Reviewerly/exHarmony huggingface dataset:
Impact of Blockchain Technology on E-Commerce

Blockchain-based technology is expected to cause significant disruption to many business applications and processes that are part of e-commerce. Given the capabilities of blockchain and relatedinnovations, and the so-called “unreliabie systems” with unique characteristics, various businesses are required to ensure trust, reliability, and enforcement between companies and consumers (B2C). For this, multiple models and procedures have developed throughout time. In this situation, it is necessary to realign business-to-business (B2B), business-to-government (B2G), and customer-to-customer <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{C}2\mathbf{C})$</tex> ties. By eliminating illegal mediators or even centralized authority, blockchain is likely to upend the core of e-commerce. Furthermore, executing integrated access to immutable data across the supply chain can fundamentally alter how companies and customers exchange knowledge and value. A newtool is used to address the issue of product tracking in supply chain management thanks to blockchain technology. This research work aims to create a network of related methodologies and approaches for cross-border e-commerce and a blockchain-based architecture for obtaining traceable goods and transactions. Blockchain technology primarily addresses challenges like intermediaries, transparency, decentralization, data security, accuracy, and transactional freedom.
A Detailed Investigation on Potential Impact of Quantum Computing on Improving Artificial Intelligence

Quantum computing is an emerging technology (QC). Several groups and research institutions are working to make quantum computing applications a reality. Artificial Intelligence (AI) is another emerging field that is becoming more established over time. The main goal of this research is to figure out how the advancement of quantum computing technology will affect AI applications. Computational methods are used to explore the growing influence of quantum computing development for a specific AI application. This study also discusses about the impact and potential of Quantum Computing (QC) on Artificial Intelligence (AI). These can be accomplished by employing cutting-edge methods to deliver usable, economically viable, and scalable technology across a wide range of industries. On the other hand, the risk of committing errors in quantum algorithms, as well as the inability to function without being super-cooled, emerges as a major challenge. Macro-energy systems and the development of sustainable energy materials are just two areas of science and engineering that quantum computing has the potential to completely transform.
Image Classification for Optimized Prediction of Leukemia Cancer Cells using Machine Learning and Deep Learning Techniques

Leukemia is a type of cancer that has a high mortality rate that affects people of all ages but is most common in children aged 5–8 years. It is usually diagnosed using one of two methods. First method is the traditional way of manually analyzing the blood samples retrieved from the microscopic images, however there is a disadvantage in this method, when the leukemia cells and normal cells are similar to each other the diagnosis process becomes more complex. The second method uses the image classification algorithms such as Deep Learning (DL) algorithms, Convolution Neural Networks (CNNs). There is an emerging trend in Machine Learning (ML) and Deep Learning (DL) models to perform image classification. This study intends to analyze ML & DL techniques for the detection and prediction of leukemia. In addition to the existing literature, this study aims to identify different ML and DL based image classification algorithms that leverage best prediction accuracy for leukemia cancer cells.
Improvement of Graph-Based Assets using Blockchain Quality Control

Traceability through different sectors including logistics and shipping has had a positive effect from blockchain technologies. This innovative solution helps establish a decentralized and drum-resistant headline through the question of data silos between organisms and authority consolidation. Nevertheless, the use of fungible tokens should represent complicated traceability operations; a new model for the projection of digital and physical goods on a blockchain is discussed. In the present token architecture, however, there is no powerful model to restore history. Blockchain explorers, which are central trustworthy actors, are the most common means for extracting past information on tokens. Any headers of the block can only be naively verified, but it takes a lot of time. According to preliminary calculations, restoring the previous 1,030 tokens on the Ethereum main net would take about 57 minutes. Challenge of this system is to eliminate competition for new chain blocks and Transactions may be handled quicker with simultaneous node development. It can be proposed that a directed acyclic graph (DAG) token definition which allows the token history to be quickly discovered, without looking at the entire blockchain. The model connects any token state from time to time and covers concepts like fusion and breakup. The implementation and assessment suggest the major enhancements to the reliability of the transitional history analysis in the DAG-based token architecture. The outcome demonstrates that the discovery of 1,030 tokens can be done in seconds and that the method maintains long-term success even though the block chain expands.
Stratification of Eye Gaze using the Proposed Convolution Neural Network Model

Humans mostly use their eyes to perceive the outside environment. Human-computer interaction is a typical application for eye gaze monitoring. It is becoming more and more important to classify open and closed eyes for real-time applications such as the detection of driver fatigue or sleepiness to reduce the number of fatal accidents and therefore mortality rates. Additionally, it is possible to forecast some diseases based on how quickly the eyes open and close. The majority of computer vision applications working with pictures face challenges like illumination and resolutions and also must consider accuracy and execution speed in real time. Therefore, the flawless categorization of the condition of the eyes being open or closed forms the objective of the proposed system. In this research, a Convolution Neural Network model is proposed that includes four convolutional blocks consisting of 8 convolution layers, 4 max pool layers,8 Batch Normalization, and 4 dropouts. The model is tuned using 8 batch sizes and Adam optimizer on a total of 20 epochs. The proposed model has obtained the value of accuracy as 0.993 and the loss is 0.0180.
Strengthening Supply Chain Integrity with Blockchain-based Anti-Counterfeiting Measures

Fake or duplicate products can endanger the health and safety of consumers. This can have a huge impact on the reputation of the genuine manufacturers and companies, which leads to a loss of revenue. It also impacts the economic growth of original manufacturers and companies through lost revenue, slander of products, replacement costs, forcing brands to break the bank to fight counterfeit goods, and increasing the risk of conflict between business partners. In order to overcome such challenges and gain the trust of consumers, blockchain-based authenticity identification systems will be used to detect the significant impact of counterfeiting, detect duplicate products, and identify them as genuine, which guarantees product identification. In this work, QR (Quick Response) codes and barcodes offer a reliable method to reduce the sale of counterfeit products due to the significant new trends in wireless technology. The fake products are detected with the help of a camera scanner by scanning the QR code, in which the product's code is linked to the blockchain in order to store the product details and ensure that each product's unique code is represented as database blocks. If the code matches, the consumer will be notified that the application has approved the request. This strategy makes sure that shoppers won't solely rely on retailers to decide whether things are authentic or fake.
Intelligent Traffic Monitoring, Prioritizing and Controlling Model based on GPS

With the increasing global population and emerging technologies, there is a pressing need for efficient traffic management systems. Unfortunately, the traditional traffic control system, which relies on tri-colour signals is unable to prioritize emergency vehicles or control traffic effectively. Resulting in accidents, loss of life, and traffic congestion. However, recent technologies such as GPS, which provides live traffic data and emergency vehicle tracking, Artificial Intelligence (AI) and Deep Learning (DL), which can predict traffic solutions, Cloud Computing it processes the data and Internet of Things (IoI), which collects real-time data and offers promising solutions. Some of the challenges include the integration of various technologies, complexity in algorithms, scalability concerns, security and privacy. In this paper, we present a study on the role of AI, IoT and GPS in traffic management, proposing a better traffic signal management system that reduces congestion, prioritizes emergency vehicles, reduces commuting time and tracks vehicles.
Model Municipal Libraries. From the Project to the Modernization of the Library Sector in Russia

The 10th All-Russian Forum of Public Libraries “Model Libraries and Modernization Quality Standard” was held on November 18—19, 2022 at the National Library of Russia (St. Petersburg). More than 600 participants registered to attend the forum in 2022 (in offline and online format). More than 230 people attended the forum in-person. The forum considered the results of implementation of the National project “Culture” in the field of creating model municipal libraries, as well as problems that have arisen in connection with the modernization in the library sector. The author reveals in detail the content of the main events of the forum — the plenary session and three Round tables (“Modernization quality standard of public municipal library”, “Collections of model libraries: formation, study, promotion”, “Municipal libraries of new generation: exchange of experience and ideas”). The article reports on summarizing at the forum of the results of implementation of the project “The Genius of the Place” 2022 and on awarding the libraries-participants that declared themselves as a “point of talents concentration”. The speakers noted that libraries achieved particular success in working with the audience in the field of creative industries. A total 21 libraries got awards. The article lists the most interesting projects (“Send the Skill”, “ProRobot”, “Master — magic touch”, etc.). The author presents the results of the All-Russian competition “Libraries. PROmotion”. 750 applications from 80 subjects of the Russian Federation participated in the competition. The winners of the competition in 2022 are Berezovsk Centralized Library System of the Sverdlovsk region, Astrakhan Library for Youth named after B. Shakhovsky, Gubkinsky Centralized Library System of Yamalo-Nenets Autonomous Okrug, National Library of the Republic of Sakha (Yakutia) and Moshinsk Library of the Nyandom Central District Library of the Arkhangelsk Region. Within the framework of the event, the exhibition of publishing products, new information technologies, goods and services “Library Context — 2022” took place. The Forum, held since 2007, over the years of its existence has become the platform of All-Russian level, where the public discussion of the results and prospects of modernization of the library sector in Russia takes place. The author marks the significance of the All-Russian Forum of Public Libraries as the largest event in the cultural life of Russia.
Kantorovich Functors and Characteristic Logics for Behavioural Distances

Abstract Behavioural distances measure the deviation between states in quantitative systems, such as probabilistic or weighted systems. There is growing interest in generic approaches to behavioural distances. In particular, coalgebraic methods capture variations in the system type (nondeterministic, probabilistic, game-based etc.), and the notion of quantale abstracts over the actual values distances take, thus covering, e.g., two-valued equivalences, (pseudo)metrics, and probabilistic (pseudo)metrics. Coalgebraic behavioural distances have been based either on liftings of $$\textsf{Set}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Set</mml:mi> </mml:math> -functors to categories of metric spaces, or on lax extensions of $$\textsf{Set}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Set</mml:mi> </mml:math> -functors to categories of quantitative relations. Every lax extension induces a functor lifting but not every lifting comes from a lax extension. It was shown recently that every lax extension is Kantorovich, i.e. induced by a suitable choice of monotone predicate liftings, implying via a quantitative coalgebraic Hennessy-Milner theorem that behavioural distances induced by lax extensions can be characterized by quantitative modal logics. Here, we essentially show the same in the more general setting of behavioural distances induced by functor liftings. In particular, we show that every functor lifting, and indeed every functor on (quantale-valued) metric spaces, that preserves isometries is Kantorovich, so that the induced behavioural distance (on systems of suitably restricted branching degree) can be characterized by a quantitative modal logic.
An Efficient Cyclic Entailment Procedure in a Fragment of Separation Logic

Abstract An efficient entailment proof system is essential to compositional verification using separation logic. Unfortunately, existing decision procedures are either inexpressive or inefficient. For example, Smallfoot is an efficient procedure but only works with hardwired lists and trees. Other procedures that can support general inductive predicates run exponentially in time as their proof search requires back-tracking to deal with a disjunction in the consequent. This paper presents a decision procedure to derive cyclic entailment proofs for general inductive predicates in polynomial time. Our procedure is efficient and does not require back-tracking; it uses normalisation rules that help avoid the introduction of disjunction in the consequent. Moreover, our decidable fragment is sufficiently expressive: It is based on compositional predicates and can capture a wide range of data structures, including sorted and nested list segments, skip lists with fast-forward pointers, and binary search trees. We implemented the proposal in a prototype tool, called $$\mathtt {S2S_{Lin}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>S</mml:mi> <mml:mn>2</mml:mn> <mml:msub> <mml:mi>S</mml:mi> <mml:mi>Lin</mml:mi> </mml:msub> </mml:mrow> </mml:math> , and evaluated it over challenging problems from a recent separation logic competition. The experimental results confirm the efficiency of the proposed system.
Failure to Achieve Domain Invariance With Domain Generalization Algorithms: An Analysis in Medical Imaging

One prominent issue in the application of deep learning is the failure to generalize to data that lies on a different distribution to the training data. While many methods have been proposed to address this, prior work has shown that when operating under the same conditions most algorithms perform almost equally. As such, more work needs to be done to validate past and future methods before they are put into important scenarios like medical imaging. Our work analyses eight domain generalization algorithms across four important medical imaging classification datasets along with three standard natural image classification problems to discover the differences in how these methods operate in these different contexts. We assess these algorithms in terms of generalization capability, domain invariance, and representational sensitivity. Through this, we show that despite the differences between domain and content variations between natural and medical imaging there is little deviation in the operation of each method between natural images and medical images. Additionally, we show that all tested algorithms retain significant amounts of domain-specific information in their feature representations despite explicit training to remove it. Thus, revealing the failure point of all these methods is a lack of class-discriminative features extracted from out-of-distribution data. While these results show that methods that work well on natural imaging work similarly in medical imaging, no method outperforms baseline methods, highlighting the continuing gap of achieving adequate domain generalization. Similarly, the results also question the efficacy of optimizing for domain invariant representations as a method for generalizing to unseen domains.
Dynamically Optimized Human Eyes-to-Face Generation via Attribute Vocabulary

Generating face from human eyes, named eyes-to-face generation, is an interesting research topic of face synthesis, which has great potential in the field of public security. One of the main challenges in eyes-to-face generation is the unbalanced information between inputs and outputs, where the outputs are complete facial images while the inputs only contain limited information in the region of eyes. The existing methods generate faces directly from eyes without considering the possibly available facial information (e.g. facial attributes), resulting in inaccurate predictions and high uncertainty in those features less correlated with eyes (e.g. hairstyle, moustache, facial contour). To address this challenge, we propose a two-stage solution (named EA2F-GAN) to dynamically optimize eyes-to-face generation via attribute vocabulary. In addition, a dataset named TEAF is constructed based on the public datasets CelebA and LFW, containing 138,934 triples of eye image, attribute vocabulary, and face image. Sufficient experimental results show that, by incorporating additional facial attributes, our proposed approach can synthesize realistic face with high consistency to the original one, significantly overwhelming state-of-the-art methods.
Multi-Modality Mobile Datasets for Behavioral Biometrics Research: Data/Toolset paper

The ubiquity of mobile devices nowadays necessitates securing the apps and user information stored therein. However, existing one-time entry-point authentication mechanisms and enhanced security mechanisms such as Multi-Factor Authentication (MFA) are prone to a wide vector of attacks. Furthermore, MFA also introduces friction to the user experience. Therefore, what is needed is continuous authentication that once passing the entry-point authentication, will protect the mobile devices on a continuous basis by confirming the legitimate owner of the device and locking out detected impostor activities. Hence, more research is needed on the dynamic methods of mobile security such as behavioral biometrics-based continuous authentication, which is cost-effective and passive as the data utilized to authenticate users are logged from the phone's sensors. However, currently, there are not many mobile authentication datasets to perform benchmarking research. In this work, we share two novel mobile datasets (Clarkson University (CU) Mobile datasets I and II) consisting of multi-modality behavioral biometrics data from 49 and 39 users respectively (88 users in total). Each of our datasets consists of modalities such as swipes, keystrokes, acceleration, gyroscope, and pattern-tracing strokes. These modalities are collected when users are filling out a registration form in sitting both as genuine and impostor users. To exhibit the usefulness of the datasets, we have performed initial experiments on selected individual modalities from the datasets as well as the fusion of simultaneously available modalities.
A pilot study on social science graduate students' data core competency

In the field of research, teaching graduate students how to manage data on a regular basis is becoming increasingly crucial. However, there is a lack of adequate evidence in the literature to determine the necessity of Research Data Management (RDM) training from the perspectives of social science student researchers. To address this gap, a pilot study was conducted to develop a data core competency measure to gauge the perceived importance of data competency among social science graduate students and assess their data core competency. Forty graduate students in a college of education at an US research university filled out a researcher-designed data core competency survey. This pilot study found that graduate students in the social sciences do not generally appreciate RDM. Consequently, the results underscore the need to foster an environment in graduate social science programs that value RDM best practices. The study suggested that future studies build on the preliminary findings by developing standardized data core competency measures.
Multiple Robots Path Planning based on Reinforcement Learning for Object Transportation

This paper proposes reinforcement learning methods to perform an object transportation task for multiple robots. This task consists of two main subtasks, path planning and motion control task. Double deep Q-learning (DDQN) model is selected to achieve path planning for an unknown environment. To increase the capability of reinforcement learning model, semi-supervised method by A* algorithm is applied during the training process. In motion control task, reinforcement learning model is designed to control a movement of a differential wheeled mobile robot. The actions of mobile robot consisting of linear and angular velocities are computed by agent. The models for motion control task are separately trained for two different purposes. The first agent is trained to deal with the path following task and the other agent is trained to handle the point following task. The agent of the point following task is utilized to control the group of robots to move with a specific formation. Proximal policy optimization (PPO) is selected for the path following task and deep deterministic policy gradient (DDPG) is selected for the point following task. Eventually, the integration of the proposed reinforcement learning models can accomplish the object transportation task for multiple robots successfully both in simulation and experiment.
Investigation of Automatic Part-of-Speech Tagging using CRF, HMM and LSTM on Misspelled and Edited Texts

Part-of-speech tagging is the process of assigning words in a given text to appropriate parts-of speech in order to reduce the disambiguation which may arise depending on the contextual usage of the words. In this paper, the problem of word sense disambiguation in Azerbaijani language is addressed by applying part of speech tagging on two varying data corpora, misspelled, and edited (clean) text using 3 different machine learning algorithms: Hidden Markov Model, Long Short-Term Memory, and Conditional Random Fields. The comparative analysis on the outcomes of the mentioned algorithms and their accuracy scores were analysed in the paper. The misspelled dataset for the experiments is provided by Unibank from their chatbot dialogues while the clean textual data was retrieved from the books and newspapers in Azerbaijani. The experiments showed that the Bidirectional LSTM has the highest accuracy scores for both edited (98.2%) and noisy (96.2%) data corpora. Suggested models can be used in the application of algorithms focuses on part of speech tags and syntactic structure of Azerbaijani language which is an agglutinative language belonging to Turkic languages family, thus enabling the research to be further investigated in other agglutinative languages with similar grammatical structure.