Reviewerly/exHarmony huggingface dataset:
Quantum Channel Marginal Problem

Given a set of local dynamics, are they compatible with a global dynamics? We systematically formulate these questions as quantum channel marginal problems, which can be understood as a dynamical generalization of the marginal problems for quantum states. These problems include as special cases channel broadcasting, channel extendibility, and measurement compatibility and are strongly connected to the generalization of the no-signaling conditions to quantized inputs and outputs. After defining the notion of compatibility between global and local dynamics, we provide a solution to the channel marginal problem that takes the form of a semidefinite program. Using this formulation, we construct channel incompatibility witnesses and show that a set of local channels are incompatible if and only if they demonstrate an advantage in a state-discrimination task.
2. Enhancement of continuous dysarthric speech

Dysarthria is a set of motor-speech disorders resulting due to neurological injuries. It affects the motor component of the motor-speech system. Disruption in muscular control makes the speech imperfect. As a result, dysarthric speech is not as comprehensible as normal speech. Most often, people with dysarthria have problems with communicating and this inhibits their social participation. Hence, for effective communication, it is extremely vital that we develop assistive speech technologies for people with dysarthria. The aim is to improve the naturalness and intelligibility of dysarthric speech while retaining the characteristics of the speaker. For this purpose, durational attributes across dysarthric and normal speech utterances are first studied. An automatic technique is developed to correct dysarthric speech to bring it closer to normal speech. The performance of this technique is compared with that of two other techniques available in the literature - a formant resynthesis technique and a hidden Markov model based adaptive speech synthesis technique. Subjective evaluations show a preference for dysarthric speech modified using the proposed approach over existing approaches.
Conditional independence testing via weighted partial copulas and nearest neighbors

This paper introduces the \textit{weighted partial copula} function for testing conditional independence. The proposed test procedure results from these two ingredients: (i) the test statistic is an explicit Cramer-von Mises transformation of the \textit{weighted partial copula}, (ii) the regions of rejection are computed using a bootstrap procedure which mimics conditional independence by generating samples from the product measure of the estimated conditional marginals. Under conditional independence, the weak convergence of the \textit{weighted partial copula proces}s is established when the marginals are estimated using a smoothed local linear estimator. Finally, an experimental section demonstrates that the proposed test has competitive power compared to recent state-of-the-art methods such as kernel-based test.
Building Knowledge Economies in Africa: An Introduction

Knowledge has emerged as a fundamental driver of economic growth and development by inter alia improving the effectiveness and efficiency of economic projects and boosting the process of finding new avenues of addressing developmental policy syndromes. Recent evidence suggests that Africa is on the threshold of significant and sustainable economic growth if its human and material resources can be effectively mobilised to support the process (Kuada & Mensah, 2017; Asongu & Tchamyou, 2019). Consequently, the World Bank’s Knowledge Economy Framework aims to explore and support the extent to which current policies in African countries affect the knowledge development process (and thereby competitiveness) on the continent. A knowledge economy is an economy in which economic prosperity largely depends on the accessibility, quality and quantity of information available, instead of the means of production (Asongu, 2017a, 2017b). This themed issue of Contemporary Social Science-‘Building Knowledge Economies in Africa’ - consists of papers that focus on, but are not limited to, the four dimensions of the World Bank’s Knowledge Economy Index. These are: information and communication technology, education, economic incentives and institutional regime, and innovation (Tchamyou, 2017). The themed issue engages with high quality contributions which, taken together, address the drivers towards knowledge-based economies. This introduction provides a context for understanding the importance of building knowledge economies in Africa and summarises the main contributions to the themed issue. The paper ends by advising scholars and policy makers regarding the risks associated with a colonial view of knowledge- notably the importance of proposing knowledge-based policies while avoiding hegemonic paradigms and hierarchical constructs. In summary, the issue consists of a set of theoretically informed, empirically robust, policy-relevant and accessible articles for both specialists and non-specialists.
PPFQ: Privacy-Preserving Friends Query over Online Social Networks

Friends query is one of the critical components in online social networks (OSNs). However, with the advent of OSNs, more and more private information of individuals (e.g., personal sensitive profiles, relationships and locations) will be collected by the service providers during the query. Some existing privacy-preserving solutions either can provide only approximate queries or need trust assumption on the service providers. To overcome these limitations, we propose PPFQ-a Privacy-Preserving Friends Query scheme over OSNs. To the best of our knowledge, this is the first work that aims at preventing attack launched by the service provider and attackers, but still allow users to make friends query based on profile and geo-distance. In PPFQ, we design a profile-based friends query protocol where user's profiles can be encrypted and searched by his friends privately with different keys. In addition, for the first time, we design a k-nearest friends query protocol to achieve location-based search on encrypted domain. By deploying the two-server secure distance computation and comparison mechanism with Batcher's sorting network, this protocol achieves accurate nearby friends retrieving while preventing the privacy of geo-location and the distance order from revealing them to the servers. We also give a comprehensive analysis of the correctness and security of PPFQ with mathematical proofs. Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction.
PROFESSIONAL STANDARDS AS A QUALITY GUARANTEE FACTOR TRAINING GRADUATES OF EDUCATIONAL ORGANIZATIONS HIGHER AND PROFESSIONAL EDUCATION

Positive modernization is peculiar to Russian education, but at the same time, there are discrepancies between the results of the training of university graduates and secondary schools with the requirements of the modern economy. This is foreground by experts especially when assessing the quality of education and recognizing the qualifications of graduates. The system of reproduction of qualified labor resources for enterprises and organizations of all sectors of the economy and forms of ownership in the country must adapt to institutional changes in the Russian system of higher and secondary vocational education, similar to the smart grids of modernized industrial energy systems. The legislative requirement for education as a single purposeful process of education and training, which is a socially significant benefit and is carried out in the interests of the individual, family, society and the state, accelerates the arrival of a new education in a new infrastructure with a changed body of pedagogical workers meeting the requirements of professional standards. To solve such a fundamental and complex task of a new education, a way is possible of segmentation of changes in the system of training graduates of universities and colleges in accordance with the transformation of the economy according to changes in technological structures and ensuring the quality of design and implementation of educational programs based on professional standards. The formation of the national qualification system, including professional standards, is characterized by the continuity of the uniform methodological basis for the system of vocational education and the national qualification system. Understanding the quality of education as a measure to achieve the goals of education, in which its customers are interested, determines the participation of these stakeholders in the procedures for the identification of educational results.
Video Analytics Using Deep Learning

Videos are a really powerful medium. Roughly 300 hours of videos are uploaded to YouTube every minute. And the number of videos created is increasing daily. With the advent of smartphones and improved hardware, the video quality is enhanced. More videos are being created and stored across domains and geographies. We have movies, advertisements, short clips, and personal videos. Most of the videos contain human faces, objects, and some movements of objects. A video might be shot during the daytime or in the night, under different lighting conditions. We have cameras capturing the movement of pedestrians on the road, manufacturing cameras for monitoring goods and products on manufacturing lines, security cameras for surveillance on airports, and number plate detection and reading systems in the parking lots, to name a few of the video analytics solutions.
Analisis Kepuasan Pengguna Google Classroom Menggunakan Pieces Framework (Studi Kasus: Program Studi Sistem Informasi Uin Jakarta)

The use of online learning media is currently widely used by all students and students in Indonesia, this is due to the covid-19 pandemic that requires students to do online learning. Media Knowledge Management System (KMS) is a must in every online learning. Online learning media is very much an option, one of which is google classroom. Google classroom is one of the most popular and used learning media. This study aims to analyze and know the level of google classroom user satisfaction by students of UIN Syarif Hidayatullah Jakarta Information System in the learning process. Satisfaction of the use of an application was formed because of the service provided by a system that can be customized to the user's wishes. This research uses PIECES framework method consisting of Performance, Informations and data, Economics, Control and security, Efficiency and Service. The result of this study obtained the average value of each domain, namely performance domain gets a value of 4,188 (Satisfied), Informations and data get a value of 4,194 (Satisfied), Economics gets a value of 4.013 (Satisfied), Control and security gets a value of 4.074 (Satisfied), Efficiency gets a value of 4,457 (Very Satisfied) and Service gets a value of 4.3 (Very Satisfied). As for the total average satisfaction level of all domains, it gets a score of 4,204 (Very Satisfied).
Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata

We address the approximate minimization problem for weighted finite automata (WFAs) with weights in $\mathbb{R}$, over a one-letter alphabet: to compute the best possible approximation of a WFA given a bound on the number of states. This work is grounded in Adamyan-Arov-Krein Approximation theory, a remarkable collection of results on the approximation of Hankel operators. In addition to its intrinsic mathematical relevance, this theory has proven to be very effective for model reduction. We adapt these results to the framework of weighted automata over a one-letter alphabet. We provide theoretical guarantees and bounds on the quality of the approximation in the spectral and $\ell^2$ norm. We develop an algorithm that, based on the properties of Hankel operators, returns the optimal approximation in the spectral norm.
Alternative workflow for GLCM-based estimation of spatial variation in seismic data using pre-selected directions

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Privacy Analysis of COVID-19 Contact Tracing Apps in the EU

This paper presents results from a privacy analysis of COVID-19 contact tracing apps developed within the EU. Though these apps have been termed advantageous, concerns regarding privacy have become an issue that has led to their slow adoption. In this empirical study, we perform both static and dynamic analysis to judge apps' privacy-preserving behavior together with the analysis of the privacy and data protection goals to deduce their transparency and intervenability. From the results, we discover that while the apps aim to be privacy-preserving, not all adhere to this as we observe one tracks users' location, while the other violates the principle of least privilege, data minimisation and transparency, which puts the users' at risk by invading their privacy.
Simulation and Algorithm Optimization of VAV Air Conditioning Room Temperature Model

With the rapid development of intelligent buildings, the research and development of VAV air-conditioning systems have become the focus of attention. This article mainly studies the temperature control loop of the VAV air-conditioning room, and optimizes the control effect of the temperature control loop of the air-conditioning room by combining intelligent algorithm and PID control. At the same time, a fuzzy PID controller is designed. Fuzzy PID control is added to the temperature control loop of the air-conditioning room. Matlab/Simulink is used for modeling and simulation. The experimental results show that the fuzzy PID control effectively improves the stability of the indoor temperature. The resulting maximum dynamic deviation is smaller, the adjustment time is shorter, and the interference resistance is better, which has certain theoretical significance for improving the comfort of the VAV system air-conditioned room.
The method of recognizing traffic signs based on the improved capsule network

Traffic sign recognition is one of the urgent problems to be solved by automatic driving technology, and it is also one of the more complex problems. For the problem that the conventional convolutional neural network has not been good enough to recognize traffic signs, this paper uses an improved capsule network .The method first uses image processing to extract features of traffic signs in a complex background, remove noise, binarize traffic signs, extract the main parts, make the characteristics of traffic signs more obvious, and then input the traffic signs into the capsule network to identify. The test results on the GTSRB data set show that the improved capsule network method has an improved recognition accuracy of 2%-5% in complex scenes, which is a great improvement compared to the traditional convolutional neural network. The experimental results show that the improved capsule network method has great reference significance for the research of autonomous driving.
Investigation of Wind Loads on Utility-Scale Seasonal Tilt Solar PV Power Plants

The solar photovoltaic (PV) sector is experiencing excellent growth in the world. This paper presents the static structural analysis of seasonal tilt photovoltaic power plant. However, dynamic analysis on the system is not yet presented exclusively. We have done computational fluid dynamic (flow simulation) on seasonal tilt solar PV power plant on utility scale. The load on the solar panel due to the different velocity of winds observed in India is applied. Various directions of the application of wind velocity are also considered in the analysis. From flow simulation, we have obtained the maximum wind loads acting on the module, and for that maximum wind load, we have done a static structural analysis. From the static force analysis, structural stability is analyzed. This study will be helpful in the analysis of similar kind of structures under a wide range of forces generated from different wind velocities.
Utilizing whatsapp-driven learning during covid-19 outbreak: Efl users’ perceptions and practices

The COVID-19 pandemic required an abrupt shift to online education In Indonesia, where WhatsApp is a popular communication tool, teachers moved from face-to-face delivery to sharing teaching materials via WhatsApp This study investigated teachers’ and learners’ perceptions, practices, and challenges of utilizing WhatsApp as a primary teaching and learning tool amid the COVID-19 pandemic A mixed quantitative and qualitative method was used to identify 119 EFL teachers’ and 198 learners’ perceptions, practices, and challenges of using WhatsApp in East Java senior high schools The findings revealed discrepancies in perceptions between EFL teachers and learners regarding WhatsApp's overall perceptions, ease of use, and usefulness Results indicate that WhatsApp can be a suitable primary tool for teaching and learning, rather than being limited to its more traditional role as a supporting tool Considerations for increasing the effectiveness of WhatsApp for teaching and learning are discussed © 2021, CALL-EJ All rights reserved
FROM﻿ RHIZOME ﻿TO ﻿CLUSTERS.﻿ ABOUT﻿ A﻿ MONOGRAPH﻿ BY﻿ M.﻿G.﻿ BRESLER﻿ «ONTOLOGY﻿ OF﻿ NETWORK﻿ BEING»

The article is a review of the monograph by M. G. Bresler’s Ontology of Network Being. The monograph is devoted to the issues of virtual being, the ontology of the Internet. It uses methods developed by the French postmodern philosophers of the late 20th century (Deleuze, Guattari). The author’s scientific novelty consists in the proposal to consider network existence not as a rhizome, but as a set of communities-clusters.
High School Cybersecurity

Cybersecurity touches nearly every aspect of daily life of students, their family, and larger society. The ubiquitous connectivity of devices and ever-connected students drives the need for cybersecurity education at a deeper and more technical level than in any previous generation.
The Design and Application of English Hybrid Teaching Mode in Higher Vocational Education under the Background of Internet

Higher vocational colleges which belong to the high-level education in vocational education shoulder the important mission of our country's economic and social construction and training talents. Because of the way, channel, goal and many other aspects of training talents of higher vocational colleges are different from ordinary higher education, the social functions are different and the future development of students is also different. Although higher vocational education is mainly based on technology application and technical training, cultural knowledge and theoretical knowledge are also very important. As a public basic course, the teaching mode of higher vocational English has always been a hot topic for educators to care about and think about. Hybrid teaching is a learning method that combines face-to-face learning and online learning in traditional classroom to realize their complementary advantages. This study analyzes and explores how to design the mixed teaching mode and its application in higher vocational English under the background of Internet.
Efficient Continual Adaptation for Generative Adversarial Networks.

We present a continual learning approach for generative adversarial networks (GANs), by designing and leveraging parameter-efficient feature map transformations. Our approach is based on learning a set of global and task-specific parameters. The global parameters are fixed across tasks whereas the task specific parameters act as local adapters for each task, and help in efficiently transforming the previous task's feature map to the new task's feature map. Moreover, we propose an element-wise residual bias in the transformed feature space which highly stabilizes GAN training. In contrast to the recent approaches for continual GANs, we do not rely on memory replay, regularization towards previous tasks' parameters, or expensive weight transformations. Through extensive experiments on challenging and diverse datasets, we show that the feature-map transformation based approach outperforms state-of-the-art continual GANs methods, with substantially fewer parameters, and also generates high-quality samples that can be used in generative replay based continual learning of discriminative tasks.
KLS-A: A Full-Life-Time Anomaly Detection Method

Anomaly detection is a significant field in the industry. The anomaly detection system could detect the possible abnormalities of equipment in time, and reduce the human and material resources loss caused by equipment abnormality. It is an effective method to analyze operating status of the equipment by collecting its status data. Anomaly detection usually relies on large amounts of equipment status data. After analyzing a large amount of normal data, a model of normal data is obtained. When abnormal data arrives, the data will be identified by analyzing if the data is different from the existing model. However, a device's Full-Life-Time must go through the cold-boot stage, when a device is cold-booted, the amount of data is usually small. As the operating time of the equipment increases, data gradually accumulates. So, the efficient anomaly detection research usually focuses on the equipment in smooth-running stage. Some traditional anomaly detection methods can be used when the amount of data is small, but they usually have limited anomaly detection capabilities. This paper is motivated by the problem, we have proposed an anomaly detection method, which could run during cold-boot and also fully explore the ability of large amount of data. We call it KLS-A. "KLS" means the three components of the algorithm: KMeans, LSTM and selector, "A" means adaptive, this means our algorithm can be easily transplanted in different equipment. Through the transformation of KMeans and LSTM, KLS-A has strong adaptability, and it can automatically adjust parameters according to the amount of data. By using a customized selector, KLS-A can freely switch algorithms under different data volumes to meet the anomaly detection at different stages of the equipment. Therefore, it can maintain a good anomaly detection effect under different stages. Meanwhile, we implemented KLS-A on Spark and applied it to the monitoring of SINOPEC's hydrocondensation tower. We monitored twenty-three indicators of the hydrocondensation tower and tested the anomaly detection ability from cold-boot stage to smooth running stage. We also used the same data set to do comparative experiments with traditional anomaly detection algorithms. The results show four abnormalities can be predicted at the earliest time by KLS-A and all stages can guarantee a high accuracy rate.