Reviewerly/exHarmony huggingface dataset:
Marketing Strategy to Improve Brand Awareness: Case Study of a Cloud Service Provider in Indonesia

The World Health Organization classified Covid-19 a global pandemic, its effects have rippled across every aspect of society, including the economy. Firms are shifting their objectives and concentrating on their information technology capability to deal with the unpredictability that this pandemic has brought onto the economy. Cloud and colocation will be required to support company operations with digital infrastructure. Many of the world’s largest cloud service providers are thinking about creating cloud regions within its boundaries as a result of its tremendous development potential. Cloudgear as one of local Indonesian cloud service provider wants to take this opportunity to grow their business. Cloudgear has a comprehensive service portfolio consisting of Infrastructure as a Service (IaaS), Software as a Service (SaaS), Professional as a Software (PaaS), professional services, and multi-cloud services. However, based on the initial interview with ten cloud engineers and specialists in different companies, most of them are not aware that Cloudgear is a local Indonesian cloud service provider. Therefore, the next challenge is how to improve brand awareness of Cloudgear by formulating the right marketing strategy. Because it will strengthen the brand and develop Cloudgear competitive advantage for the company’s future growth. This research is objected to analyse Cloudgear internal, external conditions, analyse the factors that influence the brand awareness, and propose the most suitable marketing strategy in building Cloudgear brand awareness. Brand awareness is affected by advertising, social media, word of mouth and publicity. In this research, advertising, social media, word of mouth and publicity are being analysed whether it has significant positive influence to cloud service providers brand awareness or not. The hypothesis are that each of these factors have positive influence to cloud service brand awareness by performing customers analysis. Questionnaire was distributed to two hundred samples. Then, the data were processed by SPSS using multiple linear regression method. Several tests were conducted consisted of validity test, reliability test, normality test, and linearity test. After the data were valid, reliable, normal, and linear, analysis continued by doing multiple linear regression. The results are there is significant effect of each advertising, social media, word of mouth and publicity to brand awareness of cloud service provider. Determination test is resulted these variables have 50.1% influence. When F Test was conducted, the result was these variables if tested simultaneously or simultaneously have an effect on cloud service brand awareness. Therefore, the proposed marketing strategy for Cloudgear is related to improvement in advertising, social media, word of mouth and publicity in order to increase their brand awareness as a local Indonesian cloud service provider.
Quantum discord and entropic measures of two relativistic fermions

Abstract In the present work, we study the interplay between relativistic effects and quantumness in the system of two relativistic fermions. In particular, we explore entropic measures of quantum correlations and quantum discord before and after application of a boost and subsequent Wigner rotation. We also study the positive operator-valued measurements (POVMs) invasiveness before and after the boosts. While the relativistic principle is universal and requires Lorentz invariance of quantum correlations in the entire system, we have found specific partitions where quantum correlations stored in particular subsystems are not invariant. We calculate quantum discords corresponding of the states before and after applying a boost, and observe that the state gains extra discord after the boost. When analyzing the invasiveness of the POVMs, we have found that the POVM applied to the initial entangled state reduces the discord to zero. However, discord of the boosted state survives after the same POVM. Thus we conclude that the quantum discord generated by Lorentz boost is robust concerning the protective POVM, while the measurement exerts an invasive effect on the discord of the initial state. Finally, we discuss potential implementation of the ideas of this work using top quarks as a benchmark scenario.
Chain code strategy for lossless storage and transfer of segmented binary medical data

Obtaining a 3D medical visualization is a tedious process requiring several processing steps (such as segmentation) and assigning various rendering parameters (such as color and opacity). Current systems use video/image exporting or snapshots to save results. Such vendor-dependent tools not only prevent the possibility of further interactions but also creates additional large-size data that is problematic to store in PACS over time and hard to transfer for teleradiology applications. To overcome, alternative strategies propose a representation of the visualizations, which only store segmentation masks that contains the binary form of segmented data. Unfortunately, existing compression methods are limited to effectively compress the volumetric data. In this study, lossless storage of binary segmented data is effectively performed by two newly-proposed chain code approaches. Particularly-two novel contributions are presented: 1. The dictionary of normalized angle difference is improved as a new chain symbol coding procedure, namely normalized angle difference, by adding new symbols to the dictionary aiming to generate a low-entropy symbol sequence for medical volumes. 2. A new volumetric approach that utilizes 26 symbols to encode volumetric data is developed. Each slice is visited, and the contour of the segmented object is codified such that eight different vectors for each slice (pointing to one of the four faces of each voxel, plus four towards one of its edges) are obtained. The developed methods are tested on diverse volumetric segmented data and compared to existing standards. It is shown that the proposed methods outperform well-established techniques.
Reducing MRI Inter‐Scanner Variability Using 3D Superpixel ComBat

Abstract Background Inter‐scanner variability hinders the direct comparability of multi‐site/scanner MRI data for clinical research. The ComBat method is commonly used to reduce the variability based on an empirical Bayes framework 1,2 , harmonizing the data at the feature level (e.g., region‐of‐interest measures). However, directly harmonizing the scans at the voxel‐level using ComBat has been relatively less explored. In this study, we investigated the performance of the voxel‐wise ComBat. Also, going beyond voxels, we proposed a new ComBat approach which operates on a small group of voxels called superpixels 3 . Method Eighteen subjects (10 patients with Alzheimer's disease and 8 controls; age: 68.0 [9.3] years; 10 females) participated in this study. For each subject, T1‐weighted images were acquired on each of four 3T scanners with different manufacturers or models (i.e., GE, Philips, Siemens‐Prisma, Siemens‐Trio). After the standard image preprocessing including two‐step registration by using the Statistical Parametric Mapping (SPM12) 4 , the unharmonized scans (Raw data) were aligned in the standard template space. To reduce the computational load for ComBat at the voxel level (Voxel‐ComBat), we used a three‐dimensional superpixel algorithm 3 to parcellate the images into hundreds of superpixels based on the study‐specific template, and then the ComBat was applied at the superpixel level (Figure 1). Compared to Voxel‐Combat operating on about half million voxels (computation time &gt;&gt;10,000 seconds), this superpixel ComBat (SP‐ComBat) operates on only a few hundred superpixels, significantly improving the computation efficiency (computation time &lt; 5 seconds) while maintaining the harmonization performance. The harmonized scans were used to estimate cortical thickness by employing surface‐based morphometry 5 , and the coefficients of variation of thickness measures were calculated to evaluate the harmonization performance. Result The harmonized data provided similar contrasts across scanners compared to the Raw images in visual inspection (Figure 2) and had comparable distributions of the tissue‐specific signal intensity between scanners for both Voxel‐ComBat and SP‐ComBat (Figure 3). Also, these two methods significantly reduced the inter‐scanner variation (both p ‐values &lt; 0.001) in terms of cortical thickness measures (Figure 4). Conclusion This study evaluated the feasibility and effectiveness of Voxel‐ComBat and proposed a new approach, SP‐ComBat, to optimize the efficiency of ComBat harmonization at the voxel level.
Performance Analysis of Optical Reservoir Computer with Two Delayed Inputs

In this paper, a novel reservoir computing architecture based on the single nonlinear node with two delayed inputs is presented. Its performance is investigated on time-series forecasting tasks, for two different profiles of activation functions implemented in the optical domain. Simulation results show reduced NRMSE value to around 3% for Mackey-Glass time series prediction 11 steps in advance, when sine squared optical activation function based on Mach-Zehnder modulator is used.
Challenges and struggles of public senior high school science teachers during the new normal

The rapid increase of COVID-19 has brought challenges in the delivery of basic education and forced teachers to adapt the distance learning modality. It is interesting to find out the teachers' experiences in terms of their preparation, teaching engagement, and challenges in the new normal. The study aimed to capture the lived experiences of Senior High School (SHS) science teachers in the new normal education. Specifically, it sought to (a) determine the conception of science teachers on education in the new normal, (b) describe the challenges and struggles in the new normal, and (c) unveil the coping mechanisms of science teachers in addressing the challenges and struggles. A descriptive phenomenological study based on Colaizzi’s (1978) method was utilized to describe the lived experiences of Senior High School science teachers in the new normal education and their challenges and struggles of the Science instruction. Eleven (11) participants were selected as respondents of the study using the purposive sampling technique. The study utilized two research instruments – Part A. demographic profile and Part B. interview validated by an expert on research instrumentation utilized modified Colaizzi’s seven-step method (2018) for data analysis. Three (3) major themes emerged from the analysis of the responses to the study queries: Science as a Challenging Subject, Teaching and learning in the new normal, Teaching as a Vocation. The themes derived from the analysis of responses are essential in filling the gap in the literature that sought to explore the lived experienced of teachers in the new normal.
Opportunities and Challenges of Digital India

The term "Digital India" refers to the country's transformation towards a future knowledge economy and society. By enhancing online infrastructure and internet access, the Digital India programme makes sure that government services are made available to citizens electronically. The digital India program's tagline, "POWER to EMPOWER," sums up the goal of the initiative, which is to move the country ahead economically, digitally, and effectively. Additionally, it seeks to connect Gram panchayat via broadband internet, advance e-governance, and change India into a networked knowledge economy. This essay aims to highlight various opportunities for the populace as well as various difficulties the Digital India programme has encountered.
Disease- and Drug-Related Knowledge Extraction for Health Management from Online Health Communities Based on BERT-BiGRU-ATT

Knowledge extraction from rich text in online health communities can supplement and improve the existing knowledge base, supporting evidence-based medicine and clinical decision making. The extracted time series health management data of users can help users with similar conditions when managing their health. By annotating four relationships, this study constructed a deep learning model, BERT-BiGRU-ATT, to extract disease–medication relationships. A Chinese-pretrained BERT model was used to generate word embeddings for the question-and-answer data from online health communities in China. In addition, the bidirectional gated recurrent unit, combined with an attention mechanism, was employed to capture sequence context features and then to classify text related to diseases and drugs using a softmax classifier and to obtain the time series data provided by users. By using various word embedding training experiments and comparisons with classical models, the superiority of our model in relation to extraction was verified. Based on the knowledge extraction, the evolution of a user’s disease progression was analyzed according to the time series data provided by users to further analyze the evolution of the user’s disease progression. BERT word embedding, GRU, and attention mechanisms in our research play major roles in knowledge extraction. The knowledge extraction results obtained are expected to supplement and improve the existing knowledge base, assist doctors’ diagnosis, and help users with dynamic lifecycle health management, such as user disease treatment management. In future studies, a co-reference resolution can be introduced to further improve the effect of extracting the relationships among diseases, drugs, and drug effects.
A Localized Bloom Filter-Based CP-ABE in Smart Healthcare

Wearable technology-supported cloud-based smart health (s-health) has emerged as a promising answer to increase the efficiency and quality of healthcare as a result of rapid improvements in Internet of Things (IoT) technologies. However, the issues of data security and privacy preservation have not been fully resolved. In recent years, ciphertext policy attribute-based encryption (CP-ABE), which was developed as a versatile and potent cryptographic fundamental to accomplish one-to-many encryption with fine-grained access control, has been seen as a viable answer to the security issue in the cloud. The attribute values in the access policy, however, are supplied in cleartext in standard CP-ABE. This will conveniently reveal the data owners’ privacy (patients). Because the Internet of Things (IoT) in healthcare stores sensitive data in the cloud, security is crucial. The data must always be accessed via an access key when using traditional encryption techniques. Though the data cannot be accessed right away in an emergency, this offers greater security. The healthcare IoT created the break-glass concept to address this. The encryption technique is integrated with the broken glass idea to offer data protection and simple access in emergency scenarios. The majority of research papers employ cypher text policy attribute-based encryption (CP-ABE) with the broken glass idea to secure electronic health records. For improving data accessibility in the smart healthcare environment, modified cypher text policy attribute-based encryption (MCP-ABE) with the broken glass (BG) technique is suggested. Greater information security is achieved with this method, but the access policy is also dependent on keys that are vulnerable to hacking. To analyze the access policy individually throughout the key generation process, the attribute-based encryption procedure in this case uses the bloom filter. Information about the access policy is kept intact, which enhances the security of the keys. To continue serving patients and saving their lives, this modified CP-ABE is integrated with break glass in the smart healthcare facility. The experimental results demonstrated that, when compared to the lightweight break-glass procedure, the proposed solution is likewise the best in terms of decreased overhead. The main benefit of this strategy is that it uses the bloom filter concept in the MCP-ABE process, which protects the access policy attributes, to ensure that the key is never compromised. For data access in smart healthcare to preserve patients’ lives, the proposed MCP-ABE with broken glass is best.
Expanding the horizon for breast cancer screening in India through artificial intelligent technologies -A mini-review

Introduction Breast cancer is one of the most common cancer among Indian women, with an incidence of 25.8 per 100,000 women according to the Ministry of Health and Family Welfare. Late detection is responsible for poor quality of life (QOL), and it is the leading cause of death. In metropolitan regions, one in every 22 women will have breast cancer over their lifetime; but in rural areas, one in every 60 women will develop breast cancer as per estimates. Aim and objective This paper aims to describe the various AI based breast screening technologies which are used in breast cancer screening in India. Methodology The literature search was done using “Pub Med,” “Google scholar,” and “Scopus” databases for the key terms “technology,” “cancer research,” “artificial intelligence,” “mammography”, “breast cancer,” “cancer,” and/or “neoplasia in breast.” All the relevant articles were included to support this mini review. Results We found that emerging artificial intelligent technologies namely “Niramai”, “iBreastExam,” “MammoAssist” are emerging as an hope for early detection by screening in resource poor settings, in turn, which can improve the QOL among breast cancer patients.
Deep Neural Networks for Automatic Facial Expression Recognition

Out of all non-linguistic communications, one of the most popular is face expression and is capable of communicating effectively with others. We have number of applications of facial expressions in as sorted arenas comprising of medicine like psychology, security, gaming, Classroom communication and even commercial creativities. Owing to huge intra-class distinction it is still challenging to recognize the emotions automatically based on facial expression though it is a vigorous area of research since decades. Conventional lines for this approach are dependent on hand-crafted characteristics like Scale Invariant Feature Transform, Histogram of Oriented Gradient and Local Binary Patterns surveyed by a classifier which is applied on a dataset. Various types of architectures were applied for restored performance as Deep learning proved an outstanding feat. The goal of this study is to create a deep learning model on automatic facial emotion recognition FER. The proposed model efforts more on pulling out the crucial features, thereby, advances the expression recognition accuracy, and beats the competition on FER2013 dataset.
The impact of supply chain integration on strategic performance: The mediating role of strategic vigilance

The study aimed to identify the impact of supply chain integration through strategic performance and the mediating role of strategic vigilance in industrial companies. The Jordanian population is large and medium-sized, and the study population consists of medium and large-sized industrial companies. For hypothesis testing purposes the study and its model validity, Modeling Equation Structural Analysis (SEM) was used through the AMOS16 program. The study indicates that supply chain integration (strategic integration, internal integration, external integration) has an impact on strategic performance and response. The supply chain, as the results of the study indicated, shows that the response of the supply chain affects strategic performance. The study also found that the response of the supply chain mediates the impact of strategic vigilance on strategic performance. The study recommended working to raise the level of trust and honesty, commitment and attention to the interest of each party to the supply chain to maintain a good level of external integration of the supply chain and work to improve the level of supply chain response to markets and any changes that may occur in the market through corporate restructuring and streamlining working procedures.
End-to-end Jordanian dialect speech-to-text self-supervised learning framework

Speech-to-text engines are extremely needed nowadays for different applications, representing an essential enabler in human-robot interaction. Still, some languages suffer from the lack of labeled speech data, especially in the Arabic dialects or any low-resource languages. The need for a self-supervised training process and self-training using noisy training is proven to be one of the up-and-coming feasible solutions. This article proposes an end-to-end, transformers-based model with a framework for low-resource languages. In addition, the framework incorporates customized audio-to-text processing algorithms to achieve a highly efficient Jordanian Arabic dialect speech-to-text system. The proposed framework enables ingesting data from many sources, making the ground truth from external sources possible by speeding up the manual annotation process. The framework allows the training process using noisy student training and self-supervised learning to utilize the unlabeled data in both pre- and post-training stages and incorporate multiple types of data augmentation. The proposed self-training approach outperforms the fine-tuned Wav2Vec model by 5% in terms of word error rate reduction. The outcome of this work provides the research community with a Jordanian-spoken data set along with an end-to-end approach to deal with low-resource languages. This is done by utilizing the power of the pretraining, post-training, and injecting noisy labeled and augmented data with minimal human intervention. It enables the development of new applications in the field of Arabic language speech-to-text area like the question-answering systems and intelligent control systems, and it will add human-like perception and hearing sensors to intelligent robots.
Detection of Gender and Age using Machine Learning

Abstract: Age and gender predictions of unfiltered faces classify unconstrained real-world facial images into predefined ages and gender. Due to its value in intelligent real-world applications, this study topic has undergone significant advancements. Nevertheless, traditionalmethods based on unfiltered benchmarks have proven inefficient at handling large degrees of variation in unconstrained images. Because of their superior performance, Convolutional Neural Networks (CNNs) based approaches have been employed widely in recent years for the classification of jobs, and good quality of performance in facial analysis. In this work,we propose a novel end-to-end CNN approach, to achieve robust age group andgender classification of natural realworld faces. Two-level CNN architecture includesfeature extraction and classification itself. The feature extraction process extracts a feature corresponding to age and gender, and the classification process classifies theface images according to age and gender. Particularly, we address the largevariations in unfiltered real-world faces with a robust image pre-processing algorithm that prepares and processes those facial images before being given into the CNN model.