Reviewerly/exHarmony huggingface dataset:
Efficient Conversational AI Agent to Improve Rural and Urban Healthcare

Conversational AI agents are software programs which works exactly like humans, they interpret the users and accordingly react to the inputs given by them. These agents are built considering the medical interventions required to improve the overall health of the society. The AI agent designed acts intelligently during the process of the interaction between the humans and itself. It allows the user to use the interface by asking interactive questions then it processes them and responds relatively. Conversational agents are not only web based but they can also be used on other platforms like mobile phone or any other mobile devices. Despite all these a user shall be satisfied if and only if the software is easy to use and obtains the exact results with all of the queries being answered. The main concern with this model is to give that ease to the user to interact with the agent thus solving the queries related to the symptoms suffered by the patients and hence predicting the disease at an early stage by maintaining the accuracy. There are around 100000 diseases in the world according to WHO. Most of their symptoms overlap as well hence by using this agent its possible by it to think insightfully and predict the early symptoms of the disease. In this paper we have designed a user interface and this interacts with the user to take the necessary inputs. This data is fed to the advanced Natural Language Understanding (NLU) to provide the personalized prediction based on the user interaction. The predictions done by the model uses the classification algorithms of Machine Learning. The accuracy of each of these algorithms varies. Therefore instead of considering only one algorithm and hoping it gives the best accuracy, we can use the Ensemble learning method to improve the overall prediction rate. This method gives better predictive indications as it combines many models results thereby improving the overall precision. Here we train our model using various algorithms and ensemble them to get the final results based on the technique of voting. This paper presents a front-end interface for common man using HTML and Angular JS, NLU for text pre-processing using Tensorflow method and ML model as a classifier, for the prediction which uses various machine learning algorithms like SVM, Decision Tree, Random forest etc and combines them all in a majority voting ensemble for balanced results. Therefore this model interacts with any patients be it from the rural or the urban and based on their symptoms predicts and ranks the most probable disease accurately and reliably.
The process of teacher autonomy and awareness about the context of play for children’s learning

본 연구의 목적은 2020년 개정 표준보육과정에서 강조되는 아동중심⋅놀이중심의 강조점을 기반으로하여 ‘영유아의 놀이맥락 안에서 영유아의 배움에 대한 교사의 이해와 인식은 어떠한가?’와 ‘영유아의 놀이맥락에서의 배움이 이루어질 때 발휘되는 교사의 자율성 실행과정은 어떠한가’를 연구문제로 선정하였다. 이에 영유아의 놀이가 배움으로 가는 놀이맥락에 대한 교사의 인식과 이해, 해석의 과정에서 실행되는 교사의자율성 실행과정을 총체적으로 살펴보고 제4차 표준보육과정의 현장적용 시 강조되는 영유아의 놀이 맥락에대한 교사의 이해와 영유아의 배움이 어떻게 일어나며 이때 교사가 발휘하는 자율성 실행에 대한 인식을제시하여 현장에 이해를 도와 비판이 아닌 나아가야 할 현장의 방향성 논의를 위해 현장의 상황을 진단하고성찰하는 것을 목적으로 한다. 본 연구는 어린이집에 근무중인 교사 4인을 교사발달단계 이론에 근거하여보육경력 3년 이상인 참여자를 선정하였다. 연구절차는 참여관찰, 심층면담, 교사의 반성적 저널쓰기의 과정을 통해 역동적이고 통합적인 차원에서 교사의 영유아 놀이맥락과 그 과정에서 나타나는 자율성 실행에대한 인식을 분석하였다.The purpose of this study is based on the emphasis on child-centered and play-oriented emphasized in the 2020 revised standard childcare process. The research question of this study was "What is the teacher's understanding and perception of infants' learning in the play context of infants?" and s"What is the teacher's autonomy implementation process used when learning in the play context of infants?" We would like to examine the overall process of implementing teachers' autonomy in the process of teachers' perception, understanding, and interpretation of the play context in which infants' play goes to learning. In addition, we would like to find out what is emphasized in the field application of the 4th standard childcare process. We would like to diagnose and reflect on the field situation to discuss the direction of the field, not criticism, by presenting teachers' understanding of the play context of infants and toddlers and how they learn. In this study, four teachers working at daycare centers were selected based on the teacher development stage theory, and participants with more than three years of childcare experience were selected. The research procedure analyzed teachers' perceptions of infant play contexts and autonomy implementation in the process at a dynamic and integrated level through participatory observation, in-depth interviews, and teachers' reflective journal writing processes.
Beyond Hiding and Revealing: Exploring Effects of Visibility and Form of Interaction on the Witness Experience

Our interactions with technology do not just shape our individual experiences. They also affect people around us. Although previous research has addressed such "witness" experiences, the actual effect of interaction design on the witness experience remains largely unknown. In an online study (n = 407), we explored how witnesses perceive mid-air gesture-based interactions with a hearing aid, using four video vignettes. We studied witnesses' subjective visibility of manipulations and effects (following Reeves and colleagues' taxonomy), perceived form of interaction, subjective experience, and relationships between these measures. Although visibility patterns matched the intended form, they did not lead to the supposed experience (i.e., "suspenseful" gestures did not lead to suspenseful experiences). The paper illustrates gaps in current research about witness experiences, demonstrates the need to overcome basic hiding/revealing profiles, and indicates a path forward by focusing on aesthetic forms and experiences.
Cooperative Bargaining Game Based Adaptive Video Multicast Over Mobile Edge Networks

Video delivery over wireless networks with limited network resources and dynamically changing channel quality is an important challenge, and one of the most promising solutions for tackling this problem is to employ multicast transmissions, which improves network resource utilization efficiency. This paper focuses on delivering video concurrently to multiple users over mobile networks leveraging Multicast Broadcast Multimedia Service (MBMS) and Mobile Edge Computing (MEC) technology. We propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means clustering and cooperative bargaining game-based adaptive video multicast solution (KGS) over mobile edge networks, with the goal of providing high-quality video delivery service in an envisaged MBMS service area across multiple cell sites. By taking user subgrouping, resource allocation, and bitrate adaptation into account, we establish a Cooperative Bargaining Game (CBG) based joint optimization model for multiple Multicast Broadcast Synchronized Frequency Network (MBSFN) users in mobile edge networks. Then we transform this model into a two-stage convex optimization problem and a nonlinear integer programming problem. We propose a heuristic approach to solve them and achieve a Pareto optimal video delivery strategy for all users. Finally, the efficiency of the proposed scheme is evaluated through extensive simulations.
$\mathsf {moKHS}$: A More Lightweight Model for Multi-Client Delegatable Computation

Multi-client delegatable computation over outsourced data is an important research area in the context of cloud computing. The main target of it is to produce a function value taken data owned by different users as input while assuring the correctness of the derived result efficiently verifiable. Multi-key homomorphic signature (MK-HS), which allows for computations over signed data originating from multiple users while providing authenticity for the output value, is considered as an elegant primitive to fulfill the above functionality. However, we figure out several deficiencies in the context of MK-HS: (1) the model of MK-HS inevitably induces the participation of a large set of distinct public keys during the procedures of evaluation and verification, which leads to high communication and computation complexity; (2) current MK-HS schemes based on standard assumptions cannot prevent insider corruption attack; (3) the signature succintness of existing MK-HS constructions is far from optimal. In this work, we aim to seek a more appropriate approach for realizing multi-client delegatable computation. Our main result is the introduction of a new model we call <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">message-oriented key-homomorphic signatures</i> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {moKHS}$</tex-math></inline-formula> ). This model provides more lightweight definitions and inherently resists to insider corruption attacks. Besides, we provide a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {moKHS}$</tex-math></inline-formula> construction based on standard lattices which supports circuits of bounded polynomial depth, and it achieves weak security and input-independent efficiency (in an amortized sense). Furthermore, we present a transformation to obtain an adaptively-secure one from any weakly-secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {moKHS}$</tex-math></inline-formula> scheme. Notably, the signature obtained by our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {moKHS}$</tex-math></inline-formula> scheme achieves <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimal</i> succinctness in the sense that its size is independent of both the number of users involved in the computation and the input size of the program. All these improvements show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {moKHS}$</tex-math></inline-formula> is a more preferable candidate for applications of delegating computation involving multiple users.
AJENet: Adaptive Joints Enhancement Network for Abnormal Behavior Detection in Office Scenario

With the increasing popularity of intelligent surveillance systems, abnormal behavior detection of human beings based on computer vision is attracting more attention. It aims to classify and locate the abnormal behaviors and coordinates of human beings, respectively, and is a fundamental technology for intelligent security. Existing approaches mainly focus on exploring abnormal behavior features through object detectors. However, in office scenarios, almost all abnormal behaviors are closely associated with the fine-grained feature around the nose, wrist, elbow, and other human joint points regions. Detectors for generic objects cannot adequately capture such differences between abnormal behaviors, resulting in sub-optimal performance. In this paper, we focus on human joints and take one step further to enable effective behavior characteristics learning in office scenarios. In particular, we propose a novel Adaptive Joints Enhancement Network (AJENet), which includes two closely-related components, Joints Predict block (JP) and Adaptive Key Joints Enhancement block (AKJE). JP block is used to predict the human joints and facilitates the feature learning around them implicitly. By inputting the features around joints, the AKJE block enhances the feature representations of key joints according to the abnormal behavior characteristics adaptively. Experimental results demonstrate that our method outperforms other state-of-the-art methods on the collected real office scenario Office Behavior Dataset. Besides, to verify the generalization capabilities and potential of AJENet, we construct comparisons on another generic dataset PASCAL VOC 2012 Action.
Mining Sematic Association Rules from RDF Data

Many fields rely heavily on the accurate and consistent portrayal of structured data. In order to effectively express and link information on the Semantic Web, RDF (Resource Description Framework) data is essential. Here, we present a process for extracting semantic association rules from RDF data. For our method, we employ the Apriori algorithm to mine the RDF triples for hidden connections between ideas and relationships. Using metrics such as confidence, support, and lift, we examine how well our model performs. We also give visual representations, like as scatter plots and clustered matrices, to make the correlations easier to understand and analyse. The findings validate our model's potential to unearth significant relationships, which in turn reveal important details about the RDF data's underlying semantics. Our findings are discussed, and suggestions for further study are provided.
Daily Routine Monitoring Using Deep Learning Models

Today’s healthcare systems are up against a huge challenge in terms of patient control. Especially since the COVID-19 epidemic when hospital beds have been confined to emergency cases. In many circumstances, however, hospitalization is essential when there is insufficient information about the patient. Frequently, physicians leave patients in hospitals for a few days to check their condition, which can be costly. Furthermore, acquiring reliable data from patients when symptoms are no longer present is difficult. This paper looks at how different sensors, such as the gyroscope and accelerometer present in smartwatches and smartphones, can be used to monitor patient activity. These sensors can measure properties like the angular velocity of human motions and use them to train algorithms that can recognize human movement. Furthermore, identifying a patient’s diet and activities might help get reliable data on them throughout their time away from the hospitals, a process known as remote patient health monitoring. Using the WISDM dataset, which is freely available online for study, this work proposes a CNN model to identify and forecast patient diet and activity.
Visualizing the Unseen: Exploring GRAD-CAM for Interpreting Convolutional Image Classifiers

Mathematical programming can express competency concepts in a well-defined mathematical model for a particular. Convolutional Neural Networks (CNNs) and other deep learning models have shown exceptional performance in image categorization tasks. However, questions about their interpretability and reliability are raised by their intrinsic complexity and black-box nature. In this study, we explore the visualization method of Gradient-Weighted Class Activation Mapping (GRAD-CAM) and its application to understanding how CNNs make decisions. We start by explaining why tools like GRAD-CAM are necessary for deep learning and why interpretability is so important. In this article, we provide a high-level introduction to CNN architecture, focusing on the significance of convolutional layers, pooling layers, and fully connected layers in the context of image categorization. Using the Xception model as an illustration, we describe how to generate GRAD-CAM heatmaps to highlight key areas in a picture. We highlight the benefits of GRAD-CAM in terms of localization accuracy and interpretability by comparing it to other visualization techniques like Class Activation Mapping (CAM) and Guided Backpropagation. We also investigate GRAD-CAM's potential uses in other areas of image classification, such as medical imaging, object recognition, and fine-grained classification. We also highlight the disadvantages of GRAD-CAM, such as its vulnerability to adversarial examples and occlusions, along with its advantages. We conclude by highlighting extensions and changes planned to address these shortcomings and strengthen the credibility of GRAD-CAM justifications. As a result of the work presented in this research, we can now analyze and improve Convolutional Image Classifiers with greater accuracy and transparency.
An Energy-saving Data Transmission Approach Based on Migrating Virtual Machine Technology to Cloud Computing

Introduction: Over the past few years, researchers have greatly focused on increasing the electrical efficiency of large computer systems. Virtual machine (VM) migration helps data centers keep their pages' content updated on a regular basis, which speeds up the time it takes to access data. Offline VM migration is best accomplished by sharing memory without requiring any downtime. Objective and Methodology: The objective of the paper was to reduce energy consumption and deploy a unique green computing architecture. The proposed virtual machine is transferred from one host to another through dynamic mobility. The proposed technique migrates the maximally loaded virtual machine to the least loaded active node, while maintaining the performance and energy efficiency of the data centers. Taking into account the cloud environment, the use of electricity could continue to be critical. These large uses of electricity by the internet information facilities that maintain computing capacity are becoming another major concern. Another way to reduce resource use is to relocate the VM. Results: Using a non-linear forecasting approach, the research presents improved decentralized virtual machine migration (IDVMM) that could mitigate electricity consumption in cloud information warehouses. It minimizes violations of support agreements in a relatively small number of all displaced cases and improves the efficiency of resources. Conclusion: The proposed approach further develops two thresholds to divide overloaded hosts into massively overloaded hosts, moderately overloaded hosts, and lightly overloaded hosts. The migration decision of VMs in all stages pursues the goal of reducing the energy consumption of the network during the migration process. Given ten months of data, actual demand tracing is done through PlanetLab and then assessed using a cloud service.
The Relationship among Narcissism, Cognitive Flexibility and Happiness Enhancing Activities for The Leisure Sport of Undergraduate

The purpose of this study is to investigate the relationships among narcissism, cognitive flexibility and happiness enhancing activities for the leisure sport of undergraduate. To achieve the goal of this study, a total of 250 questionnaires were distributed. Out of those returned questionnaires, insincerely replied or double-replied questionnaires were excluded and finally 233 questionnaires were analyzed for this study. The data were analyzed by frequency analysis, reliability analysis, exploratory factor analysis, confirmatory factor analysis, correlation analysis, and structural equation modeling using SPSS Version 18.0. and Amos 18.0 program. Main findings were as follows: First, narcissism had a positive effect on cognitive flexibility. Second, cognitive flexibility had a positive effect on happiness enhancing activities. Third, narcissism had a positive effect on happiness enhancing activities. Fourth, cognitive flexibility mediated the relationship between narcissism and happiness enhancing activities. Therefore, in order to encourage college students’ happiness enhancing activities, it is necessary to encourage participation in leisure sports to improve narcissism, as well as to establish a sports culture in which mutual consideration and praise are established through desirable interpersonal relationships. should be sought to improve cognitive flexibility.
No. 53439. International Bank for Reconstruction and Development and Tunisia

Loan Agreement (Third Governance, Opportunities and Jobs Development Policy Loan) between the Republic of Tunisia and the International Bank for Reconstruction and Development (with schedules, appendix and International Bank for Reconstruction and Development General Conditions for Loans, dated 12 March 2012). Washington, 2 October 2015
Analysis of SPBE and SWCSF measurement instruments using Flesch Reading Ease for state security

The world has transitioned into a digital era where both individuals and governments require technology and the internet. The number of cybercrimes perpetrated online is impacted by the rising usage of computers and the internet. A measurement instrument that can stop cybercrime is necessary. The Six-Ware Cyber Security Framework (SWCSF) and the Electronic-Based Government System (SPBE) are two measurement tools that are expected to be able to stop cybercrime from happening in an agency or organization. But are all people able to use these two instruments? This research was conducted to answer this question by analyzing readability on the SPBE and SWCSF instruments using the Flesch Reading Ease method. The results show that the two instruments were extremely difficult for respondents of all grade levels to comprehend, with the exception of those at the university level or individuals who worked with computers, the internet, and other technologies.
Detecting and Measuring Social Bias of Arabic Generative Models in the Context of Search and Recommendation

Pre-training large language models on vast amounts of web-scraped texts is a current trend in natural language processing. While the resulting models are capable of generating convincing text, they also reproduce harmful social biases. Several modern studies have demonstrated that societal biases have a significant impact on the outcome of information retrieval systems. Our study is among the recent studies aimed at developing methods for quantifying and mitigating bias in search results and applying them to retrieval and recommendation systems based on transformer-based language models. This paper explores expressions of bias in Arabic text generation. Analyses will be performed on samples produced by the generative model AraGPT2 (a GPT-2 fine-tuned for Arabic). An Arabic bias classifier (Regard Classifier) based on new transformer model AraBERT (a BERT fine-tuned for Arabic) will be used to captures the social Bias of an Arabic-generated sentence or text. For the development of this classifier, a dataset will be crowd-sourced, cleaned, and independently annotated. AraGPT2 will be used to generate more biased descriptions from the standard prompts. Our Bias Detection Model (BDM) will be based on the combination of the two transformers (AraGPT2-AraBERT) models. In addition to our quantitative evaluation study, we will also conduct a qualitative study to understand how our system would compare to others approaches where users try to find bias in Arabic texts generated using AraGPT2 model. Our proposed model has achieved very encouraging results by reaching an accuracy percentage of 81%.