Reviewerly/exHarmony huggingface dataset:
Dissipative dynamics of quantum states in the fiber channel

In this paper we consider the Liouville equation that describes the quantum nonunitary dynamics of quantum states in optical fiber. We consider a particular case of thermalization aiming to applications related to various quantum information protocols; however, the model can be generalized in various ways taking into account more features (external pump, nonlinear interactions, continuous spectra, free space propagation, etc.). In order to obtain the appropriate evolution models for states in the channel we use the $\text{su}(1,1)$ algebra formalism in the Liouville representation. The developed model is applied in cases of two different initial states as an example. The first is single- and multifrequency-mode (e.g., wavelength-division-multiplexed) weak coherent states in quantum notation as a rather simple but useful example. This particular example is of interest since weak coherent states are a commonly used tool in various fields of optics, e.g., optical communication, quantum key distribution, etc. Results for coherent states are well agreed upon with classical theories; however, in order to highlight quantum features of developed theory we also consider the case of nonclassical light, in particular, Fock states. Considered implementation of the model takes into account dichroism, retardance, thermalization, dispersion, and decoherence in the polarization domain. We derive expressions of evolved states, mean photon number, and estimate the Stokes parameters as well as the degree of polarization. Considered examples explicitly demonstrate all the features and effects of the developed approach. The described approach allows one to connect the information properties of quantum channels with its physical ones. In order to illustrate this statement we consider BB84 quantum key distribution protocol and investigate the behavior of the quantum bit error rate affected by considered physical phenomena in an optical fiber.
An Analysis of Computational Complexity and Accuracy of Two Supervised Machine Learning Algorithms—K-Nearest Neighbor and Support Vector Machine

K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are the two Supervised Machine Learning Algorithms which have been used extensively in solving classification and regression problems in various application domains—medical diagnosis, pattern recognition, and image classification being the popular ones. KNN has been attractive for its simplicity—SVM has been considered as complex. Computational complexity of KNN is less compared to SVM. However, accuracy of SVM in general has been higher compared to KNN. This paper carries out an analysis of complexity of KNN and SVM and then looks at the alternative strategies to reduce the complexity. Accuracies of both SVM and KNN are analyzed from literature survey of experimental results with different training datasets. Reduction of number of training instances and usage of kernel functions are found to be the solutions for reducing SVM complexity. Factors impacting KNN accuracy are analyzed and possible alternatives are assessed. Kernel functions and distance-weighted KNN are concluded as the solution to improve KNN accuracy to compete with SVM accuracy. To utilize the best potential of the two algorithms, the option of a hybrid algorithm combining both KNN and SVM is also proposed.
AGWS: Blockchain-enabled Small-scale Farm Digitization

Farm records hold the static, temporal, and longitudinal details of the farms. For small-scale farming, the ability to accurately capture these records plays a critical role in formalizing and digitizing the agriculture industry. A trusted exchange of these records could unlock critical insights to different stakeholders across the value chain. Recently, there has been increasing attention on digitizing small scale farming with the goal of increasing farm-level transparency and visibility, access to credit, etc. using these farm records. However, most solutions proposed so far have the shortcoming of providing granular and trusted small-scale farm digitization. To address these challenges, we present a system, called AG-Wallet System(AGWS), which leverages blockchain to formalize the interactions and data flow in small-scale farming ecosystem. Utilizing instrumentation of farm tractors, we demonstrate the ability to utilize farm activities to create trusted electronic field records (EFR). Using AGWS, we processed over one hundred thousand small-scale farm-level activity events for which we also performed automated farm boundary detection of several farms.
Speech variability: A cross-language study on acoustic variations of speaking versus untrained singing

Speech production variability introduces significant challenges for existing speech technologies such as speaker identification (SID), speaker diarization, speech recognition, and language identification (ID). There has been limited research analyzing changes in acoustic characteristics for speech produced by untrained singing versus speaking. To better understand changes in speech production of the untrained singing voice, this study presents the first cross-language comparison between normal speaking and untrained karaoke singing of the same text content. Previous studies comparing professional singing versus speaking have shown deviations in both prosodic and spectral features. Some investigations also considered assigning the intrinsic activity of the singing. Motivated by these studies, a series of experiments to investigate both prosodic and spectral variations of untrained karaoke singers for three languages, American English, Hindi, and Farsi, are considered. A comprehensive comparison on common prosodic features, including phoneme duration, mean fundamental frequency (F0), and formant center frequencies of vowels was performed. Collective changes in the corresponding overall acoustic spaces based on the Kullback-Leibler distance using Gaussian probability distribution models trained on spectral features were analyzed. Finally, these models were used in a Gausian mixture model with universal background model SID evaluation to quantify speaker changes between speaking and singing when the audio text content is the same. The experiments showed that many acoustic characteristics of untrained singing are considerably different from speaking when the text content is the same. It is suggested that these results would help advance automatic speech production normalization/compensation to improve performance of speech processing applications (e.g., speaker ID, speech recognition, and language ID).
Authente-Kente: enabling authentication for artisanal economies with deep learning

The economy for artisanal products, such as Navajo rugs or Pashmina shawls are often threatened by mass-produced fakes. We propose the use of AI-based authentication as one part of a larger system that would replace extractive economies with generative circulation. In this case study we examine initial experiments towards the development of a cell phone-based authentication app for kente cloth in West Africa. We describe the context of weavers and cloth sales; an initial test of a machine learning algorithm for distinguishing between real and fake kente, and an outline of the next stages of development.
Performance Enhancement of a Delay-Based Reservoir Computing System by Using Gradient Boosting Technology

Gradient boosting technology has been proved to be an effective scheme for enhancing the performances of spatially distributed reservoir computing (RC) systems. In this work, a gradient boosting scheme by combining two reservoirs is proposed and numerically investigated in a delayed-based RC system. The original reservoir in the delayed-based RC system is a vertical-cavity surface-emitting laser (VCSEL) under polarization-rotated optical feedback (PR-OF), and it is trained on the desired output. The other VCSEL under PR-OF or polarization-preserved optical feedback (PP-OF) is supplemented to be an extra reservoir, which is trained on the remaining error of the original reservoir. Via Santa-Fe time series prediction task and 10th-order nonlinear autoregressive moving average (NARMA10) task, the performances of the delay-based RC system are evaluated before and after supplementing the extra reservoir, and then the effectiveness of the gradient boosting technology in the delayed RC system can be analyzed. The simulated results demonstrate that adopting gradient boosting technology is effective in a delay-based RC system. Comparatively speaking, the enhanced effect is more obvious under taking a VCSEL with PR-OF as the extra reservoir.
A Blockchain-based Assistance Digital Model for First Responders and Emergency Volunteers in Disaster Response and Recovery

Disasters are getting more frequent and unpredictable due to climate change and other factors. The tasks of first responders and community volunteers are getting more challenging as the frequency and intensity of disasters increase. This study explores how blockchain technologies using Ethereum can assist in increasing speed and level of availability of needed materials and services that may be required during or in the aftermath of disasters for rescue and recovery. The federated blockchain model also provides financial transactions securely and transparently by incorporating a donation mechanism for fund payment, monetary compensation or incentivization through the use of tokens and other digital assets to the materials and service providers. Inter-government agencies and co-operation and collaboration during disaster response and recovery events can also be achieved by solving the financial budget issue.
A Time Bank System Design on the Basis of Hyperledger Fabric Framework

This paper designs and realizes a Blockchain-based Time Bank system on the basis of Hyperledger Fabric framework which is one of the permissioned blockchain networks. All of the services provided by existing Time Bank systems were recorded and conducted manually in the past. Also, jobs for matching these services were also managed by people. This way of running Time Bank costs lots of time and human resources and lacks security. This paper proposes a Time Bank system enabling all the services being executed and recorded on the blockchain. The matching tasks can directly be done through autonomous smart contracts. In addition, building a Time Bank system on blockchain benefits the transaction of" Time Credit" which plays the role of digital currencies on the system. Our Time Bank also retains a grading system allowing its members to give each other a grade for reflecting their degree of satisfaction about the results provided the system. This grading system can incentivize the members to provide a better quality of service and adopt a nicer attitude for receiving a service, which may positively endorse the development of a worldwide Time Bank system.
A comparison of PV resource modeling for sizing microgrid components

Microgrid systems are being deployed with increased frequency to meet critical building loads during unplanned power outages. Determining the appropriate type and size of components that make up a microgrid can greatly affect its ability to meet this need. This paper presents a new tool to appropriately size PV, battery, and generator capacities to meet all site loads, given a resilience goal. It uses a statistical model to simulate microgrid behavior under a large range of solar resource conditions, providing resilience planners with confidence that critical loads will be met even under extreme weather conditions. We compare this model with one that uses typical weather data for sizing microgrids, and quantify the resilience effects of using these different methods. Our method produces more conservative generator capacities and fuel requirements in an outage, predicting up to 30% more fuel required in the most extreme case. Microgrid systems that are designed using more conservative estimates are more likely to continue to serve load during emergency outages under a large range of conditions.
Teaching Research Methods in Master’s-Level LIS Programs: The United States Perspective

Training in research methodology is an essential component of educating twenty-first century information professionals and library practitioners. Traditionally, competencies in library and information science (LIS) education emphasized the fundamental knowledge of research methods and critical skills in evaluating the findings. However, librarians are not only consumers of research; they are also active contributors to scholarship and need practical skills in designing and carrying out research projects. Research competencies, including the fundamentals of quantitative and qualitative research methods, are listed in the American Library Association’s (ALA) Core Competences of Librarianship. Courses in research methods are offered in many LIS programs in the United States but are not always considered part of the core curriculum. Training in research methodology is inconsistent across LIS programs and in Master’s-level courses rarely goes beyond an overview of methods and terminology. This article summarizes the findings of a study that examined the current state of research methods training in LIS Master’s-level education in the United States and investigated how future library professionals are being prepared to be consumers of research and practitioner-researchers from the perspective of the faculty who teach the courses. The data for this study were collected using multiple techniques, including content analysis of documents, questionnaires, and semi-structured interviews. Teaching faculty, selected from ALA-accredited LIS programs, contributed their perspective through questionnaires and semi-structured interviews.
Methodological Survey to Improve the Secure Data Storage in Cloud Computing

In the modern world all the peoples having a smart device to access the whole world beyond the limit with the use of the internet so cloud computing take the major part to store the real world processed information like WhatsApp, Facebook, Twitter, Amazon, online ticket booking web sites, etc., Major problem in now a day is a security issue because trillion and trillions of people are storing the information in the cloud. Unfortunately, there are several issues reduce the cloud computing growth, such as data privacy, shared services, lack of control, lack of backup services, data leakage, API's and storage gateway. The security problem is considered a major factor that could prevent the development of cloud computing. In this paper, we explored the different types of secure data storage in cloud computing, different types of data storage model. Moreover, we review and discuss the cloud computing security issues and Comparative Study of existing secure cloud storage techniques. It stresses that even though there are lots of protection clothes airers have been suggested to enhance the cloud safety measures, but there's absolutely no a now available resolution to deal with each protection problem. Thus, the upcoming analysis needs to be centered on fixing the protection problems as well as an accountability mechanism has to be developed.
An Efficient Recommendation System on E-Learning Platform by Query Lattice Optimization

This research work is on optimizing the number of query parameters required to recommend an e-learning platform. This paper proposes a new methodology for efficient implementation by forming lattice on query parameters. This lattice structure helps to co-relate the different query parameters that in turn form association rules among them. The proposed methodology is conceptualized on an e-learning platform with the objective of formulating an effective recommendation system to determine associations between various products offered by the e-learning platform by analyzing the minimum set of query parameters.
Architecting blockchain network simulators: a model-driven perspective

Blockchain networks have been suggested to have the potential to support some of the most critical functions of modern societies. When used in such capacities, failures of blockchain networks imply catastrophes that extend beyond individuals, organizations and countries. As such, before considered for wide adoption, blockchain network protocols and technologies must undergo the highest standards of analytical and empirical validation subject to key security, reliability and performance qualities. When performing empirical evaluation, however, the sheer size of open-access blockchain networks in their envisioned scale rules out the possibility of exact reproduction and validation in a lab environment. Rather, abstract working models - simulators - of proposed technologies need to be considered. To have value as research instruments, such simulators need to be widely validated for their accuracy by the research community, and also be highly transparent and reusable for allowing quick implementation and comparison of design ideas. We claim that established software engineering paradigms, namely model-driven development and software product lines can help address this need. We outline our own effort to develop a domain meta-model and object-oriented framework for efficient and reliable derivation of specialized blockchain network simulators.
Cloud curriculum resource management platform based on Hadoop

The rapid development of online education, knowledge sharing, big data, and artificial intelligence technology has brought innovation to education. With the popularization of online education, the variety of teaching resources and the amount of curriculum resources have exploded, and traditional curriculum platforms have been unable to meet the growing demand. The paper builds a cloud computing platform based on Hadoop, which is used for big data analysis and application curriculum resource management. It elaborates on the architecture, functional design, resource storage, and implementation and test of the cloud resource platform. The cloud platform applies to and serves the field of education, which improves resource utilization and sharing, and it can provide better services for teachers, students, and staff.
The Concept of Anthropotechnical Safety of Functioning and Quality of Life

Purpose: There are many methods, techniques and technologies for the formation of directive (prescribed for use) standards of integrated security: • fundamentally new (original); • developed “by analogy with world experience” (adapted); • borrowed. Methods: The elemental base of the methodology of these norms extends to all known spheres of life: • “environmental” or the sphere of human existence in nature; • “social” or the sphere of collective relationships and interactions in the community; • “technogenic”, recultivated, or the sphere of all material created in processes of thought and subsequent productive activities of a person; • “informational” or the sphere of origin, perception, processing, accumulation, transmission and impact of information, as well as reactions to it; • “energy” or the sphere of detection (generation) of energy. Results: Modern theoretical concepts (not related to business and employment problems) combine the last two areas into one “informational and energy” sphere based on the well-known functional dependence of the “bit” and “joule” units (according to Felker). Conclusion: For each of these five spheres of life, as appropriate, legitimate (authorized for use by the official authorities) averaged standards of integrated security that have an officially regulated period of their functioning are created.
Two-phase Dynamic Routing for Micro and Macro-level Equivariance in Multi-Column Capsule Networks

The capability of multi column convolutional networks in identifying local invariant features helps improve its performance on image classification tasks to a large extent. Suppression of non maximal activations in a convolutional network, however, can lead to loss of valuable information, as scalar activations typically only ,encode the presence (or absence) of a feature in an input image, providing no additional information. Capsule networks, on other hand, learn richer representations by propagating non-maximal activations to higher layers, encoding the agreement between neurons at various layers on the presence (or absence) of a feature into a fixed-length vector. Traditional capsule networks, however encodes agreements for micro and macro-level features of an input image with same precedence. Such an uniform agreement protocol can hinder the repsentation capability of a network, especially for datasets that contain objects with independently deformable components. To address this, we propose a novel two-phase dynamic routing protocol that computes agreements between neurons at various layers for micro and macro-level features, following a hierarchical learning paradigm. Experiments on seven publicly available datasets show that a multi-column capsule network that encodes an input image following our routing protocol performs competitively or better than contemporary multi-column convolutional architectures andtraditional capsule networks on a classification task.Implementations of the networks used in this paper have been made available at: github.com/DVLP-CMATERJU/TwoPhaseDynamicRouting.
Academic Expert Finding in Indonesia using Word Embedding and Document Embedding: A Case Study of Fasilkom UI

Expertise retrieval covers the problems of expert and expertise finding. In academia, expert finding can be beneficial in finding a research partner or a potential thesis supervisor. This research finds the experts in the Faculty of Computer Science in Universitas Indonesia (Fasilkom UI) using the thesis abstract and metadata of Fasilkom UI students. The methods that are used to represent the query and expertise of the lecturers are the combination of word2vec and doc2vec, which are word embedding and document embedding, respectively. Both embeddings are able to model semantic information, which is necessary for solving the problem of vocabulary mismatch in search problems. Our result shows that representing the expertise query with word2vec leads to better performance than using doc2vec. In addition, we also found that generally, the performance of the embedding models is comparable to the standard retrieval model BM25 in retrieving experts using expertise queries in both Indonesian and English languages.
Ritz-like values in steplength selections for stochastic gradient methods

The steplength selection is a crucial issue for the effectiveness of the stochastic gradient methods for large-scale optimization problems arising in machine learning. In a recent paper, Bollapragada et al. (SIAM J Optim 28(4):3312–3343, 2018) propose to include an adaptive subsampling strategy into a stochastic gradient scheme, with the aim to assure the descent feature in expectation of the stochastic gradient directions. In this approach, theoretical convergence properties are preserved under the assumption that the positive steplength satisfies at any iteration a suitable bound depending on the inverse of the Lipschitz constant of the objective function gradient. In this paper, we propose to tailor for the stochastic gradient scheme the steplength selection adopted in the full-gradient method knows as limited memory steepest descent method. This strategy, based on the Ritz-like values of a suitable matrix, enables to give a local estimate of the inverse of the local Lipschitz parameter, without introducing line search techniques, while the possible increase in the size of the subsample used to compute the stochastic gradient enables to control the variance of this direction. An extensive numerical experimentation highlights that the new rule makes the tuning of the parameters less expensive than the trial procedure for the efficient selection of a constant step in standard and mini-batch stochastic gradient methods.