Reviewerly/exHarmony huggingface dataset:
Contribution of Social Robots to Interact with Humans and Others in Taking Over Entire Job Functions, Such as Greeting and Basic Customer Service in the Workplace

This article is based on the contribution of social robots and their interaction with human beings. This study has highlighted the entire concept of social robots and it has been described that social robots are one of the assembling technological concepts that use artificial intelligence, sensor circuits, devices, and mechanical parts. These kinds of robots behave like a human and it has all the artificial and updated necessity such as an artificial mind, artificial sensations, voice, and other essential things. On the other hand, these technological instruments can able to provides suggestions to individuals and also communicate with them. This process helps people to create an easy lifestyle and also proceed to work smoothly. This study has also highlighted the impact of social robots in industry and the workplace. It helps to improve the work procedure and also influences the employees to do better work in the working area. It can be said that these intelligent robots help to improve the working culture and also makes an impact on the working environment. Accordingly, this article also focused on the various types of social robots and their benefits. This article also discussed the innovative and effective robots that influence the working atmosphere people can easily get suggestions from these modern technologies. This paper has used a secondary qualitative method for the study and it helps to provide the essential knowledge that raises the authenticity of the research paper. Keywords : Artificial vision, artificial hearing, artificial touch, artificial intelligence, hitchBOT, Kismet, Social robot.
BREAST CANCER CLASSIFICATION USING DEEP LEARNING MODEL FOR CC AND MLO VIEWS OF MAMMOGRAMS

In this work, we explore a way for analyzing a mammography exam holistically. We present the design and implementation of a deep learning model that takes as input the two mammography views (CC and MLO), as well as all detected breast masses and micro calcifications, and produces an output consisting of a three-class classification of the entire exam: negative (or normal), benign, or malignant findings. The registration of the CC and MLO views of a mammography exam is a difficult task due to the difficulty in estimating the non-rigid deformations that can align these two views, so we propose the deep learning model classification, with the hypothesis that the high-level nature of these features will reduce the need for a low-level matching of the input data.
EVALUATION OF INTERPOLIATION ERRORS IN FUNCTIONS OF HAAR AND DOBESHI WAVELET

In this article, the evaluation of its absolute errors by interpolation of functions in the Haar and Dobeshi wavelet methods was performed. The results of both methods were compared .At the same time, the determination of coefficients was carried out using Haar's method of rapid transformation. It is known that there would be small errors in the calculation of Haar's wavelet coefficients as a result of the use of approximate computational operations on a large number of integrals. In addition, these calculations can perform a number of mathematical operations and take up extra space in computer memory. In this work, Haar's rapid conversion algorithm has been used to reduce computational operations and increase interpolation accuracy.
Contribution of Unmanned Autonomous Systems (UAS) for the Development of High Tech, Intelligent Machines Capable of Traveling by Air, Land, or Sea without A Human Crew on Board

This article is focused on the contribution of unmanned autonomous systems that are based on the Hitech autonomous system. On the other hand, it also includes an intelligent machine that has the capacity to travel by sea, land, and air without humans. There are different kinds of autonomous robots, vehicles, drones, and warehouse and factory system that helps to provide smart service to individuals. Artificial intelligence plays a major role in the autonomous system and it is responsible for developing the smart system. unmanned aerial vehicle (UAV) that is also known as a drone and is generally used for mapping, wars, product deliveries as well as surveillance. This study has also shone a light on the safe environment development that leads to providing a safe environment through proper surveillance in a large area within a limited time period. In this context, UAVs play a major role in monitoring environmental law enforcement, geographical mapping, and wildlife monitoring. Transportation systems one other advanced facilities provide extraordinary service to society. It can deliver emergency medical products such as medicine, equipment, and other package products. It has been seen that several kinds of transport and logistic markets use these drones to enhance their business and deliver products safely. Accordingly, this study has also discussed the limitations of the UAS system on land, sea, and air. It can be stated that it can be effective for society and future generations. Keywords : Search And Rescue (SAR) drones, Unmanned Aerial Vehicle (UAV), Unmanned Autonomous Systems (UAS)
Network Optimization Using Learning and Distributed Intelligence through Cognition Based Networks

Network optimisation can be achieved with the incorporation of sophisticated technology and advanced algorithms to achieve efficiency and scalability. Optimisation of the entire network with learning and distributed intelligence through cognitive-based networks is investigated in the paper. A cognition-based network enhances the capacity for network optimization and designing processes to offer better services to the customers appropriately. With the rapid growth of users, the network distribution channels are required to adopt cognitive-based architecture for optimised capacity, capable of providing secure data transmission to a wider user base. 5G network construction with SDN and NFV, along with AI and machine learning algorithms are considered to be the most efficient cognitive-based approach for network optimisation. The application of NFV helps to develop network functions in operating open hardware platforms, reducing Capex, and OpenX and improving network design efficiently. On the other hand, SDN is capable of separating the control plane and the data plan with a defined interface for programming, providing an entire view of the network with centralised control.
OLIY TA’LIM TIZIMIDA QR-KODLARDAN FOYDALANISH ISTIQBOLLARI

Maqolada ta’lim jarayoniga raqamli texnologiyalar va zamonaviy usullar keng joriy etish zarurati, oliy ta’lim tizimida QR-kod yordamida elektron resurslar haqidagi axborotlarni joylashtirish tizimini yaratish, duyoning yetakchi oliy ta’lim muassasalarida bitiruvchilariga diplom va ilovalarini taqdim etishda QR-koddan foydalanish imkoniyatlari, ulardan foydalanishning o’ziga xos xususiyatlari, shuningdek, bitiruvchilar va ish beruvchilar o’rtasidagi interaktiv axborotlar almashinuvini ta’minlashdagi roli haqida batafsil to’xtalib o’tilgan.
FRACTAL STRUCTURE AND FRACTAL MEASUREMENT

. This article is devoted to the study of fractal structure and fractal dimensions. The article explains the concept of fractals, the ability to determine the fractal sizes of parts of the human body based on fractals and, based on these data, determine or predict what disease a person suffers from. In particular, the fractal structure of the human vascular system, the size of the fractal, etc. were calculated. In addition, fractals covered the causes of climate change, the causes of sudden waves in the seas and oceans, sudden changes in the economy, and even the improvement in the social status of people living in society.
PERIODIC SOLUTIONS OF DIFFERENTIAL EQUATIONS WITH CONSTANT ARGUMENTS OF THE SECOND ORDER

In the paper a method of finding periodical solutions of the differential equation of the form x(t)  p(t)x(t 1) = q(t)x([t])  f (t) is given, where [] denotes the greatest integer function, p(t) , q(t) and f (t) are continuous periodic functions of t . This provides n - periodic soluble problem to a system of n 1 linear equations, where n = 2. Furthermore, by using the well known properties of linear system in the algebra, all existence conditions for 2 - periodical solutions are described, and the explicit formula for these solutions are obtained.
Employee Creativity during COVID-19: Role of Psychological Capital and Employee Empowerment

Employee creativity remains an essential aspect of employee work engagement since the inception of COVID-19. Due to the ramifications of the impending lockdown, physical workplaces have been transformed into virtual setting. Thereby, the prevailing situation emphasizes self-initiated actions which employees must undertake by utilizing their own creativity during COVID-19 to achieve their assigned tasks. Considering the job demands and available resources, this study investigates the link between psychological capital and employee creativity with the mediation of employee empowerment. On the basis of the data collected from university staff and faculty in Lahore, Pakistan, a quantitative research approach has been employed that uses the Hayes process model for analysis to obtain the results. The results demonstrate that psychological capital positively relates to employee creativity, while employee empowerment mediates this relationship. Furthermore, this study suggests that employee empowerment would positively explain the relationship, thus making it more suitable for employees to adopt creative ways to do their work. This research further probes the organizational design work that liberates employs to use their creative behavior to give better work related performance which impacts and modifies certain aspects of their work. Thus, enabling them to perform in unprecedented challenging situations.&#x0D; Keywords: COVID-19, employee creativity, employee empowerment, psychological capital, virtual settings
Knowledge Management Dimensions and Sustainable Competitive Advantage (SCA) of Deposit Money Banks: Mediating Role of Innovation

The primary objective of the current research was to explore how the Knowledge Management (KM) processes of Deposit Money Banks (DMBs) in Makurdi Metropolis contribute to their Sustainable Competitive Advantage (SCA). Additionally, this study determined to evaluate how innovation helps the banks to retain their competitive advantage. A total of 265 valid samples were collected from a population of 14 DMBs and 933 respondents were selected from these DMBs. The data was analysed using Structural Equation Modelling (SEM). While testing the assumptions, hypotheses 1-3 supported the proposition that KM dimensions; acquisition (β=.32, p &lt;.05), utilization (β=.26, p &lt;.05), and storage (β=.24, p &lt;.05) were directly related to SCA. No evidence was found for the full mediating role of innovation in relation to KM dimensions and SCA, as demonstrated by the data. However, innovation does play a role in certain aspects of KM by demonstrating that acquisition (β=.69, p &lt;.05), utilisation (β=.21, p &lt;.05), and storage (β=.18, p &lt;.05) are positively associated with innovation in DMBs. The findings indicated that managers who responded to KM were more innovative. The importance of DMBs lies in the recognition that knowledge is not a simple concept. A commitment to new discoveries and to new understandings is not sufficient. Enterprises need to pursue knowledge that enables them to add value.
Analysis of Public Opinion Evolution in Public Health Emergencies Based on Multi-fusion Model

This paper analyzes the public opinion topics and the emotional fluctuations of netizens during the closure of the city due to the new crown epidemic, and reveals the correlation between public emotional fluctuations and public opinion topics during various periods of public health emergencies. We use the BERT-BiLSTM fusion model to efficiently capture the two-way relationship in the sentence and improve the accuracy of sentiment classification of Weibo text at the same time, to extract the hot topic feature words at different periods of the city closure by LDA mode; finally, the SEI7R model is used to simulate the prevention and control recommendations of various periods of public opinion proposed in this paper to verify the effectiveness of the prevention and control recommendations. The experimental results show that: The F value of the BERT-BiLSTM fusion model in the classification of public sentiment polarity can reach up to 0.907, which can effectively classify the sentiment of netizens; The LDA model can effectively dig out over time The theme characteristics of the text that have gradually evolved over time. The simulation results of the SEI7R model show that the countermeasures and suggestions put forward in this paper can effectively provide a theoretical basis and methodological reference for public opinion management of public health emergencies.
RMSE Computation and Detection of Ring P. Falciparum

Malaria is a disease that is threat to our life and it is caused by a parasite named protozoan. As we all studied that there are five species of malaria falciparum, p.vivax, p. malaria, p. ovale, and p. knowlesi. Some of the common symptoms of malaria are fever, fatigue, headache, cough, abdominal pain, nausea. Some of the causes of malaria are blood transfusion, maternal foetal transmission, by infected needle because of bite of female anopheles mosquito. Earlier number of method were used like malaria microscopy, RDT (Rapid Diagnostic Test). These tests were very costly so after their failure computer vision techniques came into limelight like image processing. Here microscopic images of blood samples were taken and using various techniques of image processing like preprocessing of an image, feature extraction, segmentation were used and were very popular in last few years but nowadays may machine learning classification models are used such as Naïve Bayesian classifier, Support vector machine classifier, K-nearest neighbor and many more and are very successful in predicting accuracy of detection of malaria parasite. In this paper we are going to detect malaria parasite and also which parasite it is with the help of a pre-processed image using region filling and then using canny edge detection for smoothening of edges in combination with watershed segmentation using distance transform and at last we will predict RMSE (Root Mean Square Error) using SVM classifier. Basically, in this paper we are trying to decrease the RMSE while detecting malaria parasite and we will predict the error using SVM classifier and also with Ensemble classifier and we will compare which one is better using regression analysis.
A Crowd-Ai Dynamic Neural Network Hyperparameter Optimization Approach for Image-Driven Social Sensing Applications

Image-driven social sensing (ISS) is emerging as a pervasive sensing paradigm that collects the status of the physical world by leveraging image data from human sensors. This paper studies a dynamic hyperparameter optimization problem in ISS applications and our objective is to dynamically determine the optimal hyperparameter configuration of an AI-based ISS solution that provides the accurate label for each image in the data stream of ISS applications. Our work is motivated by the observation that current ISS solutions leverage a static hyperparameter configuration that is often configured manually by AI experts, which fails to capture the potential large dynamics of ISS applications. To address this limitation, we develop DC-HPO, a dynamic crowd-assisted hyperparameter optimization system that utilizes crowdsourced human intelligence to dynamically steer the search of the optimal hyperparameter configuration for each image in the data stream of ISS applications. The experimental results on two ISS applications from real world (i.e., intelligent urban infrastructure monitoring and city environment cleanliness assessment) demonstrate that DC-HPO outperforms existing deep convolutional neural networks, crowd-AI models, and hyperparameter optimization methods in achieving the best ISS performance while keeping the lowest computational cost.
Plurilingual Competence Intervened by Online Games for a Child Living in Multilingual Environment

This research aims to analyse the plurilingual competence of English as a foreign language intervened by online games. It is a case study to a child living in multilingual environment. Plurilingualism that is the dynamic and developing linguistic repertoire of an individual language learner becomes something researchable for a child who is studying more than one language and culture in a mulitilingual environment. Online games that have become popular recently give some effects specially relating to the language used by the child. Metalinguistics, a study focusing on the relationship between language factors and non-language factors in society is the theory used to analyse the data. There are some tools used in this research: (1) phonology, (2) syntax, (3) semantics, (4) sociolinguistics. The data are analysed and described qualitatively. Some results obtained are (1) the language background and repertoire of the child are variegated in terms of languages known and degree of competence and (2) the metalinguistics awareness in the child’s first language leads to better results when learning English, especially in the specific context observed, that is on the language used in playing online games. The child has his own competency on the spelling and he has known the meaning of some words he spells for English words used when playing the online games
Preface: Al-Kadhum 2nd International Conference on Modern Applications of Information and Communication Technology

First Page
Supplementary Figure 2, Part 1 from Assessment of Automated Image Analysis of Breast Cancer Tissue Microarrays for Epidemiologic Studies

Supplementary Figure 2, Part 1 from Assessment of Automated Image Analysis of Breast Cancer Tissue Microarrays for Epidemiologic Studies
Data from Assessment of Automated Image Analysis of Breast Cancer Tissue Microarrays for Epidemiologic Studies

&lt;div&gt;Abstract&lt;p&gt;&lt;b&gt;Background:&lt;/b&gt; A major challenge in studies of etiologic heterogeneity in breast cancer has been the limited throughput, accuracy, and reproducibility of measuring tissue markers. Computerized image analysis systems may help address these concerns, but published reports of their use are limited. We assessed agreement between automated and pathologist scores of a diverse set of immunohistochemical assays done on breast cancer tissue microarrays (TMA).&lt;/p&gt;&lt;p&gt;&lt;b&gt;Methods:&lt;/b&gt; TMAs of 440 breast cancers previously stained for estrogen receptor (ER)-α, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), ER-β, and aromatase were independently scored by two pathologists and three automated systems (TMALab II, TMAx, and Ariol). Agreement between automated and pathologist scores of negative/positive was measured using the area under the receiver operating characteristics curve (AUC) and weighted &lt;i&gt;κ&lt;/i&gt; statistics for categorical scores. We also investigated the correlation between immunohistochemical scores and mRNA expression levels.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Results:&lt;/b&gt; Agreement between pathologist and automated negative/positive and categorical scores was excellent for ER-α and PR (AUC range = 0.98-0.99; &lt;i&gt;κ&lt;/i&gt; range = 0.86-0.91). Lower levels of agreement were seen for ER-β categorical scores (AUC = 0.99-1.0; &lt;i&gt;κ&lt;/i&gt; = 0.80-0.86) and both negative/positive and categorical scores for aromatase (AUC = 0.85-0.96; &lt;i&gt;κ&lt;/i&gt; = 0.41-0.67) and HER2 (AUC = 0.94-0.97; &lt;i&gt;κ&lt;/i&gt; = 0.53-0.72). For ER-α and PR, there was a strong correlation between mRNA levels and automated (&lt;i&gt;ρ&lt;/i&gt; = 0.67-0.74) and pathologist immunohistochemical scores (&lt;i&gt;ρ&lt;/i&gt; = 0.67-0.77). HER2 mRNA levels were more strongly correlated with pathologist (&lt;i&gt;ρ&lt;/i&gt; = 0.63) than automated immunohistochemical scores (&lt;i&gt;ρ&lt;/i&gt; = 0.41-0.49).&lt;/p&gt;&lt;p&gt;&lt;b&gt;Conclusions:&lt;/b&gt; Automated analysis of immunohistochemical markers is a promising approach for scoring large numbers of breast cancer tissues in epidemiologic investigations. This would facilitate studies of etiologic heterogeneity, which ultimately may allow improved risk prediction and better prevention approaches. Cancer Epidemiol Biomarkers Prev; 19(4); 992–9. ©2010 AACR.&lt;/p&gt;&lt;/div&gt;
Data from A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images

&lt;div&gt;Abstract&lt;p&gt;Well-curated sets of pathology image features will be critical to clinical studies that aim to evaluate and predict treatment responses. Researchers require information synthesized across multiple biological scales, from the patient to the molecular scale, to more effectively study cancer. This article describes a suite of services and web applications that allow users to select regions of interest in whole slide tissue images, run a segmentation pipeline on the selected regions to extract nuclei and compute shape, size, intensity, and texture features, store and index images and analysis results, and visualize and explore images and computed features. All the services are deployed as containers and the user-facing interfaces as web-based applications. The set of containers and web applications presented in this article is used in cancer research studies of morphologic characteristics of tumor tissues. The software is free and open source. &lt;i&gt;Cancer Res; 77(21); e79–82. ©2017 AACR&lt;/i&gt;.&lt;/p&gt;&lt;/div&gt;
Towards Energy-efficient Container Data Center: An Online Migratability-aware Orchestrator

The growing demands for cloud computing have led to high power consumption in data centers. To save power, existing works attempt to reduce the number of active servers via scheduling or migrating VMs. Meanwhile, owing to the lightweight, highly-portable and scalable properties, containers have been widely used in data centers. However, comparing with VMs, containers show the migratability property, i.e., some containers can not be consolidated by migration. Thus, one has to take this property into consideration when making scheduling and migrating decisions. To this end, this paper studies the energy-efficient container orchestration problem in data centers. We frame container scheduling and migration as a single problem, and take the migratability of containers into account. We propose an online container orchestration algorithm, based on Lyapunov optimization and Markov approximation. It works without requiring future information and achieves a provable performance guarantee. Simulation results show that our algorithm can effectively reduce the power consumption of data centers.