Reviewerly/exHarmony huggingface dataset:
A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients

The application of deep learning on whole-slide histological images (WSIs) can reveal insights for clinical and basic tumor science investigations. Finding quantitative imaging biomarkers from WSIs directly for the prediction of disease-free survival (DFS) in stage I-III melanoma patients is crucial to optimize patient management. In this study, we designed a deep learning-based model with the aim of learning prognostic biomarkers from WSIs to predict 1-year DFS in cutaneous melanoma patients. First, WSIs referred to a cohort of 43 patients (31 DF cases, 12 non-DF cases) from the Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) public database were firstly annotated by our expert pathologists and then automatically split into crops, which were later employed to train and validate the proposed model using a fivefold cross-validation scheme for 5 rounds. Then, the model was further validated on WSIs related to an independent test, i.e. a validation cohort of 11 melanoma patients (8 DF cases, 3 non-DF cases), whose data were collected from Istituto Tumori 'Giovanni Paolo II' in Bari, Italy. The quantitative imaging biomarkers extracted by the proposed model showed prognostic power, achieving a median AUC value of 69.5% and a median accuracy of 72.7% on the public cohort of patients. These results remained comparable on the validation cohort of patients with an AUC value of 66.7% and an accuracy value of 72.7%, respectively. This work is contributing to the recently undertaken investigation on how treat features extracted from raw WSIs to fulfil prognostic tasks involving melanoma patients. The promising results make this study as a valuable basis for future research investigation on wider cohorts of patients referred to our Institute.
A Quantum Internet Architecture

Entangled quantum communication is advancing rapidly, with laboratory and metropolitan testbeds under development, but to date there is no unifying Quantum Internet architecture. We propose a Quantum Internet architecture centered around the Quantum Recursive Network Architecture (QRNA), using RuleSet-based connections established using a two-pass connection setup. Scalability and internetworking (for both technological and administrative boundaries) are achieved using recursion in naming and connection control. In the near term, this architecture will support end-to-end, two-party entanglement on minimal hardware, and it will extend smoothly to multiparty entanglement and the use of quantum error correction on advanced hardware in the future. For a network internal gateway protocol, we recommend (but do not require) qDijkstra with seconds per Bell pair as link cost for routing; the external gateway protocol is designed to build recursively. The strength of our architecture is shown by assessing extensibility and demonstrating how robust protocol operation can be confirmed using the RuleSet paradigm.
The Age Beyond Sports: User Experience in the World of Metaverse

A platform for augmented reality called Metaverse enables users to build interactive experiences that combine the virtual and real worlds. Additionally, it can be considered a virtual version of the concept or idea of cyberspace. People have many alternative options in Metaverse but they mostly use their choise by gaming. Therefore, this study examining the opinions of the participants who experienced the Nikeland sports game, which is the initiative of the Nike sports brand in the metaverse. Within the scope of the research, semi-structured interviews were conducted with the participants who experienced the Nikeland game. Seven themes were obtained: the age beyond sports, atmospheric, feelings, chaotic structure in access, flow, perception of reality and innovative brand in this research. As a result of the findings, although the game initiative of the Nike brand in the metaverse is still in its infancy, it has been seen that there is a belief that this initiative will come to better places in the future. As a consequent of the literature review of the metaverse and the opinions of the users who have experienced the Nikeland game, the researchers have obtained results that the concepts of sports and metaverse are not far from each other and that the sports brands in the metaverse will increase.
Exploring the role of leadership, work environment, it alignment and company performance on the digital transformation: a study on the private sector companies in western region, Saudi Arabia

Purpose: The purpose of the study is to investigate the role of leadership, work environment, IT alignment and company performance on the digital transformation of private sector companies in the Western Region of Saudi Arabia. The research problem is the lack of studies about digital transformation in Saudi Arabia. Thus, the research question can be formulated as “what are the implications of leadership, work environment, alignment of IT processes, and company performance on the digital transformation of a company?” Theoretical Framework: The framework of the study is adopted from the attributes of (1) leadership, (2) work environment, (3) IT processes alignment and (4) company performance. This framework aims to provide understanding on how these variables affect the digital transformation of a company. Methodology: Primary as well as secondary data have been collected. A survey was conducted on a sample of (461) participants through online questionnaire. The approach chosen for the study is the Quantitative Deductive Approach (QDA), and the data were analyzed using SPSS. Results and Conclusion: The results of the study concluded that there is a significant positive correlation of leadership, work environment, alignment with IT processes and company performance on the digital transformation. Research Implications: The study verifies the influence of leadership, work environment, IT processes and company performance on the digital transformation. This is helpful on the managerial level for organizations which are willing to adopt digital transformation. On the academic scope, the study adds contribution to the literature of digital transformation in Saudi Arabia. Socially, it is significant to keep pace with the digitalization trend that is evolving rapidly in the Saudi society. Originality: This study is a pioneering attempt to enhance the management practices in private sector companies to explore the role of leadership, work environment, alignment with IT processes and company performance on the digital transformation. The findings should motivate organizations to consider these variables when seeking to adopt digital transformation.
An Integrated Image Encryption Scheme Based on Elliptic Curve

Due to the extensive demand for digital images across all fields, the security of multimedia data over insecure networks is a challenging task.The majority of the existing modern encryption schemes are merely developed that ensure the confidentiality of the image data.This manuscript presents a new image encryption scheme that ensures confidentiality, user authentications, and secure key sharing among the communicating parties.Initially, the users share a secret parameter using Diffie-Hellman over the elliptic curve and pass it through SHA-256.Afterwards, the proposed scheme uses the first 128-bits for the confidentiality of the data, while the remaining 128-bits are for authentication.In the encryption algorithm, the confusion module is achieved by affine power affine transformation.At the same time, the diffusion module is attained through highly nonlinear sequences, which are generated through the elliptic curve.Experimental testing and the latest available security tools are used to verify the effectiveness of the proposed algorithm.The simulation findings and the comparison of the proposed scheme with the existing image encryption techniques reveal that the suggested scheme offers a sufficient degree of security.Furthermore, the outcome of the simulation results divulges several advantages of the proposed scheme, including a large key space, resistance to differential attacks, high efficiency, and strong statistical performance.
An Extensive Blockchain Based Applications Survey: Tools, Frameworks, Opportunities, Challenges and Solutions

Many security standards and cryptographic solutions exist for different applications such as agriculture, aircraft, banking systems and etc. but a more effective and efficient solution can be given by combining existing technologies with blockchain.This work addresses the problems of previous works such as scalability, immutability, robustness, network latency, auditability, and traceability.Satoshi Nakamoto introduced Blockchain (BC) to tackle the Address Resolution Protocol (ARP) spoofing attacks, Distributed Denial of Service (DDoS), phishing problems and various security issues.Blockchain is a technology that stores the data using a chain of blocks in an encrypted form with hashing algorithms.It uses the decentralized architecture to store the information that helps users and customers to have transparency on records.The data is stored in a distributed ledger that is tamperproof and immutable.To amalgamate the research done so far, this paper presents a systematic review of ten different applications and tools used in blockchain.The applications include academics and education, agriculture, aircraft, banking, car sharing, e-voting, healthcare, Internet of Things (IoT), Intellectual Property Rights (IPR), and Supplychain (SC).Moreover, this paper presented a taxonomy for these applications and analyzed the implementation of tools used in different domains.Different open issues and challenges and key takeaways of blockchain technology were also highlighted.Hence, this paper helps give a new insight into working with blockchain and deciding on appropriate tools and approaches for a particular application.
A Systematic Literature Review of Lightweight Blockchain for IoT

The Internet of Things (IoT) has become an essential part of our society.IoT devices are used in our houses, hospitals, cars, industry, etc., making our lives easier.Nonetheless, there are a number of serious concerns about security, privacy and performance issues in IoT.It has been proven that the aforementioned issues are strictly related to the high degree of centralisation of current IoT architecture.Thus, there is an increasing interest in adopting blockchain in IoT.However, blockchain adoption is not straightforward due to the power, storage and computational limitations of IoT.Consequently, the concept of lightweight blockchain is getting more and more attention from researchers and engineers.In this paper, we conduct a systematic literature review on the lightweight blockchain concept for IoT following the PRISMA methodology.We systematically analyse "lightweight blockchain for IoT" proposals in order to better understand the limitations of blockchain for IoT, the characteristics of the current work on this topic and further research opportunities.Specifically, we analyse the definition of lightweight blockchain that other authors give, the characteristics of the reviewed proposals, their "lightweight" aspects and their evaluation.Finally, we discuss the results of the review along with further research opportunities.Consequently, this work is mostly focused on understanding the technical and performance-related aspects of blockchain for IoT as a prelude to more specific analysis such as security (i.e., attacks, vulnerabilities, etc.).
Winnow: A Domain-Specific Language for Incremental Story Sifting

Story sifters attempt to automatically or semi-automatically extract nuggets of compelling narrative content from vast chronicles of game or simulation events. Though sifting has successfully been used to enable novel computational narrative play experiences, its utility is limited by the fundamentally retrospective nature of existing sifters, which can only recognize storyful event sequences once they have fully played out. To address this limitation, we introduce Winnow: a domain-specific language for specifying story sifting patterns that can be executed incrementally to detect potentially storyful event sequences while they are still playing out. We evaluate Winnow by applying it to several specific use cases and show that it is well-suited to the implementation of prospective as well as retrospective narrative intelligence.
Knowledge Distillation as Efficient Pre-training: Faster Convergence, Higher Data-efficiency, and Better Transferability

Large-scale pre-training has been proven to be crucial for various computer vision tasks. However, with the increase of pre-training data amount, model architecture amount, and the private/inaccessible data, it is not very efficient or possible to pre-train all the model architectures on large-scale datasets. In this work, we investigate an alternative strategy for pre-training, namely Knowledge Distillation as Efficient Pre-training (KDEP), aiming to efficiently transfer the learned feature representation from existing pre-trained models to new student models for future downstream tasks. We observe that existing Knowledge Distillation (KD) methods are unsuitable towards pre-training since they normally distill the logits that are going to be discarded when transferred to downstream tasks. To resolve this problem, we propose a feature-based KD method with non-parametric feature dimension aligning. Notably, our method performs comparably with supervised pre-training counterparts in 3 downstream tasks and 9 downstream datasets requiring 10× less data and 5× less pre-training time. Code is available at https://github.com/CVMI-Lab/KDEP.
DIGITAL DOPING FOR HISTORIANS: CAN HISTORY, MEMORY, AND HISTORICAL THEORY BE RENDERED ARTIFICIALLY INTELLIGENT?

ABSTRACT Artificial intelligence is making history, literally. Machine learning tools are playing a key role in crafting images and stories about the past in popular culture. AI has probably also already invaded the history classroom. Large language models such as GPT‐3 are able to generate compelling, non‐plagiarized texts in response to simple natural language inputs, thus providing students with an opportunity to produce high‐quality written assignments with minimum effort. In a similar vein, tools like GPT‐3 are likely to revolutionize historical studies, enabling historians and other professionals who deal in texts to rely on AI‐generated intermediate work products, such as accurate translations, summaries, and chronologies. But present‐day large language models fail at key tasks that historians hold in high regard. They are structurally incapable of telling the truth and tracking pieces of information through layers of texts. What's more, they lack ethical self‐reflexivity. Therefore, for the time being, the writing of academic history will require human agency. But for historical theorists, large language models might offer an opportunity to test basic hypotheses about the nature of historical writing. Historical theorists can, for instance, have customized large language models write a series of descriptive, narrative, and assertive histories about the same events, thereby enabling them to explore the precise relation between description, narration, and argumentation in historical writing. In short, with specifically designed large language models, historical theorists can run the kinds of large‐scale writing experiments that they could never put into practice with real historians.
Rafflesia Optimization Algorithm Applied in the Logistics Distribution Centers Location Problem

&lt;p&gt;Intelligent evolutionary algorithm is an important method to solve optimization problems. Most of their inspiration comes from the laws of nature and biology. This paper proposes a new intelligent evolutionary algorithm based on the life habits of Rafflesia, which is called Rafflesia Optimization Algorithm. It mainly consists of three stages: attracting insects, swallowing insects, and spreading seeds. In the first stage, the ROA algorithm performs the local search to find the optimal solution. In the second stage, it improves execution efficiency and solution accuracy by reducing the number of individuals. In the third stage, it performs the global search to jump out of the local optimal position. In the experimental part, this paper uses numerical functions (the CEC2013 benchmark function set) and practical application problems (the logistics distribution centers location problem) to test the performance of the ROA algorithm, and compares it with seven meta-heuristics algorithms. The experimental results prove the effectiveness and practicability of the ROA algorithm.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Designing Immersive Virtual Reality Simulation for Environmental Science Education

Recently, severe environmental changes, such as global warming, climate change and environmental pollution, have become expected, and thus environmental education is becoming essential. The purpose of environmental education is to instill awareness in students to recognize and solve environmental problems. Virtual reality provides students with a spatial and temporal experience similar to reality, and it can increase their understanding of knowledge through immersion and interaction compared to traditional learning. In previous studies, virtual reality for education has mainly focused on experience, but it is difficult to find examples for environmental education. Hence, this research proposed an immersive virtual reality simulation for environmental education based on the virtual ecosystem model. It also presented two applications developed based on this simulation. This research aims at encouraging students’ active participation and motivation to solve the environmental problems while experiencing the results of interaction related to environmental factors in a virtual environment.
Variational Hamiltonian simulation for translational invariant systems via classical pre-processing

Abstract The simulation of time evolution of large quantum systems is a classically challenging and in general intractable task, making it a promising application for quantum computation. A Trotter–Suzuki approximation yields an implementation thereof, where a higher approximation accuracy can be traded for an increased gate count. In this work, we introduce a variational algorithm which uses solutions of classical optimizations to predict efficient quantum circuits for time evolution of translationally invariant quantum systems. Our strategy can improve upon the Trotter–Suzuki accuracy by several orders of magnitude. It translates into a reduction in gate count and hence gain in overall fidelity at the same algorithmic accuracy. This is important in noisy intermediate scale quantum-applications where the fidelity of the output state decays exponentially with the number of gates. The performance advantage of our classical assisted strategy can be extended to open boundaries with translational symmetry in the bulk. We can extrapolate our method to beyond classically simulatable system sizes, maintaining its total fidelity advantage over a Trotter–Suzuki approximation making it an interesting candidate for beyond classical time evolution.
Adventuring Physics: Integration of Adventure Game and Augmented Reality Based on Android in Physics Learning

One of the difficulties in physics learning is the low motivation to learn because physics materials are considered complex, boring, and abstract for students. Therefore, this research developed an Augmented Reality (AR)-integrated game application "Adventuring Physics" based on android that can create more fun physics learning and provide concept visualization on abstract materials so that they become easier for students to understand. This study aims to develop a valid and practical application of Adventuring Physics games on physics learning. This research uses Research and Development (R&amp;D) design to develop the application. This application has two main features: 'play games' and 'augmented reality'. Furthermore, the game's concept in this application is adapted to the physics materials. Meanwhile, the AR feature in this application can create visualizations in the form of 3D image projections of the material. The results of the validity assessment show that this application is declared valid, feasible, and reliable. In addition, the results of the practicality assessment show that this application is very practical. Thus, the app is worth applying in physics learning. This research has implications as an application of Adventuring Physics that can motivate students to learn physics, apply digital technology in education, and apply it to physics learning.
Hashtag recommendation for enhancing the popularity of social media posts

Social media has gained huge importance in our lives wherein there is an enormous demand of getting high social popularity. With the emergence of many social media platforms and an overload of information, attaining high popularity requires efficient usage of hashtags, which can increase the reachability of a post. However, with little awareness about using appropriate hashtags, it becomes the need of the hour to build an efficient system to recommend relevant hashtags which in turn can enhance the social popularity of a post. In this paper, we thus propose a novel method hashTag RecommendAtion for eNhancing Social popularITy to recommend context-relevant hashtags that enhance popularity. Our proposed method utilizes the trending nature of hashtags by using post keywords along with the popularity of users and posts. With the prevalent evaluation techniques of this field being quite unreliable and non-uniform, we have devised a novel evaluation algorithm that is more robust and reliable. The experimental results show that our proposed method significantly outperforms the current state-of-the-art methods.
MVVM Demonstration Using C# WPF

Purpose: Nowadays, MVVM (Model-View View-Model) is the proven architecture for distributed software development. It encourages the development of the software components by the different independent teams and easy integration at the final stage. The individual researcher prefers direct coding or tightly coupled software modules. The Model is rapid and efficient but might create maintainability issues later. So from day one, we should introduce the best design model. In this scenario, the MVVM model is the major player. Here, we present how to implement MVVM into our project through a simple task. We will design the user interface, which is part of the UX design team, and then add functionality, which is the programming part. Finally, we will do integration and execution. The code used in this project is available to download from GitHub. Design/Methodology/Approach: We are creating a C# WPF project inside the visual studio community edition. Then we segregate our activity into two parts. In the first part, we make a Model for our project. After that, we design the user interface. The user interface interacts with the user to display the data and receive inputs from the user. It is the presentation or view layer. After completing it, we add the required view model interaction logic. Finally, we integrate all components and run the project. Findings/Result: Through the research, we realize the importance of the MVVM concept. It is a good software architecture; the researcher who has gone through the documents will find out how to implement the MVVM into their project. Some essential procedures are presented concisely so they can be adopted quickly. This architecture is independent of any language. So once we grab it, it can be implemented in our project, whatever the language we use. Originality/Value: Several documents on MVVM design using WPF are available worldwide. Most of the documents are elaborative and descriptive. It is tricky to extract the required information from long-duration content as fast-track space. Here we demonstrate practically. Using the documents as a reference, the researcher can easily integrate the MVVM concept into their project. Paper Type: Experimental-based Research.