Reviewerly/exHarmony huggingface dataset:
Web Project Development: Emergency Management

Web projects are crucial to a company’s online presence and significantly impact its success. Risk management is a component of web project management to identify, assess and potentially control threats to the success of projects. The research methodology includes systematic and analytical analyses of the members’ management of risks associated with web project users. The results and implications reveal that assessing the intensity of risk management measures for the sustainability of web projects regarding risks is imperative. The capacity for emergency management in web projects is essential for identifying, assessing, and controlling potential threats, reducing the probability of project delays or failure. Emergency management offers several critical benefits to web projects, including improved data protection, increased continuity of operations, reduced downtime, enhanced reputation, and increased efficiency. Effective emergency management throughout the web project life cycle organization ensures that the project is completed on time, within budget, and to stakeholders’ satisfaction while being prepared for potential emergencies. This research develops best practices and standards for future projects, increasing project success rates.
News text classification using Long-Term Short Memory (LSTM) algorithm

Over the past few years, the classification of texts has become increasingly important. Because knowledge is now available to users through various sources namely electronic media, digital media, print media, and many more. One of them is the development of so much news every day. LSTM is one of the algorithms of deep learning methods that can classify a text. This research proves for the LSTM algorithm on the classification of news text sentences. The data used is the news text from the Kaggle data center set i.e. aggregator news data. The results of the LSTM experiment from 10 epochs obtained with an accuracy value of 93,15% on the classification of texts into four categories, namely entertainment, bussines, science, and health.
Improving Strategy Implementation Using Blockchain Smart Contracts

Every organization develops a strategy that needs to be implemented and is undertaken through project delivery. In essence, project requirements should exactly replicate an organization’s strategy. In reality, this does not happen, and behavioral factors deviate the project delivery from the strategic objectives. This occurs as project stakeholders can have competing objectives. Resultantly, requirements that are implemented through projects that are less aligned to the strategy. This article develops a game theoretic model to simulate why such deviations occur. That explains the difference between strategy development and implementation. This article also explains how the implementation of a blockchain smart contract can eliminate this difference and help an organization implement their strategy effectively. The difference between the overall strategy and the final implementation can make a significant difference in the overall value of the company because if it does not implement strategy effectively, it could lose market share, causing reduced revenue and a significant future stock price fall.
Key Technologies of Spatiotemporal Information Service Based on Satellite Navigation Augmentation System

Considering the problems of the spatiotemporal information service of the satellite navigation augmentation system (SNAS) and greater needs from high-tech fields, we present network realtime kinematic (RTK) and precise point positioning real-time kinematic (PPP)-RTK positioning methods.We studied spatiotemporal information services and regulatory technology, and designed a SNAS service management platform that realizes the functions of regional spatial data dissemination based on SNAS and uses measurement monitoring, measurement locale setting, and historical trajectory analysis.From the results of the sample test and analysis, we established that users can directly receive high-precision network RTK services based on regional data.PPP-RTK accuracy tests were carried out using a car, and the test results show that the accuracy of determining the horizontal position and elevation was at the level of centimeters in an open environment, indicating that this method could provide reliable positioning services.
Measuring Device-Specific Physical Activity Trackability in Multi-Device Environments

Wrist-worn wearables such as smartwatches and fitness trackers have become widely used with smartphones in recent years. Interestingly, both types of devices support physical activity tracking, but that is only available when users wear or carry their devices, and thus, trackability is heavily influenced by user contexts. To analyze physical activity trackability in a multi-device environment, this paper explores device-specific coverage measures based on activity bouts.
Announcing commonmeta-ruby

Following recent announcements of the commonmeta standard for scholarly metadata and a Python package that converts several metadata formats (commonmeta-py), today I am happy to announce commonmeta-ruby, a Ruby gem and command-line tool to convert scholarly metadata using commonmeta as the internal format. commonmeta-ruby is based on the bolognese Ruby library that I started a few ago while working at DataCite, but is a major rewrite that uses commonmeta as its intermediary conversion...
A Stablecoin Ecosystem Primer

This article highlights growth and public initiatives related to digital assets, blockchain, and distributed ledger technologies (DLT) in the stablecoin sector. Stablecoins have been a rapidly growing part of the digital asset ecosystem categorized into three subsectors in the DAR Industry Taxonomy Stable & Fiat Backed sector: Fiat Collateralized Stablecoins, Crypto Collateralized Stablecoins, and Algorithmic Non-Collateralized Stablecoins.
RGB-D Video based Hand Authentication using Deep Neural Network &lt;xref ref-type="fn" rid="jist1378fn1"&gt;&lt;sup&gt;&amp;#x2020;&lt;/sup&gt;&lt;/xref&gt;

In recent years, behavioral biometrics authentication, which uses the habit of behavioral characteristics for personal authentication, has attracted attention as an authentication method with higher security since behavioral biometrics cannot mimic fingerprint and face authentications. In behavioral biometrics, extensive research has been done on voiceprints and gait. However, there are few authentication technologies that utilize the habits of hand and finger movements during hand gestures. Only color images or depth images are used for hand gesture authentication in conventional methods. In this research, therefore, we propose to find individual habits from RGB-D images of finger movements and create a personal authentication system. 3D CNN, which is deep learning-based network, is used to extract individual habits. F-measure of 0.97 is achieved when rock-paper-scissors are used as the authentication operation. F-measure of 0.97 is achieved when the disinfection operation is used. These results show the effectiveness of using RGB-D video for personal authentication.
Development of Secure and Novel Methods of Image Encryption Using an Image as Key

Encryption is one of the proven methods for securing documents from unauthorized access. Image Encryption can be applied to documents that are scanned and saved as images. Many text encryption methods are available but are not suitable for documents in image format. Three methods for image encryption are proposed in this article. Key Bitplane Encryption, Key Scan Encryption, and Key RGB Displacement Encryption are the proposed methods for encrypting images. All of these methods share the idea of using an image as the key to encrypt another image. These algorithms are examined for color and grayscale images, as well as for images of various sizes.&nbsp; The three methods outlined in the paper are examined for brute force and common attack types. For each method, a comparison using statistical methods is also given. The methods are implemented in Matlab, and the test images come from the Computer Vision Group Database.
A Class of Quadratic Matrix Equations over Finite Fields

We exhibit an explicit formula for the cardinality of solutions to a class of quadratic matrix equations over finite fields. We prove that the orbits of these solutions under the natural conjugation action of the general linear groups can be separated by classical conjugation invariants defined by characteristic polynomials. We also find a generating set for the vanishing ideal of these orbits.
An Object-Oriented Neural Representation and its Implication Towards Explainable Ai

Rapid dissemination of Artificial Intelligence and machine learning in real-world problems has raised a deliberate concern about the reliability aspects of the model. A separate sub-branch of AI solicitudes on reliability is known as Explainable AI (XAI). XAI analyses the cause and cons of the decision made by AI systems. The neural network plays a significant part in AI's ability to upgrade with the recently evolving facts and data. However, the learning capability left the model obscurest for most of its decisions. Breaking the black-box nature of the neural network model and giving understanding and insight into the model functionality will mitigate the nature of uncertainty. We have designed Object oriented neural representation to devise a feature importance technique from the co-relation between Loss and Weight distribution devised method is effective in interpreting the decision to the end user and AI practitioners with optimal time complexity. The proposed neural representation also extended to incorporate domain/business rules from which we introduced a new Loss known as business loss. After the impact of business loss, we obtained an earlier decline in the Loss and improved performance from the ablation study.
Integrating DLTs with Market Infrastructures: Analysis and Proof-of-Concept for Secure DvP between TIPS and DLT Platforms

The rise of a market for digital assets, both natively digital (e.g., non-fungible tokens, utility tokens) and those representing non-digital resources (e.g., security tokens, stablecoins, e-money tokens), is closely linked with the growing diffusion of distributed ledger technologies (DLT) platforms amonginvestors. The increasing volume of transactions involving digital assets makes it worthwhile toinvestigate how central bank money can contribute to make the settlement safer and more efficient.While this is not strictly needed at the current juncture, central banks should arguably prepare toensure that they can keep central bank money at the core of the settlement process of financiallyrelevant assets. This requires the identification of possible solutions to increase interoperabilitybetween currently independent infrastructures: those regulating the exchange of the digital assetsand those providing central bank money settlement services. A way to achieve this goal is to allowthese transactions to flow through the market infrastructures, where central bank money is safelymanaged. This implies building a “bridge” between DLT platforms and market infrastructures. This paper analyses several technical interoperability models and then describes the results of twoexperimental activities, which evaluate two different solutions for synchronizing the asset-leg andthe cash-leg of a DvP (delivery versus payment) transaction. Both use the Target Instant PaymentSettlement (TIPS) platform to provide the settlement services of the cash leg. The first, named “TIPS Hash-Link”, is a lightweight, API based and DLT agnostic protocol which enables a loosely coupled integration of the market infrastructure with the majority of DLT platforms. Inspired by the Hash‑Time Locked Contracts (HTLC) protocol, TIPS Hash-Link has been specifically tailored to overcome some failure scenarios commonly experienced with HTLC, leveraging TIPS as a trusted escrow for funds and a smart contract to coordinate the DvP operations on the DLT in a safe and consistent manner. The second one, named “TIPS-Algorand Just In Time Locking”, takes advantage of the features offered by a specific DLT platform, Algorand. In particular, it leverages a native feature of the chosen DLT that simplifies DvP transactions guaranteeing atomicity; as such, this approach needs the two systems to be directly connected to interact with each other.
Facilitate Access to Buildings Using a Navigation Mobile application

Due to the presence of a lot of buildings in most universities and the difficulty of finding classrooms, which may cause delays in attending some lectures, especially for the newly joined student, which may affect the educational process. A questionnaire was used for the female students and lectures of Umm Al-Qura University and around twenty-One responses were gathered. Based on the results obtained from the questionnaire, the researchers concluded the need to develop an application that works with the same idea as Google Maps. The data of this research was gathered through the university to show buildings, deanships, and basic facilities such as the university chapel, the library, and the university’s restaurant. The application can be used by entering the name and number of the building, so they can track the location of the current point and the point to be reached, in addition to identifying the facilities other. A plan-driven approach had been used in this research application, it is based on separate development stages with the outputs to be produced at each of these stages planned in advance, in the specification stage, the system analysis is completed, in the design, stage designing the system according to the specification, in implementation stage transfer the system design to an executable application, in testing stage the functions and the features of the system are tested. It is important to have a very detailed specification and design before moving to implementation.
Intelligent Metasurface with Machine Learning for Non-contact Hand Gesture Recognition

we propose a non-contact hand gesture recognition method based on a transmissive metasurface combined with a machine learning technique. Compared with traditional passive antennas, the intelligent metasurface is programmable and has excellent dynamic electromagnetic (EM) focusing capabilities. The slight changes in the forearm muscles caused by finger and wrist movements are dynamically captured using the intelligent metasurface. A variety of common human hand gestures can be classified with high-precision through the EM echo data from five individual focusing spots in the hand and the support vector machine classification algorithm. It can be used for wireless and non-contact hand gesture recognition for people with hand disabilities.
RESIST: Robust Transformer for Unsupervised Time Series Anomaly Detection

In the last decades, Internet of Things objects have been increasingly integrated into smart environments. Nevertheless, new issues emerge due to numerous reasons such as fraudulent attacks, inconsistent sensor behaviours, and network congestion. These anomalies can have a drastic impact on the global Quality of Service in the Local Area Network. Consequently, contextual anomaly detection using network traffic metadata has received a growing interest among the scientific community. The detection of temporal anomalies helps network administrators anticipate and prevent such failures. In this paper, we propose RESIST, a Robust transformEr developed for unSupervised tIme Series anomaly deTection. We introduce a robust learning strategy that trains a Transformer to model the nominal behaviour of the network activity. Unlike competing methods, our approach does not require the availability of an anomaly-free training subset. Relying on a contrastive learning-based robust loss function, RESIST automatically downweights atypical corrupted training data, to reduce their impact on the training optimization. Experiments on the CICIDS17 public benchmark dataset show an improved accuracy of our proposal in comparison to recent state-of-the-art methods.
Principles – The Structure of Services

Architecture is in part about structure and about the way that the various components fit together within that structure; so it’s easy to see how most current styles of enterprise architecture would naturally align themselves with a machine metaphor for the enterprise. In that sense, the nature of architecture itself is part of the problem here.
Practice – Optimizing Services

Another of the architectural concerns arising from a technology-centered approach to service architecture is that almost all of the attention is on service execution and service choreography, with little left over to assess what happens next. The service may be complete at that point from the requester’s perspective, but not to the service provider; there’s often still a fair amount of work that needs to be done after the nominal service request ends.
ORGANIZATION OF TUTORING IN THE CONTEXT OF DIGITALIZATION

Business on the Internet, in our time, is an important part of the entire economy. Modern technologies, such as the Internet, have opened the way to a large number of opportunities not only for obtaining information, knowledge, but also for earning. Recently, a new phenomenon has appeared – tutoring on the Internet. Trends such as open content creation, peer-to-peer problem solving, cloud infrastructure, and smarter audience targeting have enabled countless people to succeed in the tutoring business in a short period of time. While the rapid growth of digitalization and internet access has helped the tutoring industry to a certain extent, it is the pandemic that has changed the trajectory of online tutoring. The COVID-19 outbreak has resulted in the closure of most educational institutions, resulting in significant disruption to learning activities. This, in turn, has dramatically increased the demand for online learning platforms as they facilitate distance learning, provide 24/7 access and more personalized experiences. Due to the increased effectiveness of online learning, even governments around the world are encouraging the public to engage in e-learning, which has made the online learning market highly competitive. The authors present the main global trends in the tutoring business, consider the advantages and difficulties of starting an online tutoring business.
Online Copyright Infringement Tracker Wave 12

Wave 12 - This is independent research carried out by AudienceNet on behalf of the Intellectual Property Office (IPO). Findings and opinions are those of the researchers, not necessarily the views of the IPO or the Government
Managing Peer Review Process of Research Paper

The research paper suggests a web-based evaluation management system for dealing with the complex problem of evaluating research papers in peer reviewing. The trend of Web sites is changing into web applications that acquire logical architecture, interactivity, security, etc. The suggested model aims to tackle the problem of aggregating various ideas in the form of research papers from authors all around the world and evaluating them by assigning papers to different evaluators according to their domain expertise. After taking the papers from the authors, we first determine the plagiarism score using the REST API. Admin can often view the plagiarism score, then admin can assign those papers to evaluators whose plagiarism score is lower than our threshold score. After reviewing the papers by the respective evaluators, if the reviewer rejects a particular paper for any reason, then this system suggests all the upcoming conferences and journals related to the interesting areas of the author so that the author can submit their paper to those conferences and journals by doing some necessary updates suggested by the evaluator. This system collects all the reviews/comments from the evaluators, then analyzes those data using machine learning algorithms which provide us with the key aspects behind the rejection of the research work (like plagiarism score, quality of research, etc.). It is also helpful in determining the interest of authors in various latest technologies related to the world. This entire application is using the storage of Google Cloud which makes this application lightweight and highly scalable. Since it is very difficult to evaluate lots of research papers simultaneously by a single user, the admin divides the submission of the authors between the evaluators so that the unbiased evaluation process goes smoothly and error-free.
Prediction of Mortality Using Machine Learning Algorithms in Post-CABG Patients with ECMO Support

Background: Patients with acute myocardial infarction (AMI) undergoing emergency coronary artery bypass grafting (CABG) with postoperatively extracorporal membrane oxygenation (ECMO) support are associated with higher mortality and morbidity. To identify factors leading to poor outcome in these patients is of significant importance. In this study we predicted the mortality of these patients through machine learning algorithms considering the significant risk factors.
Named Entity Recognition Based on Deep Learning for Power Grid Information

This paper mainly studies the named entity of the power equipment maintenance data such as the power maintenance ticket, the text of the maintenance plan, etc. Based on three models of Hidden Markov Chain Model (HMM), CRF, Bi LSTM-CRF, and CRF, the effect of the model is verified on the BMEO power data set constructed by ourselves. By comparing the effects of the three models, it is concluded that the model effect of BiLSTM-CRFCRF is better than other models in terms of accuracy, precision, recall and other aspects of the model. We regard entity recognition as a problem of sequence labeling, use a labeling framework based on Python to label the text on the power maintenance side, and use the labeling method of BME O, where B represents the beginning of the current entity, and M represents the current entity. The continuation of E indicates the end of the current entity, and O indicates a non-arbitrary entity. The research on the named entity of the maintenance plan in the power field helps us to build a knowledge map in the power field, which makes the power system service more intelligent and information-based, which can help the front-line power maintenance personnel.
A Scheme of Steganography and Transmission Based on AMR-WB Fixed Codebook

As a speech compression standard designated by 3GPP, Adaptive multi-rate (AMR) is currently widely used in various mobile communications. Fixed codebook (FCB) is an important part of AMR coding parameters, and it uses the depth- first tree to search the codebook, so there is a lot of redundancy space, which provides the possibility of embedding. In order to improve the anti-interference capability in the transmission process, this paper proposes a Steganography scheme of AMR FCB with multiple pulses superimposed at the same position and completes these modifications in the original search process, so it can maintain good speech quality and enable it to be applied to actual transmission. To our knowledge, this is the first AMR FCB Steganography scheme that can be applied in practice, and this scheme can be modified and extended to other speech codecs based on algebraic code excited linear prediction (ACELP) principle.