Reviewerly/exHarmony huggingface dataset:
A Comprehensive Review and Analysis of Key Heuristics of Load Balancing Techniques in Cloud Computing

Background: The field of cloud computing has been evolving for over a decade now. Load balancing is an important component of cloud computing. Load balancing implies scheduling of cloudlets (tasks) on virtual machines. Since this is an NP-hard problem, various heuristics for load balancing have been proposed in the research literature. The heuristics have been categorized, simulated and benchmarked in various ways; however, the information is scattered across many review articles. Objective: This review aims to bring a broad range of load balancing heuristics found in the research literature under one umbrella. It includes a comprehensive list of heuristics, a holistic set of criteria for their classification, and some key performance metrics and simulation tools used for their benchmarking. An illustration of a fair and comprehensive comparison of heuristics is provided using CloudSim Plus, a recent and advanced simulation tool. Method: The simulations performed with CloudSim Plus employ a generic model of task and machine heterogeneity with Poisson arrival of cloudlets and exponential distribution of cloudlet length to emulate actual cloud-computing scenarios. The simulation results in terms of key performance metrics are used to compare four centralized load balancing heuristics including Join Shortest Queue (JSQ), Join Idle Queue (JIQ), Round Robin and Minimum Completion Time (MCT).
A Secure Steganographic Technique for Medical Images

Communication and storage of Private/Secret information have become important parts of our employment or personal life, after the inception of the Information age. The development in digital technologies and the Internet caused many breakthroughs in the realm of data communication. Unfortunately, this advancement is also offering endless opportunities for attacking the transmission media and steal confidential data. Therefore, secure transmission of data being robust to attacks is required and this objective has gained the attention of many researchers. The technique of hiding information in another information is called steganography. When digital images are used for concealing the secret information, it is called Image Steganography. An important challenge in designing a steganographic scheme is to keep up a good trade-off between features such as robustness, security and imperceptibility. Most of the methods proposed in the literature lack either a feature or the trade-off between them. The proposed method combines image encryption, Lifting Scheme based Wavelet Transform (LS-WT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) for providing the trade-off between features. Peak Signal–Noise Ratio (PSNR), Normalized Cross-Correlation (NCC), and Structural Similarity Index (SSIM) are used to evaluate the performance of the scheme.
Proposing a New Algorithm for Predicting Short-Term and Long-Term Trust-ability in Cloud Computing

Despite the huge use of cloud computing, due to its large dimensions and availability for all users, this type of network is weak and vulnerable to malicious attacks. Therefore, as a useful complement to existing security methods, trust management plays a crucial role in discovering suspicious behaviors in the cloud computing network. The critical question is, how can we find ideally and effectively users with suspicious behaviors in these complex environments. In this paper, the Markov chain model has been used to calculate the short-term reliability of users in the cloud network, and the trust management system has been proposed to mitigate the effects of complex environments to calculate the user’s status. Furthermore, a new computational model has been introduced with relevant, practical factors for calculating the long-term trust that reduces the effect of changing environmental parameters in the calculations. The simulation results show that the proposed algorithm, Markov chain trust management can more effectively detect suspicious behaviors of users in the cloud computing network, and in a meaningful way, provide a better rate of delivery of packets compared to their counterparts, and ultimately provide better security in the cloud computing network.
Investigating the effective factors on tendency toward virtual education among the students of payame noor university in Kashan and Aran-Va-Bidgol county in 2017

Aims: Understanding students' attitudes and perceptions toward virtual education is important in successful development of e-learning in higher education. The purpose of this research is to investigate the tendency towards virtual education among students of Payame Noor University. Materials and Methods: This cross-sectional study was conducted in 2017 with the objective to find out perception of students about effective factors on their tendency towards virtual education. By applying Cochran formula for finite populations, sample size was calculated 92 students. Data were collected by a reliable and valid virtual education tendency questionnaire. Data was analyzed using SPSS software. Results: The results showed that the majority of respondents were in the age range of 20–29 years old and 53% of them were females. The majority of students (67%) believed that reducing visual communication was effective on the tendency toward virtual education. A lower percentage (30%), believed that the increase in students' academic level wasn't an effective factor in trend toward virtual education. overall 57% of students disagreed with the effect of reduction of costs on tendency towards virtual education. Conclusion: Most of the students stated that their tendency towards virtual education was due to the increase in their scientific level, it is suggested that the university authorities strengthen scientific foundations of the university. If so, more students are expected to enter virtual universities.
Optimization of Degraded Document Image Binarization Method Based on Background Estimation

Binarization of document images is one of the most relevant pre-processing operations, leading to a significant decrease of the amount of information used during their further analysis.Since many document images, particularly historical, may be degraded over time, the application of some simple global thresholding methods usually lead to highly unsatisfactory results.A similar situation may occur for unevenly illuminated images, limiting the visibility of various shapes, representing not only the alphanumerical characters.A typical solution of this problem is the application of some adaptive thresholding methods, as well as more sophisticated solutions, proposed recently e.g. during Document Image Binarization Competitions (DIBCO) or TQ-DIB 2019 competition.Nevertheless, due to their relatively high computational demands, there is still a need of some faster methods, leading to high binarization accuracy for challenging benchmark datasets, such as DIBCO or Nabuco.Hence, the adaptation and optimization of the parameters of the fast thresholding method utilizing background estimation, proposed originally for the OCR purposes and verified for unevenly illuminated printed documents, is presented in this paper.The proposed solution has been optimized and verified using the state-of-the-art datasets containing 166 degraded document images together with their ground-truth binary equivalents, leading to better results, also in comparison to much slower adaptive thresholding methods.The performance of all methods used in comparisons has been determined using commonly accepted metrics, such as F-Measure, Accuracy, Distance Reciprocal Distortion (DRD) or Misclassification Penalty Metric (MPM), and relative execution time, calculated for all used image datasets.
The Usefulness of Using Virtual Reality to Assess Elderly and Dementia Friendly Hospital Design

Virtual reality (VR) provides unique tool to conduct leading-edge research in several areas with the overarching mandate of providing innovative solutions in a manner, which ensures connectivity and adaptability of interdisciplinary research such as, architectural and medical research to changing technologies. The Patient safety movement is creating a “culture” where the omission and commission of clinical actions are minimized. Thereby, reducing risk of harm often related to such events as medication errors and falls. With great increase in older adults’ health care expenses, assessing physical environment is becoming essential to reduce patient’s injuries during hospitalization and optimally reduce the cost of health care resulting from these injuries. This review focuses on the usefulness of using virtual reality in assessing the effect of architectural design on mental health disorders. It also suggests future directions for the use of virtual reality so it can be implemented to improve hospital physical design to mitigate injuries sustained by older patients suffering from dementia. Furthermore, major virtual reality test mediums, and the critique over using them in the assessment of architectural environments will be discussed.
EDUCATION: EIDOS EXTRACTION OR TECHNOLOGY

This article emphasizes that digital technologies have already been totally introduced into education. Digitalization is a global process that every day subjugates the educational space and even beyond. The authors argue that the practice of introducing digital technology affects the modernization of research areas.
Multi-user file-sharing systems based on LWE

Multi-user file-sharing plays an important role in the cloud storage. It allows different users to share files via the cloud. However, it usually faces many security issues because the cloud is usually public and semi-trusted. To overcome these issues, we propose multi-user file-sharing systems (MFS) in this paper. According to the date sources, we propose two frameworks of MFS based on the framework of proxy re-encryption (PRE), unique source (uni-source) MFS and multi-source MFS. We formalize the security models, which contain ciphertext security, secret key security and master secret security. With the frameworks, we propose two secure and efficient MFS schemes based on LWE, the uni-source MFS UM and the multi-source MFS MM. We prove that UM is secret key and ciphertext security, and MM is secret key, master secret and ciphertext security with the hardness assumptions of LWE and SIS. Furtherly, we prove they have some useful properties, such as non-interactive and anonymous.
Implementing e–Upchaar: Hospital Management Information System for public health facilities in Haryana

Background and objective: There is a paucity of reliable hospital data and available information for planning and management of health services in India.Hence, the Government of Haryana launched e-Upchaar, a Hospital Management Information System (HMIS) in the public health facilities of the state.A study was conducted to give insight into the design, development and implementation of HMIS for public health facilities, capable of meeting futuristic health demands of Haryana state and the introduction of real-time reporting, data analytics for evidence-based rational decision making.Methods: A descriptive study (August-October 2019) was conducted in the state of Haryana, India on implementation of HMIS (2015HMIS ( -2019)).Desk reviews and interviews were conducted and the data from back-end of the application was explored.Integration of an external application by the Government of India's Ministry of Health initiative 'Mera Aspataal' with e-Upchaar was also reviewed.The data extracted from the database was saved in Excel 2010 for descriptive analysis.Results: A unique health ID (UHID) was created for every patient registered in e-Upchaar.Patients (80 lakhs) and EMRs (70 lakhs) created untill 2019.Registration time (1 minute), follow up visits (15 sec), billing (2 minutes) and diagnostic reports' availability on the same day.This resulted in a significant reduction in operational costs; savings due to non-printing of X-ray films in 22 facilities amounted to 13 million INR.Conclusion: e-Upchaar is playing a pivotal role towards achieving the quality standards for effective health care delivery in Haryana.It has improved transparency and integrity in hospital operations through accurate inventory management; and brought improvement in patients' experience.
Reply to “Comment on ‘How to observe and quantify quantum-discord states via correlations’ ”

The authors of the preceding Comment [Kumar and Snizhko, Phys. Rev. A 102, 016401 (2020)] made two critical remarks regarding the protocol for detecting and quantifying quantumness of separable states presented in our paper [Hunt et al., Phys. Rev. A 100, 022321 (2019)]. One is that our protocol is not ``experiment friendly'' in comparison to full quantum state tomography. The other is that the suggested quantifier fails to distinguish different degrees of quantumness within the family of Werner states. We will show that, while being technically correct, both remarks referred to generalizations of our protocol which had been neither suggested nor intended. We will reiterate why the proposed protocol would be advantageous, compared to full or partial quantum state tomography, for detecting and quantifying discord in many-body bipartite states.
Combining the NER-OCR methods to improve information retrieval efficiency in the Indonesian posters

Event organizers in Indonesia often use websites to disseminate information about these events through digital posters. However, manually processing for transferring information from posters to websites is constrained by time efficiency, given the increasing number of posters uploaded. Also, information retrieval methods, such as Named Entity Recognition (NER) for Indonesian posters, are still rarely discussed in the literature. In contrast, the NER method application to Indonesian corpus is challenged by accuracy improvement because Indonesian is a low-resource language that causes a lack of corpus availability as a reference. This study proposes a solution to improve the efficiency of information extraction time from digital posters. The proposed solution is a combination of the NER method with the Optical Character Recognition (OCR) method to recognize text on posters developed with the support of relevant training data corpus to improve accuracy. The experimental results show that the system can increase time efficiency by 94 % with 82-92 % accuracy for several extracted information entities from 50 testing digital posters.
Multi-Directional Reconstruction Algorithm for Panoramic Camera

of view. It can be applied in virtual reality, smart homes and other fields as well. A multi-directional reconstruction algorithm for panoramic camera is proposed in this paper according to the imaging principle of dome camera, as the distortion inevitably exists in the captured panorama. First, parameters of a panoramic image are calculated. Then, a weighting operator with location information is introduced to solve the problem of rough edges by taking full advantage of pixels. Six directions of the mapping model are built, which include up, down, left, right, front and back, according to the correspondence between cylinder and spherical coordinates. Finally, multi-directional image reconstruction can be realized. Various experiments are performed in panoramas (1024×1024) with 30 different shooting scenes. Results show that the azimuth image can be reconstructed quickly and accurately. The fuzzy edge can be alleviated effectively. The rate of pixel utilization can reach 84%, and it is 33% higher than the direct mapping algorithm. Large scale distortion is also further studied.
ENHANCING THE PERFORMANCE OF IMAGE PREPROCESSING FOR CLASSIFICATION AND OBJECT DETECTION

The image preprocessing optimization is a challenging task with numerous applications including classification and object detection on which this paper is oriented the most. Enhancing the performance in terms of processing time for image preprocessing is crucial to every researcher engaged into deep learning. A few common mistakes and practices are presented in this paper which can greatly impact training time, alongside practices and tools used for diagnosing potential pitfalls. In this paper, we evaluate several common Python libraries which are used for image preprocessing and analyze the impact of different augmentation ordering with respect to central processing unit (CPU) usage.
Two Stage Text Encryption Using a Private Table of the Sumerian System

The massive use of the internet in our contemporary life relates to the increase of the exchange of information through the Internet networks.Due to the importance of the information and to preserve its confidentiality, it protects using theories as well as strategies from attack or abuse.The aim of the research is to design an algorithm to encrypt text symbols to understandable symbols using private table that contains representation of ASCII of English characters with their suitable Sumerian system numbers.Each letter of the text is converted to ASCII code then encoded as an image using the private table, After that these images are combined together and saved as a new image as a Sumerian image.This image is encrypted first with unintelligible key image that is generated from three external key's (kr, kg, and kb) to be the first stage encryption image.Then the second stage image generates from encrypting the first stage image with another input key image using XOR function which increases image security.The proposed algorithm gives double stage encryption and high degree of security level because of using Sumerian system and using different keys and security methods.Through using the histograms on all tested images, the result image demonstrate the efficiency of the system visually.
Mathematical Model of the Water Quality in Tlogo Ampel Watershed

Abstract The observation station of the study is placed at one point of Tlogo Ampel River in Wonokoyo Village, Kapongan Subdistrict, Situbondo Regency with location of 7°70’44 “South Latitude and 114°08’99” East Longitude. Tlogo Ampel River is one of the rivers that through the settlement of society and industry so that naturally experience of degradation of water quality. Sampling is done every month by the related institution that is Sampean River Basin Management Institute. To predict the value of water quality is needed mathematical model change the water quality of Tlogo Ampel watershed. The research parameters included pHysical properties (Temperature and TSS), organic chemical properties (pH, BOD, COD, DO, PO4P and NO3N) as well as microbiological properties (total coli and fecal coli). The results show the Polynomial equation of order 6 for parameters: temperature, TSS, pH, BOD, COD, DO, PO4P, NO3N, total coli and fecal coli respectively are : y = 0,0002x 6 - 0,0078x 5 + 0,1203x 4 - 0,9031x 3 + 3,4485x 2 - 6,2299x + 27,595; y = -0,0027x 6 + 0,1591x 5 - 3,1236x 4 + 27,249x 3 - 108,22x 2 + 157,53x + 46,997; y = 0,0002x 6 - 0,0085x 5 + 0,1342x 4 - 1,0398x 3 + 4,0441x 2 - 7,4089x + 12,505; y = -0,0002x 6 + 0,0032x 5 + 0,0302x 4 - 0,9626x 3 + 6,8118x 2 - 16,907x + 17,482; y = 0,0005x 6 - 0,0328x 5 + 0,7417x 4 - 7,2595x 3 + 31,838x 2 - 55,407x + 47,365; y = -0,001x 6 + 0,0406x 5 - 0,6408x 4 + 4,9244x 3 - 18,785x 2 + 31,901x - 11,853; y = -1E-05x 6 + 0,0005x 5 - 0,0098x 4 + 0,0897x 3 - 0,3839x 2 + 0,6547x - 0,0902; y = 0,0004x 6 - 0,0143x 5 + 0,1694x 4 - 0,9131x 3 + 2,3917x 2 - 3,2352x + 4,1306; y = 0,0035x 6 - 0,269x 5 + 6,3886x 4 - 67,262x 3 + 337,4x 2 - 741,18x + 612,7; y = 0,0011x 6 - 0,0963x 5 + 2,2449x 4 - 22,035x 3 + 100,4x 2 - 193,42x + 140,76
Voting Credential Management System for Electronic Voting Privacy

Electronic voting requires voting privacy to protect the voter anonymity. We present a novel design framework for credential management called Voting Credential Management System (VCMS) which preserves voting privacy against advanced attackers who do not only monitor the voting transactions and communications but are also capable of compromising an authority involved in the credential management and generation. Such requirement against the advanced threat model based on an authority compromise is inspired by the recent attacks in voting privacy and is adopted in the state of the art credential management systems. VCMS achieves such properties by building on the well-established cryptographic primitives and by separating the voting token (the VCMS output credential used for the voting) and the intermediate key token (which is used within VCMS and bridges the registration/certificate with the voting token). VCMS is specifically applicable to electronic voting and is simpler than other sophisticated credential management systems achieving comparable security properties.
Learning What They Think vs. Learning What They Do: Two Mechanisms of Vicarious Learning

Vicarious learning is a vital component of organizational learning. However, as new technologies emerge that have the potential to improve vicarious learning among individuals and between humans and algorithms, our lack of conceptual clarity about it’s underlying mechanisms may impede our ability to benefit from them. We theorize and model two fundamental processes underlying vicarious learning: observation of actions (learning what they do) vs. belief sharing (learning what they think). The analysis of our model points to two key insights. First, vicarious learning through belief sharing is not universally better than simple mutual observation of actions and outcomes. Specifically, enabling mutual observability of actions and outcomes is superior to sharing of beliefs when the task environment features few alternatives with large differences in their value and there are no time pressures. Second, the superiority of vicarious learning through belief sharing in more complex task environments with time constraints does not require any differential wisdom ex ante among the learners. Both results are shown to be the consequence of a key mechanism – how vicarious learning affects self-confirming biases.
A Translation-Based Approach to Morphology Learning for Low Resource Languages

“Low resource languages” usually refers to languages that lack corpora and basic tools such as part-of-speech taggers. But a significant number of such languages do benefit from the availability of relatively complex linguistic descriptions of phonology, morphology, and syntax, as well as dictionaries. A further category, probably the majority of the world’s languages, suffers from the lack of even these resources. In this paper, we investigate the possibility of learning the morphology of such a language by relying on its close relationship to a language with more resources. Specifically, we use a transfer-based approach to learn the morphology of the severely under-resourced language Gofa, starting with a neural morphological generator for the closely related language, Wolaytta. Both languages are members of the Omotic family, spoken and southwestern Ethiopia, and, like other Omotic languages, both are morphologically complex. We first create a finite- state transducer for morphological analysis and generation for Wolaytta, based on relatively complete linguistic descriptions and lexicons for the language. Next, we train an encoder-decoder neural network on the task of morphological generation for Wolaytta, using data generated by the FST. Such a network takes a root and a set of grammatical features as input and generates a word form as output. We then elicit Gofa translations of a small set of Wolaytta words from bilingual speakers. Finally, we retrain the decoder of the Wolaytta network, using a small set of Gofa target words that are translations of the Wolaytta outputs of the original network. The evaluation shows that the transfer network performs better than a separate encoder-decoder network trained on a larger set of Gofa words. We conclude with implications for the learning of morphology for severely under-resourced languages in regions where there are related languages with more resources.