Reviewerly/exHarmony huggingface dataset:
Big Data Processing Frameworks for Handling Huge Data Efficiencies and Challenges: A Survey

The increasing expansion of digital data collected from many sources renders traditional storage, processing, and analysis methods obsolete. For these restrictions, new technologies for processing and storing very massive datasets have been developed. Big data processing is required to extract relevant information from it. Transforming data into information and knowledge is what processing implies. Big data processing is the process of dealing with massive amounts of data and changing it from its raw form into useable information in a more understandable manner. As a result, numerous big data processing execution frameworks have emerged, but determining and selecting the appropriate framework for processing your big data applications is a significant challenge. Therefore, this paper investigates the possible influence of big data challenges and discusses in depth the most well-known approaches to big data processing, which are divided into five classes: batch processing, streaming processing, real-time processing, interactive processing, and hybrid processing, as well as the variety of the most popular frameworks associated with them such as Apache Hadood, Dryad, Samza, IBM Infosphere, Storm, Amazon Kinesis, Drill, Impala, Flink, and Spark. Furthermore, this study presents a comparison among the several features of the frameworks by highlighting their drawbacks and strengths. Thus, it can be used as a guideline for picking the best application framework in IT analytics and will help business users make faster decisions.
A Fully-Nested Encoder-Decoder Framework for Anomaly Detection

Abstract Anomaly detection is an important branch of computer vision. At present, a variety of deep learning models are applied to anomaly detection. However, the lack of abnormal samples makes supervised learning difficult to implement. In this paper, we mainly study abnormal detection tasks based on unsupervised learning and propose a Fully-Nested Encoder-decoder Framework. The main part of the proposed generating model consists of a generator and a discriminator, which are adversarially trained based on normal data samples. In order to improve the image reconstruction capability of the generator, we design a Fully-Nested Residual Encoder-decoder Network, which is used to encode and decode the images. In addition, we add residual structure into both encoder and decoder, which reduces the risk of overfitting and enhances the feature expression ability. In the test phase, a distance measurement model is used to determine whether the test sample is abnormal. The experimental results on the CIFAR-10 dataset demonstrate the excellent performance of our method. Compared with the existing models, our method achieves the state-of-the-art result.
Discrete Cosine Transform Incremental Least Mean Square Over Wsns and Agent Localization

A Discrete Cosine Transform-based technique is developed for the Incremental Least Mean Square (ILMS) approach, hereafter called DCTILMS, in Wireless Sensor Networks (WSNs). The proposed DCTILMS simultaneously reduces the Mean Square Deviation (MSD) and accelerates the convergence rate of the Mean Square Error (MSE) . The new transform-based technique is implemented in the agent localization scenario by considering a fixed number of anchors with known positions around a set of sensor agents with unknown positions. The transient and steady-state MSD results have been derived in closed-form expressions. In the simulations, the proposed DCTILMS is compared to existing techniques. The DCTILMS is shown to outperform the traditional ILMS methods. We show that the empirical results are in excellent agreement with the theoretical expressions of the MSD.
A Study on Datasets Used in Multilingual Opinion Mining

Sentiment analysis has been around for a long time, and every day we use text with strong opinions about things. There is a lot of information that needs wrangling to understand, so automation has become important. In the sentiment analysis process use of proper data set has become an important task. In experimentation of the multilingual domain selection of the dataset become very crucial. This survey paper discusses recent progress in sentiment analysis and different datasets and related work done in the dataset. The main aim of this paper is to discuss different available datasets for multilingual opinion mining and evaluation of the dataset done by various researchers.
Intelligent Facial Expression Evaluation to Assess Mental Health Through Deep Learning

Expressions have been one of the most primary forms of non-verbal communication for humans. This is very natural and has formed an irreplaceable part of our lives. The facial expressions have also been highly useful parameters in the understanding of human behavior and analysis of the human psyche. The psycho-therapists have been engaged in this practice for a long time and the rise of awareness about mental health issues have been leading to an increase in the number of patients. Therefore, there is a need for an accurate analysis tool for achieving the facial expression detection. To provide a solution to this problem, this research article illustrates a facial expression technique through the use of Feature Extraction along with Convolutional Neural Networks and Decision tree. The approach has been effectively evaluated for its accuracy which has resulted in highly satisfactory results.
Prototypical Concept Representation

Concepts are building blocks of human thinking. For machines, concept understanding has also been increasingly important, which makes concept representation a fundamental problem in artificial intelligence. While many concepts have their instances, the massive amount of information carried by instances has long been ignored in current concept representation, which limits the usage of these concepts in applications. In this paper, inspired by prototype theory in cognitive science, we propose prototypical concept representation for machines, which represents each concept with a distributed prototype derived from representations of its instances. For prototypical representation learning, we further introduce a novel model named Prototypical Siamese Network (PSN). PSN is trained under the supervision of <small>isA</small> determination, one of the most important concept-related applications. Results of extensive experiments demonstrate that, our method achieves state-of-the-art performance, thus validating the effectiveness of prototypical concept representation.
Smart License based Vehicle Security System using Fingerprint Sensor

Recently, vehicle stealing is becoming common problem in many countries. Also, driving without a Driving license is another issue for the society. Therefore, every vehicle manufacturer as well as owners of costly vehicles use inbuilt anti- theft system that provides security to the vehicles. There are few difficulties with existing security systems. In this paper, an improved system is proposed to overcome these difficulties. The system is designed using a smart driving license card that will help in road safety and vehicle security. The Smart driving license card which helps to limit the vehicles operation based on three parameters as driving license expiry date, vehicle ownership and the category of the vehicles. The system uses very few components like Driving license Card (DL), Fingerprint sensor, Crystal OSC, Atmega328 Controller, Ignition system which is affordable for common users. The system also uses Atmega 328 Controller to serve as the entire brain of the system. The programmable controller can encrypt only sum of the authorized driver’s license and the fingerprints of the trusted people which ensures the security of vehicles. The system is developed and tested that gives satisfactory results.
Set-Based Intent-Expressive Trajectory Planning and Intent Estimation

This letter proposes a novel approach to intent-expressive motion planning and intent estimation for agents/robots with uncertain discrete-time affine dynamics. In contrast to the more commonly considered stochastic settings, our intent-expressive trajectory planning approach is set-based and leverages the active model discrimination framework for designing optimal inputs to attain certain target/goal states, while allowing an observer/teammate to clearly infer the intended goal based only on observations of a partial trajectory before it has reached its target/goal state, despite worst-case uncertainties. Further, in tandem with the planning algorithm, we also propose an intent estimation algorithm that can be used by the observer/teammate to determine the intended goal from observations of a noisy, partial trajectory.
Critical Considerations on the Digital Potentialities, Vocations, and Needs of Teachers in Training in Bolivia

Currently, there is interest in the scope of digital literacy in pre-service teachers due to the incorporation of ICT in education and its increasing use by society. This work reviews the research articles related to the ICT use for the training of future teachers. The study has a critical perspective of the Bolivian educational model showing it is not possible to assess digital literacy by focusing on the competence models of digital skills conceived from the global vision of UNESCO and the EU. We carried out a systematic literature analysis by selecting documents (n = 14) from three databases: Google Scholar, Scielo, and Eric; the researchers also consulted virtual libraries of the Bolivian Teacher Training Schools. The review stands that there is no concrete proposal for incorporating ICT appropriate to the Bolivian approach. Therefore, it is necessary to reflect, debate, and deepen the integration of ICT and its correspondence with the educational model of Bolivia under three components: the digital divide, the interaction assumed by the government, and the current scope of ICT as instrumental use in education. Understanding and evaluating ICT incorporation on the Bolivian educational model triad are necessary to consider the native people’s worldviews with a decolonized orientation.
Variations of Solar Radiation in Typical Resource Regions of China During 1961–2016

Based on the total solar radiation, horizontal direct radiation and scattered radiation data of six typical resource regions in China from 1961 to 2016, this paper focuses on analyzing the variation trend of solar radiation by using climate tendency estimation method, cumulative anomaly method, Morlet wavelet analysis method and Mann-Kendall mutation analysis method. The results are as follows: in addition to the Zhengzhou six typical resources scattered radiation in a downward trend, basic and change rule and the plane direct radiation and total radiation change law basic similar, Kashgar, the level of the direct radiation decreases in Lhasa, Golmud trends, by contrast, the level of the direct radiation level direct radiation biggest drop in Zhengzhou. The mutation years were mainly concentrated in the 1980s and 1990s.
A Wearable Multi-Joint Wrist Contour Measuring Device for Hand Shape Recognition

Recently, various types of hand shape recognition systems have been developed for human–machine interfaces. However, most wearable recognition systems cannot robustly handle the variations in attachment positions of the devices. Thus, we propose a hand shape recognition system using a wearable multi-joint wrist contour measuring device to realize robust and effective recognition of hand shape, regardless of attachment position variations. In particular, this device can measure the wrist contour and band flexion data to recognize hand shape. The wrist contour data are measured using photo-reflectors mounted inside the device, and the band flexion data are measured using photo-interrupters installed at the device joints. Additionally, the attachment position information is extracted from the wrist contour or band flexion data using dimensional compression or attachment position recognition to achieve robustness against the position variations. Subsequently, the extracted information is incorporated into the hand shape recognizer. The results of the recognition experiment demonstrate that the attachment position information extracted from the band flexion data using dimensional compression could effectively realize robust hand shape recognition, considering variations in the device attachment position.
On the Sensitivity of LSTMs to Hyperparameters and Word Embeddings in the Context of Sentiment Analysis

Recurrent neural networks are still providing excellent results in sentiment analysis tasks, variants such as LSTM and Bidirectional LSTM have become a reference for building fast and accurate predictive models. However, such performance is difficult to obtain due to the complexity of the models and the hyperparameters choice. LSTM based models can easily overfit to the studied domain, and tuning the hyperparameters to get the desired model is the keystone of the training process. In this work, we provide a study on the sensitivity of a selection of LSTM based models to various hyperparameters and we highlight important aspects to consider while using similar models in the context of sentiment analysis.
A forefront insight into the integration of AI and blockchain technologies

Artificial intelligence (AI) and blockchain: these two technologies have recently been the trendiest and most revolutionary, progressive technologies. Blockchain technology can automate payments in cryptocurrency and provide admittance in a decentralized, safe, and trustworthy way to shared records, transactions, and log ledgers. Also, with smart contracts, blockchain can handle interactions between participants without an interceder. Furthermore, AI provides intelligence and smart decision-making for human-like machines. This chapter presents a thorough analysis of AI and blockchain integration ability, possibilities, and applications. The incorporation of AI was evaluated as well as how blockchain will influence industry 4.0. We also recognize and address the open issues of AI and blockchain fusion.
Funnel Random Forest: Inliers-Focused Ensemble Learning for Improved Prognostics of Heart Failure

Despite the overall high performance and explainability of Decision Tree-based classifiers, such as the Random Forest (RF), their algorithmic generalisation is inherently susceptible to outliers. Especially with real-world data, as in the clinical setting, outliers may occur more frequently. Nevertheless, care needs to be exerted to ensure only some outliers are removed to ensure the clinical utility of Machine Learning-based approaches leveraged for predictive analytics. To this purpose, statistical methods, such as the interquartile range-based method, can be used to partly remove some outliers; however, these strategies are often hand-crafted, relying to subjective heuristics that does not generalise to unseen data. Conversely, this study developed and validated a method that is implemented intrinsically to the RF model to handle outliers by identifying them proactively, prior to the training procedure, by leveraging a fusion of information theory-based metrics. By leveraging the Gini Impurity of the RF and blending it with mutual information-related scores as indicators of features’ interactivity to provide a ‘funnel’ for filtering out outliers, 11.04% less outliers than those from standard statistical methods were identified. This funnel technique led to higher training performance by about 2% on benchmark medical datasets. The data relate to clinical records of patients with heart failure (HF) and the RF was leveraged to predict their survival. The classification performance of the proposed model (Funnel RF) was evaluated against published models on the same benchmark clinical datasets and found more accurate and reliable by 9.67% and 23.05% respectively. Considering its higher performance and explainability, findings support the application of this novel method for clinical decision support in aiding prediction of prognostics in patients with HF.
The Effects of Self-representation &amp; Immersive Experiences on Social Presence in Emerging Markets

As smartphone adoption surges in new markets, casual gaming has become a fast growing vertical. Emerging markets typically experience three challenges: network connection, network bandwidth, and device storage capacity [1]. To deploy new technologies such as augmented reality with high fidelity assets, novel approaches are needed. High fidelity assets are necessary to generate a sense of presence, or being there, in virtual environments [2]. In our paper, we explore the deployment of a mobile application to 2.5 million users across South Asia. This application allows users to build their own interactive 3D avatar. Our work discusses novel approaches to deploy high fidelity assets in emerging markets. We next explore two topics central to virtual worlds: self-representation and self-presence [3]. Key to virtual environments are the realism and connection to avatars [4]. The stronger the human-avatar connection, the more likely virtual experiences are to have an impact on physical world identity. While self-representation has long been a concept explored in research, only recently have emerging markets gained technology for culturally accurate virtual representations. As such, our work is one of the first large scale field explorations of self-avatars’ impact on identity in new markets. Our paper explores the quantitative and qualitative engagement with a user’s self-avatar. We find that self-representation, particularly clothing and animations can strongly influence user’s connections to their virtual self. This leads to positive associations with the user’s avatar. Long term implications of virtual identity via mobile experiences are discussed.
Cryptogovernance for Security Supervision in the Internet of Things

The current undeveloped study of academic lab safety calls for effective pre-alarm and protection measures. Internet of Things (IoT) is one of effective tools to gather data and yield data analysis for academic governance. In this paper, a cryptogovernance model of security supervision is proposed to facilitate the cryptogovernance effectiveness. Integrated with blockchain and IoT, cryptogovernance could gather crucial security data, lend security measures to the data storing and security supervision of hazardous substances in academic laboratory, improve the governance capacity from the source, and convert the current governance mode into penetrating governance of the higher education production process. Challenges and risks of devices and data stored in central servers are explored. Cryptogovernance algorithm in rules to tackle security supervision problems, such as reveal of personal privacy data or data with national security, poorer data trustworthiness with the risk of being deleted and tampered with, insufficient automation functions and resource monitoring capabilities are compared and analyzed. Potential challenges are assessed and recommendations are developed to address security supervision issues.
Stream processing in the semantic web

In this chapter, the concept of semantic data streams and semantic stream processing are discussed as topics of interest in the context of smart cities and collective intelligence. The discussion is done from the perspective of the semantic web. In order to do so, the semantic web technology basis is first elaborated. The RDF-Stream format is discussed as a means of representing semantic data stream, with TripleWave as a possible means of generating these streams. The semantic web technologies described here can be found useful in the processing of semantic IoT data streams by reusing the advantages of the semantic web technologies in data stream environments. Some challenges and barriers in reasoning over RDF streams are discussed.
Capturing the Progression of Acute Conditions and the Dynamics of Clinical Decision-Making Using Bayesian Networks

Developing clinical decision support systems (CDSS) that accurately capture the way clinicians gather information and make decisions during patient’s care is challenging. The modelling problem is especially difficult for acute conditions that evolve rapidly over a short timescale. Previous researchers have proposed various time-based Bayesian networks (BNs) applicable to a stationary process rather than the evolving situation encountered in acute medical conditions. In this paper, we propose a method for developing CDSS using progressive BNs that capture the evolving nature of acute conditions, as well as the dynamics of clinical decision making. To describe and validate our methodology we developed a BN for predicting the likelihood of survival of combat casualties in the prehospital setting: field and en route care. To overcome the typical lack of data for this type of problem, we use a combination of expert knowledge and available data to produce a causally coherent BN structure. The accuracy of the model is demonstrated using a 10-fold cross validation. The model had an excellent discrimination ability with an area under the ROC curve of 0.84 and 0.82 for the BN in field and en route care, respectively. The proposed model can be used to potentially support clinical decision making in several ways, such as: patient triage, or enabling precision medicine with treatments targeted to risk.
Ensembles Learning Algorithms with K-Fold Cross Validation to Detect False Alarms in Wind Turbines

Wind farms demand a specialized maintenance management. Supervisory control and data acquisition and condition monitoring systems are used to control wind turbines, which generate vast volumes of data. Large amounts of complex data can be efficiently classified using machine learning techniques. Several machine learning algorithms have been applied to fault detection and energy prediction in wind turbines, but the literature is scarce for alarm classification, and almost non-existent for the detection and diagnosis of false alarms. The innovation of this paper is the implementation of ensemble tree algorithms for the prediction and classification of alarms and detection of false alarms. To analyze the methodology, three different ensemble algorithms have been evaluated: Bagging, Boosted and RUSBoost; and different K-fold cross validation has been applied to validate the results and compared with holdout validation. The methodology is evaluated on a real dataset from three wind turbines. The results indicate an accuracy of 99.1%, and the $$F_1$$ is 0.995, this demonstrates that the ensemble tree algorithm is a reliable method for the prediction of alarms in wind turbines. Subsequently, the misclassifications produced by the higher accuracy algorithm are studied. The causes of these misclassifications are analyzed, together with the maintenance log and the alarm log. The case study proves that the proposed methodology detects that more than 17% of false alarms. These results demonstrate that the proposed methodology is effective at detecting and identifying false alarms.