in web text. Based on this, the concept of event ontology mode and the method of extracting event ontology mode are proposed, and the method is used to establish the environmental pollution emergency ontology mode based on shared vocabulary. Finally, the ontology model of environmental pollution emergencies was formalized by using description logic, and the domain event ontology model was implemented by using the event ontology model to verify its validity and conceptual consistency.
Cognitive Regionology: The Experience of Modeling Regional Socio-Economic Processes

Introduction. In the current situation, the society is subject to extremely dynamic changes and strategic developments are becoming obsolete more rapidly. It is cognitive modeling of complex semi-structured systems that is one of the modern methods that presents technological solutions in response to the challenge of obsolescence of strategies. The practice of strategizing Russia’s regions makes it possible to single out the regional dimension as a self-sufficient subject of cognitive modeling. The objective of the article is to summarize many years of experience of applying the method of cognitive modeling of regional socio-economic processes and the application of the obtained models in educational, scientific and administrative activities of the region on the basis of the study conducted. Materials and Methods. The database of Analytic software application was used as the information resource. Cognitive modeling was the main method employed and was considered in more detail; statistical methods, comparative analysis, and an expert survey were also used. Results. Specific examples of research conducted in the Nizhny Novgorod Region, the Samara Region, and the Republic of Mordovia have shown the advantages of using cognitive modeling technology in the educational, scientific, and administrative activities in a region. The factor-digital cognitive model of a region becomes the basis for organizing trainings on the strategy of sustainable development at the regional and interregional levels. Analytic software application supports the research process and is integrated into the electronic information and educational environment of regional universities. Discussion and Conclusion. The cognitive modeling method makes it possible to solve the problems of static and dynamic analysis of a region as a complex system in various subject areas. The factor-digital models of regions obtained using Analytic software application are of a universal nature and are relatively easily modified under the framework conditions of any other region of Russia on remote access platforms.
Modeling software architecture design on data storage security in cloud computing environments

Cloud-based computation is known as the source architecture of the upcoming generation of IT enterprise. In context to up-coming trade solutions, the Information Technology sections are established under logical, personnel, and physical control, it transfers application software and large database to appropriate data centers, where security and management of database with services are not trustworthy fully. So this process may face many challenges towards society and organizations and that not been well understood over a while duration. This becomes one of the major challenges days today. So in this research, it focuses on security-based data storage using cloud, which plays one of the important aspects bases on qualities of services. To assure user data correctness in the cloud system, a flexible and effective distributed technique with two different salient features was examined by utilizing the token called homomorphic with erasure-coded data for distributed verification, based on this technique it achieved error data localization and integration of storage correctness. Also, it identifies server misbehaving, efficient, and security-based dynamic operations on data blocking such as data append, delete, and update methods. Performance analysis and security show the proposed method is more effective resilient and efficient against Byzantine failure, even server colluding attacks and malicious data modification attacks.
Word-Embedding-Based Traffic Document Classification Model for Detecting Emerging Risks Using Sentiment Similarity Weight

With the increase in traffic accident rates, traffic risk detection is becoming increasingly important. Moreover, it is necessary to provide appropriate traffic information considering user locations and routes and design an analysis method accordingly. This paper proposes a word-embedding-based traffic document classification model for detecting emerging risks using a quantity termed sentiment similarity weight (SSW). The proposed method detects emerging risks by considering and classifying the importance and polarity of keywords in traffic document. Conventional sentiment analysis methods fail to utilize semantically significant keywords unless they are included in a sentiment dictionary. In this study, through word imputation using an established similarity dictionary and by widening the limited utilization range, the proposed method overcomes the disadvantage of sentiment dictionaries. The proposed method is evaluated through three tests. In the first, the similarity between keywords is measured, and thus model accuracy is evaluated. In the second test, three classifiers for emerging risk classification are compared. In the last test, emerging risk detection is assessed according to whether the proposed SSW is applied, and its effectiveness is therefore verified. The evaluation results demonstrate that the proposed traffic-related document classification model using the SSW has an f-measure of 0.907, indicating satisfactory performance. Therefore, the proposed SSW can be effectively used as a parameter in traffic-related document classification and enables the detection of emerging risks.
Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology

Microscopic examination of tissues or histopathology is one of the diagnostic procedures for detecting colorectal cancer. The pathologist involved in such an examination usually identifies tissue type based on texture analysis, especially focusing on tumour-stroma ratio. In this work, we automate the task of tissue classification within colorectal cancer histology samples using deep transfer learning. We use discriminative fine-tuning with one-cycle-policy and apply structure-preserving colour normalization to boost our results. We also provide visual explanations of the deep neural network’s decision on texture classification. With achieving state-of-the-art test accuracy of 96.2% we also embark on using a deployment friendly architecture called SqueezeNet for memory-limited hardware.
Ranking vs. Classifying: Measuring Knowledge Base Completion Quality.

Knowledge base completion (KBC) methods aim at inferring missing facts from the information present in a knowledge base (KB) by estimating the likelihood of candidate facts. In the prevailing evaluation paradigm, models do not actually decide whether a new fact should be accepted or not but are solely judged on the position of true facts in a likelihood ranking with other candidates. We argue that consideration of binary predictions is essential to reflect the actual KBC quality, and propose a novel evaluation paradigm, designed to provide more transparent model selection criteria for a realistic scenario. We construct the data set FB14k-QAQ where instead of single facts, we use KB queries, i.e., facts where one entity is replaced with a variable, and construct corresponding sets of entities that are correct answers. We randomly remove some of these correct answers from the data set, simulating the realistic scenario of real-world entities missing from a KB. This way, we can explicitly measure a model's ability to handle queries that have more correct answers in the real world than in the KB, including the special case of queries without any valid answer. The latter especially contrasts the ranking setting. We evaluate a number of state-of-the-art KB embeddings models on our new benchmark. The differences in relative performance between ranking-based and classification-based evaluation that we observe in our experiments confirm our hypothesis that good performance on the ranking task does not necessarily translate to good performance on the actual completion task. Our results motivate future work on KB embedding models with better prediction separability and, as a first step in that direction, we propose a simple variant of TransE that encourages thresholding and achieves a significant improvement in classification F1 score relative to the original TransE.