Reviewerly/exHarmony huggingface dataset:
A Mixed Reality Based Approach for Enhancing Drug Dispensing Process Using Microsoft Hololens

Technology is considered as the key of modernity of any field of our life. Every department of pharmacy uses many types of modern technologies, starting from drug design to drug clinical studies to industrial pharmacy and through drug dispensing to medical records and so on. But what if we can go further, what if we can go beyond its normal usage, where technology introduces real solutions for our life, and mainly in pharmacy!Mixed reality is considered one of the newest applied sciences in daily life that introduces many brilliant solutions for daily operations. Although this technology is quite old from many years, the lack of applied hardware for customers with quite cost made people and developers not use it wildly. In this research, we introduce an integrated approach for drug despising in pharmacy, built on the top of mixed reality, to make pharmacists reach the maximum usage of current drug and patient information to make drug despising process more proper to patient and pharmacists.We introduce UI design for modular system and its prototype by using Microsoft mixed reality glasses HoloLens, conjugation with other technologies that try to introduce solutions for the problems that face the pharmacists in drug dispensing process. Starting from receiving prescription, and reviewing drug information, to providing patients with information they need. All these data are displayed in a good order for pharmacists, while they speak to patients. Our approach is evaluated mainly by making a survey with four pharmacists and obtain their opinion on our proposed system.
An Improved Bayesian TRIE Based Model for SMS Text Normalization

Normalization of SMS text, commonly known as texting language, is being pursued for more than a decade. A probabilistic approach based on the Trie data structure was proposed in literature which was found to be better performing than HMM based approaches proposed earlier in predicting the correct alternative for an out-of-lexicon word. However, success of the Trie-based approach depends largely on how correctly the underlying probabilities of word occurrences are estimated. In this work we propose a structural modification to the existing Trie-based model along with a novel training algorithm and probability generation scheme. We prove two theorems on statistical properties of the proposed Trie and use them to claim that is an unbiased and consistent estimator of the occurrence probabilities of the words. We further fuse our model into the paradigm of noisy channel based error correction and provide a heuristic to go beyond a Damerau-Levenshtein distance of one.
Security at the Edge

The Internet has become an essential part of daily life for almost everyone in society having grown far beyond its roots in the 1970s as the ARPANET, a network that was principally the domain of scientists and engineers. The popularity of the HTTP, developed at CERN in the late 1980s led to the widespread use of the term ‘the web’ as a generic name for the Internet for many years, at least in the public domain. Of course, the Internet is much more than just web browsing and, in recent years, the term cyberspace has become the most popular term to describe interactions over the Internet. Yet, an unambiguous definition of the term is difficult to formulate . Financial institutions underpinning the economy and the operation of national critical infrastructures, such as monitoring and control of the electricity supply, are now dependent on the Internet. A consequence of this is that cyberattacks become more costly for the victims and perversely more attractive to the criminals who carry them out . The advent of the Internet of Things (IoT) and edge computing as a new paradigm creates the potential for enhanced productivity but at the same time opens up new opportunities for cyberattacks while still being exposed to existing attach vectors such as the well-known denial of service attack (DDoS), which can take place in many forms . In this chapter, we described the challenges in building an edge system that is secure against cyberattack. We begin by briefly reviewing the architecture of communications over the Internet and later consider the new challenges that follow from operating the hardware with values of voltage, frequency and current that enable more energy efficiency.
Partitive Case and eyse-marked Subjects in Korean

Some eyse-marked phrases in Korean are interpreted as a subject in the sentence. We claim that based on the possibility of topicalization, eyse-marking in those phrases cannot be the realization of structural case, i.e., nominative. It cannot be locative either, since it is not allowed to be focalized. In other words, it can mark the subject but it is neither nominative nor locative. Considering its semantic properties and distributions, we propose that it is partitive. First of all, it produces “part of” meaning: An eyse-marked subject refers to some members of a group, and it cannot refer to an individual. Such a restriction follows naturally from the fact that an individual cannot be divided in parts. Also the fact that there is a morphological overlap between ablative –eyse and partitive –eyse becomes another strong argument for our claim since the similarities between partitives and ablatives from diachronic as well as synchronic perspective have been found in many languages. Our analyses lead to the conclusion that –eyse has three basic functions: locative, ablative, and partitive.
In the Class of Elena Fabianovna Gnesina

The article is dedicated to Elena Fabianovna Gnesina, an outstanding Russian teacher-pianist, creator and head of a complex of musical educational institutions. The article analyzes E.F. Gnesina’s pedagogical heritage which has become a reference point not only for special music, but also for music and pedagogical institutions in Russia. The author of the article also considers in detail the method of pianists’ initial training, developed by E.F. Gnesina and recorded in a number of her piano exercises. The article also analyzes the principles of building students’ educational repertoire, developed by E.F. Gnesina.
Prediction of Direct Normal Irradiance Using Artificial Neural Networks Under Oujda Climate

Providing the knowledge of solar irradiance under such location is nowadays one of the crucial issue and over the world. This paper presents our performed work regarding the prediction of direct normal irradiance (DNI) using artificial neural networks (ANN) method under Oujda climate (Eastern of Morocco). To explore ANN, we have developed and validated a Matlab code with different statistical performance indicators. DNI is the most sensitive component of solar irradiance because of its high variability. For this purpose, various parameters are chosen as inputs: Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI), Temperature (T), Wind Speed (WS), Wind Direction (WD), and Barometric Pressure (BP), which are recorded from the meteorological station installed at a roof of Mohammed first University in Oujda for two years of measurement period (2015–2016). Making combinations of mentioned variables leads to detect the best ANN architecture with accurate correlations and low errors. To assess the performance of the ANN method, we calculated different statistical indicators in addition to the correlation coefficient of training, testing and validation of the ANN models. Results show that the most appropriate model for our prediction case includes seven inputs (GHI, DHI, T, WS, WD, BP and RH) with 28 hidden neurons in which performs with low errors; 0.16%, 6.6%, 4.2% for the bias, root mean square and mean absolute errors respectively.
Fingerprint Authentication System for BaaS Protocol

Over the past many years, several corporations have benefited from the implementation of cloud solutions among the organization. Due to the advantages such as flexibility, mobility, and cost saving, the number of cloud users is expected to grow rapidly. Consequently, organizations want a secure system, credit to manifest its users so as to make sure the practicality of their services and information hold on within the cloud storages are managed in a private environment. In the current approaches, the user authentication in cloud computing is predicated on the credentials submitted by the user like secret, token and digital certificate. Unfortunately, these credentials can often be stolen, accidentally revealed, or hard to remember. In view of this, we propose a fingerprint-based authentication system to support the user authentication for the cloud environment. We take into account a distributed state of affairs wherever the biometric templates are hold on within the cloud storage, whereas the user authentication is performed without the leak of any sensitive information.
End-to-End Continual Rare-Class Recognition with Emerging Novel Subclasses

Given a labeled dataset that contains a rare (or minority) class containing of-interest instances, as well as a large class of instances that are not of interest, how can we learn to recognize future of-interest instances over a continuous stream? The setting is different from traditional classification in that instances from novel minority subclasses might continually emerge over time—and hence is often referred as continual, life-long, or open-world classification. We introduce RaRecognize, which ( i ) estimates a general decision boundary between the rare class and the majority class, ( ii ) learns to recognize the individual rare subclasses that exist within the training data, as well as ( iii ) flags instances from previously unseen rare subclasses as newly emerging (i.e., novel). The learner in (i) is general in the sense that by construction it is dissimilar to the specialized learners in (ii) , thus distinguishes minority from the majority without overly tuning to what is only seen in the training data. Thanks to this generality, RaRecognize ignores all future instances that it labels as majority and recognizes the recurring as well as emerging rare subclasses only. This saves effort at test time as well as ensures that the model size grows moderately over time as it only maintains specialized minority learners. Overall, we build an end-to-end system which consists of (1) a representation learning component that transforms data instances into suitable vector inputs; (2) a continual classifier that labels incoming instances as majority (not of interest), rare recurrent, or rare emerging; and (3) a clustering component that groups the rare emerging instances into novel subclasses for expert vetting and model re-training. Through extensive experiments, we show that RaRecognize outperforms state-of-the art baselines on three real-world datasets that contain documents related to corporate-risk and (natural and man-made) disasters as rare classes.
Towards Performance Optimization for Hadoop MapReduce Applications

Apache Hadoop is a widely used open-source distributed platform towards big data processing and provides YARN based distributed parallel processing framework on low cost commodity machines. However, YARN adopts static resource management (that is, the number of containers available per node and the size of each container are static in nature) depending on pre-configured default resource units called containers leading to poor performance to deal with various sort of MapReduce applications. In addition, during the last wave of a job, many available resources occur frequently being idle because YARN does not consider the wave behavior in tasks of MapReduce applications. To take advantage of idle resources resulting in performance improvement, the important parameter, the number of map tasks is needed to optimize based on the available resources and governed by split size. Therefore, this parameter is optimized through the split size tuning based on the available resources. To address the drawback of static resource management of yarn in Hadoop, the numbers of concurrent containers per machine are tuned to optimize the node performance for running each MapReduce application. As per experimental results, the proposed system that optimizes the selected parameter on optimized concurrent containers can achieve the performance gains of MapReduce applications while reducing the optimization overheads.
A Constant Round Write-Only ORAM

The write-only oblivious RAM (ORAM) is proposed to efficiently protect the privacy of applications such as cloud storage synchronization and encrypted hidden volumes. For N blocks with size B = Ω(log2N), the most efficient write-only ORAM, DetWoORAM, achieves O(B) communication complexity with O(logN) rounds per logical write. We propose a two-level write-only ORAM and achieve O(B) communication complexity with O(1) rounds. Similar to the traditional bucket-based ORAM schemes, we set a rate for the write operation to further reduce the communication complexity. The top-level stores data blocks in a flat array and the write pattern is protected by writing blocks uniformly at random. The second level employs a binary tree to store the position map of data blocks. To avoid recursive storage, a static position map for blocks in the second level is used. Both the analysis and experiments show that, besides the achieved low communication complexity and rounds, the stash sizes in the top level and the second level are bounded to O(B) and ω(B), respectively.
Academician Zh. I. Alferov and the Russian Academy of Sciences Library. (To the 90&lt;sup&gt;th&lt;/sup&gt; birthday anniversary)

The article is devoted to the activity of Zhores I. Alferov as a curator of the Russian Academy of Sciences Library (BAN), which is little known to a wide audience. It reports about his role in the construction of a new book depository for the funds suffered after the 1988 tragic fire, in expanding international contacts in the Academy of Sciences; participation in the publication of unique materials of the “Academicians’ case of 1929–1931”, and “Annals of the Library of the Russian Academy of Sciences”; initiative in attracting the attention of students of the Academic University of the Russian Academy of Sciences to the history of the Library. Zh. I. Alferov supported establishing the Council for defending PhD theses on specialty 05.25.03 "Library Science, Bibliography and Book Science” in BAN that worked during 1998–2007. For nine years, more than 20 people defended their dissertations. The author emphasizes the special role of Zh. I. Alferov in discussing library science issues in the academic community at the meetings of the Presidium of the Russian Academy of Sciences and the St. Petersburg Scientific Center of the Russian Academy of Sciences. His openness, responsiveness and democratic attitude toward the staff of the Russian Academy of SciencesLibrary made him a respected person of all library staff. Evidence of this is the fundamental bibliographic index of Zh. I. Alferov’s publications, which was issued in 2010 in the famous series “Materials for the biobibliography of scientists”.
Doctoral Colloquium—A Qualitative Study of Virtual Reality as a means of Providing Cultural Context in Literature Classrooms at Minority Serving Institutions

This Doctoral Colloquium intends to make a focused and strictly limited test of the effectiveness of virtual reality in the literature classroom in terms of qualitative evidence gathered via short answers as well as final interviews in which students reflect upon the usefulness of VR in their learning experience. Final interviews will include both fact-based reading comprehension questions, and questions that ask the participants to reflect upon their experience. Written questions will involve both reading comprehension and also ask students to describe levels of engagement and learning.
Large-width machine learning algorithm

We introduce an algorithm, called Large Width (LW), that produces a multi-category classifier (defined on a distance space) with the property that the classifier has a large 'sample width.' (Width is a notion similar to classification margin.) LW is an incremental instance-based (also known as 'lazy') learning algorithm. Given a sample of labeled and unlabeled examples, it iteratively picks the next unlabeled example and classifies it while maintaining a large distance between each labeled example and its nearest-unlike prototype. (A prototype is either a labeled example or an unlabeled example which has already been classified.) Thus, LW gives a higher priority to unlabeled points whose classification decision 'interferes' less with the labeled sample. On a collection UCI benchmark datasets, the LW algorithm ranks at the top when compared to 11 instance-based learning algorithms (or configurations). When compared to the best candidate from instance-based learners, MLP, SVM, decision tree learner (C4.5) and Naive Bayes, LW is ranked at second place after only MLP which comes at first place by a single extra win against LW. The LW algorithm can be implemented in parallel distributed processing to yield a high speedup factor and is suitable for any distance space, with a distance function which need not necessarily satisfy the conditions of a metric.
Cloud based ERP Model using Optimized Load Balancer

Enterprise Resource Planning (ERP) and Cloud computing are turning out to be increasingly more significant in the field of Information Technology (IT) furthermore, Communication. These are two distinct segments of current data frameworks, and there are a few inside and out examinations about Enterprise Resource Planning on cloud computing framework. ERP frameworks are related with a few issues, for example, shared synchronization of multi-composed assets, constrained customization, massive overhauling cost, arrangement mix, industry usefulness, reinforcement support and innovation refreshes. These issues render ERP frameworks execution excruciating, complex and time-devouring and create the need for a huge change in ERP structure to upgrade ERP frameworks foundation and usefulness. Cloud Computing (CC) stages can defeat ERP frameworks inconsistencies with financially savvy, redid and profoundly accessible figuring assets. The objective of this examination is to blend ERP and CC benefits to lessen the factor of consumption cost and execution delays through a proposed system. For this reason, investigate the unmistakable issues in current ERP frameworks through a complete correlation between ERP when moving to CC condition. Also, a conventional structure is proposed for “Cloud-based ERP frameworks”.
Intelligent Effectiveness Evaluation of College Ideological Guiding based on Computer Data Analysis and Mining

Intelligent effectiveness evaluation of the college ideological guiding based on computer data analysis is conducted in this manuscript. The novelties of the paper can be summarized as following aspects. (1) As a traditional and mature means of data management, database system is mainly used for transaction processing, the traditional models are optimized for obtaining the high efficient model. (2) When technicians use the Python for the computer network development, they can directly apply the network protocol library resources contained in Python, the proposed research work use the Python to implement the system. (3) Apriori algorithm automatically performs pruning when generating candidate sets, this model is used to construct the fuzzy evaluation model. The performance of the model is validated through the simulations.
A Quantum Pipeline for an Executable Quantum Instruction Set Architecture

The existence of algorithm to machine gap has been accepted by the quantum computing domain experts, as the research community is struggling to realize the real power of quantum computing. The prime factor contributing to this gap is the large amount of quantum error correction (QEC) that is required during the execution of a quantum algorithm. For serial quantum computers, with just one processing zone, the coherence time of the qubits would have to grow with the increase in number of qubits, which is not realistic. To this end, the researchers have proposed a "Software-Enabled Vertical Integration and Co-Design", which helps in developing optimized quantum compilers and produce easily verifiable solutions. The other option is to either increase the coherence time, decrease the time per quantum gate operation. However, both of these does not seem to be feasible, because both will lead to increase in execution time, albeit reducing the quantum error rate. Therefore, the feasible solution is to parallelize the quantum information processing (QIP), or the quantum instructions. In this paper, we design a simple quantum instruction set architecture (QISA) and a quantum pipelined architecture (QuPA). The proposed QISA is very similar to the classical instruction set architecture, except that the opcode is initially in an equal superposed state. Also the proposed QISA is grouped based on a certain common prefix, which helps to decode a particular (or group of) operation(s) with a higher probability. We demonstrate how the proposed QISA can be executed over the proposed QuPA with much less quantum cost compared to a general serial architecture. We show that the proposed QuPA has a gate cost of O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) compared to the gate cost of the serial architecture, which is O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ), where m ≤ n.
Motion Capture of Modern Greek Verbs: Measuring aspects and relations among actions

In the context of human motion analysis and human-centered computational sensing, this work presents a methodology for the investigation of the relations among actions of a set of (Modern Greek) motion verbs. The actions denoted by these verbs fall in the motion categories of pushing, pulling, hitting, and beating. Motion data were collected with motion capture technology, and measures of correlation and distance are used to identify existing relations among actions. Finally, hierarchical clustering analysis was applied to identify groups of actions. The results are in line with a semantic categorization of the corresponding verbs. The overall experimental procedure and data analysis indicate that the employed methodology could be useful in promising applications of motion recognition or motion clustering, aiming at the identification of related captured actions.
Geostatistical estimation analysis of typical Carlin gold deposit in Hunan province, China

Abstract In this paper, 3D mining modelling software was used to construct the 3D geological model of Typical Carlin gold deposit in Hunan province of China, and the geostatistics theories are also involved for the resource estimation which offer references for additional exploration and mining plans. Access database was constructed for gold deposit, topo DTM, mineralised body models and grade models are constructed by Surpac, and also analysis of the spatial grade distribution in reality was completed with statistic softwares. By using of geostatistics, assay samples are composited and analysed statistically and outliers influence was reduced to certail level. Experimental Variation functions were constructed for the strike, dip and vertical directions. Certain attributes are estimated by Ordinary Kriging estimation method. Comparing to the traditional estimation methods, estimation under 3D block model can obtain more reliable analysis results, which have significant reference values for dynamic management of mine operation.