Reviewerly/exHarmony huggingface dataset:
Supervised Multi-view Latent Space Learning by Jointly Preserving Similarities Across Views and Samples

In multi-view learning, leveraging features from various views in an optimal manner to improve the performance on predictive tasks is a challenging objective. For this purpose, a broad range of approaches have been proposed. However, existing works focus either on capturing (1) the common and complementary information across views, or (2) the underlying between-view relationships by exploiting view pair similarities. Besides, for the latter, we find that the obtained similarities cannot representatively reflect the differences among views. Towards addressing these issues, we propose a novel approach called MELTS (Multi-viEw LatenT space learning with Similarity preservation) for multi-view classification. MELTS first utilizes distance correlation to explore hidden between-view relationships. Furthermore, by assuming that different views share certain common information and each view carries its unique information, the method leverages both (1) the similarity information of different view pairs and (2) the label information of distinct sample pairs, to learn a latent representation among multiple views. The experimental results on both synthetic and real-world datasets demonstrate that MELTS considerably improves classification accuracy compared to other alternative methods.
Square Root and Inverse Square Root

Adaptive computation of the square root and inverse square root of the real-time correlation matrix of a streaming sequence {xk∈ℜn} has numerous applications in machine learning, data analysis, and image/signal processing. They include data whitening, classifier design, and data normalization [Foley and Sammon 75; Fukunaga 90].
Quantum Satellite Wide Area Network Construction and Performance Simulation Based on Quantum Repeater

research-article Share on Quantum Satellite Wide Area Network Construction and Performance Simulation Based on Quantum Repeater Authors: Kaijie Han School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, China School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, ChinaView Profile , Min Nie School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, China School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, ChinaView Profile , Guang Yang School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, China School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, ChinaView Profile , Jing Wang School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, China School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, ChinaView Profile , Ting Gao School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, China School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, ChinaView Profile , Feng Tian School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, China School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, ChinaView Profile , Jingqi Li School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, China School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, ChinaView Profile Authors Info & Claims ICCIP 2021: 2021 the 7th International Conference on Communication and Information Processing (ICCIP)December 2021 Pages 158–163https://doi.org/10.1145/3507971.3507998Online:07 March 2022Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Geothermometry, Geobarometry, and Pseudosections

Building upon the award-winning second edition, this comprehensive textbook provides a fundamental understanding of the formative processes of igneous and metamorphic rocks. Encouraging a deeper comprehension of the subject by explaining the petrologic principles, and assuming knowledge of only introductory college-level courses in physics, chemistry, and calculus, it lucidly outlines mathematical derivations fully and at an elementary level, making this the ideal resource for intermediate and advanced courses in igneous and metamorphic petrology. With over 500 illustrations, many in color, this revised edition contains valuable new material and strengthened pedagogy, including boxed mathematical derivations allowing for a more accessible explanation of concepts, and more qualitative end-of-chapter questions to encourage discussion. With a new introductory chapter outlining the “bigger picture,” this fully updated resource will guide students to an even greater mastery of petrology.
APPLYING CLUSTER ANALYSIS OF EDUCATIONAL CONTENT FOR IDENTIFYING SIMILAR DOCUMENTS

Research was conducted on the intellectual support of the process for improving the quality of the educational content forming professional competencies of higher education programs. At the same time, the identification of relevant working curricula of disciplines, taking into account the context of entities in documents with automatic extraction of entities and relations between them, has allowed to implement it without laborious processing and adaptation of knowledge bases. The subject of the study is the content of working curricula (syllabus), which are defined as a set of data that characterize the results of training and the content of the discipline. As a result of the work [1], the author created a corpus of texts from the documents of working curricula in the disciplines of the specialty "Information Systems". Existing approaches to the intellectual support of the formation of educational programs based on ontological models of systems [2-4] based on knowledge and rules, heuristic algorithms for the automated compilation of curricula, methods of expert assessments and cognitive maps do not allow to effectively take into account and quickly track changes in the market labor, and also in the space of educational content. In turn, the formation and actualization of ontological models, systems of rules and precedents by experts for all existing subject areas the preparation of educational programs is an extremely labor-intensive process that requires the involvement of a representative composition of experts in each of the subject areas to ensure the required accuracy.
Exploring bias in F-score computation methods of multi-class segmentation models

The F-score is a commonly used measure to compare model performances in machine learning applications. However, different computation methods of this metric may lead to significantly different results and introduce bias and limited comparability between different studies. We investigated three commonly used methods of F-score computation and analyzed the effect of different handling strategies of special cases, such as empty channel, undefined precision, and undefined recall, on each of the compared methods for a multiclass segmentation task. We observed variability of up to 20% in the F-score for a dental tooth structure segmentation task. To benchmark different computation methods, we performed simulations with controlled F-score. The outcome of the study indicates that only one approach, namely Ftp,fp, led to consistently unbiased F-score. In this approach, the true positives, false positives, and false negatives over all segmentation channels are added up, and the F-score is computed from the sum. The other computation methods introduced biases of up to 22.57%. Based on the findings of the present study, we recommend an unbiased approach like Ftp,fp to be used for performance computation, which will foster benchmarking and comparisons of studies.
Identifying Suitable Tasks for Inductive Transfer Through the Analysis of Feature Attributions

Transfer learning approaches have shown to significantly improve performance on downstream tasks. However, it is common for prior works to only report where transfer learning was beneficial, ignoring the significant trial-and-error required to find effective settings for transfer. Indeed, not all task combinations lead to performance benefits, and brute-force searching rapidly becomes computationally infeasible. Hence the question arises, can we predict whether transfer between two tasks will be beneficial without actually performing the experiment? In this paper, we leverage explainability techniques to effectively predict whether task pairs will be complementary, through comparison of neural network activation between single-task models. In this way, we can avoid grid-searches over all task and hyperparameter combinations, dramatically reducing the time needed to find effective task pairs. Our results show that, through this approach, it is possible to reduce training time by up to 83.5% at a cost of only 0.034 reduction in positive-class F1 on the TREC-IS 2020-A dataset.
Experiments

Experiments are a fast-growing method in IR. What kinds of knowledge do they generate about the actual world and how does the nature of the samples affect what we find and its utility?
C-SAR: Class-Specific and Adaptive Recognition for Arabic Handwritten Cheques

We propose C-SAR, a Class-specific and Adaptive Recognition algorithm for Arabic handwritten Cheques. Existing methods suffer from low accuracy due to the complex structure of Arabic script and high-dimensional datasets. In this paper, we present an adaptive algorithm that implements a class-specific classification to address these challenging issues. C-SAR trains a set of class-specific machine learning models of Support Vector Machines and Artificial Neural Networks features extracted using angular pixel distribution approach. Furthermore, we propose a class-specific taxonomy of Arabic cheque handwritten words. The proposed taxonomy divides the Arabic words into groups over three layers based on their structural characteristics. Accordingly, C-SAR performs classification on three phases, i.e., 1) similar and non-similar structures, for binary classification, 2) classes with similar structures into another two categories, and 3) class-specific models to recognize the Arabic word from the given image. We introduce benchmark experimental results of our method against previous methods on the Arabic Handwriting Database for Text Recognition. Our method outperforms the baseline methods with at least 5% accuracy having 90% average classification accuracy.
Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information

Detecting and anticipating global proliferation expertise and capability evolution from unstructured, noisy, and incomplete public data streams is a highly desired, but extremely challenging task. In this article, we present our pioneering data-driven approach to support the non-proliferation mission to detect and explain the evolution of proliferation expertise and capability development globally from terabytes of publicly available information (PAI), focusing on our knowledge extraction pipeline and descriptive analytics. We first discuss how we fuse nine open-source data streams, including multilingual data, to convert 4 TB of unstructured data to structured knowledge and encode dynamically evolving proliferation expertise representations—content and context graphs. For this, we rely on natural language processing (NLP) and deep learning (DL) models to perform information extraction, topic modeling, and distributed text representation (aka embedding) learning. We then present interactive, usable, and explainable descriptive analytics to refine domain knowledge and present it in a human-understandable form. Finally, we introduce future work avenues that will leverage our dynamic knowledge representations and descriptive analytics to enable predictive and prescriptive inferences to achieve real-time domain understanding and contextual reasoning about global proliferation expertise and capability evolution.
Perspective on Power Smart Customer Service Based on AI

At present, the power customer service system is only used as an auxiliary means of front-line customer service. However, with the continuous development of speech recognition, natural language processing, knowledge graph and other technologies, as well as the continuous integration and application of relevant AI technologies in the field of power customer service, the original customer service mode will be disruptive changed from service scenario and interaction mode. The smart customer service system based on AI technology will further play an important role in improving quality and efficiency, and help state grid to build a first-class energy Internet enterprise. Based on the current situation and future development and application requirements of power customer service AI, relying on the construction results of “two libraries and one platform” of AI of State Grid Corporation, this paper studies the power smart customer service process and key technologies, and analyzes the construction scenario of power smart customer service application from the aspects of service navigation, service quality detection and customer service operation, Support the construction of smart customer service application scenarios and systems of State Grid Corporation.
Quantum dynamical resource theory under resource non-increasing framework

We define the resource non-increasing (RNI) framework to study the dynamical resource theory. With this definition, we propose several potential quantification candidates under various free operation sets. For explicit demonstrations, we quantify the quantum dynamical coherence in the scenarios with and without post-selective measurements. Correspondingly, we show that the maximally incoherent operations (MIO) and the incoherent operations (IO) in the static coherence resource theory are free in the sense of dynamical coherence. We also provide operational meanings for the measures by the quantum discrimination tasks. Moreover, for the dynamical total coherence, we also present convenient measures and give the analytic calculation for the amplitude damping channel.
Autoencoder with Channel Estimation for Marine Communications

Due to the special characteristics of the underwater environment such as pressure and temperature, many wireless communication technologies that can be implemented in a terrestrial environment cannot be implemented well in underwater environments. Therefore, it is very important to study the new generation of MWCNs. To solve the challenges mentioned in Sect. 1.2.2 , this chapter proposes a novel Orthogonal Frequency Division Multiplexing (OFDM) autoencoder featuring CNN-based channel estimation for marine communications with complex and fast-changing environments. We demonstrate that the proposed OFDM autoencoder system can be generalized to work under various channel environments, different throughputs, while outperform the traditional OFDM counterparts, especially when working at high throughputs. In addition, since OFDM systems require accurate channel estimations to function properly, this chapter also proposes a new channel estimation algorithm for OFDM systems that combine the power of deep learning with the philosophy of super-resolution reconstruction, which uses Dense convolutional neural Networks (Dense-Net) to reconstruct low-resolution pilot information images into high-resolution full Channel Impulse Responses (CIRs). The Dense-Net structure has the characteristics of dense connections and feature multiplexing. Simulation results show that under slow fading, the proposed channel estimator can estimate the CIRs perfectly. Under fast fading, the proposed channel estimator outperforms existing learning-based algorithms with fewer neural network parameters. Therefore, the proposed novel autoencoder scheme and the powerful channel estimator are potentially attractive approaches for MWCNs.
Graph-Based Information Block Detection in Infographic With Gestalt Organization Principles

An infographic is a type of visualization chart that displays pieces of information through information blocks. Existing information block detection work utilizes spatial proximity to group elements into several information blocks. However, prior studies ignore the chromatic and structural features of the infographic, resulting in incorrect omissions when detecting information blocks. To alleviate this kind of error, we use a scene graph to represent an infographic and propose a graph-based information block detection model to group elements based on Gestalt Organization Principles (spatial proximity, chromatic similarity, and structural similarity principle). We also construct a new dataset for information block detection. Quantitative and qualitative experiments show that our model can detect the information blocks in the infographic more effectively compared with the spatial proximity-based method.
Basic Cryptography: Asymmetric Key Encryption

We previously mentioned a second type of encryption called asymmetric key (or public/private key) cryptography. This is much more recent than symmetric key, and involves some complex math. It is far slower than symmetric key, hence not suitable for encryption of large documents (normally it only encrypts 128 to 512 bits). It is however, well suited to key management. Unlike symmetric key that uses the same key for encryption and decryption, asymmetric key uses a matched pair of keys (one public, one private) for each user. This is not intuitive as is symmetric key. You don’t lock you house with one key and unlock it with a different key. Asymmetric key cryptography is central to many real-world cryptographic systems.
Integrating Mobile Applications ICT and Digital Tasks Through Online English Language Classrooms

The successful integration of Mobile Applications and Information and Communication Technology depends on teachers’ awareness with reference to digital teaching resources, digital equipment and ability to integrate the same effectively and creatively to the learning content so as to promote critical thinking. The former researches have proved that social network models are strong motivators in the path of English language learning among digital natives. The concept of integration of technology in both classroom context and personal context will enhance better learning environment and reduce the anxiety among learners of English. M-Learning and ICT can transform English language learning classroom as a forum for language learning, practice critical thinking and problem solving skills, and sharpen esthetic sense thus to attain cognitive development. This paper emphasizes on the importance of Digital pedagogy and reveals various Mobile applications that will be resourceful for the technocrats to enrich language skills.
Applicable technologies to improve teaching processes at the higher education level

&#x0D; &#x0D; &#x0D; The general objective of the study is to analyze the role of the technologies applied in the improvement of higher-level teaching processes at the Toribio Rodríguez de Mendoza National University. The approach is mixed since the documentary analysis and the survey are used to obtain the data; the population consists of the main theoretical contents, Background and theories on the technologies to be used to improve higher-level teaching procedures and by 10 teachers from the Toribio Rodríguez de Mendoza National University, to whom a small survey was applied in order to identify the technological resources applied by teachers to support the teaching process learning, which promote the development of content and its implementation at the Toribio Rodríguez de Mendoza National University. The methods used were: Systemic, hermeneutic, hypothetical-deductive, statistical and analytical method. The incorporation of ICT into regular teaching practice is becoming closer to reality every day, thanks to the commitment and interest of teachers in the subject and the spread of educational portals on the Internet, where teaching resources for the classroom can be found.&#x0D; &#x0D; &#x0D;
The Strategy of Provision Time Length Improvement of Medical Record Documents in Bhayangkara Lumajang Hospital with PDCA Method – Indonesia

Background: Each hospital must follow minimum service standards regarding waiting timesand the time for providing outpatient medical record documents. According to the results ofobservations, the time for providing outpatient medical record documents on the morning shift hasa total average of 22 minute while the afternoon shift has a total average of 21 minute.Methods: The purpose of this study is to improve and develop strategies to speed up the time ofproviding medical record documents at Bhayangkara Lumajang Hospital with the PDCA (Plan,Do, Check, Action) method.Results: The results and discussion can be concluded, namely the plan (planning) in an effort toimprove the time of providing medical record documents which is quite long, namely by makingtracers. Do (implementation) of the results of the plan in an effort to improve the length of timefor providing medical record documents at Bhayangkara Lumajang Hospital, by implementing theMCH system, applying tracers to medical record documents. The check can be concluded that thetime for providing medical record documents has not met the expected standard.Conclusion: Action (improvement) as a result of the agreement in Brainstorming, Action concludedthat Bhayangkara Lumajang Hospital will maintain the standard time for providing medical recorddocuments and apply the use of tracers from researchers.
Intelligent English automatic translation system based on computer corpus

Machine translation system, also known as automatic translation system, generally refers to a relatively complex process of automatically by computer, and generally refers to the translation of sentences or full texts in this process. At present, systems are generally divided into two categories: one is statistical-based machine translation systems, and the other is case-based machine translation systems. Corpus-based machine translation system involves many disciplines, among which linguistics, mathematics and computer science are the most basic. Multi-disciplinary cooperation constitutes a rapidly developing machine translation system based on corpus. The rapid development of machine translation system has accelerated the cultural, economic and other exchanges between different countries. Looking at the current development situation, we can see that the rapid development of corpus linguistics and computer linguistics has greatly improved the performance of today's machine translation system.