Reviewerly/exHarmony huggingface dataset:
Development of Modified Blum-Blum-Shub Pseudorandom Sequence Generator and its Use in Education

Abstract In information security systems, the algorithm of the Blum-Blum-Shub (BBS) generator, which is based on the use of a one-way function and is a cryptographically secure pseudorandom number generator, became widespread. In this paper, the problem of the analysis of modified algorithms of the BBS generator operation is considered to improve their statistical characteristics, namely, the sequence repetition period. It has been established that in order to improve the characteristics of the classic BBS algorithm, it is necessary to systematize approaches to change the recurrent equation itself, the relationship between the current and the previous members of the sequence. For this purpose, a generalized unified model of the modification of the classical BBS algorithm is derived. The repetition period with computational complexity were analyzed for classical algorithm and 80 proposed modifications. A gain in statistical characteristics is improved with slight increase in the required computing power of the system. The proposed modified BBS pseudorandom sequence generator can be used in training of students when teaching cryptographic stability of information security systems. The study of this generator combines the knowledge of students acquired in both digital electronics and mathematics.
Robust Estimation of Discrete Distributions under Local Differential Privacy

Although robust learning and local differential privacy are both widely studied fields of research, combining the two settings is just starting to be explored. We consider the problem of estimating a discrete distribution in total variation from $n$ contaminated data batches under a local differential privacy constraint. A fraction $1-\epsilon$ of the batches contain $k$ i.i.d. samples drawn from a discrete distribution $p$ over $d$ elements. To protect the users' privacy, each of the samples is privatized using an $\alpha$-locally differentially private mechanism. The remaining $\epsilon n $ batches are an adversarial contamination. The minimax rate of estimation under contamination alone, with no privacy, is known to be $\epsilon/\sqrt{k}+\sqrt{d/kn}$, up to a $\sqrt{\log(1/\epsilon)}$ factor. Under the privacy constraint alone, the minimax rate of estimation is $\sqrt{d^2/\alpha^2 kn}$. We show that combining the two constraints leads to a minimax estimation rate of $\epsilon\sqrt{d/\alpha^2 k}+\sqrt{d^2/\alpha^2 kn}$ up to a $\sqrt{\log(1/\epsilon)}$ factor, larger than the sum of the two separate rates. We provide a polynomial-time algorithm achieving this bound, as well as a matching information theoretic lower bound.
Text Implicates Prosodic Ambiguity: A Corpus for Intention Identification of the Korean Spoken Language

Phonetic features are indispensable in understanding the spoken language. Especially in Korean, which is wh-in-situ and head-final, the addressee of spoken language sometimes finds it hard to discern the speaker’s original intention if not provided with the sentence prosody. However, acoustic information may not be guaranteed for all spoken language processing, due to the difficulty of managing and computing speech data. This article suggests a corpus that aims to distinguish utterances with ambiguous intention from clear-cut ones, utilizing the prosodic ambiguity of the text input. In detail, the resulting classification system decides whether the given text input is one of fragment, statement, question, command, rhetorical question/command, or indecisive, taking into account the intonation-dependency of the text. Based on an intuitive understanding of the Korean language engaged in the data annotation, we construct a corpus with seven intention categories, train classification systems, and validate the utility of our dataset with quantitative and qualitative analyses.
Factors Hindering the Adoption of DevOps in the Saudi Software Industry

DevOps has gained high importance in the global software industry due to the ease of software development, testing and deployment it provides. However, the Saudi software industry has not been able to adopt DevOps at a great pace due to various factors. This study, thus, aimed to examine different factors that hindered the adoption of DevOps in the Saudi software industry. Also, recommendations are provided at the end for Saudi Arabia to enhance the adoption of DevOps in its software industry. To accomplish the aims, this study used a literature review and interviews to gather data and examine it to produce the findings. The findings of the study highlight lack of support from organizational management and lack of laws as the major factors for the adoption of DevOps in the Saudi software industry.
Photometric single-view dense 3D reconstruction in endoscopy

Visual SLAM inside the human body will open the way to computer-assisted navigation in endoscopy. However, due to space limitations, medical endoscopes only provide monocular images, leading to systems lacking true scale. In this paper, we exploit the controlled lighting in colonoscopy to achieve the first in-vivo 3D reconstruction of the human colon using photometric stereo on a calibrated monocular endoscope. Our method works in a real medical environment, providing both a suitable in-place calibration procedure and a depth estimation technique adapted to the colon's tubular geometry. We validate our method on simulated colonoscopies, obtaining a mean error of 7% on depth estimation, which is below 3 mm on average. Our qualitative results on the EndoMapper dataset show that the method is able to correctly estimate the colon shape in real human colonoscopies, paving the ground for true-scale monocular SLAM in endoscopy.
Influencing Factors and Intervention Strategies of Teachers' Work Engagement in Primary and Secondary Schools

In recent years, the job burnout rate of teachers in my country has been on the rise. With the rise of positive psychology, researchers began to study teachers' work engagement. After consulting the literature, combined with the actual situation of primary and secondary school teachers, it is found that the main factors affecting the work engagement of primary and secondary school teachers are: teachers' salary, teachers' professional identity, teachers' professional development platform, and school management system. Therefore, it is possible to improve the work commitment of primary and secondary school teachers from the aspects of improving the existing system, improving the level of school management, and teachers' own efforts.
EFL Learners’ Anxiety When Using LMS and WhatsApp : A Mixed-methods Study

Are there any differences between the students' foreign language classroom anxiety induced by WhatsApp and LMS? 2. How do the students perceive their online language learning using either WhatsApp or LMS?
Decentralized non-convex optimization via bi-level SQP and ADMM

Decentralized non-convex optimization is important in many problems of practical relevance. Existing decentralized methods, however, typically either lack convergence guarantees for general non-convex problems, or they suffer from a high subproblem complexity. We present a novel bi-level SQP method, where the inner quadratic problems are solved via ADMM. A decentralized stopping criterion from inexact Newton methods allows the early termination of ADMM as an inner algorithm to improve computational efficiency. The method has local convergence guarantees for non-convex problems. Moreover, it only solves sequences of Quadratic Programs, whereas many existing algorithms solve sequences of Nonlinear Programs. The method shows competitive numerical performance for an optimal power flow problem.
Pengembangan website dan promosi pemasaran Chlorella Jelly Genki di Kecamatan Tampan Kota Pekanbaru

Chlorella Jelly Genki is a kind of health drink to increase the immune system or the level of people's immunity from various diseases including Covid-19 which is packaged in plastic cups that can be sold commercially at a price of Rp. 10,000/cup. Within 10 months from September 2020 to July 2021, 5321 cups have been sold with consumers from various circles including academics from universities such as students and lecturers at the Riau University and Riau Islamic University, Government Employees, Members of the Riau Regional House of Representatives, and the congregation of mosques such as the Rodatul Jannah mosque, Pekanbaru and the general public. To further increase the marketing reach of Chlorella Jelly Genki to the wider community, various strategies or efforts are needed, including developing websites using digital technology and marketing promotions through community service activities by involving student groups as the driving force through integrated Community Service Activities. Abdimas in 2021. This Community Service activity has been carried out from May 2021 to early August 2021, the report of which is presented in this paper. The first way is by developing a digital technology website and marketing promotion through social media in the form of a home page, which can be done under the name: http://genkinusantara.com/. While the second way is to directly invite the target community group to come to the Algae Research Center. The results of this community service show that of the 244 people who were invited, 115 people or 47.1% of them expressed their willingness to become a distributor or selling agent of Chlorella Jelly Genki. While 26 people (10.7%) were still unsure or still needed further consideration to become a distributor or sales agent, 94 people (38.5%) firmly stated that they were not or not yet willing and 94 people (38.5%) ) people (3.7%) did not provide an answer. All participants who attended stated that Chlorella Jelly Genki was delicious and very suitable to be developed in Riau and also in Indonesia.
A Family of Neyman-Pearson-Based Detectors for Noise-Type Radars

We derive a detector that optimizes the target detection performance of any single-input single-output noise radar satisfying the following properties: it transmits Gaussian noise, it retains an internal reference signal for matched filtering, all external noise is additive white Gaussian noise, and all signals are measured using heterodyne receivers. This class of radars, which we call noise-type radars, includes not only many types of standard noise radars, but also a type of quantum radar known as quantum two-mode squeezing radar. The detector, which we derive using the Neyman-Pearson lemma, is not practical because it requires foreknowledge of a target-dependent correlation coefficient that cannot be known beforehand. (It is, however, a natural standard of comparison for other detectors.) This motivates us to study the family of Neyman-Pearson-based detectors that result when the correlation coefficient is treated as a parameter. We derive the probability distribution of the Neyman-Pearson-based detectors when there is a mismatch between the pre-chosen parameter value and the true correlation coefficient. We then use this result to generate receiver operating characteristic curves. Finally, we apply our results to the case where the correlation coefficient is small. It turns out that the resulting detector is not only a good one, but that it has appeared previously in the quantum radar literature.
Detecting block ciphers generic attacks: An instance-based machine learning method

Cryptography facilitates selective communication through encryption of messages and or data. Block-cipher processing is one of the prominent methods for modern cryptographic symmetric encryption schemes. The rise in attacks on block-ciphers led to the development of more difficult encryption schemes. However, attackers decrypt block-ciphers through generic attacks given sufficient time and computing. Recent research had applied machine learning classification algorithms to develop intrusion detection systems to detect multiple types of attacks. These intrusion detection systems are limited by misclassifying generic attacks and suffer reduced effectiveness when evaluated for detecting generic attacks only. Hence, this study introduced and proposed k-nearest neighbors, an instance-based machine learning classification algorithm, for the detection of generic attacks on block-ciphers. The value of k was varied (i.e., 1, 3, 5, 7, and 9) and multiple nearest neighbors classification models were developed and evaluated using two distance functions (i.e., Manhattan and Euclidean) for classifying between generic attacks and normal network packets. All nearest neighbors models using the Manhattan distance function performed better than their Euclidean counterparts. The 1-nearest neighbor (Manhattan distance function) model had the highest overall accuracy of 99.6%, a generic attack detection rate of 99.5% which tallies with the 5, 7, and 9 nearest neighbors models, and a false alarm rate of 0.0003 which is the same for all Manhattan nearest neighbors classification models. These instance-based methods performed better than some existing methods that even implemented an ensemble of deep-learning algorithms. Therefore, an instance-based method is recommended for detecting block-ciphers generic attacks.
Physics-Informed Quantum Communication Networks: A Vision Towards the Quantum Internet

Quantum communications is a promising technology that will play a fundamental role in the design of future networks. In fact, significant efforts are being dedicated by both the quantum physics and the classical communications communities on developing new architectures, solutions, and practical implementations of quantum communication networks (QCNs). Although these efforts led to various advances in today's technologies, there still exists a non-trivial gap between the research efforts of the two communities on designing and optimizing the performance of QCNs. For instance, most prior works by the classical communications community ignore important quantum physics-based constraints when designing QCNs. For example, many existing works on entanglement distribution do not account for the decoherence of qubits inside quantum memories and, thus, their designs become impractical since they assume an infinite lifetime of quantum states. In this paper, we bring forth a novel analysis of the performance of QCNs in a physics-informed manner, by relying on the quantum physics principles that underly the different components of QCNs. The need of the physics-informed approach is then assessed and its fundamental role in designing practical QCNs is analyzed across various open research areas. Moreover, we identify novel physics-informed performance metrics and controls that enable QCNs to leverage the state-of-the-art advancements in quantum technologies to enhance their performance. Finally, we analyze multiple pressing challenges and open research directions in QCNs that must be treated using a physics-informed approach to lead practically viable results. Ultimately, this work attempts to bridge the gap between the classical communications and the quantum physics communities in the area of QCNs to foster the development of the future communication networks towards the quantum Internet.
Aroaro - A Tool for Distributed Immersive Mixed Reality Visualization

In this research demo we present three immersive scenarios on three XR modalities - VR, immersive AR on HoloLens and 2D AR on Android. These scenarios are a network of Harry Potter characters in VR, a map-based visualization of a soldier&#x0027;s history with rich attributes including images and sound on HoloLens, and a visualization of car racing on Android. These visualizations have been created with Aroaro our distributed mixed reality data visualization tool.
Genuinely Multipartite Entanglement vias Shallow Quantum Circuits

Multipartite entanglement is of important resources for quantum communication and quantum computation. Our goal in this paper is to characterize general multipartite entangled states according to shallow quantum circuits. We firstly prove any genuinely multipartite entanglement on finite-dimensional spaces can be generated by using 2-layer shallow quantum circuit consisting of two biseparable quantum channels, which the smallest nontrivial circuit depth in the shallow quantum circuit model. We further propose a semi-device-independent entanglement model depending on the local connection ability in the second layer of quantum circuits. This implies a complete hierarchy of distinguishing genuinely multipartite entangled states. It shows a completely different multipartite nonlocality from the quantum network entanglement. These results show new insights for the multipartite entanglement, quantum network, and measurement-based quantum computation.
Scalable and Adaptable End-To-End Collection and Analysis of Cloud Computing Security Data: Towards End-To-End Security in Cloud Computing Systems

Cloud computing provides customers with enormous compute power and storage capacity, allowing them to deploy their computation and data-intensive applications without having to invest in infrastructure. Many firms use cloud computing as a means of relocating and maintaining resources outside of their enterprise, regardless of the cloud server&#x0027;s location. However, preserving the data in cloud leads to a number of issues related to data loss, accountability, security etc. Such fears become a great barrier to the adoption of the cloud services by users. Cloud computing offers a high scale storage facility for internet users with reference to the cost based on the usage of facilities provided. Privacy protection of a user&#x0027;s data is considered as a challenge as the internal operations offered by the service providers cannot be accessed by the users. Hence, it becomes necessary for monitoring the usage of the client&#x0027;s data in cloud. In this research, we suggest an effective cloud storage solution for accessing patient medical records across hospitals in different countries while maintaining data security and integrity. In the suggested system, multifactor authentication for user login to the cloud, homomorphic encryption for data storage with integrity verification, and integrity verification have all been implemented effectively. To illustrate the efficacy of the proposed strategy, an experimental investigation was conducted.
Portraying pedagogic experience: Faculty of History-Pedagogy of Voronezh State Pedagogic Institute

The article illustrates the possibility of using the portraiture method to describe a complex structured object – a faculty in the dynamics of its formation and development to the stage of reprofiling. The description of the Historical-Pedagogic Faculty of Voronezh State Pedagogic Institute is proposed. Stratification (changes in status, structure, areas of activity) and self-identification (assessment of certain types of activity by its participants) were used as aspects of portraiture.The self-identification aspect is revealed, first of all, through the author's position: Emma V. Panicheva is one of the founders of the faculty, who headed the profile department for a long time; Marina V. Shakurova graduated from the Historical and Pedagogic Faculty of Voronezh State Pedagogic Institute in 1985.The required integrity of the image against the background of constant stratification dynamics is maintained by deep value meanings, end-to-end life tasks, understanding of the role of the individual.The portrait of the faculty is based on the history of the concept of the phenomenon (the creation of Young Pioneer faculties in the early 1960s), fragments of the chronology of the existence of the Historical-Pedagogic Faculty of Voronezh State Pedagogic Institute (three stages – Young Pioneer faculty, the Historical- Pedagogic Faculty, the historical faculty with the profile of training “history –upbringing work”), a description of the role of the dean's office and the profile department (the department of theory and methodology of Young Pioneer and Komsomol work; later, the department of theory and methodology of upbringing work).
Pedagogic literacy as pupils’ modern educational result

The article reveals the concept of pedagogic literacy as one of the topical and demanded results of general and additional education of children. The author introduces the concept of pedagogic literacy and describes its three levels – elementary (general cultural) pedagogic literacy as a quality necessary for every person in modern society, pre-professional pedagogic literacy as the basic result of pre-professional pedagogic training of schoolchildren and professional pedagogic literacy as an indicator of the quality of pedagogue's professional activity. The potential of identifying the key educational results of pre-vocational pedagogic training of schoolchildren in concretising its mission as a stage of continuous pedagogic education and specifying the tasks of pre-vocational pedagogic training within the framework of the implementation of state educational standards is demonstrated. The article presents the experience of the formation of pedagogic literacy of schoolchildren in the framework of the educational project “Ushinsky Gymnasium” of Ushinsky Yaroslavl State Pedagogic University.
How to Attain Communication-Efficient DNN Training? Convert, Compress, Correct

This paper introduces CO3 -- an algorithm for communication-efficient federated Deep Neural Network (DNN) training. CO3 takes its name from three processing applied which reduce the communication load when transmitting the local DNN gradients from the remote users to the Parameter Server. Namely: (i) gradient quantization through floating-point conversion, (ii) lossless compression of the quantized gradient, and (iii) quantization error correction. We carefully design each of the steps above to assure good training performance under a constraint on the communication rate. In particular, in steps (i) and (ii), we adopt the assumption that DNN gradients are distributed according to a generalized normal distribution, which is validated numerically in the paper. For step (iii), we utilize an error feedback with memory decay mechanism to correct the quantization error introduced in step (i). We argue that the memory decay coefficient, similarly to the learning rate, can be optimally tuned to improve convergence. A rigorous convergence analysis of the proposed CO3 with SGD is provided. Moreover, with extensive simulations, we show that CO3 offers improved performance when compared with existing gradient compression schemes in the literature which employ sketching and non-uniform quantization of the local gradients.
Scheduling Coflows for Minimizing the Total Weighted Completion Time in Heterogeneous Parallel Networks

Coflow is a network abstraction used to represent communication patterns in data centers. The coflow scheduling problem in large data centers is one of the most important $NP$-hard problems. Many previous studies on coflow scheduling mainly focus on the single-core model. However, with the growth of data centers, this single-core model is no longer sufficient. This paper considers the coflow scheduling problem in heterogeneous parallel networks. The heterogeneous parallel network is an architecture based on multiple network cores running in parallel. In this paper, two polynomial-time approximation algorithms are developed for scheduling divisible and indivisible coflows in heterogeneous parallel networks, respectively. Considering the divisible coflow scheduling problem, the proposed algorithm achieve an approximation ratio of $O(\log m/ \log \log m)$ with arbitrary release times, where $m$ is the number of network cores. On the other hand, when coflow is indivisible, the proposed algorithm achieve an approximation ratio of $O\left(m\left(\log m/ \log \log m\right)^2\right)$ with arbitrary release times.