Reviewerly/exHarmony huggingface dataset:
ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN TECHNOLOGY FOR SECURE DATA AND PRIVACY

Data, which is then processed in order to extract the desirable characteristics, serves as the input for a number of different AI algorithms. However, the facts that can be found on the internet are incredible and tough to authenticate. Given the complexity of the internet, it is quite challenging to validate the data for the consumers. Consequently, in this research, we suggested using SecNet as a solution. An architecture that assists in the protection of data storage, the processing of data, and the sharing of large-scale Internet settings is known as SecNet. The primary objective of this architecture is to enhance the performance of artificial intelligence algorithms across a variety of data sources in order to provide a cyberspace that is more safe and to make use of actual big data. This architecture combines and supplies the following three primary components: 1) The trading of data based on a blockchain is carried out with the ownership of the data being assured. This enables the interchange of accurate data in a wide-ranging environment and contributes to the formation of actual "big data. 2) The protection of an AI-based secure computing platform that is powered by artificial intelligence to develop more astute security standards and contribute to the establishment of a cyberspace that is more reliable. 3) The trustworthy value-sharing Security Service buy Mechanism gives participants a fantastic opportunity to gain Economic Rewards for the provision of data or services, which makes data sharing easier and ultimately leads to improved AI performance".
The Rise of Cyber Crime in Pakistan: A Threat to National Security

This research paper investigates the unprecedented and rapidly growing threat of cyber space and cyber warfare to Pakistan's national security in the context of non-traditional dangers. Utilizing a descriptive approach and qualitative data collection method, the study analyzes various secondary sources, including books, articles, and internet search engines, to understand the current state of cyber security in Pakistan. The findings reveal that Pakistan faces multiple cyber threats from nations such as India, Russia, Israel, the United States, and China, and lacks advanced information technology (IT), effective policy implementation, and adequate educational progress in IT. The paper concludes that Pakistan must prioritize the development of cyber space technology, enhance advanced technology, educate its population in advanced IT, and learn from first world nations' cyber security strategies to protect its government, financial system, military locations, and citizens' personal information from cyber threats. Proper implementation of existing laws, policies, and penalties is also crucial to mitigate hacking and other forms of cyber-crime, ensuring a more secure digital future for the nation.
Denoising with Graphics Processing Units and Deep Learning in Non-invasive Medical Imaging

Medical imaging is not only essential to the diagnostic process, but also plays a very important role in determining the course and effectiveness of treatment. In the last few decades, tremendous technological innovations have been made in the field of non-invasive medical imaging. Among them, imaging methods represented by computed tomography and magnetic resonance imaging are indispensable in current clinical medicine because they can acquire biological structures and functions in three to four dimensions with high spatial resolution non-invasively. However, the acquisition of data with high spatial resolution generally leads to a decrease in the signal-to-noise ratio. A longer acquisition time is required to improve the signal-to-noise ratio. However, for non-invasive medical image acquisition in clinical settings, a long acquisition time is impractical and results in a decrease in signal-to-noise ratio, especially in high spatial resolution images. It is thus essential to develop effective denoising techniques as post-processing and also to adapt the optimal denoising method in accordance with the user’s objectives. This review provides a brief overview of denoising techniques as post-processing for medical imaging, and introduces our work on fast and accurate denoising methods using graphics processing units and denoising with deep learning.
Exploring Word Embeddings for Text Classification: A Comparative Analysis

For language tasks like text classification and sequence labeling, word embeddings are essential for providing input characteristics in deep models. There have been many word embedding techniques put out in the past ten years, which can be broadly divided into classic and context-based embeddings. In this study, two encoders—CNN and BiLSTM—are used in a downstream network architecture to analyze both forms of embeddings in the context of text classification. Four benchmarking classification datasets with single-label and multi-label tasks and a range of average sample lengths are selected in order to evaluate the effects of word embeddings on various datasets. CNN routinely beats BiLSTM, especially on datasets that don't take document context into account, according to the evaluation results with confidence intervals. CNN is therefore advised above BiLSTM for datasets involving document categorization where context is less predictive of class membership. Concatenating numerous classic embeddings or growing their size for word embeddings doesn't greatly increase performance, while there are few instances when there are marginal gains. Contrarily, context-based embeddings like ELMo and BERT are investigated, with BERT showing better overall performance, particularly for longer document datasets. On short datasets, both context-based embeddings perform better, but on longer datasets, no significant improvement is seen.In conclusion, this study emphasizes the significance of word embeddings and their impact on downstream tasks, highlighting the advantages of BERT over ELMo, especially for lengthier documents, and CNN over BiLSTM for certain scenarios involving document classification.
Influence of augmented reality and virtual reality on real estate investment decisions: understand consumer perspective in Indian AEC industry

Purpose Augmented Reality (AR) and Virtual Reality (VR) technologies possess the potential to transform the scenario of making real estate investment decisions through the immersive experience they offer. From the literature it was observed that the research in this domain is still emergent and there is a need to identify the latent variables that influence real estate investment decisions. Therefore, by examining the effects of these technologies on investment decision-making, the purpose of the study is to provide valuable insights into how AR and VR could be applied to enhance customers' property buying experiences and assist in their decision-making process. Design/methodology/approach From an extensive review of the literature four latent variables and their measure were identified, and based on these a survey instrument was developed. The survey was distributed online and received 300 responses from the respondents including home buyers, developers, AEC professionals and real estate agents. To validate the latent variables exploratory factor analysis was used whereas to establish their criticality second-order confirmatory factor analysis was used. Findings From the results, the four latent constructs were identified based on standard factor loadings (SFL) that is Confident Value Perception (CVP, SFL = 0.70), Innovative Investment Appeal (IIA, SFL = 0.60), Trusted Property Transactions (TPT, SFL = 0.58) and Effortless Property Engagement (EPE, SFL = 0.54), that significantly influence investor decision-making and property purchase experience. Originality/value This study contributes to the literature on real estate investment decisions by providing empirical evidence on the role of AR and VR technologies. The identified key variables provided practical guidelines for developers, investors and policymakers in understanding and leveraging the potential of AR and VR technologies in the real estate industry.
Investigating the role of Product Owner in Scrum teams: Differentiation between organisational and individual impacts and opportunities

The role of the Product Owner in agile software development is critical being accountable for maximising the value, although becoming proficient in this role is a complex process. The Product Owner’s responsibilities vary between different industries. The literature has not fully explained the role of a Product Owner. We shed light on the factors that influence the role at levels of organization, team, stakeholder, and the individual. We conduct a systematic literature review in combination with a focus group consisting of practitioners to better understand the problem and to address the persistent gaps in theory and practice. Rather than exposing generic competencies associated with the role of Product Owner, our findings show that the role is tailored to fit the unique organisational context, including at the team level, and involves managing the organisational environment, including the institutional culture and politics. We also find an inherent tension arising from the fact that although the role of Product Owner is affected by the organisational level, the performance of the role is strongly influenced by attributes at the individual level. In particular, the individual’s ability to establish and manage networks and relationships within the organisation is mediated by his or her communication skills.
3D hand mesh recovery through inverse kinematics from a monocular RGB image

Recovering 3D hand mesh from a monocular RGB image has a wide range of application scenarios such as VR/AR. The parametric hand model provides a good geometric prior to the shape of hand, and is commonly used to recover the 3D hand mesh. However, the rotation parameters of hand model are not easy to learn, which influences the accuracy of model-based methods. To address this problem, we take advantage of the inverse kinematic chains of hand to derive an analytical method, which can convert hand joint locations into rotation parameters. By integrating such analytical method into the neural network, we propose an end-to-end learnable model named IKHand to recover the 3D hand mesh. IKHand comprises detection module and mesh generation module. Detection module predicts the 3D hand keypoints while mesh generation module takes these keypoints to generate the 3D hand mesh. Experimental results show that our proposed method can generate impressive and robust 3D hand meshes under several challenging conditions, and can achieve competitive results on FreiHAND dataset as well as RHD.
Learning and predicting the unknown class using evidential deep learning

Abstract In practical deep-learning applications, such as medical image analysis, autonomous driving, and traffic simulation, the uncertainty of a classification model’s output is critical. Evidential deep learning (EDL) can output this uncertainty for the prediction; however, its accuracy depends on a user-defined threshold, and it cannot handle training data with unknown classes that are unexpectedly contaminated or deliberately mixed for better classification of unknown class. To address these limitations, I propose a classification method called modified-EDL that extends classical EDL such that it outputs a prediction, i.e. an input belongs to a collective unknown class along with a probability. Although other methods handle unknown classes by creating new unknown classes and attempting to learn each class efficiently, the proposed m-EDL outputs, in a natural way, the “uncertainty of the prediction” of classical EDL and uses the output as the probability of an unknown class. Although classical EDL can also classify both known and unknown classes, experiments on three datasets from different domains demonstrated that m-EDL outperformed EDL on known classes when there were instances of unknown classes. Moreover, extensive experiments under different conditions established that m-EDL can predict unknown classes even when the unknown classes in the training and test data have different properties. If unknown class data are to be mixed intentionally during training to increase the discrimination accuracy of unknown classes, it is necessary to mix such data that the characteristics of the mixed data are as close as possible to those of known class data. This ability extends the range of practical applications that can benefit from deep learning-based classification and prediction models.
Sistem Informasi Penjadwalan Mata Pelajaran Menggunakan Algoritma Artificial Bee Colony Berbasis Web

Course Scheduling Information System Using Web-Based Artificial Bee Colony AlgorithmOne of the educational institutions in Cicalengka District and Bandung Regency is SMK Yadika Cicalengka. has difficulties with scheduling and plotting. At SMK Yadika Cicalengka, the existing scheduling system is still done manually using Microsoft Excel. This becomes less effective because it takes quite a long time and requires an assessment of each teacher's teaching availability every day. This research aims to create a scheduling system that can optimize the time to create subject schedules and accurately divide teaching assignments. Using the artificial bee colony method, this research develops the system and achieves optimal results by imitating how bee colonies search and exploit food sources effectively. In the case of this research, iteration and solution selection will be performed to reduce the breadth of the problem. The results of this study show that the created system is more efficient in time scheduling, with an average value of 96%.
Bitcoin market networks and cyberattacks

This paper assesses the interconnectedness across Bitcoin trading platforms leading to and in the aftermath of large-scale cyberattacks using a network topology and daily data over the period 1 January 2015–31 May 2019. We find significant volatility spillovers, especially when the frequency and the magnitude of cyberattacks rise. While this suggests the presence of informational inefficiencies, these are not uniformly distributed, as the observed heterogeneity in volatility spillovers depends on the size of the cyberattack. Moreover, cyberattacks appear to change the network structure of Bitcoin trading platforms. Specifically, while the Bitcoin in U.S. dollar platform remains systemically important, the Bitcoin trading platforms of the Yen and Australian dollar have become more relevant in recent times. From a practitioner and market professional perspective, our results suggest that an informed investment strategy can exploit such volatility spillovers to generate abnormal returns. From a policy point of view, the easing of Bitcoin regulation in Asia seems to be linked with the increasingly prominent importance of the trading platforms of the Yen and Australian dollar.
Look for Patterns in Names

Nonprofit Communications ReportVolume 21, Issue 10 p. 3-3 Picking the Right Platform Look for Patterns in Names First published: 09 September 2023 https://doi.org/10.1002/npcr.32255Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume21, Issue10October 2023Pages 3-3 RelatedInformation
Underrepresentation of Local Researchers in Geophysical Studies at the Bosumtwi Impact Crater: Insights from A Systematic Review

Impact cratering is an important aspect of planetary evolution. Geophysics plays a complementary role in identifying impact craters on Earth given the non-unique geological characteristics associated with such craters. The Bosumtwi impact crater in Ghana represents one of the world's most well-preserved and young mid-sized impact craters, and this study aims to evaluate the current state of geophysical research conducted in this area. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique was employed for data collection and analysis, which involved identifying and screening relevant sources of data. Results indicated that the maximum number of publications (14) on the Bosumtwi impact crater was recorded in 2007, with 15% of these articles being affiliated with Ghanaian institutions. Furthermore, only two articles reported funding from Ghana. The major geophysical methods applied in studies of the Bosumtwi impact crater include various techniques that have confirmed the presence of shock-metamorphosed rocks. Although geophysical methods cannot provide unambiguous evidence for an impact origin of the Bosumtwi crater, they did provide additional constraints in establishing its impact origin. This study highlights the lack of local financial support for research in Ghana and African countries in general, with the underrepresentation of Ghanaian geophysical researchers being a concerning outcome. The absence of hazard studies such as the creation of unstable cliffs and the long-term effects of the meteorite impact on inhabitants of the Bosumtwi impact crater is particularly significant. Further research is necessary to fully understand the implications of this underrepresentation. Moreover, this study highlights the importance of research at the Bosumtwi impact crater for achieving the UN Sustainable Development Goals.
PriRPT: Practical blockchain-based privacy-preserving reporting system with rewards

In order to obtain evidence of a crime timely, most authorities encourage whistleblowers to provide valuable reports by rewarding them with prizes. However, criminals will try their best to delete or tamper with the reports and even threaten and revenge the whistleblowers to escape punishment. Hence, to make the reporting system work, it is essential to ensure the integrity of reported messages and the anonymity of the reporting and rewarding procedures in the reporting system. Most existing schemes for this problem are generally based on ring signatures, which incur high computational overhead and imperfect anonymity. In this paper, we introduce a novel practical blockchain-based privacy-preserving reporting system with rewards dubbed as PriRPT. Specifically, the proposed scheme integrates the permissioned blockchain system, keyed-verification anonymous credential (KVAC), and structure-preserving signatures on equivalence classes (SPS-EQ) to provide reliable auditing of reports, and support anonymous reporting and anonymous rewarding simultaneously. In addition, we achieve higher efficiency in the reporting and rewarding protocol by replacing costly zero-knowledge proofs with KVAC and SPS-EQ. We also formalize the scheme along with security proof and provide rigorous evaluations on an open blockchain platform (JUICE) and a personal laptop to demonstrate its practicability.
EMPLOYEE ENGAGEMENT &amp; IT’S EFFECTIVENESS AT AMAZON AND ACCENTURE

In recent days Employee Engagement is booming talent manager and employee engagement goes hand in hand Involving employees helps create a culture that motivates employees to collaborate, share ideas, and create breakthrough solutions. Providing excellent customer service and support, company value, teamwork with colleagues and contributing to a healthy working environment in his IT industry where customer experience is essential for customer retention and loyalty. An organization with a reputation for high employee engagement may be more attractive to her top IT talent. Companies that value employee well-being, provide opportunities for advancement, and promote a healthy work culture are more feasible to attract potential prospects, to Analyze the relationship between employees Gender and Employee Engagement at workplace, Co-relation, the sample consist of 126 employees from both the companies i.e 64 from Accenture and 62 in Amazon, the opportunity to build workplaces where employees feel empowered, valued and inspired to do their best work KEYWORD : Effectiveness, Employee Engagement, Effectiveness, Employee retention Corporate Culture.
Learning an ensemble dehazing network for visible remote sensing images

Image dehazing is an important preprocessing task since haze extremely degrades the image quality and hampers the application of remote sensing vision system. Although the deep learning-based method has been successful in image dehazing, there has been little effort to harmonize convolutional neural networks and transformer to better satisfy removing haze. In particular, local and global representation learning are equally important for the challenging image dehazing task. To this end, we propose an effective ensemble dehazing network (EDHN) for visible remote sensing images. Specifically, we introduce two key backbone modules for the developed ensemble framework, including operation-wise attention module and transformer module. The operation-wise attention module is designed for restoring spatially varying degradation, and the transformer module is employed to refine haze-free background textures and structures. Furthermore, residue channel prior and feature aggregation block are also incorporated into our ensemble architecture to further guide image reconstruction and boost image restoration. Experimental results show the superiority of our proposed EDHN and demonstrate the favorable performance against recent dehazing approaches.
Hierarchical Knowledge Propagation and Distillation for Few-Shot Learning

Recent research efforts on Few-Shot Learning (FSL) have achieved extensive progress. However, the existing efforts primarily focus on the transductive setting of FSL, which is heavily challenged by the limited quantity of the unlabeled query set. Although a few inductive-based FSL methods have been studied, most of them emphasize learning superb feature extraction networks. As a result, they may ignore the relations between sample-level and class-level representations, which are particularly crucial when labeled samples are scarce. This paper proposes an inductive FSL framework that leverages the Hierarchical Knowledge Propagation and Distillation, named HKPD. To learn more discriminative sample-level representations, HKPD first constructs a sample-level information propagation module that explores pairwise sample relations. Subsequently, a class-level information propagation module is designed to obtain and update the class-level information. Moreover, a self-distillation module is adopted to further improve the learned representations by propagating the obtained knowledge across this hierarchical architecture. Extensive experiments conducted on the commonly used few-shot benchmark datasets demonstrate the superiority of the proposed HKPD method, which outperforms the current state-of-the-art methods.