Reviewerly/exHarmony huggingface dataset:
DLoader: Migration of Data from SQL to NoSQL Databases

Data is increasing exponentially in the modern world which requires more proficiency from the available technologies of data storage and data processing. This continuous growth in the amount of structured, semi-structured, and unstructured data is called as big data. The storage and processing of big data through traditional relational database systems are not possible due to increased complexity and volume. Due to improved expertise of big data solutions in handling data, such as NoSQL caused the developers in the previous decade to start preferring big data databases, such as Apache Cassandra, MongoDB, and NoSQL. NoSQL is a modern database technology designed for fast read and write operations and provides horizontal scalability to store large amount of voluminous data. Large organizations face various challenges to shift their relational database framework to NoSQL database framework. In this paper, we proposed an approach to migrate the data from a relational database to the NoSQL database. We have specifically done transformation for Cassandra and MongoDB from MySQL database. The experiments show that the proposed approach successfully transforms the relational database to a big data database, and the performance analysis of such transformed databases shows that Cassandra database requires less storage space and offers a better performance.
On the Number of 0-Tandems in Simple nD Digital 0-Connected Curves

A 0-tandem is a configuration of two voxels (n-cells) sharing exactly one vertex. We propose a formula connecting the number of 0-tandems in a simple nD digital open or closed 0-connected curve $$\gamma $$ with the number of cells in $$\gamma $$ . Our formula generalizes the formula by Brimkov et al. (2006) from closed to open curves, and the formula by Maimone and Nordo (2015) from open curves in 3D to open or closed curves in nD. We also propose an alternative formula valid for 3D curves.
Classification of Indoor–Outdoor Scene Using Deep Learning Techniques

Scene classification is a process in which a computer’s visualizations of a scene are mapped to segments. Then, the machine applies deep learning to do the task. Indoor scene classification is more challenging than outside scene classification due to unpredictability. Over the last several years, numerous approaches for indoor scene classification have been created, and each of them is faced with a unique set of difficulties. Accuracy is the greatest issue with all of them. Since DL methods, in particular, CNNs, can automatically filter features without negatively impacting overall performance, they have become a practical solution for scene classification. CNN’s are a long-term technique used to classify images. Large-scale training dataset is needed to train for CNNs. Additionally, developing an entirely new CNN architecture is complicated from the bottom up. In this case, transfer learning, which provides desirable outcomes with small datasets, is the best approach. This work introduces a novel method for transferring images to the class using the CNN model and later also the VGG-19 pretrained model. The VGG-19 network is extraordinarily deep and was trained on a large number of different pictures including complex classification problems. Independently trained models are used to show the values through indoor and outdoor training programs. The experiment has been done on two different datasets like SUN397 and Places365 using both algorithms CNN and VGG-19. The comparison has been made of these algorithms into with existing AlexNet model and it is found that the VGG-19 has outperformed among all.
Automated Detection of Type 2 Diabetes with Imbalanced and Machine Learning Methods

Diabetes mellitus is an abominable disease, if left untreated, may turn fatal. This paper presents rigorous experimentation to ascertain a machine learning model that can implement an accurate diagnosis of diabetes mellitus. While many research efforts have been publicized till date to detect diabetes, none of them addresses the class imbalance problem. This paper carries out the experiments on Pima Indian Diabetes in three categories a. experiments with traditional machine learning algorithms, b. experiments with ensemble machine learning methods, and c. experiments with machine learning algorithms after imbalanced data pre-processing techniques. The performance of the solutions has been evaluated using F1-score, Precision, Recall, Area Under Precision-Recall curve, Area Under Receiver Operating Characteristic curve, and Classification Accuracy. The experimental results show improved performance with the proposed approach as compared to the state-of-the-art methods.
A Literature-Based Perspective on Human-Centered Design and Evaluation of Interfaces for Virtual Reality in Robotics

Over the past decade, the use of virtual reality applications is increasing in the field of robotics, which is set to increase even further with the advent of “Metaverse”. Metaverse is considered as an “embodied internet” which will seamlessly integrate machines, robots, and computer applications including those in virtual and augmented reality. In this work, we review the current state of research and discuss the underlying problems with the current design and evaluation methods for the state-of-the-art virtual reality interfaces employed in the field of robotics. To overcome these issues, we present our perspective on the methods for design and evaluation of such interfaces. Our perspective takes into account human-centered design methods along with the functional measures of evaluating the performance of the interfaces. This interdisciplinary perspective emphasizes rigorously designed empirical experiments that require close collaborations among engineers, designers, human-computer interaction researchers and cognitive scientists.
Operations on Generating Functions

The first chapter explored several simple sequences. As a sequence is indexed by natural numbers, the best way to express an arbitrary term of a sequence seems to be a closed form as a function of the index (n).
Rectangularization of Digital Objects and Its Relation with Straight Skeletons

The rectangular partitioning of a digital object, A (without holes) is presented here. The partitioning is obtained in such a way that the set of connected output rectangles are related to the straight skeleton of the corresponding digital object. The given digital object, A is imposed on background grid of size, g (say) and its inner isothetic cover, P is obtained which is the maximum area orthogonal polygon inside the digital object. The combinatorial rules are formulated to apply those on P to partition it into a set of rectangles such that it is related to the straight skeleton of P. The partitioning algorithm discussed here runs in $$O(n/g \log n/g)$$ where n being the number of pixels on the periphery of digital object and g being the grid size. The experimental result shows the efficiency of the algorithm.
DanVeer: A Secure Resource Funding Mobile Application

In today’s environment, a lot of emphasis is given on the global adoption of standard practices for mobile application development. In the current research work, the standard practices for mobile application development are reviewed and a new app “DanVeer” has been proposed which is a crowdfunding application. It is a native android application designed developed in Java and implements concepts of blockchain. The paper focuses on the various software engineering aspects of mobile application development such as architecture, testing and debugging of apps, app review and analysis, applications beyond smartphones and tablets, maintenance of apps, app security and privacy and similar others.
Mitigating Ransomware Victimisation Through the Sharing Economy

This chapter examines the role of the ‘No More Ransom' platform on scarce resource sharing between the public and private sectors to mitigate ransomware victimisation. The chapter investigates the effectiveness of the platform with the most prominent sharing economy variables; low barrier accessibility, transaction cost, and trust-building. The chapter employs each sharing economy variable on three beneficiaries of the platform victims, law enforcement agencies (LEAs) and private companies. Based on Europol documents, semi-structured elite interviews and online expert webinars, the chapter's central argument is that the ‘No More Ransom' platform provides a low barrier to entry for victims, LEAs, and private companies to access the platform. It also reduces the transaction cost for all parties sharing decryption tools. However, for more efficient resource sharing, the addition of robust trust-building mechanisms in the platform for the private sector will reduce the possible trust issues between LEAs and the private sector and motivate and encourage more private sector participation. Increasing private sector participation, as a result, will reduce the transaction cost of obtaining decryption tools by victims.
Neuroevolution-Based Earthquake Intensity Classification for Onsite Earthquake Early Warning

Earthquake warning systems are adopted as the last resort for providing automated actions preventing secondary hazards due to earthquakes. However, existing methodologies do not provide site-specific warnings related to the intensity of the earthquake at the warning site. Moreover, the use of evolutionary techniques is also not explored for earthquake early warning (EEW). In this article, a warning methodology for onsite alert during a strong earthquake (Modified Mercalli Intensity > V) with potential for damage is proposed. Neuroevolutionary technique is applied on a balanced dataset to enhance precision and recall from 64.09% and 46.68% to 95.03% and 94.47%, respectively, after the proposed dataset balancing, which provided 90.02% warning accuracy.
A Myhill-Nerode Theorem for Finite State Matrix Automata and Finite Matrix Languages

We propose a deterministic version of finite state matrix automaton (DFSMA) which recognizes finite matrix languages (FML). Our main result is a generalization of the classical Myhill-Nerode theorem for DFSMA. Our generalization requires the use of two relations to capture the additional structure of DFSMA. Vertical equivalence $$\equiv _v$$ captures that words sharing the same vertical location, horizontal equivalence $$\equiv _h$$ captures that words sharing the same horizontal location. A finite matrix language is defined to be regular if relations $$\equiv _v$$ and $$\equiv _h$$ exist that satisfy certain conditions, in particular, they have finite index. We show that the language associated to a DFSMA is regular, and we construct, for each finite matrix language, a DFSMA that accepts this language. Our result provides a foundation for learning algorithms for DFSMA.
Secure Management of Digital Academic Certificates Using Blockchain Technology

The education sector is striving/needed to bring innovation in its different activities and the blockchain technology (BT), which is proposed for Bitcoin cryptocurrency, has promising property in securely decentralizing many applications including the ledger for banking systems. In this paper, we employ blockchain in academics for secure storage and easy retrieval/online verification of the hard-earned certificates to be used in diverse areas namely jobs, higher educations, forgery-detection, etc. Note that the academic certificates are indispensable for everyone’s lives, and it is the time to keep such important property in non-destructible manner but with easy accessibility to all concerned. The basic idea is that an educational institution while publishing the results of examinations, in addition to the distribution of certificates/marksheets to the students must incorporate the same in blockchain as smart certificates and make them available in peer-to-peer (P2P) network. This avoids the maintenance of registers for results kept by colleges, universities, education sectors, etc.; however, it supports for proofs of degrees claimed by students and online quick verification for any issue including fake certificate submission. An institution based on the departments, disciplines and various programs run creates multiple blockchains corresponding to the registers maintained, though the first block called the genesis block contained institute’s identity, would be the common for all. For conceptualization, a prototype design for management of blockchain-based smart certificates with various uses is presented.
An Expert System for the Detection and Mitigation of Social Engineering Attacks (Sea) Using Machine Learning Algorithm

Social engineering is a mechanism of convincing an Internet user to disclose their private information. It is a technique of influencing people, so that they pretend to share their private information. Using social engineering attack (SEA) individual’s or the systems are targeted by the criminals, trying to access the password and bank information’s or secretly install malicious software, this also gain access to your computer. The attackers not only capture an individual’s information, also access information in various business environments such as discussion with the corporate clients, interaction between the employees through a chat service. This can be identified, and its effect can be analyzed using advanced machine learning models. In the research work we have proposed and demonstrated the implementation of a model using machine learning, to analyze the malicious behavior of the attackers and for the detection of SEA. This method is applied for both off-line text and in real-time situations to identify whether a human Chatbot or off-line chat is performing a SEA or not. In the proposed method the text message is analyzed and verified for linguistic errors and the model used to detect and separate the chances of any attacks. The technique proposed is also analyzed using both factual and semi-synthetic dataset to obtain the result with better accuracy. The model developed will detect phishing links (uniform resource locator -URLs) from the webpage and classify the URL whether it is legitimate or illegitimate URL using machine learning algorithm.
An Improved Method for Cross-Domain Pedestrian Re-identification

Cross-domain pedestrian re-identification (ReID) aims to transfer a model trained on a certain dataset to another unlabeled dataset for testing, which has good robustness. There are two main ways to achieve this so far, one of which is based on generative adversarial network, which aims to let the source domain samples have the style characteristics of the target domain samples, so that reducing the difference in feature distribution between the two domains. The other one is domain adaptation, by mapping the source domain and target domain samples to the same feature space and make the distance in the space as close as possible. This article discusses the reasons for the weak generalization ability of cross-domain models from the macro- and micro-perspectives. Firstly, from the perspective of macro, according to the human parsing model CE2P and the semantic segmentation model DeepLabv3+ , a background segmentation method is proposed. When testing on large differences between two domains such as Market-1501 and MSMT17, the average accuracy of mAP has increased by 1–2%, and the first hit rate top-1 has increased by 3–5%, but it has decreased on small differences between two domains such as Market-1501 and DukeMTMC. Secondly, from the perspective of micro, we combine the advantages of SpCL and cluster contrast, and the labeled samples of the source domain are used to guide the unlabeled samples of the target domain to train. Besides, we select the hardest three samples saved in last epoch when updating, according to the experimental results, compared with the hardest sample in cluster contrast, the MAP and top-1 have better results in three datasets. Finally, we combine with the background segmentation method when testing on the MSMT17, the MAP has increased 0.6%, and top-1 has increased 0.9%. Although the development of unsupervised pedestrian re-recognition is very hot, and the result is close to supervised pedestrian re-recognition, when the model performance reaches to a commanding height, it is more difficult to be improved. However, on the basis of unsupervised, domain adaptation only needs to add the source samples, and it is easy to be improved for more than 2%, so the domain adaptive method still has a larger application prospect.
Speckle Measurement Systems

Further to the discussion of optical measurement systems with optical sensors in Chap. 7 , the present in this chapter focuses on a special interference effect, the so-called speckle interferences (Bloise in Speckle photography and speckle interferometry and their applications to mechanic solid problems. Research Signpost, 2008 [1]; Goodman in Speckle phenomena in optics. Roberts and Company Publishers, 2007 [2]; Jones and Wykes in Holographic and speckle interferometry. Cambridge University Press, 1989 [3]; Donges and Noll in Lasermesstechnik. Hüthig, 1993 [4]). The speckle effect occurs when coherent light is incident onto a rough surface, the surface roughness being in the order of, or larger than, the wavelength of the incident light. In speckle interferometry, usually multiple beams are interfering with each other. Thus, in Sect. 8.1 of the present chapter, we discuss multiple-beam interference in detail. Then, in Sect. 8.2, the properties of speckle image detection will be presented, and in general, speckle interferometry principle will be explained. Finally, in Sect. 8.3, different speckle interferometer setups will be illustrated.
Trustworthy Communications

The sources used to construct the Research Genealogy are analyzed by type and by internal structure. Examples of trustworthy sources are provided for each source type.
Distributed Research Networks

Successful medical researchMedical research requires a fairFair and meticulous peer reviewPeer review processProcess, safe and secure dataData storageStorage, and verifiable statistical analysisStatistical analysis. Initially, the medical researchMedical research systemSystem utilized a centralized systemSystem to disseminate informationInformation. A centralized systemSystem is advantageous because of its ability for speedy disseminationDissemination of informationInformation; however, it is vulnerable to the influence of possible bias and offers a clear target for hackers. With technologyTechnology advancementsAdvancement, the centralized systemSystem morphed into a decentralizedDecentralized model that encourages improved collaborationCollaboration but can either foster innovationInnovation or limit the diversity of ideas depending on the circumstances. More recently, blockchainBlockchain technologyTechnology has made the advancementAdvancement to a distributed networkNetwork systemSystem possible. A distributed networkNetwork is less vulnerable to hackingHacking or corruption but slows down publicationPublication speed. This chapterChapter will review the advantages and disadvantages of the three major types of networksNetwork (centralized, decentralizedDecentralized, and distributed), as well as propose a humanHuman-computerComputer hybridHybrid researchResearch networkNetwork that could provide a solution for improvingImproving the quality and circulationCirculation of researchResearch. Such an approach would address the need for meticulous peer reviewPeer review by offering greater peer reviewPeer review rigorRigor with a larger pool of peer reviewers available. Additionally, the implementation of such a networkNetwork through a blockchain inscription method would allowAllow for secure, long term data storageStorage.
Largest Area Parallelogram Inside a Digital Object in a Triangular Grid

A combinatorial algorithm to construct Largest Area Parallelogram (LAPT) inside a digital object lying on a Triangular grid is proposed in this work. An inner triangular cover (ITC) is first constructed, where the sides of ITC lies on the grid line and within the object. After the ITC is constructed, the proposed algorithm maintain few lists and a set of rules to find the LAPT. It is observed that the algorithm runs in $$O(k\cdot \frac{n}{g}\lg \frac{n}{g})$$ time where n number pixel on the boundary of the digital object, g is grid size, and k is the number of convexities.
Computational Model of the Mind: How to Think About Complex Systems in Nature

The collaborationCollaboration between the areas of computationComputation and the biological sciencesScience in the last two decades has changed in essential ways the scope and methodsMethod of researchResearch in their shared field. In this chapter, we describe two fundamental principles: computational equivalency and the suboptimal paradigm of evolution. These principles permeate current researchResearch in computationComputation and biologyBiology. We illustrate these principles with three examples: animalAnimal behaviorBehavior, plant behavior, and human interactionInteraction. These examples are described formally using computational languagesLanguage. These descriptions are designed to provide the reader with a concrete framework where these and similar ideas could be studied, tested, and developed. The computational descriptions of these systems are simple and easily accessible to readers. This accessibility makes appropriate the introduction of these ideas in the traditional school curriculum. Our current studentsStudent are the future custodians of our society. They need novel ideas to respond to the novel challenges of modern society.
Decision-Making in Integrated Contexts: Epistemological, Methodological and Cognitive Choices—Towards New Requirements in Psychological Research?

This chapter examines the epistemological and methodological conditions of decisionDecision-making in cognitive psychologyPsychology and social psychologyPsychology. Willing to apply an approachApproach that guarantees the objectivity and the validity of the choicesChoice, a very large number of previous studies used the laboratory by applying experimental paradigms, which led to formal (in economics) or cognitive (in medicineMedicine) modelsModel. These modelsModel comprise a limited number of always-linear variables which were supposed to represent real situations accurately. More recently, decisionDecision-making situations of a social natureNature (forest fires, large-scale maritime pollution, managing global warming) led to a paradigm shift by immediately considering the situations to be treated as holistic and complex. DecisionDecision-making needs to processProcess unified dynamic systemsSystems, dynamic. Nowadays, while some disciplines use algorithms, the vast majority of the situations treated in social sciencesScience, social use cognitive data and globalism as tools to reduce uncertaintyUncertainty. In such an approachApproach, the creation of contexts or integrated systemsSystems, integrated turns out to be particularly heuristic, as, for instance, the “crossing” of archaeologyArchaeology and cognitive psychologyPsychology, which enriches each of the disciplines mutually while helping to design a more unified scienceScience.