Reviewerly/exHarmony huggingface dataset:
Landsat Collection 2 radiometric calibration updates

Since the release of Collection 1 in 2016 Landsat imagery in the U.S. Geological Survey (USGS) archive have been organized and managed as a collection of consistently calibrated and processed Earth image data that have been acquired globally over the last nearly 50 years. In addition to Level-2 products and improved geometric accuracy, Collection 2 brings several radiometric updates. In this paper, we will address the Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) absolute and relative gain updates, a change in the OLI bias calculation method and the Landsat 5 Thematic Mapper (TM) thermal band calibration update.
Alpha-N-V2: Shortest Path Finder Automated Delivery Robot with Obstacle Detection and Avoiding System

An improved version of Alpha-N, a self-powered, wheel-driven Automated Delivery Robot (ADR), is presented in this study. Alpha-N-V2 is capable of navigating autonomously by detecting and avoiding objects or obstacles in its path. For autonomous navigation and path planning, Alpha-N uses a vector map and calculates the shortest path by Grid Count Method (GCM) of Dijkstra’s Algorithm. The RFID Reading System (RRS) is assembled in Alpha-N to read Landmark determination with Radio Frequency Identification (RFID) tags. With the help of the RFID tags, Alpha-N verifies the path for identification between source and destination and calibrates the current position. Along with the RRS, GCM, to detect and avoid obstacles, an Object Detection Module (ODM) is constructed by Faster R-CNN with VGGNet-16 architecture that builds and supports the Path Planning System (PPS). In the testing phase, the following results are acquired from the Alpha-N: ODM exhibits an accuracy of [Formula: see text], RRS shows [Formula: see text] accuracy and the PPS maintains the accuracy of [Formula: see text]. This proposed version of Alpha-N shows significant improvement in terms of performance and usability compared with the previous version of Alpha-N.
Predicting Human Actions Using a Hybrid of ReliefF Feature Selection and Kernel-Based Extreme Learning Machine

Human activity recognition (HAR) is a growing field that provides valuable information about a person. Sensor-equipped smartwatches stand out in these studies in terms of their portability and cost. HAR systems usually preprocess raw signals, decompose signals, and then extract attributes to be used in the classifier. Attribute selection is an important step to reduce data size and provide appropriate parameters. In this chapter, classification of eight different actions (brushing teeth, walking, running, vacuuming, writing on the board, writing on paper, using the keyboard, and stationary) has been performed with smartwatch motion sensor data. Forty-two different features have been extracted from the motion sensor signals and the feature selection has been performed with the ReliefF algorithm. After that, performance evaluation has been performed with four different machine learning methods. With this study in which the best results have been obtained with the kernel-based extreme learning machine (KELM) algorithm, estimation of human action has been performed with high accuracy.
Extracting JSON Values with JsonPath

XPath is used to extract values from XML documents. JsonPath performs this task for JSON documents. Chapter 10 introduces you to JsonPath.
Normalization of Facial Occlusion in Face Recognition

Face recognition accuracy is determined by face detection results. Detected faces will be in view of clear and occlusion faces. If detected face has occlusion than recognition accuracy is reduced. This research is directed to increase recognition rate when detected occlusion face. In this paper is proposed normalization occlusion faces by Principal component analysis algorithm. After applying normalization method in occlusion faces false reject error rate is decreased.
Visualization in multiobjective optimization

tutorial Share on Visualization in multiobjective optimization Authors: Bogdan Filipič Jožef Stefan Institute, Ljubljana, Slovenia Jožef Stefan Institute, Ljubljana, SloveniaView Profile , Tea Tušar Jožef Stefan Institute, Ljubljana, Slovenia Jožef Stefan Institute, Ljubljana, SloveniaView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 951–974https://doi.org/10.1145/3319619.3323374Published:13 July 2019Publication History 0citation110DownloadsMetricsTotal Citations0Total Downloads110Last 12 Months16Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
CMDIfication process for textbook resources

Interoperability between heterogeneous resources and services is the key to a correctly functioning digital infrastructure, which can provide shared resources at once. We analyse the establishment of a standardised common infrastructure covering metadata, content, and inferred knowledge to allow collaborative work between researchers in the humanities. In this paper, we discuss how to provide a CMDI (Component MetaData Infrastructure) profile for textbooks, in order to integrate it into the Common Language Resources and Technology Infrastructure (CLARIN) and thus to make the data available in an open way and according to the FAIR principles. We focus on the 'CMDIfication' process, which fulfils the needs of our related projects. We describe a process of building resources using CMDI description from Text Encoding Initiative (TEI), Metadata Encoding and Transmission Standard (METS) and Dublin Core (DC) metadata, testing it on the textbook resources of the Georg Eckert Institute (GEI).
A smart-contract-based blockchain for a healthcare IoT network

The demand for IoT systems in healthcare services is increasing widely and the data generated from these networks should be transferred and stored in a highly secure manner. Blockchain technology helps in maintaining the privacy and integrity of the electronic health records (EHR) data with the help of smart contracts that authenticate the users and maintain confidentiality in the network. Integrating blockchain into IoT systems improves the overall security of the network. In this paper, we propose an architecture based on Hyperledger Fabric which is a private blockchain platform that is used for storing the EHR data which are collected from various IoT sensors. The deployed smart-contract helps in performing some of the basic database functionalities onto the blockchain.
A coupled map lattice-based image encryption approach using DNA and bi-objective genetic algorithm

The paper presents a coupled map lattice (CML) and deoxyribonucleic acid (DNA)-based image encryption algorithm that uses genetic algorithm (GA) to get the optimised results. The algorithm uses the chaotic method CML and DNA to create an initial population of DNA masks in its first stage. The GA is applied in the second stage to obtain the best mask for encrypting the given plain image. The paper also discusses the use of two more chaotic functions, i.e., logistic map (LM) and transformed logistic map (TLM) with DNA-GA-based hybrid combination. The paper evaluates and compares the performance of the proposed CML-DNA-GA algorithm with LM-DNA-GA, TLM-DNA-GA hybrid approaches. The results show that the proposed approach performs better than the other two. It also discusses the impact of using a bi-objective GA optimisation for image encryption and applies the same to the all three discussed techniques. The results show that bi-objective optimisation of the proposed algorithm gives balanced results with respect to the selected fitness functions.
Blockchain Technology and It’s Applications in E-Governance Services

Blockchain Technology is one of the most popular technologies of present days. This technology has the capability to eliminate the requirement of third party to validate the transactions over the Peer-to-Peer network. Due to various features of Blockchain like smart contract, consensus mechanism, network transactions are completed securely, efficiently and timely. This technology is very useful in many areas including medical, IoT, e-Governance services, smart cities, taxation, supply chain, banking etc. In this paper, we discuss the Blockchain Technology in detail, its data structure, open source platform like Ethereum and Hyperledger, technical aspects of this technology, possible applications of this technology, challenges and limitations in adaptation of this technology.
Self-calibration system for pragmatic failure in English-Chinese translation based on big data

Aiming at the problems of long time, high energy consumption and low accuracy of the current English-Chinese translation pragmatic self-calibration system, a design method of English-Chinese translation pragmatic self-calibration system based on big data is proposed. In the hardware part of the system, the framework of the pragmatic error self-calibration system is designed. The speech is converted into digital signals by the speech recognition module, and the recognised digital signals are translated into Chinese by the functions in the translation module. In the software part of the system, the sample risk minimisation algorithm is adopted to keep the loss function in the sample minimum, and the calibration model is built according to the linear search and feature selection results. The experimental results show that the energy consumption coefficient of the designed system varies from 0 to 1.5. The average calibration accuracy is 95% and the calibration accuracy is high.
Multi-party computation review for secure data processing in IoT-fog computing environment

In the last decade internet of things (IoT) technology becomes useful in almost every application fields. IoT-cloud-based architecture is insufficient to handle the requirement of IoT like latency, bandwidth, and volume of data. Fog computing can address those problems by providing processing and services to end-users at the edge network. In centralised system computation and processing was done in a centralised way. Some of the issues in that centralised system are malicious behaviour, node capture, and failure. The solution for this would be if multiple parties do the computation then the decision can be reliable, trustworthy. In this paper, authors first used IoT-fog-cloud architecture to explain the need to multi-party computation in IoT applications. Secondly, authors review existing work in term of the basic working principle of multi-party computation and already used applications area. Lastly paper described the solution approach to perform the secure multi-party computation in term of blockchain technology.
An Integrated Environment for Cloud Voting System using Edge Computing

Accurate voting system is the primary component of democracy to represent people’s actual mandate in governance. It plays a vital role in smooth functioning of administration at its various levels, like centre, state, municipality, corporation, etc. With the proliferation of Information and Communications Technology (ICT), voting process in India is all set for a major upheaval. This process is successfully leveraging the benefits of Information and Communication Technology (ICT) to administer delivery of multifaceted Electronic Services (E-Services) like Electronic Voting (E-Voting), Electronic Banking (E-Banking), Electronic Healthcare (E-Healthcare), Electronic Education (E-Education), etc to populace. Furthermore, to achieve data security during these Electronic Communication (E-Communication), Cloud Computing may be used as an interface for the transacting parties to deliver various electronic services to end users. Hence, any type of conventional service delivery system can be enhanced to its equivalent technology based platform so as to recover the issues faced through the conventional system. Even, this electronic communication can be used to monitor real time data that changes frequently with reference to time. In this paper, authors have proposed a Cloud Voting System (CVS) to deliver prompt results to all stakeholders in a flawless manner, which will explore research scope to perform Electronic transaction through strict user authentication protocol and will resolve the Latency problem of Cloud Voting System (CVS). During our research work, we will also gradually explore the prospect and limitations of our proposed model with reference to real world parameters.
Written Testimony for the House Committee on Science, Space, and Technology on November 13, 2019

Making open the default for research plans, data, materials, code, and outcomes will reduce friction in discovery and maximize return on research investments. Extending existing policy frameworks about transparency and openness across federal agencies will help improve research efficiency. These frameworks can help decision-makers navigate situations in which principles of security and privacy are in conflict with principles of transparency and openness. Rule-making should be informed by the best available evidence. Sometimes the best available evidence is based on data that cannot be transparent, has high uncertainty, or has unknown reproducibility. Developing tools that clarify uncertainty will improve policymaking and shape research priorities.
QUASE: AN Ontology-Based Domain Specific Natural Language Question Answering System

Since early days Question Answering (QA) has been an intuitive way of understanding the concept by humans. Considering its inevitable importance it has been introduced to children from very early age and they are promoted to ask more and more questions. With the progress in Machine Learning &amp; Ontological semantics, Natural Language Question Answering (NLQA) has gained more popularity in recent years. In this paper QUASE (QUestion Answering System for Education) question answering system for answering natural language questions has been proposed which help to find answer for any given question in a closed domain containing finite set of documents. The QA system mainly focuses on factoid questions. QUASE has used Question Taxonomy for Question Classification. Several Natural Language Processing techniques like Part of Speech (POS) tagging, Lemmatization, Sentence Tokenization have been applied for document processing to make search better and faster. DBPedia ontology has been used to validate the candidate answers. By application of this system the learners can gain knowledge on their own by getting precise answers to their questions asked in natural language instead of getting back merely a list of documents. The precision, recall and F measure metrics have been taken into account to evaluate the performance of answer type evaluation. The metric Mean Reciprocal Rank has been considered to evaluate the performance of QA system. Our experiment has shown significant improvement in classifying the questions in to correct answer types over other methods with approximately 91% accuracy and also providing better performance as a QA system in closed domain search.
A novel approach for improving data locality of MapReduce applications in cloud environment through intelligent data placement

In this world of big data, hosting storage and analytics as cloud service is extremely relevant. In multi-user environments, there are chances for load imbalance during data placement. MapReduce like frameworks move computation towards data. However, because of load imbalance, some nodes cannot start computation on the node on which data is stored and may be compelled to start computation on some other nodes. This results in deteriorating data locality. In this case, data have to be copied to the computing node. This data transfer increases the job completion time. This paper proposes a data placement policy for clouds in which the data and virtual machines are collocated in the same set of physical servers. The physical servers in the cloud are grouped into partitions created using the minimum spanning tree. Experimental results show that this proposal improves node utilisation and reduces execution time over default placement in the cloud environment.
MSA: a task scheduling algorithm for cloud computing

Cloud computing is an effective technology to perform huge-scale and complex computing. Achieving a minimum makespan is the prime motto of any task scheduling algorithm for a cloud computing environment. This paper aims to propose a new task scheduling algorithm named as Min-sufferage scheduling. While selecting a task for execution, the Min-sufferage picks up the task with minimum Sufferage value. The proposed method has been tested on numerous set of tasks and resources. The experimental results indicate that the proposed Min-sufferage algorithm results in less makespan as compared to Sufferage algorithm.
A Sentiment Lexicon-Based Analysis for Food and Beverage Industry Reviews. The Greek Language Paradigm

The purpose of this research is to implement a methodology to detect and quantify customers’ opinions which referred to the Food and Beverage (F&B) sector using the Greek language. Due to the large and continuously opinionative data produced by the evaluations of the customers’ reviews, the F&B companies, and/or other stakeholders face difficulties to extract all the necessary data and to proceed to further analysis. As far as the Greek market is concerned, the F&B sector is one of the most dynamic sectors. Delivery or take away food or coffee is very common, with the vast majority of consumers to order from aggregators’ platforms (online digital markets). In this study, 8,950 customers’ reviews are extracted from 690 companies selected randomly from a total of 6,795 companies covering the most popular capitals of Greece and presented in the most used common e-platform. The mining of customers’ reviews covers a month period during the year of 2018 and the evaluated functions are the quality of food, the customer service, the image of the company, the pricing, and the quantity of food. As it appears, the sentiment analysis in an aspect-level using the lexicon-based technique should approach methodologically the problem by identifying not only the relevant information but also the particular expressions and phrases the evaluators use over the Internet. The extracted keywords and phrases from the customers’ reviews are used to form the corresponding dictionaries of the functions and to proceed in the sentiment classification. The method is tested in an annotated dataset of 2,000 customers’ reviews and, overall, the findings are expected to contribute towards the design and implementation issues of a sentiment lexicon particularly devoted to the Greek F&B industry.
Recommendations to Ensure the Circuit's Safety in the In-Circuit Reengineering of the Printed Circuit Configuration in the Information Security Management System of the Radio-Electronic Industry Companies

The conceptual technology of in-circuit circuit analysis of the configuration of the printed circuit of a radio-electronic device is reviewed including in-circuit measurements of the device radio-technical characteristics. It is shown that the in-circuit analysis does not involve the dismantling of the printed circuit board or the breaking of the electrical circuits connections in the basic circuit arrangement of functional modules in the printed circuit structure. The in-circuit analysis allows for the selective formation of a list of elements in the device without the risk of electrical damage to radio elements of the structure. Major design and technological recommendations are proposed and formulated for ensuring circuit safety in the in-circuit reengineering of the printed circuit configuration in the information management safety system of radio-electronic industry companies.
Identifying the Faces from Poor Quality Image / Video

Face biometric is becoming more popular because of its wide range of applications in authorizing the person either from an image or from the video sequence. The bottleneck in face recognition is Pose angle variation, varying light condition, Partial Occlusion, Blur in the image or Noise. The proposed method first removes the noise from the image using Adaptive Median Filter (AMF) then Discrete Cosine Transform(DCT) is applied to normalize the illumination problem. The algorithm is further used to remove the motion blur using Lucy Richardson’s method by calculating the Point Spread Function (PSF). The Pose variation problem is then addressed with Global Linear Regression(GLR). Then the Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA) are applied to the normalized image to get the feature vector. This combined feature score is used to recognize the image using K-Nearest Neighbor (K-NN). The result shows a maximum accuracy of 92% and 87.5% with Pose angle variation between (0°, 22°) and (22°, 40°) respectively. The pose variation greater than this shows an average accuracy of 77.5%. The result also shows a maximum computation speed of 0.018 Seconds.
Preregistration in experimental linguistics: Applications, challenges, and limitations

Our current publication system neither incentivizes publishing null results nor direct replication attempts, which biases the scientific record toward novel findings that appear to support presented hypotheses (referred to as “publication bias”). Moreover, flexibility in data collection, measurement, and analysis (referred to as “researcher degrees of freedom”) can lead to overconfident beliefs in the robustness of a statistical relationship. One way to systematically decrease publication bias and researcher degrees of freedom is preregistration. A preregistration is a time-stamped document that specifies how data is to be collected, measured, and analyzed prior to data collection. While preregistration is a powerful tool to reduce bias, it comes with certain challenges and limitations which have to be evaluated for each scientific discipline individually. This paper discusses applications, challenges and limitations of preregistration for experimental linguistic research.
An improved key management scheme in cloud storage

Nowadays, cloud services are used by numerous people all around the globe. One of its major applications is in the field of cloud storage. Users can store data in cloud without the need to have hardware resources for their storage. They will just have to pay for the amount of resources they use. For storage applications, usually the user provides the cloud, with the data to be stored. Cloud encrypts this data and returns a key to the user. So the user needs to store only this key for decryption. Storage of this key is a matter of concern. If the key is lost, then the probability of data loss is very high. In order to avoid this, a large number of key management techniques have been proposed. This paper proposes a key management scheme that regenerates the key using the attributes of the user in case it is lost.
Software Architecture

There is usually one member of the development team who has the title “software architect,” and there may be others on the team who, while untitled, function in that capacity. You may or may not be the titled, or functioning, architect of your team, but you are certainly the architect of the code you are assigned to develop. Depending on your position on the team, some of the things discussed in this chapter may be beyond your direct control or sphere of influence. Nevertheless, even if you are only responsible for a small module in your project, you can still approach your module as something you are going to architect as opposed to something you’re just going to hack together. Consequently, when I talk about software architects, I mean you.