Reviewerly/exHarmony huggingface dataset:
The Teacher Evaluation and Management System Innovation Based on the Artificial Intelligence Algorithms

The information construction is an important part of the process of the modern educational system construction, and it is a basic and long-term work, whose construction status directly affects the overall level of running a school and the image of the school. The functions of this system can meet the needs of the current teaching evaluation and play an active role in further improving the teaching quality and strengthening the construction of the information technologies in colleges and universities. Both in the theory and in practice, the current teacher performance management is still mainly confined to the level of the teacher performance evaluation. Most of its theories and practices are mainly contained in the theory and the practice of the teacher performances, especially in the theory and practice of the teacher performance evaluation.
A Double Correction of D-S Evidence Theory for Information Authenticity Screening

A lot of information and communication security monitoring devices have been introduced in the current information and communication network structure. Although these devices can detect whether the real-time data is normal or not according to various monitoring indicators, they cannot detect the authenticity of the original data. Based on this situation, a method based on double strategy is proposed to identify the threat of information by considering the algebra statistics and geometric trends of the data.
Maintaining Wavelet Synopses for Sliding-Window Aggregates

The IoT era has brought forth a computing paradigm shift from traditional high-end servers to "edge" devices of limited processing and memory capabilities. These devices, together with sensors, regularly produce very high data volumes nowadays. For many real-time applications, storing and indexing an unbounded stream may not be an option. Thus, it is important that we design algorithms and systems that can both work at the edge of the network and be able to answer queries on distributed, streaming data. Moreover, in many streaming scenarios, fresh data tend to be prioritized. A sliding-window model is an important case of stream processing, where only the most recent elements remain active and the rest are discarded. In this work, we study the problem of maintaining basic aggregate statistics over a sliding-window data stream under the constraint of limited memory. As in IoT scenarios the available memory is typically much less than the window size, queries are answered from compact synopses that are maintained in an online fashion. For the efficient construction of such synopses, in this work, we propose wavelet-based algorithms that provide deterministic guarantees and produce almost exact results. Our algorithms can work on any kind of numerical data and do not have the positive-numbers constraint of techniques such as the exponential histograms. Our experimental evaluation indicates that, in terms of accuracy and space-efficiency, our solution outperforms the exponential histograms and deterministic waves techniques.
Towards a Deep Learning Autoencoder algorithm for Collaborative Filtering Recommendation

Deep learning has received leapfrog progress in the realm of Machine learning such as image processing, speech recognition, the natural language processing, and recommendation systems. The traditional recommendation algorithm has existed the matter of cold start and data sparsity. To alleviate such problems, we propose a deep autoencoder algorithm for collaborative filtering recommendation AE-CF algorithm, which incorporates autoencoder and collaborative filtering recommended algorithms. The proposed AE-CF algorithm learn deep latent factors from users feature data and ratings. We evaluate the proposed AE-CF algorithm by applying the MovieLens dataset, a public dataset for movie recommendations. The experimental results demonstrate that AE-CF algorithm can effectively reduce the recommendation error and thus improve the recommendation quality.
From Precision Medicine to Precision Health: A Full Angle from Diagnosis to Treatment and Prevention

Health Intelligence, a term that encompasses a broad range of techniques and methods from artificial intelligence and data science that provide better insights and improved decision making about individuals' health and well-being, is increasingly used in today's medicine and healthcare services. Here we discuss some applications of precision medicine and health, innovative approaches that utilize health intelligence to improve diagnosing people's illnesses and making decisions about different treatment and prevention options in a timely manner.
The development of the fuzzy-based infant incubator

In the Bio-medical field, preterm infant care is one of the most important and popular areas of study. The newborns are at a higher risk of mortality and are called high-risk infants because of the gestational age or their birth weight put them at higher-than-average risk of disease and death. For that purpose, the preterm baby requires surrounding exactly similar as in the womb to cope with the external environment. This paper addresses the development of the fuzzy-based infant incubator which controlled the temperature and humidity. The objective of this paper is to design and develop the fuzzy-based controlled incubator to regulate temperature and humidity at a maximum error of 0.5℃ and 5% respectively. The novelty of this paper is in the temperature and humidity range regulated
Affine registration of multispectral images of historical documents for optimized feature recovery

Abstract Multispectral (MSI) imaging of historical documents can recover lost features, such as text or drawings. This technique involves capturing multiple images of a document illuminated using different wavelengths of light. The images created must be registered in order to ensure optimal results are produced from any subsequent image processing techniques. However, the images may be misaligned due to the presence of optical elements such as filters, or because they were acquired at different times or because the images were captured from different copies of the documents . There is little prior work or information available about which image registration techniques are most appropriate. Image registration of multispectral images is challenging as the illumination changes for each image and the features visible in images captured at different wavelengths may not appear consistently throughout the image sequence. Here, we compare three image registration techniques: two based on similarity measures and a method based on phase correlation. These methods are characterized by applying them to realistic surrogate images and then assessed on three different sets of real multispectral images. Mutual information is recommended as a measure for affine image registration when working with multispectral images of documentary material as it was proven to be more robust than the other techniques tested.
Relaxing non-interference requirements in parallel plans

Abstract The aim of being able to reason about quantities, time or space has been the main objective of the many efforts on the integration of propositional planning with extensions to handle different theories. Planning modulo theories (PMTs) are an approximation inspired by satisfiability modulo theories (SMTs) that generalize the integration of arbitrary theories with propositional planning. Parallel plans are crucial to reduce plan lengths and hence the time needed to reach a feasible plan in many approaches. Parallelization of actions relies on the notion of (non-)interference, which is usually determined syntactically at compile time. In this paper we define a semantic notion of interference between actions in PMT. Apart from being strictly stronger than any syntactic notion of interference, we show how semantic interference can be easily and efficiently checked by calling an off-the-shelf SMT solver at compile time, constituting a technique orthogonal to the solving method.
Tic tac types: a gentle introduction to dependently typed programming (functional pearl)

Tic-Tac-Toe is a simple, familiar, classic game enjoyed by many. This pearl is designed to give a flavour of the world of dependent types to the uninitiated functional programmer. We cover a journey from Tic-Tac-Terrible implementations in the harsh world of virtually untyped |Strings|, through the safe haven of vectors that know their own length, and into a Tic-Tac-Titanium version that is too strongly typed for its own good. Along the way we discover something we knew all along; types are great, but in moderation. This lesson is quickly put to use in a more complex recursive version.
On the Impact of Cluster Configuration on RoCE Application Design

RDMA over Converged Ethernet (RoCE) allows RDMA-enabled NICs to operate in datacenter networks. This study focuses on identifying how different aspects of datacenter cluster configuration impact the latency, and throughput, and CPU utilization of different ways of transferring data in RoCE (RDMA verbs). We look into the impact of colocated applications competing for both the CPU and access to the NIC as well as the impact of the network MTU. We find that RDMA applications do not fairly share the NIC, large frames should not be used, and that correct verb choice is dependent on many variables, including application access patterns, object size, and the load of both the local and remote CPU.
Career Recommendation System for Scientific Students Based on Ontologies

Students are usually unaware of their own skills.They choose to follow the trend, rather than the proper pathway.Which negatively affects the professional sector, and the development of the country.Orienting students, and guiding them would offer considerable benefits.Building the appropriate student's profiles is the golden key for an accurate orientation.To do so, relying on the simple use of the grade point average (GPA) will not be sufficient, and mislead the guidance.Instead, studying their personality and skills has to be done, in order to provide them with their reel orientation.The presented solution aims to orient students to the most suitable career, based on a mathematical model, valid for all education systems, and takes into account the trades trends and students capabilities.
Recommendation algorithm for mobile E-commerce based on cone depth learning

Aiming at the real user – commodity behavior data of Alibaba mobile e-commerce platform, the big data development of mobile e-commerce is analyzed in this paper, and a recommendation algorithm based on the mobile e-commerce deep learning is put forward. The study of the recommendation algorithms applies to the information age with the rapid growth of data. The results have verified that the recommendation algorithm proposed in this paper can extract useful information features from extensive information and recommend to the users who need such information to meet the information needs of different users, save the time required for users to search for the information, and improve the utilization of the information. These results indicate that the mobile e-commerce recommendation algorithm based on the cone depth learning has good practicality. It is concluded in this paper that the recommendation algorithm has a relatively high recommendation effect in the mobile e-commerce scenario and that its ideas and results have essential value for the study of mobile e-commerce.
A proof-search system for the logic of likelihood

Abstract The cut-free Gentzen-type sequent calculus LLK for the logic of likelihood (LL) is introduced in the paper. It is proved that the calculus is sound and complete for LL. Using the introduced calculus LLK, a decision procedure for LL is presented.
Feature Model for Extensions in Modeling Languages

Extension is a common term used in model-driven engineering. However, it is expressed in different ways by different modeling languages. A class diagram modeler uses extension to mean inheriting from a class. An aspect modeler extends a base with an aspect. Despite the different ways the term is being expressed, it generally refers to adding/changing structure/behaviour of a model in some way. We observe that model extensions vary in several ways. For example, in some cases, such as in use case diagram extensions, the extended model (base model) is information complete, meaning that it requires no further information in order for it to be useful. In other languages, the base model is not useful on its own and must be completed with an extension. Extensions also vary in terms of granularity, e.g., inheritance between two classes is an extension at a low level of granularity (between two elements, i.e., classes, of the same class diagram) compared to extension between two models. This paper presents a feature model for extensions in modeling languages. We discuss how extensions vary in terms of granularity, the completeness of the base model, whether or not the extension model requires to specify matching information, and the changes the composed model does to the base. Using this feature model, we explore extensions in several popular modeling languages and report our findings.
A Software Tool to Convert Requirements to Test Cases

As software test design is a manual process, test cases are generally prepared manually. We use a structured method to extract business requirements and feed these requirements in a tool (TestAlgo). The tool translates these requirements and automatically creates test cases and business requirements models such as process models. We suggest that this tool can not only save time and effort in creating automated test cases but also handle changes in requirements efficiently.
To the 90th Birth Anniversary of E.A. Fenelonov, the Librarian, Leader, Scientist

The article is devoted to the 90th birth anniversary of E.A. Fenelonov, the prominent figure of library construction, scientist-librarian and the Honored worker of culture of the RSFSR. The authors present his biographical data, data on the most important stages of his life and activity: study at the Moscow State Library Institute, postgraduate study, defence of the dissertation thesis “Organization of library service for rural population”, leadership of the newly organized library of the Lipetsk region; work as the Head of Department (then Division) of libraries of the Ministry of Culture of RSFSR, the Head of the Main information-computing centre for culture and arts of the Ministry of Culture of RSFSR, the Deputy Director for scientific and methodical work of the V.I. Lenin State Library of the USSR (nowadays — the Russian State Library, RSL), the research scientist in the Research Department of the RSL. The paper describes the general characteristic features of his administrative activity and scientific creativity. The authors emphasize his role in the centralization of the library network, creation of depository libraries, introduction of open access and computers in the librarianship, participation in the development of the “Regulations on Librarianship in the USSR” (1984), the laws “On Librarianship”, “On the Legal Deposit Copy of Documents”, etc. The paper considers the main works of E.A. Fenelonov, dedicated to the management of librarianship, comprehensive planning of library construction, economic efficiency of libraries, organization of the library network, library zoning, activities of the centralized library systems, methodical work of libraries. The authors reveal the personality of E.A. Fenelonov as organizer, manager, scientist and person.
A Cloud-ready Scalable Annealing Processor for Solving Large-scale Combinatorial Optimization Problems

This paper presents a CMOS annealing processor (CMOS-AP) that accelerates ground state searches of the Ising model. The main feature of this processor is its inter-chip connection interface for making a larger chip. A credit card sized compute node integrating two CMOS-APs was also developed as an interface with existing computer systems. The compute node can handle up to 61,952 spins at a time. A performance evaluation using the node improved the CPU speed by 55 times in solving a minimum vertex cover problem, one of the NP-hard combinatorial optimization problems. Finally, we describe a cloud interface for the compute node to make the CMOS-APs more useful and to promote application development for it.
A Method for Sharing Cell State for LSTM-Based Language Model

With the rapid growth in the integration of AI technology in various Natural Language Processing, the demands on the development within the fields of Natural Language Understanding and Natural Language Generation have been rapidly increasing as well. Both of these techniques analyze language, as it is naturally spoken or written by users, and must contend with a degree of ambiguity not present in formal language. For that reason, Language Modeling has been used as a key role in this area. Recently, the emerging field of deep learning, which applies complex Deep Neural Networks for machine learning tasks, has been applied to language modeling. Long-Short Term Memory (LSTM), a type of a recurrent neural network, has been adopted and has achieved reasonable results than the traditional language models. However, although LSTM-based language models have shown reasonable results by memorizing preceding cells’ values, it is difficult to memorize all the information of preceding cells because they only use a 2-dimensional matrix to memorize all the information. To compensate for this limitation of memorizing problems, we propose a method for sharing cell state for a neural network-based language model, which considers all preceding cell states as a cell-stack. Our model achieved better performance compared to a traditional LSTM-based language model improving average perplexity scores from 133.88 to 124.32 for various time steps and from 141.29 to 133.62 for various hidden sizes, respectively.
Semi-supervised NMF with Local and Global Label Embedding for Data Representation

Nonnegative Matrix Factorization (NMF), as a popular feature extraction technique, has recently attracted increasing attentions in high-dimensional data analysis, due to the strong ability of dimension reduction and semantic representation. In order to utilize the partial label information in practice, several semi-supervised NMF methods have been proposed. However, existing semi-supervised NMF variants only consider the global label constraint of data, but ignore the local label information embedded in geometric structure, which is very crucial for data representation. To address the above issue, we propose an improved NMF method, namely local and global regularized semi-supervised NMF (LGNMF), by considering the local and global label constraints simultaneously. Specifically, the local label constraint is depicted in a graph, which is pre-computed by metric learning. In addition, to explore the global label information, we construct an indicator matrix to restrict the coefficient matrix in LGNMF. For the formulated LGNMF, a multiplicative update rule (MUR) is developed. Extensive experiments on several real datasets demonstrate the superiority of the proposed method over the state-of-the-art methods in terms of clustering accuracy and normalized mutual information.
Pre-tonic /ʌɪ/ ∼ /ɨ/-variation in Contemporary English

This article is concerned with the question of why the pre-tonic /ʌɪ/ of a word such as biology never changes to /ə/ or /ɪ/, whereas the pre-tonic /ʌɪ/ of a word such as direct often monophthongizes to a qualitatively reduced vowel. That is, b/ʌɪˈ/ology vs. d/ʌɪˈ/rect ∼ d/ɪˈ/rect or d/əˈ/rect. It is argued that in general, pronunciations with /əˈ/ or /ɪˈ/ are more rhythmic than unreduced pronunciations with /ʌɪˈ/, which might count for an English speaker as a stress clash (i.e., /ˌʌɪˈ/). However, in the case of some words, the reduction of pre-tonic /ʌɪ/ is inhibited by (1) structure preservation in word-formation (e.g., /ˈʌɪ/rony → /ʌɪˈ/ronic); (2) the existence of an alternative stress pattern such as ˈmigrate, with primary stress occurring upon the diphthong /ʌɪ/ itself (rather than upon the syllable immediately following it); and (3) orthography, which means that in the case of some orthographic combinations/individual letters (e.g., ai of Nairobi, a of Naomi), the diphthong /ʌɪ/ makes more sense than the short monophthongs /ə/ or /ɪ/. In few cases (e.g., Italic languages vs. italic characters), there are also semantic reasons to prefer a reduced vowel in one environment vs. the diphthong /ʌɪ/ in a different environment.
Comparison of Model-Based Diagnosis Methods: Testability Modeling &amp; Rodon

This paper provides a detailed analysis and summary of diagnosis model generation rules of Rodon. These rules are different from testability modeling theory. Two cases, a binary tree and a multi-branch tree, are used to compare and illustrate the differences. Dependency matrices and diagnosis trees of these cases are generated by using testability modeling and Rodon respectively. By comparing the diagnosis results, the similar and dissimilar rules are analyzed in detailed. In the binary tree case, the diagnosis model generation rules of Rodon is same as the rules of isolation tree generation rules of testability modeling. In the multi-branch tree case, Rodon can generate a multi-branch tree, but the testability modeling method cannot generate it. Although the testability modeling method has such shortage, it can generate a dependency matrix, which is a binary matrix with higher computational effectiveness and lower storage requirements. Based on comparison, several update suggestions for testability modeling are discussed.
Application of Innovative Geochemical and Mineralogical Techniques to Understanding the La Victoria Epithermal Gold Deposit, Peru

Journal Article Application of Innovative Geochemical and Mineralogical Techniques to Understanding the La Victoria Epithermal Gold Deposit, Peru Get access Kelsey L Rozon, Kelsey L Rozon Department of Earth Sciences, Western University, London, ON, Canada Search for other works by this author on: Oxford Academic Google Scholar Neil R Banerjee, Neil R Banerjee Department of Earth Sciences, Western University, London, ON, Canada Corresponding author: neil.banerjee@uwo.ca Search for other works by this author on: Oxford Academic Google Scholar Lisa L Van Loon, Lisa L Van Loon Department of Earth Sciences, Western University, London, ON, CanadaLISA CAN Analytical Solutions Inc., Saskatoon, SK, Canada Search for other works by this author on: Oxford Academic Google Scholar Bill Pearson Bill Pearson Eloro Resources Ltd., Toronto, ON, Canada Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 25, Issue S2, 1 August 2019, Pages 800–801, https://doi.org/10.1017/S1431927619004732 Published: 01 August 2019
Microsoft HoloLens Evaluation Under Monochromatic RGB Light Conditions

Mixed reality technologies provide more natural interaction with virtual objects integrated in physical environments. Considering global lighting conditions, the spatial mapping has limitations, which manifest when scanning under limited light intensity. This paper evaluates the impact of red, green and blue monochromatic lighting sources on the spatial map scanning and distance measurement process of the Microsoft HoloLens holographic computer and head-mounted display. The paper also compares luminous power values, measured by MS HoloLens, with the ones obtained from a professional luminous flux meter.