Reviewerly/exHarmony huggingface dataset:
Implications of the blockchain technology adoption by additive symbiotic networks

A vibrant debate has been initiated around the potential adoption of blockchain technology for enhancing the development of industrial symbiosis networks, particularly for promoting the creation of additive symbiotic networks. Despite the potential benefits of trust creation and elimination of intermediary entities, adopting such innovative technologies promises to disrupt the current supply chains of those symbiotic networks. The literature on these topics is still beginning; thus, the present research intends to contribute. A framework for understanding the implications of adopting the blockchain technology in the supply chain structure (specifically, in the dependency dimension) of an additive symbiotic network was developed, considering a network theory lens. The case study method was deemed to be suitable for carrying out this research. A case study related to an additive symbiotic network is described in detail, with the development of two scenarios: scenario I “as-is” for the current state of the network and scenario II “to-be” considering the adoption of the blockchain technology. Results show that adopting blockchain technology impacts the supply chain structure of additive symbiotic networks. More specifically, there are implications for the power distribution among the network’s stakeholders.
A novel approach to detect fraud in Ethereum transactions using stacking

Abstract Ever since Ether is launched as a digital currency, its rise has been rapid. It is currently the second most valuable digital currency in the world. There are more than 1 million transactions happening on the Ethereum network every day, and this number is expected to continue to increase. Due to the increasing number of transactions, fraudulent transactions have also increased, which has resulted in a large amount of money being lost and has also destroyed the livelihoods of many individuals. Due to their similarity to valid transactions, it is extremely difficult to distinguish between them. Additionally, Ethereum's pseudo‐anonymity adds to the difficulty of identifying the parties involved. Since there are millions of transactions every day, it would be difficult to manually verify each one. Therefore, a mechanism for validating these transactions is needed. In this context, this paper proposes a novel approach to detecting fraudulent accounts associated with these transactions by implementing machine learning algorithms among the given set of transactions. We propose a framework for creating a stacking classifier by combining several standalone classification algorithms and creating a meta‐learner based on the output of each base algorithm. The algorithms include Logistic Regression, Naive Bayes, Decision Trees, Random Forests, AdaBoosts, KNNs, SVMs, and Gradient Boosts. As a result of combining these algorithms, a powerful classifier with the ability to detect fraudulent transactions. A variety of machine learning models were trained and evaluated on the test set using various metrics. Based on the results of the individual algorithm the Random Forest algorithm achieved the highest accuracy of 95.47%, followed by Gradient Boosting at 94.61% which is an ensemble algorithm using the boosting technique. The Stacking classifier that combines Multinomial Naive Bayes and Random Forest as the base learners and logistic regression as the Meta learner achieved the highest accuracy of 97.18% with an F1 score of 97.02%. Based on the results of all the stacking models developed, it is concluded that algorithms tend to perform better when combined properly. When compared to the other approaches, the proposed approach has outperformed the others, making it feasible in the real world to detect fraudulent transactions.
An imbalance-aware nuclei segmentation methodology for H&amp;E stained histopathology images

A key step in computational pathology is to automate the laborious process of manual nuclei segmentation in Hematoxylin and Eosin (H&E) stained whole slide images (WSIs). Despite lots of efforts put forward by the researchers to develop automated nuclei segmentation methodologies in the literature, the segmentation performance is still constrained due to several challenges, including overlapping and clumped nuclei, scanners with different resolutions and nuclei with varying sizes and shapes. In this paper, we introduce an imbalance-aware nuclei segmentation methodology to deal with class imbalance problems in H&E stained histopathology images. The introduced methodology involves the following improvements: (1) the design of a preprocessing stage with a variety of resize-split, augmentation and normalization techniques, and (2) an enhanced lightweight U-Net architecture with a generalized Dice loss layer. To prove its effectiveness and efficiency, a comprehensive experimental study is carried out on a well-known benchmark, namely the MonuSeg2018 dataset. According to the results, the proposed methodology outperforms various recently introduced studies in terms of well-known evaluation metrics, such as Aggregated Jaccard Index (AJI) and Intersection of Union (IoU).
Spatio-Channel Attention Blocks for Cross-modal Crowd Counting

Crowd counting research has made significant advancements in real-world applications, but it remains a formidable challenge in cross-modal settings. Most existing methods rely solely on the optical features of RGB images, ignoring the feasibility of other modalities such as thermal and depth images. The inherently significant differences between the different modalities and the diversity of design choices for model architectures make cross-modal crowd counting more challenging. In this paper, we propose Cross-modal Spatio-Channel Attention (CSCA) blocks, which can be easily integrated into any modality-specific architecture. The CSCA blocks first spatially capture global functional correlations among multi-modality with less overhead through spatial-wise cross-modal attention. Cross-modal features with spatial attention are subsequently refined through adaptive channel-wise feature aggregation. In our experiments, the proposed block consistently shows significant performance improvement across various backbone networks, resulting in state-of-the-art results in RGB-T and RGB-D crowd counting.
The control of tax corruption: evidence from nonfungible token market in China

Purpose In 2021, nonfungible token (NFT) has emerged and grown as a new digital asset and became a carrier for cryptocurrency holders in China. NFT opens the door of the digital world for creators’ rights and the realization of economic interests. However, potential problems such as money laundering, terrorist financing and tax avoidance risks have increased in China due to the lack of regulations. As tax control is an important tool used by the government to adjust the economy and market, this study aims to investigate the future market capitalization of NFT and provide value orientations to control the NFT market in China with a tax control approach based on the positive experience of other countries. Design/methodology/approach In this study, least squares and expert estimation are applied to predict the future market capitalization based on the global market, which can provide an understanding of the current NFT market and the significance of its tax control. In addition, the tax control and interpretation of Chinese taxation institutions and structures are also explored. Findings Results include the probable tax structure or policy that national institutions can carry out over different transactions. Conclusions show that introducing tax control to regulate and monitor the rise of state revenue and decline of illegal financing activities. Establishing tax control in the Chinese NFT market can provide a centralized guarantee to ensure the safety and legality of transactions and enable further progress. Originality/value This study puts forward new ideas on the future development of nonprofitable tokens based on blockchain technology from the perspective of taxation in China.
Improved ellipse-fitting phase demodulation technique to suppress the effect of light source intensity noise in interferometric system

An improved ellipse-fitting algorithm phase demodulation (EFAPD) technique is proposed to reduce the influence of light source intensity noise on a system. In the original EFAPD, the sum of the intensities of coherent light (ICLS) is an important part of the interference signal noise, which makes the demodulation results suffer. The improved EFAPD corrects the ICLS and fringe contrast quantity of the interference signal by an ellipse-fitting algorithm, and then calculates the ICLS based on the structure of pull-cone 3 × 3 coupler, so as to remove it in the algorithm. Experimental results show that the noise of the improved EFAPD system is significantly reduced compared with that of the original EFAPD, with a maximum reduction of 35.57 dB. The improved EFAPD makes up for the deficiency of the original EFAPD in suppressing light source intensity noise, and promotes the application and popularization of EFAPD.
Resource Man and the Smart Wife

research-article Share on Resource Man and the Smart Wife: Implications for Sustainability in the Home Author: Yolande Strengers Monash University Monash UniversityView Profile Authors Info & Claims InteractionsVolume 30Issue 2March - April 2023pp 36–40https://doi.org/10.1145/3582566Published:28 February 2023Publication History 0citation308DownloadsMetricsTotal Citations0Total Downloads308Last 12 Months308Last 6 weeks25 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
Automated design of freeform imaging systems for automotive heads-up display applications

The freeform imaging system is playing a significant role in developing an optical system for the automotive heads-up display (HUD), which is a typical application of augmented reality (AR) technology. There exists a strong necessity to develop automated design algorithms for automotive HUDs due to its high complexity of multi-configuration caused by movable eyeballs as well as various drivers' heights, correcting additional aberrations introduced by the windshield, variable structure constraints originated from automobile types, which, however, is lacking in current research community. In this paper, we propose an automated design method for the automotive AR-HUD optical systems with two freeform surfaces as well as an arbitrary type of windshield. With optical specifications of sagittal and tangential focal lengths, and required structure constraints, our given design method can generate initial structures with different optical structures with high image quality automatically for adjusting the mechanical constructions of different types of cars. And then the final system can be realized by our proposed iterative optimization algorithms with superior performances due to the extraordinary starting point. We first present the design of a common two-mirror HUD system with longitudinal and lateral structures with high optical performances. Moreover, several typical double mirror off-axis layouts for HUDs were analyzed from the aspects of imaging performances and volumes. The most suitable layout scheme for a future two-mirror HUD is selected. The optical performance of all the proposed AR-HUD designs for an eye-box of 130 mm × 50 mm and a field of view of 13° × 5° is superior, demonstrating the feasibility and superiority of the proposed design framework. The flexibility of the proposed work for generating different optical configurations can largely reduce the efforts for the HUD design of different automotive types.
Dynamic Replication Policy on HDFS Based on Machine Learning Clustering

Data growth in recent years has been swift, leading to the emergence of big data science. Distributed File Systems (DFS) are commonly used to handle big data, like Google File System (GFS), Hadoop Distributed File System (HDFS), and others. The DFS should provide the availability of data and reliability of the system in case of failure. The DFS replicates the files in different locations to provide availability and reliability. These replications consume storage space and other resources. The importance of these files differs depending on how frequently they are used in the system. So some of these files do not deserve to replicate many times because it is unimportant in the system. This paper introduces a Dynamic Replication Policy using Machine Learning Clustering (DRPMLC) on HDFS, which uses Machine Learning to cluster the files into different groups and apply other replication policies to each group to reduce the storage consumption, improve the read and write operations time and keep the availability and reliability of HDFS as a High-Performance Distributed Computing (HPDC).
Improvement Performance of the Random Forest Method on Unbalanced Diabetes Data Classification Using Smote-Tomek Link

Most of the health data contained unbalanced data that affected the performance of the classification method. Unbalanced data causes the classification method to classify the majority data more and ignore the minority class. One of the health data that has unbalanced data is Pima Indian Diabetes. Diabetes is a deadly disease caused by the body's inability to produce enough insulin. Complications of diabetes can cause heart attacks and strokes. Early diagnosis of diabetes is needed to minimize the occurrence of more severe complications. In the diabetes dataset used, there is an imbalanced data between positive and negative diabetes classes. Diabetes negative class data (500 data) is more than diabetes positive class (268), so it can affect the performance of the classification method. Therefore, this study aims to apply the Smote-Tomeklink and Random Forest methods in the classification of diabetes. The research methodology used is the collection of diabetes data obtained from Kaggle, as many as 768 data with eight input attributes and 1 output attribute as a class, pre-processing data is used to balance the dataset with Smote-Tomeklink, classification using the random forest method, and performance evaluation based on accuracy, sensitivity, precision, and F1-score. Based on the tests conducted by dividing data using 10-fold cross-validation, the Random Forest algorithm with Smote-TomekLink gets the highest accuracy, sensitivity, precision, and F1-score compared to Random Forest with Smote. The Random Forest algorithm with Smote-Tomeklink has 86.4% accuracy, 88.2% sensitivity, 82.3% precision, and 85.1% F1-score. Thus, using Smote-Tomeklink can improve the performance of the random forest method based on accuracy, sensitivity, precision, and F1-score.
A Review of Art and Real World Applications of Intelligent Perception Systems

Sensory data and AI/ML techniques are crucial to several robotics applications, which is why perception in robots is a hot topic. Some of these applications include: object recognition, scene understanding, environment representation, activity identification, semantic location classification, object modeling, and pedestrian/human detection. Robotic perception, as used in this article, is the collection of machine learning (ML) techniques and methods that allow robots to process sensory data and form conclusions and perform actions accordingly. It is clear that recent development in the field of ML, mostly deep learning methodologies, have led to improvements in robotic perception systems, which in turn make it possible to realize applications and activities that were previously unimaginable. These recent advancements in complex robotic tasks, human-robot interaction, decision-making, and intelligent thought are in part due to the fast development and widespread usage of ML algorithms. This article provides a survey of real-world and state of the art applications of intelligent perception systems in robots.
Educational services export: competition in new geopolitical conditions

Geopolitical tensions are on the rise. The competitive struggle in the field of educational services export does not stand aside either. In a fierce confrontation, a rich arsenal of modern technologies is used. Using the information onslaught, the ideology, the mentality of the younger generation, its worldview positions become the goal of this struggle. Russia has always been a competitive country in the export of educational services. However, in today’s conditions of fierce competition, it is necessary to defend one’s positions, find advantages in the Russian educational system, relying both on accumulated experience and using innovative approaches. In order to be competitive in the modern world community, during the transition to a new technological order, it is necessary to offer innovative products in the field of education, expand the range of the subjects of the educational environment on various regional platforms.
Self-Spin Enabled Docking and Detaching of a UAV-UGV System for Aerial-Terrestrial Amphibious and Independent Locomotion

As an amphibious system, aerial-terrestrial vehicles have demonstrated remarkable environmental adaptability. In order to expand their synergistic advantages, in this letter, an Aerial-Terrestrial vehicle consisting of a pair of unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) is proposed, which is capable of performing UAV-UGV autonomous docking and detaching. Under the docking modality, the proposed vehicle can perform amphibious locomotion to adapt to rugged terrains. Under the detaching mode, the distributed UAV and UGV subsystems gain enhanced accessibility to the confined space and can share both air and land information, which is promising to accomplish collaborative missions. In order to achieve the docking and detaching of the two subsystems, a screw-like binding mechanism is proposed. To implement the binding mechanism, it is separated into two pieces, i.e., ‘screw' and ‘nut,’ which are duel-functioned as the landing gear of the UAV or the UGV roof, respectively. To improve the success rate of docking, a mathematical model of the binding mechanism is established to calculate the ideal curve of the guiding surface, which provides important cues for the mechanism fabrication. With such a binding mechanism, UAV or UGV can execute docking or detachment via screwing-like self-spinning motion. The spinning torque of the UAV and UGV are calibrated by torque measurement tests. As a result, each independent vehicle can generate sufficient power to lock or detach via its original actuators, thus omitting the extra actuators. The full stage control, including air-ground docking-detaching and amphibious locomotion, are validated experimentally.
Photonic reservoir computing using a self-injection locked semiconductor laser under narrowband optical feedback

Photonic time-delay reservoir computing (TDRC) using a self-injection locked semiconductor laser under optical feedback from a narrowband apodized fiber Bragg grating (AFBG) is proposed and numerically demonstrated. The narrowband AFBG suppresses the laser’s relaxation oscillation and provides self-injection locking in both the weak and strong feedback regimes. By contrast, conventional optical feedback provides locking only in the weak feedback regime. The TDRC based on self-injection locking is first evaluated by the computational ability and memory capacity, then benchmarked by the time series prediction and channel equalization. Good computing performances can be achieved using both the weak and strong feedback regimes. Interestingly, the strong feedback regime broadens the usable feedback strength range and improves robustness to feedback phase variations in the benchmark tests.
Algorithm of quantum engineering of large-amplitude high-fidelity Schrödinger cat states

We present an algorithm of quantum engineering of large-amplitude [Formula: see text] high-fidelity [Formula: see text] even/odd Schrödinger cat states (SCSs) using a single mode squeezed vacuum (SMSV) state as resource. Set of [Formula: see text] beam splitters (BSs) with arbitrary transmittance and reflectance coefficients sequentially following each other acts as a hub that redirects a multiphoton state into the measuring modes simultaneously measured by photon number resolving (PNR) detectors. We show that the multiphoton state splitting guarantees significant increase of the success probability of the SCSs generator compared to its implementation in a single PNR detector version and imposes less requirements on ideal PNR detectors. We prove that the fidelity of the output SCSs and its success probability are in conflict with each other (which can be quantified) in a scheme with ineffective PNR detectors, especially when subtracting large (say, [Formula: see text]) number of photons, i.e., increasing the fidelity to perfect values leads to a sharp decrease in the success probability. In general, the strategy of subtracting up to [Formula: see text] photons from initial SMSV in setup with two BSs is acceptable for achieving sufficiently high values of the fidelity and success probability at the output of the generator of the SCSs of amplitude [Formula: see text] with two inefficient PNR detectors.
Mediation effect of organisational leadership on internal business environment and organisational commitment: insights from Oman

Many studies have been conducted in this region applying specific theoretical frameworks in the real sector. However, fewer studies showed that leadership can be a positive mediator towards a positive internal business environment and organisational commitment. Hence, this study takes a different approach in the theory application. In contrast, the study applies the mediation role of leadership (transactional and transformational) between internal business environment and organisational commitment in the public sector. The study has conducted a self-administered survey to collect the data. Out of 412 participants in total, 267 was an adequate sample size for this study. The case study showed that transformational and transactional leadership, as mediators over the internal environment, significantly affect organisational commitment in a public service organisation in the Sultanate of Oman. Moreover, exciting results are discussed while concluding remarks are provided throughout the conclusion, including theoretical and practical implications, limitations, and future works of the study.
THE THEORY OF COGNITIVE MODELING LITERARY TEXT SPACE

The article presents the theory of cognitive modeling of literary text space aimed at a comprehensive cognitive interpretation of proxeme text code. A typology of text cognitive constructs is proposed. This typology includes six types such as: literary concept, cognitive-plot matrix of literary text, text cognitive links, cognitive coordinates of literary text, cognitive text space models, cognitive text temporal models. The research presents the author’s algorithm for modeling and interpreting text proxeme constructs applied to studying the cognitive model of nominative field of concept SPACE and offers the typology of literary space nominees consisting of proxemes’ types such as: (1) words with prepositions as space markers, (2) verbs of movement or location in space, (3) place names, (4) landscape units as landscape nominations. It is revealed that proxemes have the form of a word or phrases that can be single-component or multi-component nominees. According to the method of functioning proxemes are divided into personalised proxemes and non-personalised proxemes. The existence of anthropocentrically marked and anthropocentrically unmarked models of literary space is justified.