Reviewerly/exHarmony huggingface dataset:
التحليل الكيميائي للمياه المعباء في محافظة دهوك

Drinking water is considered one of the basic human needs that cannot be dispensed with for any reason and the water must have quality standards in terms of taste and color in addition to the physical, chemical and biological specifications. The bottled water industry has flourished in recent decades, and the demand for it has increased in most parts of the world and for various reasons, perhaps the most prominent of which is the quality of bottled water and the regularity of that quality in different seasons, while the water changes its quality due to the quality of the source ,in addition to the possibility of contamination during transportation or when it is poorly stored, which It leads to a change in some physical and chemical properties of water. On this basis, specific samples were selected for all companies that manufacture bottled water at Duhok Governorate area, and then all samples were examined, both chemical and physical, to obtain laboratory results for these samples.
E-Government and Citizen Satisfaction in Jordan: EGOVSAT Testing Using Sem Approach

Governments around the world have been in focus to facilitate citizens' access to and adoption of e-services by ensuring that they benefit from digital development. Therefore, ensuring citizens' satisfaction with the provided services is critical for that adoption success. The current study evaluates satisfaction with e-government among Jordanian citizens through testing the EGOVSAT model. Quantitative approach through a descriptive exploratory method was adopted. A self-administered questionnaire was developed for data collection purposes. Through convenience sampling, 483 respondents were included, and structural equation modelling through SMART PLS was utilised to test the model, and the result indicated that the proposed dimensions accounted for 53.2% of the variance in citizen satisfaction with e-government services. Moreover, reliability was identified as the strongest driver followed by efficiency and utility respectively, whereas customisation was identified as a negative driver and flexibility as an insignificant one entailing an issue that needs to be addressed. Following garnered results, implications were discussed.
Hybrid Techniques for Object Detection using Deep Learning

Deep learning is a one of the major concept of Artificial Intelligence and Machine learning, which deals with the object detection task. On the other hand, a new targeted dataset is built according to commonly used existing datasets, and two networks called Single Shot Multi box Detector (SSD) and You Only Look Once (YOLO) are chosen to work on this new dataset. Through experimentation strengthen the understanding of these networks, and through the analysis of the results, learn the importance of targeted and inclusive datasets for deep learning. In addition, to this optimize the networks for efficient utilization when integrated with the necessary system or application. Further, explore the applications corresponding to these networks. The implementation includes two major concepts. The first concept is Object detection. Object detection is the process of object recognition and classification. There are several Training sets available online for training an object detection model. But the models are not trained to detect the same object from different geographical regions. The second concept is lane detection and steering suggestion. The model detects using the concept of radius or curvature of the road and also distance of the car from both the lane lines. Using these parameters it also gives steering suggestions such as move right or left by a certain distance. In addition to this it gives the distance and speed attributes of the surrounding objects such as cars, motorcycles, etc. Finally, the model developed is capable of detecting all the parameters required in order to be integrated and to create a self-driving car and it can be used efficiently in India. Using the above parameters that are obtained from the model the car can navigate through lanes in real-time. Its improved performance is due to the fact that it can detect road specific objects and because it is specifically trained for Indian roads.
Gaussian Immature Bayes Classifier and Uneven Lay down Conception for Information Flow Group in Frequent Concept Drift

The requirement of acting class in streaming environments, investigators have exuberance a range of circulation classification procedures by way of managing idea flow. However, routine idea waft is a tough disaster in data flow as the span of the statistics isn't always fixed more than the point in time frame. As a result of taking into consideration the habitual concept flow, this manuscript propose a novel classifier representation, referred to as Real Gaussian naïve Bayes classifier for the information circulate categorization. To carry out the confront of habitual concept float, the first part is to construct the use of the difficult set idea intended for detect the idea waft. After that, Gaussian naïve classifier is tailored accurately to replace active information devoid of the usage of the museum facts. Also, the class is featured using the posterior possibility and the goal characteristic which focus the couple of criterion. The predicted RGNBC version is experiment through two big datasets and the effects are proven from corner to corner the existent MReC-DFS set of rules via sen, spe and corr. from the results, we ought to find out that the projected version attain the utmost accurateness of 63.97 % while evaluated with the aid of the available set of rules.
GraphFrames

GraphFrames are an abstraction of DataFrames that are used to do Graph Analytics. Graph Analytics stems from the mathematical Graph Theory. Graph Theory is a very important theory used to represent relationships between entities, which we can use to perform various analyses. You are using Graph Theory in your everyday life when using Google. Google introduced the PageRank algorithm that is based on Graph Theory. It tries to identify the most influential website that suits your search in the best way.
"DNS SEHAT" Implementation on Automatic Replicated Recursive Domain Name System on PT. X

Thanks to the existence of Search engines, all of informations and datas could be easily found in the internet, one of the search engine that users use the most is Google. Google still be the most popular search engine to provide any informations available on the internet. The search result that Google provide, doesn't always give the result we wanted. Google just displayed the results based on the keyword we type. So sometimes, they show us the negative contents on the internet, such as pornography, pornsites, and many more that seems to be related to the keyword, whether the title or the other that makes the result going that way. In this paper, we will implement the "DNS SEHAT" to pass along client's request queries so the Google search engine on the client's side will provide more relevant search results without any negative contents.
A fast robust template matching method based on feature points

A method for template matching based on the feature matches between a target image and the template is proposed. Firstly, two sets of feature points from two images were extracted by ORB algorithm, and then the key points were matched to get a number of matching point pairs. Secondly, the wrong matches were removed to leverage feature numbers to improve quality. Finally, we explored a grid framework to locate the target object. Experiments demonstrated the great performance of our method.
Digital watermarking for healthcare: a survey of ECG watermarking methods in telemedicine

Innovations in healthcare have introduced a radical change in medical environment including patient diagnostic data and patient biological signal facilities and processing. The adoption of telemedicine services usually leads to an incremental trend in transmission of electronic sensitive data over insecure infrastructures. Since integrity, authenticity and confidentiality are mandatory features in telemedicine, the need to guarantee these requirements with end-to-end control arises. Among the various techniques implemented for data security, digital watermarking has gained considerable popularity in healthcare oriented applications. The challenge the watermarking insertion has to overcome is to avoid changes in health and medical history of a patient to a level where a decision maker can make a misdiagnosis. This paper presents a survey of different applications of electrocardiogram watermarking for telemedicine. The most recent and significant electrocardiogram watermarking schemes are reviewed, various issues related to each approach are discussed and some aspects of the adopted technique, including classification and performance measures, are analysed.
Complex Dual Tree Wavelet Multi-Level Feature Based Transformation Parameter Estimation for 3d Medical Image Registration

3D image registration of CT and MRI data is carried out using DTCWT sub bands by considering the features from all 64 bands. The features are selected by considering Mattes Mutual Information Metric and the optimizer algorithm estimates the optimum transformation parameters from all the 64 bands. Transformation parameters from eight low pass bands from each octave are averaged to identify optimum registration parameters. Similarly, for registration of high pass bands mean of transformation parameters from 56 bands are identified. The proposed registration algorithm is suitable for register multimodal medical images and the proposed algorithm is validated for more than 20 3D images. Mutual information and joint entropy is estimated to demonstrate the advantages of proposed algorithm overt that of intensity based algorithm. With features identified from 56 bands with six orientations the registered image is found to consist of features from both input images with closeness level measured to be within 12%.
Crypto-compression scheme based on the DWT for medical image security

Ensuring the confidentiality of exchanged data is always a great concern for any communication. Also, the purpose of compression is to reduce the amount of data while preserving important information. This reduction leads to the archiving of more information on the same storage medium and minimises the transfer times via telecommunication networks. Indeed, the combination of encryption and compression guarantees both confidentiality and authentication of information. In addition, it reduces processing time and transmission on public channels and increases storage capacity. In this paper, we propose a new approach of a partial or selective encryption for medical Images based on the discrete wavelet transform (DWT) coefficients and compatible with the norm JPEG2000. The obtain results prove that, the proposed scheme provides a significant reduction of the processing time during the encryption and decryption, without tampering the high compression rate of the compression algorithm.
A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development (Preprint)

<sec> <title>BACKGROUND</title> Knowledge graph embedding is an effective semantic representation method for entities and relations in knowledge graphs. Several translation-based algorithms, including TransE, TransH, TransR, TransD, and TranSparse, have been proposed to learn effective embedding vectors from typical knowledge graphs in which the relations between head and tail entities are deterministic. However, in medical knowledge graphs, the relations between head and tail entities are inherently probabilistic. This difference introduces a challenge in embedding medical knowledge graphs. </sec> <sec> <title>OBJECTIVE</title> We aimed to address the challenge of how to learn the probability values of triplets into representation vectors by making enhancements to existing TransX (where X is E, H, R, D, or Sparse) algorithms, including the following: (1) constructing a mapping function between the score value and the probability, and (2) introducing probability-based loss of triplets into the original margin-based loss function. </sec> <sec> <title>METHODS</title> We performed the proposed PrTransX algorithm on a medical knowledge graph that we built from large-scale real-world electronic medical records data. We evaluated the embeddings using link prediction task. </sec> <sec> <title>RESULTS</title> Compared with the corresponding TransX algorithms, the proposed PrTransX performed better than the TransX model in all evaluation indicators, achieving a higher proportion of corrected entities ranked in the top 10 and normalized discounted cumulative gain of the top 10 predicted tail entities, and lower mean rank. </sec> <sec> <title>CONCLUSIONS</title> The proposed PrTransX successfully incorporated the uncertainty of the knowledge triplets into the embedding vectors. </sec>
Automatic Extraction of Acronyms from Text

A brief introduction to acronyms is given and motivation for extracting them in a digital library environment is discussed. A technique for extracting acronyms is given with an analysis of the results. The technique is found to have a low number of false negatives and a high number of false positives. Introduction Digital library research seeks to build tools to enable access of content, while making as few as possible assumptions about the content, since assumptions limit the range of applicability of the tools. Generally, the broader the assumptions the more widely applicable the tools. For example, keyword based indexing [5] is based on communications theory and applies to all natural human textual languages (allowances for differences in character sets and similar localisation issues not withstanding) . The algorithm described in this paper makes much stronger assumptions about the content. It assumes textual content that contains acronyms, an assumption which is known to hold for...
Video Cloud Services for Hospitals: Designing an End-to-End Cloud Service Platform for Medical Video Storage and Secure Access (Preprint)

<sec> <title>UNSTRUCTURED</title> The amount of medical video data that has to be securely stored has been growing exponentially. This rapid expansion is mainly caused by the introduction of higher video resolution such as 4K and 8K to medical devices and the growing usage of telemedicine services, along with a general trend toward increasing transparency with respect to medical treatment, resulting in more and more medical procedures being recorded. Such video data, as medical data, must be maintained for many years, resulting in datasets at the exabytes scale that each hospital must be able to store in the future. Currently, hospitals do not have the required information and communications technology infrastructure to handle such large amounts of data in the long run. In this paper, we discuss the challenges and possible solutions to this problem. We propose a generic architecture for a holistic, end-to-end recording and storage platform for hospitals, define crucial components, and identify existing and future solutions to address all parts of the system. This paper focuses mostly on the recording part of the system by introducing the major challenges in the area of bioinformatics, with particular focus on three major areas: video encoding, video quality, and video metadata. </sec>
Collaborative studio in a virtual environment

© 2002 IEEE. Recently, virtual environments (VE) have been increasingly used as settings for design and research. Using VE to visualize ideas from the initial steps of design, the architect is challenged to deal with perception of space, solid and void, without translations to and from a two dimensional media. The goal of the authors' studies was to identify how designers use and communicate early design ideas by using immersive three-dimensional (3D) VEs and how they describe 3D volumes using a different media. A series of experiments were undertaken, including navigation- and perception-tasks, designing in IVE, transcription of design, remote communication between design partners and controlled observations. They explored initial intentions of 3D-immersive design schemes, textual descriptions and collaborations within IVE. They discuss frameworks and factors influencing how architectural students communicate their proposals in an immersive Virtual Environment Design Studio, and how this approach of design studio enables to understand volumes and spatial relationships.
Experimental research problems of extracurricular individualization of masters' training in education management

Information library resources are significant educational resources in higher education institutions The development of the ability of foreign undergraduates to effectively acquire, evaluate and use these resources in extracurricular time can improve their level of information literacy and their ability to self-study For this purpose, we have developed a technology of extracurricular individualization for professional training of foreign masters under the guidance a librarian with a complex of information library resources (CILR) This educational technology establishes an algorithm of interrelated educational activities between foreign undergraduate and counseling library staff in extracurricular time To test the effectiveness of this technology, we conducted an experiment at the faculty of Pedagogical Education of Moscow State University named after M V Lomonosov During the experiment we used the Training and methodical complex on discipline “Informatization of educational process”, which developed by us To conduct the experiment, we have developed a methodical instrument of objective preliminary, current and final diagnostics to test the knowledge and skills of information literacy of foreign undergraduates and their ability to self-study through information library resources Based on the analysis of experimental data, we have made the conclusions that the proposed technology accelerates and improves the development of students ‘ skills in search, processing, analysis, storage and management of professionally relevant information, inculcates an information culture for them and forms need for independent work.
Deep learning approach for voice pathology detection and classification

A non-invasive cum robust voice pathology detection and classification architecture is proposed in the current manuscript. In place of the conventional feature-based machine learning techniques, a new architecture is proposed herein which initially performs deep learning-based filtering of the input voice signal, followed by a decision-level fusion of deep learning and a non-parametric learner. The efficacy of the proposed technique is verified by performing a comparative study with very recent work on the same dataset but based on different training algorithms.The proposed architecture has five different stages.The results are recorded in terms of nine (9) different classification score indices which are – mean average Precision, sensitivity, specificity, F1 score, accuracy, error, false-positive rate, Matthews Correlation Coefficient, and the Cohen’s Kappa index. The experimental results have shown that the use of machine learning classifier can get at most 96.12% accuracy, while the proposed technique achieved the highest accuracy of 99.14% in comparison to other techniques.
E-Voting: Blockchain Based Voting For Secure Vote Casting

blockchain is decentralized immutable ledger technology maintaining integrity. So to conduct tamperproof election it’s one of the approach towards it. Smart contracts are Self executed code that is written on Ethereum platform in blockchain. An E-voting system should be completely secure and does not allow voting twice that is double spending in blockchain. So it should be completely transparent. In research work electronic voting application is implemented and tested using smart contract on Ethereum platform with the help of metamask wallet. The results of ballots and votes will be stored on Ethereum blockchain with the help of consensus algorithm proof of stake. This consensus is used in validating a transaction with concept of majority approval. Current electronic voting system requires a centralized authority to control the procedure from ballot input to result output and for monitoring of election. While blockchain technology provide decentralized system which is open across connected nodes. Blockchain assets provide increased level of system security from hacking and fraud. Every transaction in blockchain is time-stamped and signed digitally with the help of cryptographic algorithms, and it assigns unique hash value to every block so it can be trace easily. Blockchain technology is one of solutions because it embraces a decentralized system and the entire databases are owned by many users. The blockchain technology also has much vulnerability due to which many attacks like 51% attack, Double Spending attack, DDOS attack, Sybil attack, Eclipse attack and Routing attack can be performed on it.
Dynamic Bayesian networks for evaluation of Granger causal relationships in climate reanalyses

Earth and Space Science Open Archive This work has been accepted for publication in Journal of Advances in Modeling Earth Systems (JAMES). Version of RecordESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary. Learn more about preprints. preprintOpen AccessYou are viewing the latest version by default [v1]Dynamic Bayesian networks for evaluation of Granger causal relationships in climate reanalysesAuthorsDylanHarriesiDTerence JohnO'KaneiDSee all authors Dylan HarriesiDCorresponding Author• Submitting AuthorCommonwealth Scientific and Industrial Research Organisation (CSIRO)iDhttps://orcid.org/0000-0002-2476-6989view email addressThe email was not providedcopy email addressTerence John O'KaneiDCommonwealth Scientific and Industrial Research Organisation (CSIRO)iDhttps://orcid.org/0000-0002-2137-5915view email addressThe email was not providedcopy email address
Chronicle

In connection with the anniversary of the Kazan University, the Department of Social Hygiene undertook the work "History of the Department of Hygiene of the Kazan University for 125 years." Placed in this issue Kaz. med zhurn. "article" The first hygienist of the Kazan department Fyodor Kalaydovich "is the beginning of this work. In connection with the same anniversary, the Department of Social Hygiene turned to the medical press with a letter "To the old doctors of Kazan" (see No. I, K.M.Zh., 1929) with a request to deliver material to the history of the medical faculty of Kazan University. that (memories, works, photographs, etc.). This appeal is primarily directed to hygienists, sanitary doctors and community physicians. The department notes with satisfaction that responses to this appeal have begun to arrive.