Reviewerly/exHarmony huggingface dataset:
Adaptive Sprint Planning Based on Risk Management

Context: The paper presents explicit risk management applied to Scrum in the Sprint planning context. Standard Scrum handles risks only implicitly, so this paper describes Scrum extension.Objective: The purpose of this paper is to present an illustrative case of an adaptive Sprint planning that is based on explicit risk management.Method: An extensive literature review combined with the interviews as well as participant observations were used as research methods.Results: As a result, an explicit risk management has been suggested and presented on example. Decision making is empowered with recommendations. According to the literature review research performed by the authors, this paper fills in the gap in the literature of explicit risk management in the context of Scrum. Another interesting contribution of this paper is that it synthesizes some important basic definitions of software risk management, like risk, risk factor and uncertainty.Conclusions: Risk management methods that are applied are simple and easy to use. This approach supports project teams in Sprint plan adjustments, so it strengthens adaptability and it supports teams in taking actions that are quantified in case of their business value.
A New Graph Grammar Formalism for Robust Syntactic Pattern Recognition

I introduce a formalism for representing the syntax of recursively structured graph-like patterns. It does not use production rules, like a conventional graph grammar, but represents the syntactic structure in a more direct and declarative way. It can handle iterative, hierarchical and nested recursive structure in more than one dimension.This is used to develop an error-tolerant algorithm for syntactic pattern recognition of spatial configurations, which can handle complex patterns of up to 1000 symbols and can cope with variability in geometric relationships, blurry and indistinct symbols, overlapping symbols, and erased patches. I achieve this by integrating all aspects of pattern recognition (feature detection, segmentation, parsing, filling in missing symbols, top-down and bottom-up inference) into a single process, to exploit the synergy between them.This is backed up by a mathematical theory in which the grammar and the pattern are both represented as networks and parsing is seen as the construction of a homomorphism from the pattern to the grammar.
Organisation Techniques and Meeting Deadlines

Effective organisation techniques and the ability to break down larger projects into a series of more manageable time framed steps are key to student progression and success. Competent organisation skills help to overcome academic weaknesses and abilities in time management can alleviate negative emotion, such as stress around study tasks. As such, this chapter told from the dyslexic learners’ perspectives provides advice on developing proficient organisation strategies. These include developing systems to control events, using technology, using plans, daily to-do-lists, and making timetables into visual posters to display on walls. Visual examples of different types of academic calendars, timetable templates and to-do-lists are provided so that the reader can select methods suitable for how they work, and advice on using organisation to maintain motivation and prevent procrastination of work is supplied. Meeting deadlines and multitasking on several assignments due at the same time are anxiety provoking experiences for students with dyslexia, so guidance on ways to cope with this, such as using support networks for encouragement and motivation, starting work on assignments early in the term to meet deadlines, and identifying and breaking down tasks into smaller components by imposing self-made goals to make the work more manageable is presented.
PAWS: Personalized Arm and Wrist Movements With Sensitivity Mappings for Controller-Free Locomotion in Virtual Reality

Virtual Reality (VR) headsets equipped with multiple cameras enable hands-only teleportation techniques without requiring any physical controller. Hands-only teleportation is an effective alternative to controllers for navigation tasks in virtual reality - allowing users to move from one point to another instantaneously. However, the current implementation of hands-only techniques does not consider users' physical attributes (e.g., arm's reach). Thus, a hands-only teleportation technique can lead to different user experiences based on physical attributes. We propose PAWS, a personalized arm and wrist-based teleportation technique that incorporates users' physical attributes for improved teleportation experiences. We first evaluate different degrees of teleportation personalization with no-, partial, and full personalization. We find that full personalization offers faster locomotion - but at the cost of degraded performances with distant targets due to increased sensitivity. We hence further explore different combinations of mapping functions (e.g., sigmoid, quadratic) to personalize motor movements and find that asymmetric functions result in improved performance. Overall, our results show that PAWS helps users to navigate quickly in virtual environments.
Fairness Resource Allocation Based on Blockchain for Secure Communication in Integrated IoT

Abstract The emerging Space-Air-Ground, Artificial intelligence (AI), blockchain and Vehicle-toeverything (V2X) technology in Integrated Internet of Things (IoT) enables vehicles to communicate with other vehicles and roadside units (RSU), which improves communication efficiency and driving safety. Due to the rich data in the Integrated IoT, it is easy for illegal persons to steal data or deceive users. While reasonably allocating resources to complete the transmission task, it is still a challenge to ensure the communication security of vehicle users in Integrated IoT. In this paper, we study the cost of vehicle users transmitting tasks by Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) in Integrated IoT. In order to protect the information security, we establish the identity authentication and matching model to obtain a better channel environment of Integrated IoT. Moreover, we consider dynamic pricing based on bandwidth resource occupancy to ensure user experience and server load in Integrated IoT. Under the constraints of task tolerable delay, we fix the bandwidth on the V2V side and decouple the task decomposition and bandwidth allocation in Integrated IoT. Then, we propose algorithms Blockchain based Vehicle Identity Authentication (BVIA) and Delay Weight Fairness Bandwidth Allocation (DWFBA). In this way, vehicle users and RSUs entering the Integrated IoT system are authenticated and matched with devices with good trust value, and the fairness of users resource allocation is guaranteed. Simulation results show that BVIA algorithm can reduce communication overhead, DWFBA algorithm can control user costs, and effectively reduce the number of task failures in Integrated IoT.
IoT Security Challenges and Emerging Solutions: A Comprehensive Review

The proliferation of Internet of Things (IoT) devices has revolutionized the way we interact with technology, embedding connectivity into every facet of modern life. However, this rapid growth has introduced a host of security challenges that threaten the integrity, privacy, and functionality of IoT ecosystems. This paper presents an abstract review of the complex landscape of IoT security challenges, ranging from vulnerabilities in devices to data breaches and sophisticated cyberattacks. Amid these challenges, emerging solutions offer promising avenues to fortify the security posture of IoT environments. Blockchain technology holds the potential to enhance data integrity and establish trust, while hardware-based security mechanisms bolster the resistance of devices against intrusions. Novel approaches such as AI-powered anomaly detection contribute to real-time threat identification, and privacy-preserving techniques strive to strike a balance between data utility and confidentiality. Additionally, the concept of zero trust architectures is transforming the fundamental approach to securing IoT networks. This paper not only underscores the gravity of IoT security challenges but also highlights the proactive measures that are shaping the future of IoT security. Through an exploration of case studies and a discussion of the inherent challenges and trade-offs, this review elucidates the delicate International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 09 | September - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM25662 | Page 2 balance between fortifying IoT systems and maintaining usability. As IoT continues its rapid evolution, the imperative to ensure robust security measures becomes paramount, necessitating collaborative efforts and innovative solutions to safeguard the promises of an interconnected world. Keywords: IoT,DDoS, blockchain, Cyberattacks
Deep Learning Approach in Anti-Phishing Technology

A significant rise in electronic trade, or consumer-to-consumer online transactions, has occurred recently as a result of advances in Internet and cloud technologies. Malware, phishing, ransomware, man- in-the-middle attacks and other techniques are just a few of the ways cybercriminals utilize to begin a cyber attack. Phishing is a form of social engineering or a cyber security assault in which nefarious actors send messages acting as reliable individuals. Phishing attacks use complex strategies, techniques, and technologies to obtain sensitive data, including content injection, online social networks etc. Real-time identification of phishing sites is challenging with the available anti-phishing tools. Therefore, a protected and open system is required that allows the user to directly verify the site. The long short- term memory (LSTM) algorithm and the convolutional neural network (CNN) were combined to develop a hybrid deep learning model (LCR). The goal of this research was to develop a web browser named Phishing Detection Browser (PD Browser) that can identify phishing attacks using deep learning. Keywords— Phishing, Anti-Phishing, Deep learning, Hybrid Deep Learning Model
Exploring the Potential of Blockchain in Data Science

Blockchain technology has recently gained significant attention in various industries due to its unique features such as decentralization, immutability, and transparency. These features make it an ideal technology for data science, which involves the analysis and interpretation of large amounts of data. This paper explores the potential of blockchain in data science and discusses the various ways in which blockchain can enhance the data science process. The paper first provides an overview of blockchain technology and its key features. It then discusses the current challenges faced by data science and how blockchain can help to address these challenges. Specifically, the paper highlights how blockchain can improve data quality, data security, and data privacy. The paper also presents several use cases where blockchain has been successfully applied in data science. These use cases include supply chain management, healthcare, and finance. The paper analyses these cases and explains how blockchain has helped to improve data management and decision-making processes. Finally, the paper concludes with a discussion of the limitations and future research directions of blockchain in data science. While blockchain has several potential benefits, it is not a one-size-fits-all solution. Further research is needed to explore the scalability and interoperability of blockchain in data science. Overall, this paper highlights the potential of blockchain in data science and provides a comprehensive analysis of its impact on data management and decision-making processes. Keywords—Data Science, Blockchain, Decentralized, cryptography, Smart Contract.
The Construction of Cybersecurity

AbstractPresently, the world is facing drastic changes and the pandemic both unseen in a century. Various security challenges keep emerging. World economy encounters strong headwinds on its path toward recovery, and global development suffers major setbacks. Geopolitics has a significant impact on cybersecurity, with global industrial and supply chains disrupted. Cybersecurity expands to digital security, data security faces severe challenges, and the situation of cybersecurity becomes complex and severe.
Prediksi Kecepatan Angin Jangka Menengah dengan Artificial Neural Network untuk Estimasi Daya Listrik Tenaga Angin (Studi Kasus: Kota Sabang)

Indonesia, as a country at the equator, has a very large renewable energy potential that can be used as a source of electrical energy. Electricity consumption in Indonesia, especially in Aceh, continues to increase annually because of population growth and increasing economic needs. Recently, the construction of power plants has been considered to be environmentally friendly and economical. One of the efforts that can be made is the development of wind-power plants. The availability of certain wind speeds was expected. Therefore, accurate prediction data must be used as the basis for building wind power plants. To increase the accuracy of wind speed prediction by looking at the error rate in predicting the amount of wind speed generated using an Artificial Neural Network with feed-forward and feed-backward functions from the back propagation algorithm (BPNN). The results of the application using the Neural Network algorithm with a back propagation Neural Network (BPNN) to predict wind speed show that the Neural Network algorithm can predict wind speed with an error of 0.0036. In July 2021, the estimated energy demand is 81.5 KWH.
Data Access Control for Secure Authentication Using Dual VC Scheme Based on CP-ABE in DID

In a Decentralized Identifier (DID), the Holder does not depend on a third party but controls self-identity information and is authenticated by the Verifier. Therefore, the Verifier can request verification data for access control from the Verifiable Data Registry (VDR) and credentials to authenticate the Holder. Data access control should be used when requesting data access such that only authorized Verifiers can access it. Consequently, studies on secure and efficient data access control have been conducted, and among them a scheme using Ciphertext Policy Attribute-based Encryption (CP-ABE) is underway. However, when the CP-ABE scheme is applied to the DID, the Holder’s extended Self-Sovereign Identity (SSI), which proves that the Holder has approved access to the Holder’s data stored in the VDR, is not ensured. Furthermore, the VDR does not verify the Verifier’s data access rights, resulting in unauthorized verification and illegal access to data by the user. And issue infringement of the Holder’s privacy, where Verifiers can infer the Holder by sharing and connecting the same DID-based Verifiable Presentations (VPs) of the Holder. Also, it leads to overheads in the amount of computation and search time for encryption/decryption. Therefore, in this paper, we propose a data access control for secure authentication by solving the security vulnerabilities of CP-ABE and using a CP-ABE-based dual Verifiable Credential (VC) scheme in DID.
Towards Detecting Freezing of Gait Events Using Wearable Sensors and Genetic Programming

Freezing of gait (FOG) is one of the most common manifestations of advanced Parkinson’s disease. It represents a sudden interruption of walking forward associated with an increased risk of falling and poor quality of life. Evolutionary algorithms, such as genetic programming (GP), have been effectively applied in modelling many real-world application domains and diseases occurrence. In this paper, we explore the application of GP for the early detection of FOG episodes in patients with Parkinson’s disease. The study involves the analysis of FOG by exploiting the statistical and time-domain features from wearable sensors, followed by automatic feature selection and model construction using GP. Efforts to use data from wearable sensors suffer from challenges caused by imbalanced class labels, which affect the task of GP model development. Thus, the cost-sensitive approach is incorporated into GP to tackle the imbalanced problem. The standard metrics, such as sensitivity, specificity, and F1-score, were used for testing the final model. With 30 repetitions, the average performance of the GP model has shown promising results in detecting the occurrence of FOG episodes in Parkinson’s disease.
Administrasi keuangan

Abstract — In the world of education,there are several components of educationthat are very instrumental in supportinglearning activities both directly andindirectly. One of the most importantcomponents is educational facilities andinfrastruncture. Administration ofeducational facilities and infrastructure isa very supportive thing for achieving thegoals of education. The teaching andlearning process will be more succesful ifeducational facilities and infrastucture areadequate. Education infrastructure andfacilities must always be complete. Thegovernment must always strive tocontinuously aquip aducational facilitiesand infrastructure for all levels and levelsof education. this article aims to determinethe administration of educational facilitiesand infrastructure in Indonesia along withthe obstacles faced and the efforts taken toovercome these obstacles. This research isa descriptive study with a qualitativeapproach. Data collection is done by themethod of observation, interviews anddocumentation. The data analysistechnique in this study is an interactiveanalysis consisting of data reduction, datapresentation and conclusion drawing.Check the validity of the data using sourcetriangulation and method triangulation
Pengiriman Pesan Whatsapp Massal Otomatis dengan XPath Address Finder, Checker, dan Fitur Update

WhatsApp is a reliable, secure, simple calling and messaging service that is available for free. This service was originally developed as the short message service alternative, and now supports location, document, video, photo, and text exchange features. This service has over two billion active users in one hundred and eighty countries. With some of these advantages, this service can be used as an alternative to SMS to convey information in bulk. For example, broadcasting information or reminders of certain things or advertising. By using WhatsApp, the costs required for the broadcasting process will be very small compared to using SMS. However, it would be very inefficient and time-consuming if the process of broadcasting the information was still done manually by retyping or copy-pasting messages from target to target. This reseach aims to develop an application that can automate the process of sending mass messages automatically through the WhatsApp service. In addition, a Virtual Contact File creation feature was also developed to help users add contacts from targets. To improve the adaptability to HTML structure changes on the WhatsApp Web website, an XPath address update feature on key elements was also developed. To support the update feature, the XPath address finder and checker on certain elements in a web page function were also developed.
Ansatz-Agnostic Exponential Resource Saving in Variational Quantum Algorithms Using Shallow Shadows

Variational Quantum Algorithms (VQA) have been identified as a promising candidate for the demonstration of near-term quantum advantage in solving optimization tasks in chemical simulation, quantum information, and machine learning. The standard model of training requires a significant amount of quantum resources, which led us to use classical shadows to devise an alternative that consumes exponentially fewer quantum resources. However, the approach only works when the observables are local and the ansatz is the shallow Alternating Layered Ansatz (ALA), thus severely limiting its potential in solving problems such as quantum state preparation, where the ideal state might not be approximable with an ALA. In this work, we present a protocol based on shallow shadows that achieves similar levels of savings for almost any shallow ansatz studied in the literature, when combined with observables of low Frobenius norm. We show that two important applications in quantum information for which VQAs can be a powerful option, namely variational quantum state preparation and variational quantum circuit synthesis, are compatible with our protocol. We also experimentally demonstrate orders of magnitude improvement in comparison to the standard VQA model.
Nonlinear Granger Causality using Kernel Ridge Regression

I introduce a novel algorithm and accompanying Python library, named mlcausality, designed for the identification of nonlinear Granger causal relationships. This novel algorithm uses a flexible plug-in architecture that enables researchers to employ any nonlinear regressor as the base prediction model. Subsequently, I conduct a comprehensive performance analysis of mlcausality when the prediction regressor is the kernel ridge regressor with the radial basis function kernel. The results demonstrate that mlcausality employing kernel ridge regression achieves competitive AUC scores across a diverse set of simulated data. Furthermore, mlcausality with kernel ridge regression yields more finely calibrated $p$-values in comparison to rival algorithms. This enhancement enables mlcausality to attain superior accuracy scores when using intuitive $p$-value-based thresholding criteria. Finally, mlcausality with the kernel ridge regression exhibits significantly reduced computation times compared to existing nonlinear Granger causality algorithms. In fact, in numerous instances, this innovative approach achieves superior solutions within computational timeframes that are an order of magnitude shorter than those required by competing algorithms.
Exact instanton transseries for quantum mechanics

We calculate the instanton corrections to energy spectra of one-dimensional quantum mechanical oscillators to all orders and unify them in a closed form transseries description. Using alien calculus, we clarify the resurgent structure of these transseries and demonstrate two approaches in which the Stokes constants can be derived. As a result, we formulate a minimal one-parameter transseries for the natural nonperturbative extension to the perturbative energy, which captures the Stokes phenomenon in a single stroke. We derive these results in three models: quantum oscillators with cubic, symmetric double well and cosine potentials. In the latter two examples, we find that the resulting full transseries for the energy has a more convoluted structure that we can factorise in terms of a minimal and a median transseries. For the cosine potential we briefly discuss this more complicated transseries structure in conjunction with topology and the concept of the resurgence triangle.