Reviewerly/exHarmony huggingface dataset:
User centered design for the development of health technology: Towards sustainable healthcare systems

User-oriented problems in healthcare application design are a major threat to intervention effectiveness. Despite recommendations that end-users should be involved in the development of these technologies, few studies involve their end-users in systematic and meaningful ways to ensure that applications are customized to their needs and preferences. User-centered design (UCD) involves end-users throughout the development process to ensure that all design aspects and support tasks are easy to operate and of value. This study provides an overview of UCD Methods: to aid researchers in the selection of appropriate methods to improve application design. Additionally, we aim to showcase how end-users can be successfully involved in each stage of the development process. An overview of UCD Methods: has been developed by (1)using an interdisciplinary theoretical approach, drawing on theories from communication science, health psychology, marketing, and health informatics, (2)identifying specific stages of development in which end-user participation is crucial; and (3)understanding barriers to end-user participation. Best practices of UCD will be discussed. Each method has its unique value in contributing to UCD; personas can be used to make design decisions, think-a-loud Methods: are valuable in receiving user feedback about which aspects of technology are confusing. Prototypes are useful to gather user requirements at an early stage of the development while how focus groups are fundamental in getting consecutive feedback about these prototypes. Barriers to end-user participation are a cognitive burden and a lack of affinity with technology. Knowledge about end-user values and needs is essential to create meaningful and effective interventions. Adopting a UCD method will have an immense benefit to end-users as increased usability can have advantages such as reduced stress level, improved accessibility, and reduced threat of damage. This study will help researchers, developers, and designers to create more user-friendly and sustainable healthcare systems.
Quantum Computing Significance on Multidimensional Data

Quantum computing is an emerging field of study and gains importance due to the fact that with the introduction of quantum computers, many challenges and changes are presented for the existing algorithms. The main reason for this is the exponential speed of such computers. This study analyzes some of the benefits and implications of quantum computing on geometrical problems such as the multidimensional search for window queries with R-Trees. A review of the window query on R-Trees in classical computing is done to consider its adaptability to quantum computers by applying the Grover's quantum search algorithm from a theoretical point of view. Thereby, the query time complexity in worst-case scenarios could be improved to quadratic search time.
Reflective Safety Clothes Wearing Detection in Hydraulic Engineering Using YOLOv3-CCD

The construction site of the hydraulic engineering has a high danger factor and the correct wearing of reflective safety clothes ensures the safety of workers. Therefore, the detection and testing of reflective safety clothes wearing is an important task in the construction site of a hydraulic engineering. However, the traditional manual supervision strategy has the problems of low efficiency, narrow scope, and poor real-time performance in complex working conditions. Based on YOLOv3 that is a classical target detection model, this paper proposes a reflective safety clothes detection algorithm (YOLOv3-CCD) based on an attention mechanism and an improved loss function. By adding the CA (Coordinate Attention) mechanism module to the backbone network, the characterization ability of the target feature is enhanced, so as to solve the Long-Term dependencies problem in the detection process; The loss function is changed from IOU-Loss (Intersection Over Union Loss) to CIOU-Loss (Complete-IOU Loss), so that the network takes the aspect ratio into consideration when selecting the prediction box, which improves the accuracy of target positioning; In the post-processing of the algorithm, we improved NMS (Non-Maximum Suppression) to solve the problem of dense target detection being missed. Experimental results show that compared with the original YOLOv3 network model, the algorithm has stronger robustness and the overall detection accuracy is 1.8% higher than that of the original network. Moreover, the detection speed is 32 frames per second, which is faster than the original network.
Forecast of the Trend in Sales Data of a Confectionery Baking Industry Using Exponential Smoothing and Moving Average Models

Starch-containing foods such as bread, pastries, and cakes are usually baked at a moderately high temperature in an oven.When these products are later exposed to room temperature, the associated gelatinized starch begins to harden which causes retrogradation and molecular realignment.Due to this circumstance, manufacturers need to have a fairly accurate estimate of products demand in order to determine the precise amount of baking powder and additives for use in their production so as not to incur losses in their business arising from the stale and consequentially unsalable products.This research was therefore focused on selecting the best forecasting model using a prominent confectionery firm in Abeokuta, Ogun State, Nigeria as a case study.The study was based on 24-week operational period sales data collected from the company.The moving average model and the exponential smoothing model were the two forecasting models considered in this research.The data obtained was thoroughly reviewed and the results of the forecasting models were compared.The most effective model was the exponential smoothing model as it produced the lowest mean absolute percentage error on the average of 3.7347 for the cumulative days of sales under review as against the 15.1713 for the moving average model.However, the exponential smoothing model was considered the best forecasting model for minimizing forecasting error in this study.
The Role of Smart Systems in Enhancing the Performance of Knowledge Management in Libraries Based on the Adoption of Using Expert System and Robots

Purpose: This study aims to enhance the performance of Knowledge management (KM). Additionally, the advantages and the applications of this export system and the smart systems are analyzed.&#x0D; &#x0D; Theoretical framework: Selecting an algorithm isn’t an easy process. With a deep exploration of techniques and algorithms, the appropriate algorithm should be chosen and implemented to ascertain the solution for the problems like analyzing the trend of the business, identifying the age group, and finding the most desired articles and publications.&#x0D; &#x0D; Design/methodology/approach: Contented and Expressive review approaches are implemented to conduct the research. The investigators significantly studied the materials associated with robots and expert systems in the reference to knowledge management in libraries. The results are obtained using the data visualization tool tableau. the Genetic algorithm is also used to analyze the results.&#x0D; &#x0D; Findings: Smart systems are not easy to implement in knowledge management because knowledge management contains a large number of datasets. It has to be categorized first, then needs to be analyzed and the decisions must be taken accordingly.&#x0D; &#x0D; Research, Practical &amp; Social implications: The expert systems and the robots are to be implemented in the KM so the knowledge management will have enhanced performance with the help of the implementation of smart systems.&#x0D; &#x0D; Originality/value: In the study, the Genetic algorithm is used to find the analysis results. This algorithm was chosen because it works well in a noisy environment and is also easy to understand along with this GA is compared with the neural network algorithm.
Blockchain and its Applications: A Conceptual Legal Primer

ABSTRACT Blockchain is a potent buzzword and a potentially transformative technology. Diverse businesses are looking to leverage this technology to reap its alleged benefits of increased efficiency, reduced costs, enhanced transparency, and improved traceability. Yet, significant confusion persists about the structure, utility, and applicability of blockchain technology. A clear understanding of what blockchain is and how it works is especially important for lawyers advising clients on the applications that use, or purport to use, it. This article aims to assist lawyers and regulators by providing conceptual clarity about what blockchain is, how it works, and its main use cases. These use cases include smart contracts, cryptoassets, and some central bank digital currencies. These applications may transform business practices, or even entire monetary and payment systems, so conceptual clarity among lawyers will only become more vital.
Development of the Digital Accounting and Its Impact on Financial Performance in Higher Education

The emergence of digital society in industry 4.0 is one of the most visible changes in the XXI century. The creation of digitization has led to significant changes in accounting and financial management as features of an innovative University. This study aims to analyze the development of digital accounting and examine its impact on economic performance in higher education by using IBM SPSS Statistics 25 and the feasibility of the investment using the payback period approach. The development of digital accounting is based on a web 2.0-based ICT system using the Software Development Life Cycle (SDLC) method with the waterfall model and applying the latest financial accounting standards. Analysis of the impact of accounting digitization was carried out on 247 educational staff as a population and involved 152 respondents as samples selected using the Slovin formula. Data analysis consisted of descriptive statistics, correlation, and multiple regression analysis with an error rate of 5%. The findings showed a positive correlation between financial performance and accounting digitization. Accounting digitization significantly affected financial arrangement with a P-value of 0.000 is smaller than the alpha (α) of 0.05 or sig.T&lt; 0.05, which means significant. The investment feasibility test using the payback period concludes that digital accounting is feasible to implement in higher education. The eligibility criteria based on the results of calculating the rate of return on investment is three years and two months, faster than the required payback period of four years.&#x0D; &#x0D; Received: 18 December 2022 / Accepted: 10 February 2023 / Published: 5 March 2023
The Readiness of Accounting Departments Management and Implementation of the Industrial Revolution Curriculum 4.0

The Industrial Revolution 4.0 brings a threat to accounting graduates because many fields of accounting work can be replaced by information technology. For this reason, the accounting department in higher education must be ready with the industrial revolution 4.0 curriculum. This study aims to analyze readiness of managers of accounting study programs and the implementation of the industrial revolution 4.0 curriculum. The research is categorized as interpretive qualitative. Data were collected using in-depth interviews, focus group discussions (FGD), and documentation. The key informants in this research are the manager of the accounting study program and a team of experts from the Indonesian Institute of Accountants. The results of the study stated that the accounting department had prepared various steps to implement the industrial revolution 4.0 curriculum. The readiness of the accounting department manager and the implementation of curriculum 4.0 is carried out in several stages, namely adjusting the graduate profile, redesigning the curriculum, changing learning methods, improving the quality of human resources, improving laboratory infrastructure, and establishing partnerships with stakeholders. This study also recommends courses that are relevant and in accordance with the industrial revolution 4.0. The implication of this research is that the accounting study program has guidelines for implementing the industrial revolution 4.0 curriculum.
Iterative Integrated Planning and Scheduling Model in Project Management

Abstract Quite often, project planning and scheduling are used interchangeably but they have different roles in the successful completion of a project. On a higher level, the project plan is the master blueprint which drives the project towards achieving objectives while the project schedule details the specific tasks to be executed by individual or set of groups to achieve project's goal. Project planning primarily involves selecting the appropriate procedures and methodologies required to deliver the project on time. Project scheduling, on the other hand, converts the defined plans, scope, and cost into an operational timeline with specific set of objectives within allocated budget. Main factors in planning and scheduling those differentiate these two phases in the project management specified in the below table. FactorsPlanningSchedulingDeals withWhat and HowWhen and WhoDefinitionPlanning is process of developing a tool to design and produce the desired unique deliverables.Scheduling is the process that defined the time frame in which the project's deliverables will be accomplished and the sequence of tasks.Focus onOverall project objectives and business value creation.Assign various sets of tasks to the available resources for a specified time duration.ImpactFrom beginning until end of the project.During project execution and closure. Some organizations integrate the jobs and functions of planning and scheduling in a single position. There are benefits to this approach because a project scheduler can't fully understand the nitty gritty when it's time to have resources on the floor without also having firsthand information about the project's entire scope in place. This approach gives remarkable results when the project is small to medium with less engineering disciplines involved. However, for the medium to large scape project will require dedicated functions of planning and scheduling independently.
Financial Literacy As a Necessary Component of the Highly Qualified Personnel Training System

The article is devoted to the analysis of the problem of financial literacy of young people based on the results of a sociological survey, in which 158 students of Omsk universities of various areas of training took part. The relevance of teaching financial literacy in a modern university according to a 5-point system was rated by about 80% of respondents at least 4 points. Thus, the results of the study allow us to conclude that it is necessary to introduce special courses on financial literacy in the system of training highly qualified personnel or to include relevant sections in the study of disciplines of economic-mathematical and information-technological cycles.
Fowlkes-Mallows Correlated Cohen Kappa Coefficient Block Matching based Multi-Layer Perceptron Classification for Motion Estimation in VLSI

Motion Estimation (ME) constitutes an essential process in video coding with lesser processing time. Many methods were designed for efficient motion estimation in the VLSI architecture. However, the consumption of power was not reduced through existing techniques. Fowlkes–Mallows Correlated Cohen Kappa Coefficient Block Matching-based Multi-Layer Perceptron Classifier (FMCCKCBM-MLPC) Model is introduced to handle such limitations. FMCCKCBM-MLPC Model is used for increasing the motion estimation of video series in the VLSI architecture circuits. Multi-Layer Percepton is used for examining the feature and performing classification with the multiple layers. Input is sent to the hidden layer 1. In hidden layer 1, the segmentation process is performed by Fowlkes–Mallows Regularized Principal Component Regression to reduce the time loss. In hidden layer 2, the block matching is carried out by Cohen Kappa Coefficient with the segmented blocks. With Cohen Kappa Coefficient, the repeated blocks are predicted with lesser loss in the video series. This helps to enhance motion estimation in the VLSI architecture. FMCCKCBM-MLPC Model is computed in terms of power and time consumption. The simulation result of the FMCCKCBM-MLPC Model minimizes the area, PSNR, delay and power consumption of motion estimation in the video series with existing techniques.
A Systematic Review of Pedagogical Practices in the Education 4.0

Demands of the twenty-first century and industrial's needs are obviously changing, while many jobs and skills were not needed or, to some certain levels, are irrelevant in the job market, rendering some graduates locally and internationally unemployed, which leads some time to an economic dilemma.The current rising in technological evolution in the business world is known as the Industry 4.0 revolution.Simultaneously, education 4.0 is designed to bridge the gap between graduate job market requirements, the technology landscape changes, and the content of learning in institutions of learning globally.The primary purpose of this study was to perform a literature review to identify and classify the pedagogical practices that were most effectively used in education 4.0 to promote the acquisition of 21st-century skills among graduates.Mainly, a systematic review approach was used to explore past studies on Pedagogical Practices in Education 4.0; from 2018 to 2022 applying the PRISMA method in the review process.The study identified the stimulation of students -instructors' readiness and involvement as crucial elements to facing the challenges of education 4.0.It is a critical factor in obtaining education 4.0 resolution skills and enhancing character education in developing soft skills, curriculum design, learning modules, assessment strategy, and classroom experience.In conclusion, effective integration of education 4.0 into the learning institutions is visible with a paradigm shift by infusing education 4.0 elements into the curriculum, its pedagogical practices and stimulating enthusiasm among students and instructors.Thus, an adequate budget must be allocated to learning institutions with innovative monitoring systems, especially for developing countries, not to lag behind developed
Improving performance of decision threshold moving-based strategies by integrating density-based clustering technique

&lt;abstract&gt; &lt;p&gt;Class imbalance learning (CIL), which aims to addressing the performance degradation problem of traditional supervised learning algorithms in the scenarios of skewed data distribution, has become one of research hotspots in fields of machine learning, data mining, and artificial intelligence. As a postprocessing CIL technique, the decision threshold moving (DTM) has been verified to be an effective strategy to address class imbalance problem. However, no matter adopting random or optimal threshold designation ways, the classification hyperplane could be only moved parallelly, but fails to vary its orientation, thus its performance is restricted, especially on some complex and density variable data. To further improve the performance of the existing DTM strategies, we propose an improved algorithm called CDTM by dividing majority training instances into multiple different density regions, and further conducting DTM procedure on each region independently. Specifically, we adopt the well-known DBSCAN clustering algorithm to split training set as it could adapt density variation well. In context of support vector machine (SVM) and extreme learning machine (ELM), we respectively verified the effectiveness and superiority of the proposed CDTM algorithm. The experimental results on 40 benchmark class imbalance datasets indicate that the proposed CDTM algorithm is superior to several other state-of-the-art DTM algorithms in term of G-mean performance metric.&lt;/p&gt; &lt;/abstract&gt;
Supervised, Unsupervised and Semi-Supervised Word Sense Disambiguation Approaches

Word Sense Disambiguation (WSD) aims to help humans figure out what a word means when used in a certain setting. According to the Neuro Linguistic Programming (NLP) community, WSD is an AI-complete issue with no human solution in sight. WSD has found widespread usage in a wide variety of applications, including but not limited to: Machine translation (MT), Information Retrieval (IR), Data Mining (DM), Information Extraction (IE), and Lexicology (Lex). It is discovered that WSD may be learned effectively using a variety of different methodologies, including supervised, semi-supervised, and unsupervised methods. These methodologies are sorted into groups according to the kind and quantity of annotated (identified) corpora (data) they need as the primary source of information utilized to distinguish between senses. The unsupervised method employs unannotated (unidentifiable) corpora for training, whereas the semi-supervised method requires a less number of annotated corpora than supervised methods. All these three strategies will critically be discussed in this study.
How Duolingo's AI Learns what you Need to Learn: The language-learning app tries to emulate a great human tutor

It's Lunchtime when your phone pings you with a green owl who cheerily reminds you to “Keep Duo Happy!” It's a nudge from Duolingo, the popular language-learning app, whose algorithms know you're most likely to do your 5 minutes of Spanish practice at this time of day. The app chooses its notification words based on what has worked for you in the past and the specifics of your recent achievements, adding a dash of attention-catching novelty. When you open the app, the lesson that's queued up is calibrated for your skill level, and it includes a review of some words and concepts you flubbed during your last session.
Real-Time Evaluation of Perception Uncertainty and Validity Verification of Autonomous Driving

Deep neural network algorithms have achieved impressive performance in object detection. Real-time evaluation of perception uncertainty from deep neural network algorithms is indispensable for safe driving in autonomous vehicles. More research is required to determine how to assess the effectiveness and uncertainty of perception findings in real-time.This paper proposes a novel real-time evaluation method combining multi-source perception fusion and deep ensemble. The effectiveness of single-frame perception results is evaluated in real-time. Then, the spatial uncertainty of the detected objects and influencing factors are analyzed. Finally, the accuracy of spatial uncertainty is validated with the ground truth in the KITTI dataset. The research results show that the evaluation of perception effectiveness can reach 92% accuracy, and a positive correlation with the ground truth is found for both the uncertainty and the error. The spatial uncertainty is related to the distance and occlusion degree of detected objects.
Efficient Cyber-Evidence Sharing Using Zero-Knowledge Proofs

A common challenge for organisations managing confidential customer information is to respect obligations due to legal requirements while advocating the privacy of their users’ data. In the context of digital forensics and cyber security scenarios, we define cyber evidence sharing as the task of verifying that mutual knowledge exists about confidential information or digital evidence, without revealing or disclosing the information itself. An attractive cryptographic solution to this problem is the concept of Zero-Knowledge Proofs (ZKPs). In this paper, we propose a flexible and efficient approach for sharing cyber evidence based on using ZKPs. We present a protocol that allows a verifier to establish the proof of knowledge of hash digest information. This provides computational security and can be implemented efficiently using a Merkle-tree data structure. The protocol has been implemented, the resulting proof-of-concept system is evaluated, and its efficiency is demonstrated. We believe that it could be a valuable tool for use in practical real-world applications.