Reviewerly/exHarmony huggingface dataset:
Blockchain Based Vehicular Trust Management and Less Dense Area Optimization

In the vehicular ad hoc network, the variation in the network is increasing rapidly due to convergence of cities into a smart city. With the rapid increase of vehicles, many problems arise in the network such as cost, scalability, security and trust management. To address these problems, we proposed the trust management system through blockchain based authentication of the vehicles by assigning unique identification. If still malicious vehicles exist in the network, trust is achieved by calculating the data that is sent from the vehicles so that network does not get stuck due to malicious data. In addition, we also proposed the crowdsensing concept using vehicles' sensors data and we minimize the cost of deploying the sensors and also optimize the density of area with respect to chance minimum occurrence of vehicles. We performed these solutions on the etheriam environment. vehicle to vehicle and vehicle to infrastructure communication got secured from malicious vehicles and malicious data. In crowdsensing, cost is reduced as compared to the wireless sensor network.
Kant’s Antinomies of Pure Reason and the ‘Hexagon of Predicate Negation’

Abstract Based on an analysis of the category of “infinite judgments” in Kant, we will introduce the logical hexagon of predicate negation. This hexagon allows us to visualize in a single diagram the general structure of both Kant’s solution of the antinomies of pure reason and his argument in favor of Transcendental Idealism.
Isolating Real-Time Safety-Critical Embedded Systems via SGX-Based Lightweight Virtualization

A promising approach for designing critical embedded systems is based on virtualization technologies and multi-core platforms. These enable the deployment of both real-time and general-purpose systems with different criticalities in a single host. Integrating virtualization while also meeting the real-time and isolation requirements is non-trivial, and poses significant challenges especially in terms of certification. In recent years, researchers proposed hardware-assisted solutions to face issues coming from virtualization, and recently the use of Operating System (OS) virtualization as a more lightweight approach. Industries are hampered in leveraging this latter type of virtualization despite the clear benefits it introduces, such as reduced overhead, higher scalability, and effortless certification since there is still lack of approaches to address drawbacks. In this position paper, we propose the usage of Intel's CPU security extension, namely SGX, to enable the adoption of enclaves based on unikernel, a flavor of OS-level virtualization, in the context of real-time systems. We present the advantages of leveraging both the SGX isolation and the unikernel features in order to meet the requirements of safety-critical real-time systems and ease the certification process.
Design and Research of Composite Web Page Classification Network Based on Deep Learning

This paper proposes a network model that combines long and short feature extractors to solve the problem of automatic classification of web pages. By analyzing the current major portal websites, the main categories of original corpus are formulated. By analyzing the composition of webpage content, the composite extraction features of long and short feature extractors are designed. The attention mechanism is introduced in the short feature extraction network to enhance the ability of short text information extraction. For the longer text, the long feature extraction network combines the attention mechanism of the word and segment to capture information. In the last layer of the classification, the correction mechanism is used for model fusion, which further improves the accuracy of classification. The experimental results show that the proposed method has higher classification accuracy. The classification indicators under the first-level label all reached 0.94 or higher, and 0.90 under the secondary label. The composite feature extraction network designed in this paper has better anti-noise ability and classification efficiency, and can achieve higher classification accuracy.
Boosting IoT Efficiency and Security through Blockchain: Blockchain-based Car Insurance Process - A Case Study

The evolution of Internet of Things (IoT) enables addressing problems with limited human assistance. It offers communication across various modern smart city applications, for example, using communication technologies like wireless sensor networks that would generate a huge amount of data. It is imperative to maintain reliable and trustworthy interactions over things to guarantee proliferation of IoT applications. As these applications are being rolled out in many fields, sensitive data need to be safeguarded against misuse and/or leakage. Moreover, promptness and real-time responses should feature all IoT services. This paper explores, through an experimental framework, the integration of blockchain into IoT in order to investigate its impact, security enhancements, and limitations or open challenges it might bring. A private fork on Ethereum blockchain platform is developed to connect IoT devices (Raspberry pi) deployed to detect car crashes so, that, the process of insurance claims are expediated. The same scenario is also deployed on public Ethereum network in order to offer a comprehensive overview of both public and private blockchain platforms and their related security issues and challenges. The findings formulate a rigid foundation upon which a proper and efficient IoT paradigm can be provisioned to unlock blockchain technology potentials in different other domains besides IoT.
A New Pupil Detection Algorithm Based on Circular Hough Transform Approaches

In this paper a new pupil detection algorithm based on circular Hough transform approaches is presented. The proposed algorithm includes an adaptive quantitative binarization procedure, a pupil reconstruction stage by using certain morphological operations and the image contour detection performed with the Laplace operator. To save the computing resources, the circular Hough transform is applied on the edge of the binarized eye image and provides better results for noisy eye image captured in nonuniform and variable lighting conditions. The CHT based algorithm achieves a detection rate at five pixels higher than 82% for three different and highly representative databases, totalizing 1324 eye images.
Quality Factors Enhancement of Requirement Engineering: A Systematic Literature Review

Software requirement engineering is among the most important issues for starting any software project. The most-reported problem in requirement engineering (RE) is the difficulty to identify quality requirements. Sometime analysts may face incorrect and incomplete requirements, which may become the reason for project failures from the perspective of dissatisfaction of stakeholders. Hence, the quality in each phase of RE is important. The main focus of this article is to categorizing, Identifying, and synthesizing the existing researches on quality enhancement of the RE process. The systematically reviewing the relevant studies based on the Kitchenham systematic review methodology is the main objective of this study. This review identifies the methods which are used for enhancing the quality of RE by improving requirement elicitation, requirement analysis and specification, and requirement validation process. In this research, we are not specific to any phase of RE. Hence, the techniques obtained for quality enhancement purpose can target different phases as we are going to deal with the overall quality of requirements to enhance different quality factors as defined by IEEE standards. In general, 3 defined research questions have been invested for the sake of discussion on explored results. Likewise, 44 articles were selected from an initial set of 101 research papers. In particular, research articles answering formulated question have been included in this SLR.
Ontology summit 2019 communiqué: Explanations

With the increasing amount of software devoted to industrial automation and process control, it is becoming more important than ever for systems to be able to explain their behavior. In some domains, such as financial services, explainability is mandated by law. In spite of this, explanation today is largely handled in an unsystematic manner, if it is handled at all. The decisions of modern artificially intelligent systems, such as those built on deep neural networks, are especially difficult to explain. The goal of the recent Ontology Summit 2019 was concerned with the role of ontologies for explaining the functioning of a system. More specifically, the Ontology Summit focused on critical explanation gaps and the role of ontologies for dealing with these gaps. The sessions examined current technologies and real needs driven by risks and requirements to meet legal or other standards. The sessions covered explainable artificial intelligence, commonsense reasoning and knowledge, the role of narrative, and explanations in the fields of finance and medicine. The goal of this Communiqué is to foster research and development of approaches to explanations and to drive towards explanation support which can be incorporated into both knowledge engineering processes and ontology design best practices.
Security analysis of the W-OTS$^+$ signature scheme: Updating security bounds

In this work, we discuss in detail a flaw in the original security proof of the W-OTS${^+}$ variant of the Winternitz one-time signature scheme, which is an important component for various stateless and stateful many-time hash-based digital signature schemes. We update the security proof for the W-OTS${^+}$ scheme and derive the corresponding security level. Our result is of importance for the security analysis of hash-based digital signature schemes.
An Ontology Driven Question Answering System for Legal Documents

This paper presents an ontology-driven question- answering system for Motor Vehicle Department(MVD). The MVD uses the motor vehicle act that regulates almost all the aspects of road transport vehicles. It defines a set of offenses and related penalties. At present all this information are unstructured, so in this work, we propose a Natural Language Processing(NLP) based structured representation for these offenses and its related penalties. We have used Semantic Role Labeling(SRL) to identify the roles of each word in the offenses. We use the result of SRL to create the ontology.
Coordinated Motion Planning of Dual-arm Space Robot with Deep Reinforcement Learning

In this paper, we focus on coordinated motion planning of dual-arm robot. The kinematics model of the robotic arm is established by Denavit-Hartenberg (D-H) coordinate method and the mathematical model of the cooperative motion planning problem is established. The rapidly-exploring random trees (RRT) algorithm and the deep deterministic policy gradient (DDPG) algorithm are used to carry out dual-arm coordinated motion planning, respectively. The simulation results show that these algorithms can effectively complete the robot arm motion planning task, but the RRT improved algorithm cannot balance the planning efficiency and result optimization. Compared with the RRT algorithm, the DDPG algorithm trains the model through continuous trial and error to optimize its planning strategy. The trained model can be used to obtain an optimized path and it can ensure the efficiency of the planning with the optimized strategy.
k-NN Sampling for Visualization of Dynamic Data Using LION-tSNE

Dimensionality reduction algorithms are often used to visualize multi-dimensional data, which are mostly non-parametric. Non-parametric methods do not provide any explicit intuition for adding new data points into an existing environment which limits the applicability of visualization for Big Data scenario. The LION-tSNE (Local Interpolation with Outlier coNtrol t-Distributed Stochastic Neighbor Embedding) method was proposed to overcome the limitations of existing techniques. The LION-tSNE algorithm uses random sampling method for tSNE model design which creates an initial visual environment then new data points are added to this environment using local-IDW(Inverse Distance Weighting) interpolation method. The randomly selected sample data often suffer from non-representativeness of the whole data which creates inconsistency in the tSNE environment. To overcome this problem two new sampling methods are proposed which are based on k-NN (k-Nearest Neighbor) graph update properties. It is empirically shown that proposed methods outperform existing LION-tSNE method with 0.5 to 2% more k-NN accuracy and results are more consistent. The study is done on five differently characterized datasets with three different initial solutions of tSNE. The proposed method results are statistically significant which is done by statistical method pairwise t-test.
Blockchain Technology, Sustainability and Business: A Literature Review and the Case of Dubai and UAE

The importance of innovation across all sectors is emphasized by the UAE leadership (United Arab Emirates, UAE Vision 2021). The UAE National Innovation Strategy (NIS) aims to take innovation to new heights by creating an innovation enabling environment, innovation champions, and priority sectors (United Arab Emirates, 2015). Among these priorities are technology and sustainability. UAE has encouraged numerous technology activities, mainly The Smart Government and The Smart City initiatives. Within this broader innovation and smart city framework, the Dubai blockchain Strategy was recently launched. The purpose of this paper is to elaborate on the UAE framework and identify business areas that blockchain can have a significant impact. UAE, and Dubai in particular, has been global markets and finance hub. In this paper, we examine the potential applications of blockchain technology in UAE’s finance industry, exchanges, and marketing. We also discuss the role of blockchain technologies for sustainability. Blockchain is identified as one of the most important emerging technologies (Mulligan et al. in World economic forum: Blockchain beyond the hype—A practical framework for business leaders, 2018). We conclude that UAE and Dubai have rightly identified and promoted blockchain technologies and more strategic investment would be beneficial.
A study of learning motivation of senior high schools by applying unity and mblock on programming languages courses

Abstract This study aims to explore whether high school students can enhance students’ learning motivation through challenging learning situationsin programming courses. This study was conducted for 12th-grade students in high school. The experiment lasted for eight weeks. Measurements included Pre- and post-tests measure MSLQ (Motivated Strategies for Learning Questionnaire) and the performance of programming. The experimental group learning design the game interactive project in Unity with C# programming language, and the control group learning build Arduino interactive project in mBlock with block-based programming. The results showed that there was no significant difference in performance, but the learning motivation of the experimental group was lower than that of the control group.
Collaborative learning with corrupted labels

Deep neural networks (DNNs) have been very successful for supervised learning. However, their high generalization performance often comes with the high cost of annotating data manually. Collecting low-quality labeled dataset is relatively cheap, e.g., using web search engines, while DNNs tend to overfit to corrupted labels easily. In this paper, we propose a collaborative learning (co-learning) approach to improve the robustness and generalization performance of DNNs on datasets with corrupted labels. This is achieved by designing a deep network with two separate branches, coupled with a relabeling mechanism. Co-learning could safely recover the true labels of most mislabeled samples, not only preventing the model from overfitting the noise, but also exploiting useful information from all the samples. Although being very simple, the proposed algorithm is able to achieve high generalization performance even a large portion of the labels are corrupted. Experiments show that co-learning consistently outperforms existing state-of-the-art methods on three widely used benchmark datasets.
A Comparative Study on Various Text Classification Methods

With the exponential growth in the enhancement of modes of information exchange, the spread of text has become not only substantially faster, but also widespread. Due to this, text has become an indispensable part of all kinds of decision-making. Hence, it has become imperative to analyse the methods that can help make sense of this text as efficiently as possible. We shall make an attempt at the same by discussing various tools to make this very task increasingly productive. We shall try to analyse the relationship between the way an algorithm works and how it performs on various sets of data having different types of featurization. We shall analyse featurization techniques such as bag of words/N-grams, Tf-Idf vectorization, average Word2Vec and Tf-Idf Word2Vec.
Clusterix-Like BigData DBMS

Abstract Commercial OLAP systems are economically unavailable for organizations with limited financial capabilities. Analytical processing of large amounts of data in these organizations can be accomplished using open-source software systems on a cost-effective cluster platform. Previously created Clusterix-like DBMS using a regular query processing plan is not efficient enough. Therefore, research on such systems was developed with a focus on a full load of processor cores and using the GPU acceleration (systems Clusterix-N, N—from new) up to the development of a system comparable in efficiency to the open-source system Spark, which is currently considered the most promising. The development methodology was based on the constructive system modeling methodology.
Semi-autonomous methodology to validate and update customer needs database through text data analytics

To develop highly competitive products, companies need to understand customer needs (CNs) by effectively gathering and analysing customer data. With the advances in Information Technology, customer data comes not only from surveys and focus groups but also from social media and networking sites. Few studies have focused on developing algorithms that are devised exclusively to help to understand customer needs from big opinion data. Topic mining, aspect-based sentiment analysis and word embedding are some of the techniques adopted to identify CNs from text data. However, most of them do not consider the possibility that part of the customer data analysed is already known by companies. With the aim to continuously enhance company understanding of CNs, this paper presents an autonomous methodology for automatically classifying a set of text data (customer sentences) as referring to known or unknown CN statements by the company. For verification purposes, an example regarding a set of customer answers from an open survey questionnaire regarding the climate system of a car is illustrated. Results indicate that the proposed methodology helps companies to validate and update the customer need database with an average of 90 % precision and 60 % recall.
Object Detection and Gesture Control of Four-Wheel Mobile Robot

In this paper, the development of a four wheel mobile robot with gesture-controlled robot arm manipulator system is presented. The accelerometer and wifi based gesture control model is used to manipulate the motion of its arm with human hand. The glove data is used to control the movement of the robot arm for pick and place operation. The object detection algorithm is also implemented to detect the object autonomously. An ultrasonic sensor is used to detect the upcoming object. Under this work, only single object is considered. The prototype model of four-wheel mobile robot with two movable arms is developed. The proposed object detection algorithm is implemented on the physical model to check the effectiveness of the algorithm. After object detection operation, the gesture control based controller is used for pick and place activity. The novelty of this research is collaborative use of object detection algorithm and gesture control scheme.
Trajectory Outlier Detection on Trajectory Data Streams

The detection of abnormal moving on trajectory data streams is an important task in spatio-temporal data mining. An outlier trajectory is a trajectory grossly different from others, meaning there are few or even no trajectories following a similar route. In this paper, we propose a lightweight method to measure the outlier in trajectory data streams. Furthermore, we propose a basic algorithm (Trajectory Outlier Detection on trajectory data Streams-TODS), which can quickly determine the nature of the trajectory. Finally, we propose an Approximate algorithm (ATODS) to reduce the detection cost. It is space approximate algorithm which can effectively reduce the amount of calculation. The cost of ATODS algorithm can satisfy the demand of trajectory data streams. Our method are verified using both real data and synthetic data. The results show that they are able to reduce the running time without reducing the accuracy.
Sentiment Analysis on Kerala Floods

In twenty-first century, Twitter has been a very influential social media platform, be it in election or gathering aids for a disaster. In this work, we propose to study about the effect of natural calamities like the recent Kerala flood, their effect on the people and reactions of people from different stratus of society. The direction of our research will be towards sentimental analysis using RStudio and the Twitter app. This research highlights the reactions of people on a public platform during the calamity. Word cloud and other data visualization techniques are used for our study. The research also highlights on how the government reacts and how aid is provided in the dire time of need. We predict that our research will be useful to human society as it showcases a lot about human behaviour, its goodness and shortcomings. The paper also throws light on the Hurricane Michael calamity and gives a comparative study on relations like the differences of opinion among the people as well as their similarities during and after a calamity.
AR Guides Implementation for Industrial Production and Manufacturing

This paper introduces an original approach for the implementation of Augmented Reality automated systems for industrial production and manufacturing. Based on analysis of the up-to-date problems of AR devices practical use like limited usability of AR goggles, low performance and quality of image recognition in real time there is proposed a new approach of accented visualization. The paper addresses some problems of machine vision application for production quality control, which helps solving the challenges for AR guides and improving the efficiency of their implementation in practice. In particular, augmented reality allows users to undergo interactive training and receive active assistance during the execution of technological operations without the need to refer to a set of documentation in paper or electronic form. Within the developed framework there is presented a new solution architecture that combines the capabilities of neural networks, Ontologies and Big Data processing facilities. The proposed approach is recommended to improve the quality of recognizing production operations and manufactured products using machine vision technologies and neural networks in industrial applications.