Reviewerly/exHarmony huggingface dataset:
Grocery Store Management System

A Grocery store management system helps the Grocery shops to streamline all the store operations well and run the business without any hassle. With a POS software for a Grocery store, you can offer the best customer experience, increase your customer retention rates and attract more customers. You may use it to keep expenses in check, boost sales, and make customers happier. Optimal inventory levels, reduced costs, and continued competitiveness in the dynamic grocery retail market are all possible outcomes of an investment in an inventory management system. A Grocery software is a fast, reliable, and one-stop solution that facilitates easy and efficient operations by automating with the help of technology. The Grocery stores can manage all their store operations in a single software which includes Sales, Purchase, Inventory, Accounting, CRM, Loyalty, and many more. Thus, the basic functions, to manage a store, carried out are:1. Receiving of incoming consignments (goods) 2. Safe keeping of goods (Custody)3. Disposal of undesirable goods.4. Inventory Management.5. Housekeeping and record maintenance.
The Students’ Perception towards the Use of Icebreakers, Warmers, and Energizers in EFL Classrooms: A Tertiary Study

This study examines first-year students’ perceptions in the English Language Teaching Department at Tishk International University regarding using icebreakers, warmers, and energizers in the classroom. The research aims to assess the effectiveness of these activities in enhancing student engagement and participation, as well as their impact on the overall learning experience. The study contributes valuable insights into the practical applicability of these techniques in an educational setting. Additionally, the positive perceptions of the students underscore the potential of these activities in establishing a favorable and comfortable learning environment. Data were collected through surveys and interviews, employing both quantitative and qualitative methods. For the quantitative aspect, 32 participants (10 males &amp;amp; 22 females) were surveyed, while the qualitative segment involved interviews with 10 students (five males &amp;amp; five females) at Tishk International University. The study’s results indicate that the overwhelming majority of students hold a positive perception of the use of these activities. They view them as beneficial for creating a relaxed and comfortable learning atmosphere, fostering interaction and collaboration among classmates, and boosting motivation and interest in the language. The study suggests that incorporating these activities into EFL classrooms can significantly enhance the overall student learning experience and contribute to a more dynamic and interactive classroom environment. The findings are consistent with existing literature in the field.
The development of an explainable machine learning system for predicting brain atrophies from electronic health records

Abstract Background The number of elderly living with dementia is projected to dramatically increase, especially in low‐income and middle‐income countries (LMICs). While obtaining brain scans to detect atrophies and significant radiology findings related to dementia might be costly, the use of electronic health record (EHR) for elderly patients could facilitate identifying patients at a high risk based on routinely collected data. This works, evaluates the use of an interpretable machine learning‐based system that predicts brain atrophies and small vessel disease based on EHR data. Method We develop a machine learning predictive system to predict atrophies or small vessel disease from retrospective anonymized EHR data recorded for patients above the age of 50 years from a multispecialty outpatient medical center from Ramallah, Palestine. The patient labelling involved using natural language processing to identify cases in unstructured radiology reports for structural brain Magnetic Resonance Imaging (MRI). For input features, we extracted and preprocessed 30 features for the most commonly collected clinical features such as demographics and laboratory results. The dataset was split into training and test sets, where data preprocessing and augmentation, and hyperparameter tuning were applied before training multiple models including Xgboost, Support Vector Machines, Random Forest, and Logistic Regression. To better understand the relative impact each feature had on the model’s prediction, shapely feature importance analysis was conducted for the top performing model. Result The top preforming model was Random Forest, with a performance of Area Under the Precision‐Recall Curve (AUPRC) of 0.989, and 0.842 for Area Under Receiving Operating Curve (AUROC). The feature importance analysis revealed that age, sex, Vitamin D, Vitamin B12 and red blood cell distribution width were the features with highest impact on the model’s prediction Conclusion Predicting atrophies from EHRs for elderly visiting outpatient clinics could assist in case‐finding for dementia patients and ultimately help identify patients that would benefit from an MRI. In future works, we aim to further investigate building low‐cost diagnostic tools based on a smaller feature set, which could be valuable for facilities from LMICs.
Explainable Artificial Intelligence using Model Agnostic Methods in detecting Alzheimer’s disease

Abstract Background Alzheimer’s disease (AD) results in cognitive dysfunction among older people, making their lives nearly impossible. Early intervention is likely to be the most effective way to slow its progression. Machine learning models are critical to determine whether a patient is demented or not at an early stage. But the most highly accurate models are non‐linear and less transparent, so the logic behind the predictions are not revealed. In this study, a neural network model was created, and two model‐agnostic methods were applied on it to understand the important features the model used to make predictions. Method A total of 25 features were selected from Oasis‐2 dataset of 150 participants. A feature selection is performed using KRUSKAL algorithm and 10 potential features were selected. A feed forward neural network model with sigmoid hidden neurons and softmax output neurons was created for classification of demented and non‐demented cases. Then, two model agnostic methods, LIME (Local Interpretable Model‐Agnostic Explanations) and SHAP (SHapley Additive exPlanations) were applied on the model to identify the features contributing to the prediction. LIME was used to generate a human‐readable explanation for a single prediction by approximating the behavior of the model locally around the instance being predicted. SHAP is a method based on the concept of Shapley values from cooperative game theory. It assigns a contribution value to each feature of the model, indicating how much that feature contributes to the final prediction Result Neural network resulted in an accuracy of 91.4%. It is found that Clinical Dementia Rating (CDR) is the most significant feature, followed by the normalized brain volume (nWBV). CDR is also observed to positively influence the detection of dementia cases, whereas the nWBV has a negative impact, which is also true clinically. Additionally, the contribution of each feature to the prediction is shown, which may assist in the evaluation of the model’s trustworthiness. Conclusion The goal of this research is to create a neural network model that is highly accurate and highly interpretable so that any non‐technical stakeholder can understand the reasoning behind the predictions. So, accuracy combined with interpretability increases the reliability of AD prediction systems.
Research on the Methods and Strategies of Heart-to-Heart Conversation of University Counselors in the New Era

Heart-to-Heart conversations represent a primary mode of operation for counselors in higher education. They serve as a crucial means for counselors to fulfill their fundamental mission of cultivating virtue and character, reinforcing ideological and theoretical education, and guiding values among university students. These conversations play a pivotal role in establishing positive teacher-student relationships and stand as essential indicators for assessing the capabilities and dedication of academic counselors. In the context of the new era, academic counselors must closely align with the changing ideologies and behaviors of the "Z-generation". By adopting the perspective of "Comprehensive Nurturing", emphasizing education for all, throughout the entire process, and in all dimensions, they should explore innovative approaches and strategies in heart-to-heart conversations. This exploration aims to better guide the thoughts and behaviors of university students, empowering them to become competent individuals capable of shouldering the responsibilities of national rejuvenation in this new era.
The Correlation between Business English Freshmen's Learning Motivation and Self-efficacy

Researchers have conducted numerous studies on the phenomenon of learners' motivation in foreign language acquisition; however, there is a paucity of research examining the relationship between English learning motivation and self-efficacy among Business English freshmen. This study aims to address three key questions to explore the correlation between English learning motivation and self-efficacy among Business English majors: (1) What is the level of English learning motivation among first-year business English majors? (2) What is the overall status of self-efficacy in English learning among first-year business English majors? (3) What is the association between English learning motivation and self-efficacy among first-year business English majors? Subsequently, employing SPSS data analysis, this research yields several noteworthy findings: firstly, the level of English learning motivation among first-year business English majors is significantly high; secondly, their perceived self-efficacy in mastering the language falls within a moderate range; finally, the relationship between English learning motivation and self-efficacy demonstrates a positive correlation (R=.595*).
Legal Service Development for Industrial Enterprises in the Greater Bay Area with Patent Circulation Driven by Intelligent Valuation

Based on the research on industrial enterprises in the Greater Bay Area, this paper analyzes the patent legal risks faced by industrial enterprises in the Greater Bay Area, the market demand for legal services of patent agents and the patent transaction process, and puts forward the idea of legal services for industrial enterprises in the Greater Bay Area according to the characteristics of patent circulation driven by intelligent valuation. Driven by intelligent valuation, industrial enterprises in the Greater Bay Area will usher in brand-new development opportunities, and their legal services will expand from single intellectual property management to multiple fields such as intellectual property risk warning and dispute resolution.
An Example of a Supporting Combination by Using GA to Evolve More Advanced and Deeper CNN Architecture

It has become an annual tradition for Convolutional Neural Networks (CNNs) to continuously improve their performance in image classification and other applications. These advancements are often attributed to the adoption of more intricate network architectures, such as modules and skip connections, as well as the practice of stacking additional layers to create increasingly complex networks. However, the quest to identify the most optimized model is a daunting task, given that state of the art Convolutional Neural Network (CNN) models are manually engineered. In this research paper, we leveraged a conventional Genetic Algorithm (GA) to craft optimized Convolutional Neural Network (CNN) architectures and pinpoint the ideal set of hyper parameters for image classification tasks using the MNIST dataset. Our experimentation with the MNIST dataset yielded remarkable results. Compared to earlier semi-automatic and automated approaches, our proposed GA demonstrated its efficiency by swiftly identifying the perfect CNN design, accomplishing this feat in just 6 GPU days while achieving an outstanding accuracy of 95.50%.
Breach Notification in the General Data Protection Regulation

The EU General Data Protection Regulation (GDPR) introduced new standards for data breach notification. Articles 33 and 34 of the Regulation require that in the event of a data breach, the supervisory authority and data subjects must be informed. This paper discusses the European legal framework for data breach notification and its implications for organizations, data subjects, and supervisory authorities. By analyzing the main provisions, deadlines, and requirements of the Regulation, it examines the problems and possibilities of the data breach notification system provided for in the Regulation. It highlights the transformative impact of the breach notification provisions on data security, privacy, and liability. By examining breaches from the perspectives of legal obligations, organizational responsibilities, and individual and user rights, we aim to shed light on the complex dimensions of this critical element of data protection and its profound impact on data protection practices in the digital age. Ultimately, this study serves as a benchmark for the GDPR’s breach notification provisions with the US California Consumer Protection Act and the Canadian Privacy and Electronic Documents Act. As technology continues to evolve with artificial intelligence, big data, blockchains, and the Internet of Things, new security gaps and data processing methods will emerge that will set new standards for data breach notification.
DIGITALIZATION OF HIGER EDUCATION IN THE CONTEXT OF THE COVID-19 AND THE POST-PANDEMIC PERIOD: METHODOLOGICAL ISSUES: METODOLOGICAL PROBLEMS

The article discusses expanding the research base in the field of educational psychology through the analysis of methodological issues that arose during the process of systemic digiti­zation of general and professional education in Russia during the COVID-19 pandemic and the post-pandemic period. Digitization during these periods accelerated the transformation of socialization through educational means in a virtual learning environment. The fundamental problem of digital transformation in education is that the digital format of education in its modern distance form contradicts the contemporary humanistic paradigm of education, char­acterized by the priority of subject-subject relationships, ideas of inclusion, and interests in socialization in the process of learning and upbringing. Prospects for further research lie in studying the transformation of modern educational methodology in the context of digitiza­tion, creating relevant psychological foundations for virtual pedagogical communication to develop social-emotional skills and emotional intelligence.
KEY GUIDELINES OF EDUCATIONAL POLICY IN THE CONTEXT OF BUILDING A NEW MODEL OF HIGHER EDUCATION

The author examines the main aspects of the modern university activity related to the transformation of the educational system and current socio-cultural conditions of society. Possible strategies for organizing the educational process in a modern Russian university were formulated based on the integration of existing developments in higher education man­agement. These strategies create conditions for responding to the educational needs of young people, taking into account the value orientations of the youth community. The proposed solu­tions are based on the principles of interdisciplinarity, integration of science and education, flexibility, and adaptability. A model for designing and developing educational programs in the context of higher education transformation is described.
THE RELATIONSHIP BETWEEN STUDENTS' TENDENCY TO MANIPULATION AND THE RESULTS OF PROJECT ACTIVITIES IN UNIVERSITY EDUCATION

The results of a scientific study are presented, with participants being students enrolled in the Bachelor's program in "State and Municipal Management," whose future professional activities are closely related to interpersonal relationships in professional communication. The experimental base for the study was provided by the Western Branch of the Russian Academy of National Economy and Public Administration under the President of the Russian Federa­tion (RANEPA). The students' inclination toward manipulation was assessed using T. Bant's scale. The research showed that the inclination to use manipulative techniques and methods in the process of project-based learning at the university negatively affects the overall results of project activities of individual creative groups.
ITor-SDN: Intelligent Tor Networks Based SDN for Data Forwarding Management

Tor (The Onion Routing) network was designed to enable users to browse the Internet anonymously. It is known for its anonymity and privacy security feature against many agents who desire to observe the area of users or chase users’ browsing conventions. This anonymity stems from the encryption and decryption of Tor traffic. That is, the client’s traffic should be subject to encryption and decryption before the sending and receiving process, which leads to delay and even interruption in data flow. Keys Exchange between network devices plays a fatal role in performing cryptographic procedures. To overcome delay or interruption problems, we utilized the Software-Defined Network (SDN), Machine Learning (ML), and Blockchain (BC) techniques, which support the Tor network to intelligently speed up exchanging the public key via the proactive processing of the Tor network security management information. Consequently, the combination network (ITor-SDN) keeps data flow continuity to a Tor client.We simulated and emulated the proposed network by using Mininet and Shadow simulations. The results show that the proposed network improves the overall performance by approximately 55% by executing the ITor-SDN network combination approach.
Unmanned Aerial System Trajectory Tracking based on Diversified Grey Wolf Optimization Algorithm

Trajectory tracking is one of the most important aspects of an unmanned aerial system (or quad-copter) for selecting the optimal path from the source to the destination. This article presents a mathematical framework and approach for addressing the challenge of nonlinear systems like quad-copter. A novel control system for the quad-copter’s positions ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> ) and attitudes (roll (ϕ ), yaw (ψ ), pitch (θ )) has been proposed based on optimisation techniques that are integrated with proportional-integral (PI) controllers. Astute position update methods such as helical, circular, etc. have been introduced using different algorithms like particle swarm optimization (PSO), grey Wolf optimization (GWO), and the diversified grey wolf optimizer (DGWOA) algorithm. Following that, in an iterative procedure, a variety of leadership levels are used to update the individual’s position, and the leadership is modified through the use of an adaptive mechanism. For validation, the proposed algorithm’s effectiveness is evaluated based on the convergence rate compared to that of other meta-heuristic algorithms. Owing to its inadequate exploration, PSO leads to challenges with parameter selection, whereas GWO is easy to get to the local optimum. The concept and execution of DGWOA have been implemented to update the Unmanned Aerial Systems (UAS) controlled parameters in order to overcome these limitations. The proposed algorithm’s performance for path planning in a complex and cluttered environment is investigated. The simulation shows that the DGWOA algorithm has a faster response as compared to the reference, ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> ) has been improved with (92.87, 96.95, and 99.69) percentage along with eliminating the shortcoming of PSO & GWO.
Blind Channel Estimation and Data Detection with Unknown Modulation and Coding Scheme

Blind signal processing techniques of channel and noise power estimation, modulation and channel coding scheme (MCS) recognition, and data detection have played crucial roles in wireless transmission scenarios, where the receiver cannot obtain <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information of the incoming signals. Each of these blind signal processing tasks has been extensively studied in the literature. Few works studied the combination of partial tasks jointly. However, to the best of our knowledge, an overall problem that involves all the aforementioned tasks has not been investigated previously. Simply cascading the solution of each individual task is apparently not a suitable approach for the overall problem. To address the above issues, we design a joint <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">blind</i> receiver that jointly improves the performance of parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">estimation</i> , MCS <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recognition</i> , and data <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">detection</i> . Thus, we refer to it as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">blind estimation</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recognition</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">detection (BERD)</i> receiver. The multipath fading channel, noise power, and MCS of the transmitter are all unknown to the receiver side; the required side information is only the pool of MCS candidates and perfect synchronization. In this BERD receiver, we first propose an expectation-maximization-based channel and noise power estimator block, which solves the non-convex maximum likelihood estimation problem in a tractable way. Then, a soft-information detector and regenerator block is designed to detect the transmitted information bits and regenerate more reliable symbols. The BERD receiver iterates between these two blocks, and consequently, the accuracy of channel estimation is improved and in turn helps the data detection. Finally, a multistage likelihood-based fusion and decision block is proposed to make the final decision on the adopted MCS, the information bits, and the unknown channel information. Numerical results are provided to show the superior performance of the proposed BERD receiver compared to the existing schemes, in terms of data detection, MCS recognition, and channel estimation.