Reviewerly/exHarmony huggingface dataset:
Secure Authentication Key Agreement Protocol with eCK Model in Heterogeneous IoT Environment

Internet of Things (IoT) has been developed increasingly and is a next wave in the era of computing. IoT is the concept in which many of the objects are getting networked and connected anytime and anyplace. It uses any network and any service in a heterogeneous circumstance. According to recent research, it is estimated nearly 30 billion devices on IoT; therefore, the security issues on IoT turn into a vital fundamental in present tendency. Although IoT brought convenience life, most existing IoT devices were controlled by multiple units (or parties), including owners, employees, and maintenance vendors. Distinct units may join different Certificate Authorities (CAs) for the public key management. How to share a common session key between IoT devices certified by distinct CAs is a practical issue worth to be studied. Therefore, we propose an authentication key agreement protocol using elliptic curve algorithm with extended Canetti-Krawczyk (eCK) security that can solve heterogeneous IoT session key problem for distinct CAs and satisfies all security requirements of AKA protocols.
Analytical agility at the edge of the network through auction mechanisms (Conference Presentation)

With the proliferation of edge operations—especially across multiple domains—having the ability to configure and run mission-critical edge analytics is mandatory for the success of coalition missions. With the rise of cloud computing—especially hybrid clouds—small scale data centers are fast becoming a viable option for military data analysis problems. However, having fixed resource requirements hinders the task satisfiability in dynamic situation; this paper provides a flexible resource allocation mechanism in which the provider chooses the amount of resources allocated for a job. Novel auction mechanisms are deployed to increase the efficiency in the allocation.
Approximate graph spectral decomposition with the Variational Quantum Eigensolver

Spectral graph theory is a branch of mathematics that studies the relationships between the eigenvectors and eigenvalues of Laplacian and adjacency matrices and their associated graphs. The Variational Quantum Eigen- solver (VQE) algorithm was proposed as a hybrid quantum/classical algorithm that is used to quickly determine the ground state of a Hamiltonian, and more generally, the lowest eigenvalue of a matrix <i>M</i> &isin; R<sup>nxn</sup>. There are many interesting problems associated with the spectral decompositions of associated matrices, such as partitioning, embedding, and the determination of other properties. In this paper, we will expand upon the VQE algorithm to analyze the spectra of directed and undirected graphs. We evaluate runtime and accuracy comparisons (empirically and theoretically) between different choices of ansatz parameters, graph sizes, graph densities, and matrix types, and demonstrate the effectiveness of our approach on Rigetti's QCS platform on graphs of up to 64 vertices, finding eigenvalues of adjacency and Laplacian matrices. We finally make direct comparisons to classical performance with the Quantum Virtual Machine (QVM) in the appendix, observing a superpolynomial runtime improvement of our algorithm when run using a quantum computer.*
The biotechnological network model analysis of a distributed information technology system

The article provides a comparative analysis of the information system based on the terminal and peer-to-peer architecture using wireless technology for network bandwidth. We give the research result of guaranteed data packets delivery on the example of the terminal architecture. The structure of the technical system information taken for research is also given. We analyzed the model of the distributed information system on three types as QS (queue system) as well. The Lemma model is taken to describe the system.
Sifat Kesedaran Fonologi Dan Peranan Artikulasi Dalam Komunikasi Bahasa Melayu Pendidikan Awal Kanak-Kanak

The nature of phonological awareness and articulation of the role of communication in the Malay language early childhood education is a social phenomenon. There are, therefore, problems in terms of variations in the nature of phonological awareness, its relation to articulation factors, clarity, consistency, articulation sound factors, and language comprehension factors in terms of phonological awareness among students studying kindergarten. Therefore, this study describes the significance of phonological awareness among five- to six-year-olds. From a methodological point of view, it uses the Design of Mixed Methods through a sequential approach, which is a quantitative study first followed by a qualitative study. This study uses the phenomenon study method, which is the study to aim to understand the meaning of events and the relevance of individuals in certain situations. It also uses the following two types of phenomena, which are significant meanings faced by pupils and general conditions experienced by students. This study uses interview techniques and document analysis. The findings of this study indicate that there is a significant positive low correlation   (r = .18) between phonological awareness and the role of articulation or pronunciation among students aged five to six years of study in kindergarten. It has also been found that in terms of phonological awareness, students perform almost exactly the task of conceptual speech words, syllable structure awareness tasks, rhyme awareness tasks, phonetic replacement tasks, and phonemic awareness tasks. It also suggests further studies.
Variable word length: a quantum-proof encryption solution

Quantum computing is coming; and, with it, the security of modern cryptography will be compromised. Technologies such as industrial control systems, banking, and smartphones depend on cryptography to ensure the confidentiality and integrity of operations. Any data sent over the internet relies on cryptography. In some cases, information is so sensitive that if the encryption is broken years in the future the results could be catastrophic. A post-quantum cryptographic solution is required today. Variable World Length is a proposed quantum-proof symmetric cryptography algorithm. It is low-labor, low-latency, and low-power; with a keyspace of 10<sup>511</sup> in its simplest form (for comparison, AES-256 is 2<sup>256</sup>). Variable Word Length performs bit manipulation to send “words” of varied length. Without the key, an attacker would need to brute force all possible combinations. Due to the design of the algorithm, some of these combinations will decrypt real plaintext that was not the encrypted message. Additionally, the keyspace can be increased without an exponential tax on computing resources.
LibGuides: The READ Japanese Collection : Home

There is a browsing collection on the 22nd floor of the W.E.B. Du Bois Library that has been carefully curated to provide a range of reading materials for people who are building up their Japanese reading muscles.
Deep Learning for Textual Emotion Mining

Textual Emotion Mining (TEM) is extensively a widespread and established research problem. It has got a substantial amount of research attention in the previous decade. Over the parallel period, deep learning approaches have demonstrated remarkable success in the area of natural language processing. As a result, deep learning based TEM has attracted the eyes of people from both academia and industry in recent years. This manuscript surveys the current emerging research on the impact of deep learning techniques in TEM domain and contributes in a two-fold manner. Firstly, it provides a summary of the relevant research results concentrated on the application of deep learning approaches for different tasks under the umbrella of TEM. Secondly, it evaluates the viability of deep learning based emotion mining research, examines existing challenges, opportunities and provides future directions in the field. After a thorough survey of the available literature in the relevant domain, to the best of the found understanding, it is the first paper that surveys deep learning efforts in the TEM area.
Information-Theoretic Perspective of Federated Learning.

An approach to distributed machine learning is to train models on local datasets and aggregate these models into a single, stronger model. A popular instance of this form of parallelization is federated learning, where the nodes periodically send their local models to a coordinator that aggregates them and redistributes the aggregation back to continue training with it. The most frequently used form of aggregation is averaging the model parameters, e.g., the weights of a neural network. However, due to the non-convexity of the loss surface of neural networks, averaging can lead to detrimental effects and it remains an open question under which conditions averaging is beneficial. In this paper, we study this problem from the perspective of information theory: We measure the mutual information between representation and inputs as well as representation and labels in local models and compare it to the respective information contained in the representation of the averaged model. Our empirical results confirm previous observations about the practical usefulness of averaging for neural networks, even if local dataset distributions vary strongly. Furthermore, we obtain more insights about the impact of the aggregation frequency on the information flow and thus on the success of distributed learning. These insights will be helpful both in improving the current synchronization process and in further understanding the effects of model aggregation.
Subject Guides: English 1121: ARTICLES

Research basics for students writing about literature, current social issues, or discourse communities.
LibGuides: 12 Month Children's Programming: Storytime

Created by Jessica Jean Walker for INFO 260A: Children's Programming and Services (Fall 2019), Instructor Wrenn-Estes
Improving the measurement robustness of active stereo 3D sensors by optimization of shifted aperiodic fringe patterns

Pattern projection-based 3D sensors are widely used for contactless, non-destructive optical 3D shape measurements. In previous works, we have shown 3D measurement systems based on stereo matching between two cameras with GOBO-projected aperiodic fringe patterns. In this contribution, we demonstrate a method to optimize the projection patterns for high measurement robustness, i.e., high completeness of the resulting point cloud with low probability of outliers. To calculate the 3D coordinates of an object point by triangulation, a pixel correspondence between the two cameras must be found. The search for such pixel correspondences can be broken into two parts: a coarse correspondence search and a sub-pixel-accurate refinement. The former is responsible for the completeness and correctness of the 3D result, while the quality of the latter determines the accuracy. The correctness of the correspondence search depends on the property of the projection pattern to uniquely encode each point on the measurement object. If the pattern is very self-similar, the points are not well distinguishable from each other and there is a high probability of mismatches during correspondence search. We introduce a mathematical measure to evaluate the self-similarity of a GOBO-projected fringe pattern. This measure operates on patterns, which we simulate with a simplified 1D model. Based on this measure and its derivatives, we developed an algorithm to optimize the fringe patterns. We compare results achieved with unoptimized and optimized fringe patterns.
RLG and PCC: An Interview with Karen Smith-Yoshimura<sup>*</sup>

It is important to remember that there were two bibliographic networks, OCLC and the Research Libraries Information Network (RLIN), which together with the Library of Congress enabled the sharing of bibliographic and authority records amongst PCC members. Karen Smith-Yoshimura was active in the early years of the Program for Cooperative Cataloging (PCC) representing RLIN. In this interview with Smith-Yoshimura in the summer of 2019, Joan Schuitema poses five questions which provide an opportunity for Smith-Yoshimura to share her memories about her participation in PCC prior to RLG’s merger with OCLC.
Client Satisfaction and Preferences Towards Nonverbal Communication(NVC) of Health Care Providers in a Therapeutic Setting, Mangaluru

Non verbal communication refers to the communication and interpretation of information by any means other than language. Non verbal communication is used to express and communicate thoughts, feelings and emotions to establish and maintain relationships and to influence others. In health care setting it is very important to maintain a healthy provider- client relationship. The present study studies the non verbal behaviors between doctors and nurses and ranks the client preference during interaction. The results revealed that there is no significant difference in the non verbal behaviours used by doctors and nurses. Henry Garret ranking was used to rank the preferences rated by Clients. Hence it is concluded that NVC are very much essential in health care.
A Parallel LSTM-Based Missing Body Feature Point Completion in Video Frames

The ability to guess the posture of a body is an essential process for activity recognition. OpenPose, a deep learning-based model, detects body feature points that form the posture of a subject from moving images.OpenPose is often used for action recognition. However, there are cases where the feature points cannot be detected due to image blurring or a part of the human body being shielded, for example. Conventional interpolation methods estimate missing values, but the estimation accuracy of these methods is not high when a particular feature point is missing in a large number of consecutive frames. We propose a deep learning-based point estimation model that performs data completion of missing body feature points, which cannot be captured by OpenPose. The model is constructed based on Long Short-term Memory (LSTM) that takes consecutive frames in a video. In order to improve estimation accuracy, we revised the model by considering the following two extensions, namely, the division of body parts and the use of relative coordinates. Experimental results show that the proposed model successfully completes body feature points within an error of 8.15 pixels, which is three times better than the conventional interpolation methods and five times better than a LSTM without the proposed extensions.
Data Storing and Processing

Many commercial and industrial IoT systems collect large amounts of data. It is currently expected that IoT devices will generate 80ZB (zettabytes, 1ZB = 1 billion terabytes) over the next decade. In most situations, due to the high volume and potential complex computational pipeline, real-time analysis of this data is very difficult, or impossible. As such, efficient and large-scale data storage capabilities are an essential component in IoT systems: we store the data and use edge or cloud hardware to process it over minutes, hours, or even days.
Corrigendum to “Dataset on powered two wheelers fall and critical events detection”

[This corrects the article DOI: 10.1016/j.dib.2019.103828.].
LibGuides: Free E-Resources During the COVID-19 Pandemic: Jove Science Videos

Many providers are temporarily making their services or content available for free in order to support distance learning.
DATA ANALYSIS IN REAL LIFE PROBLEMS: A STUDY ON THE PSYCHOLOGICAL APPROACH OF STUDENTS TOWARDS THEIR SUBJECT AND TEACHER

The prime purpose of this study is to understand and highlight the reasons for the psychological approach of students toward their subject and teacher. This paper tries to cover all aspects of students psyche especially the general phenomenon of attitude in their subjects. Now a days many of the students studying at tertiary level, show varieties of attitudinal changes in the class room which creates enormous psychological tension to their teachers, that they find very difficult to express. Therefore, this paper is an attempt to identify and understand the problems that they face in the class and within themselves. This paper brings out the facts by incorporating all the hidden information and opinions provided by the students through Google forms. Thus, it can be an insight into all the psychological issues of studies. We discuss the statistical approach of analysis of variance for the verification of data analysis. It can be the best tool to examine the relations to compare and get the mean value among groups. Hence, whatever data we have taken for analysis, which gives the correct results for this test.
Convolutional Autoencoder and Deep Infomax for epileptic EEG signals clustering.

Convolutional Autoencoder and Deep Infomax for epileptic EEG signals clustering. This work of thesis wants to show how Convolutional Autoencoder and Deep Infomax networks can help in the diagnosis of epilepsy, because nowadays the duty belongs to five doctors. Different tests have been effectuated using two different networks and applying different initial conditions such as: epoch length, number of channel observed and different composition of traning set. Unluckily, hoped results have not been achieved, but important starting points for furhter developments are presented.
Learning to Update Natural Language Comments Based on Code Changes

We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies. We propose an approach that learns to correlate changes across two distinct language representations, to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications. We train and evaluate our model using a dataset that we collected from commit histories of open-source software projects, with each example consisting of a concurrent update to a method and its corresponding comment. We compare our approach against multiple baselines using both automatic metrics and human evaluation. Results reflect the challenge of this task and that our model outperforms baselines with respect to making edits.
Research and Design of College Teaching Log Management System

At present, many colleges and universities use manual filling in the teaching log, which has certain limitations. Based on the web system, the whole teaching log is tracked by the web system. For the teachers, it provides the schedule management function with friendly operation. For the teaching administrators, considering that different colleges have different algorithms for calculating class hours, the system specially designs and implements the public edition module. The teaching administrators edit the specific formula, namely the class hour calculation strategy. The users can use the select strategies to achieve one click calculation of class hours.
Cryptocurrency Challenges Sovereign Currency

All national and international monetary structures have evolved to assist in the creation and management of sovereign fiat currencies. This sovereign currency status quo was suddenly upended with the arrival of the first cryptocurrency, Bitcoin, in 2008 which introduced a peer-to-peer digital fiat currency without the need of a central banking system, through a trustless, fungible and tamper-resistant distributed accounting system known as blockchain. The response to the threat posed by cryptocurrency has ranged from declaring it illegal, attempting to regulate it, ignoring it, treating it as a commodity and/or like any other financial asset and regulating it as such; or more recently seriously considering state-backed digital currency. Presently the assessment appears to be that of â€˜co-existenceâ€™ with central banks providing national/sovereign currency, primarily digital currency, and cryptocurrency vying with gold as a back-up or â€˜insuranceâ€™ against the perils of a sovereign fiat currency.
A Collaborative Sparse Representation-Based Approach for Pattern Classification

Sparse representation-based classification (SRC) has been an important approach in pattern classification and widely applied to various fields of visual recognition. However, there are some practical factors to consider in real-world problems, such as pixel damage, block occlusion, illumination, and position change. In recent years, researchers have brought a large number of improvements and proposed various effective algorithms. We improve the degree of sparse representation (SR) based on the probabilistic collaborative representation framework in this paper. Furthermore, a new algorithm is proposed and successfully applied to face recognition. The effectiveness and efficiency of our method are demonstrated experimentally.
LibGuides: Starting up STEAM:: Home

A Libguide to creating Science, Technology, Engineering, Arts, and Math programs within urban schools
Twister Generator of Poisson Random Numbers with the Use of Cumulative Frequency Technology

The widely known generators of Poisson random variables are associated with different modifications of the algorithm based on the convergence in probability of a sequence of uniform random variables to the created stochastic number. However, in some situations, this approach yields different discrete Poisson probability distributions and skipping in the generated numbers. This paper offers a new approach for creating Poisson random variables based on the complete twister generator of uniform random variables, using cumulative frequency technology. The simulation results confirm that probabilistic and frequency distributions of the obtained stochastic numbers completely coincide with the theoretical Poisson distribution. Moreover, combining this new approach with the tuning algorithm of basic twister generation allows for a significant increase in length of the created sequences without using additional RAM of the computer
A Study on Undergraduate Students’ Satisfaction of the Self-regulated English Class based on the Utilization of Movies

The purpose of this study is to investigate the effects of self-regulated English class based on the utilization of movie, Good Will Hunting for the consequent learning satisfaction of undergraduate students. To find out the students’ learning satisfaction of this instructional class method, this study utilized a questionnaire and interview with 82 students for the analysis. The questionnaire consisted of questions on subject opinions about cognition, learning motivation and learning behaviors. The difference in satisfaction between the experimental group and the control group was analyzed using the independent t-test. The results of this study were as follows. First, it showed effects in power of memory, meta-cognitive ability, and cognitive ability. Second, it had a substantial effect on controlling emotion, keeping moderate tension and learning motivation. Lastly, it had a positive effect on the undergraduate students’ satisfaction for learning practice, time management and behaviors. However, it did not enhance comprehensive ability, learning motivation, or utilizing learning sources. This study holds significance in observing how cognitive ability, learning motivation, and learning behavior of students are influenced by self-regulated English class based on utilization of movie. (Jungwon University)