Reviewerly/exHarmony huggingface dataset:
Robust one-sided self-testing of two-qubit states via quantum steering

Entangled two-qubit states are the core building blocks for constructing quantum communication networks. Their accurate verification is crucial to the functioning of the networks, especially for untrusted networks. In this work we study the self-testing of two-qubit entangled states via steering inequalities, with robustness analysis against noise. More precisely, steering inequalities are constructed from the tilted Clauser-Horne-Shimony-Holt inequality and its general form, to verify the general two-qubit entangled states. The study provides a good robustness bound, using both local extraction map and numerical semidefinite-programming methods. In particular, optimal local extraction maps are constructed in the analytical method, which yields the theoretical optimal robustness bound. To further improve the robustness of one-sided self-testing, we propose a family of three measurement settings steering inequalities. The result shows that three-setting steering inequality demonstrates an advantage over two-setting steering inequality on robust self-testing with noise. Moreover, to construct a practical verification protocol, we clarify the sample efficiency of our protocols in the one-sided device-independent scenario.
Pengembangan Video Animasi Iklan Berbasis Motion Graphic untuk Meningkatkan Hasil Belajar Bahasa Indonesia di Sekolah Dasar

This study aims to determine how the development, feasibility, teacher and student responses and the effectiveness of motion graphic-based animation video media developed at SDN 06 and 07 Bengkulu City. The method used is the Research and Development method using the ADDIE model. The research instruments used were interviews, questionnaires, knowledge learning outcomes tests in the form of multiple choice questions (Multiple Choices) given through pretest and posttest and performance assessment rubrics to measure learning outcomes in skills aspects. The results showed that the developed media met the eligibility and requirements to be used as learning media. This can be seen from the material validation of 0,91, language validation 0,82 and design validation 0.86. The teacher and student responses showed a positive response to the developed media. It is also quite effective in improving students' Indonesian learning outcomes. Seen from the learning outcomes of the knowledge aspect, the significance value at (2-tailed) is 0,000 and the N-Gain Score is 70%. Meanwhile, the learning outcomes in the skill aspect of the experimental class were higher than the control class. From these data, it appears that there are differences in learning outcomes achieved by students in the experimental and control classes. Thus, it can be concluded that motion graphic-based animation video media is quite effective in improving Indonesian language learning outcomes in advertising materials in elementary schools.
Pengembangan LKPD Menulis Pantun Berbasis Pedagogi Genre Berbantuan Kamus Rima Kata Online di Sekolah Dasar

The purpose of this study was to describe the development, feasibility, and response, as well as to determine the effectiveness of LKPD Writing Pantun Based on Genre Pedagogy Assisted by Online Rima Kata Dictionary in Elementary Schools. This type of research is Research &amp; Development (R &amp; D), with the ADDIE model (Analyze, Design, Development, Implementation, Evaluation). The research sites are at SDIT Al-Ahsan and SDN 69 Seluma on February 21 – March 8, 2022. Based on the needs analysis, students and teachers need LKPD in learning to write pantun. The LKPD design is made by combining the LKPD structure format, genre pedagogical approach, and the rima kata dictionary media used. Design this LKPD using the Canva app. This LKPD is suitable for use with very high content validity in material and language aspects (1), and high media aspects (0.7), with medium inter-rater reliability in material aspects (40%), strong language aspects (70%), and weak media aspect (30%). The teacher's response to the use of the LKPD was very good, as was the response of the students (97.8%). This LKPD is effectively used and proven to improve student learning outcomes in writing pantun
The Capacity of Heavy Metal Remediation by Cyperus alternifolius, Chrysopogon zizanioides (L.) Roberty, and Aloe vera (L.) Burm.f. under Industrial and Urban Wastewater Treatment

Abstract Urban wastewater (UWW) and industrial wastewater (IWW) release lots of heavy metals into the environment, leading to various risks regarding agricultural production, food quality, human and animal health, and environmental safety. Phytoremediation is known as one of the best and most affordable strategies to eliminate or reduce heavy metals in different environments. This study examined the phytoremediation ability of three plant species (Cyperus alternifolius, Chrysopogon zizanioides (L.) Roberty, and Aloe vera (L.) Burm.f) to absorb heavy metals of Zn, Cr, Pb, Cu, Mn, Ni, and Mg from the contaminated soil with UWW and IWW for 14 months. The result showed that all three examined plants reduced heavy metal pollution of the soil using the phytostabilization method. The metal accumulation index (MAI) of the root in V. zizanioides was higher than C. alternifolius and A. vera. While MAI of the shoot in A. vera was higher than C. zizanioides and C. alternifolius. Based on the results, it is suggested to cultivate C. zizanioides and A. vera in the polluted areas to decrease the amount of soil contaminants and create a green belt. Keywords: Heavy metal, Industrial and urban wastewater, Metal accumulation index, Phytoremediation, Phytostabilization
Comparative QoE Study of VoD and Passive Gaming Videos using Cross-Correlated QoS Metrics

In this paper, we compare the end-user Quality of Experience (QoE) of two of the most popular streaming applications: Video on Demand (VoD) applications and Passive Video Game (PVG) streaming applications.
Explainable AI and Its Applications in Healthcare

Due to the lack of high-end graphics or tensor processing units, previously, deep neural networks could not be implemented as state-of-the-art Artificial Intelligence (AI) algorithms. Rather, linear models were preferred, and they were easy to understand and interpret. Things started changing with the advent of more advanced processing units, in the last decade, when the algorithms took on real-world problems. The models began getting bigger and better.
Mobile Apps as Tools for Jordanian EFL Teachers' Professional Development

The present study investigated Jordanian EFL teachers' use of mobile apps as tools for their professional development, particularly self-directed content knowledge development. One hundred and eighty-six teachers of different cities of Jordan responded to a researcher-designed questionnaire. The data collected from the participants were tabulated and discussed in light of the five research questions. A significant result of this study was that 95.2% of the participants use their mobile apps as a vehicle to improve their English, and that the language areas attracting the participants' attention the most were vocabulary and grammar. Another finding of this research was that the most popular social media, language learning, and news-in-English apps used by the participants for the purpose of professional development were Facebook, BBC Learning English, and BBC News, respectively.
Philosophy in Russian Higher Education: Mission and Difficulties

The solutions proposed in the article are aimed at optimizing the curriculum of philosophy and the effective development of general cultural competencies within the framework of non-humanitarian directions of higher education. The author draws attention to the fact that the basic general cultural competencies, which should be realized as a result of the study of philosophy, coincide with the basic social functions of philosophy. The basic social functions of philosophy (philosophical and methodological) are considered and the main difficulties in the implementation of the relevant competencies are noted. The implementation of the methodological function of philosophy is complicated by the gap between humanitarian and special subjects; insufficient erudition of teachers of philosophy in the field of natural and technical sciences prevents the convincing presentation of the advantages of philosophical thinking. The active participation of teachers of philosophy in scientific research (for the implementation of socio-humanitarian examination) is recommended. Nevertheless, it is concluded that the goal of the philosophy curriculum— the formation of a critically thinking creative personality – is in contradiction with the prevailing social trends; therefore, its achievement is unlikely before the implementation of the humanitarization of the technogenic society. Small improvements in the curriculum and the development of new pedagogical methods do not solve the main problem — the need to reorganize the higher education system in the modern world.
Absorbers as detectors for unbound quantum systems

Complex absorbing potentials are frequently employed in quantum calculations. One of the advantages that such absorbers provide is the ability to attenuate outgoing waves in simulations of unbound systems, thus allowing for truncating the numerical domain. Here we argue that the absorber may also be used to probe outgoing waves so that physical information about absorbed particles may be retained. Moreover, under certain conditions, the physical information extracted via the absorber is subject to loss in coherence, as is also the case when collapsing the wave function upon measurement. Both these aspects demonstrate clearly how, and when, the effect of introducing a complex absorbing potential corresponds to that of a detector.
Machine Learning with Python

Machine Learning (ML), a subfield of computer science and a subset of Artificial Intelligence (AI), has application in many different areas including online shopping, medicine, video surveillance, email spam, etc. The main idea is to develop algorithms that can learn from data and make predictions based on this learning. There are many types of ML algorithms applied in the various domains. They are divided in three main categories, i.e., supervised, unsupervised, and reinforcement. Python has an arsenal of libraries that support the implementation of these algorithms, including NumPy, SciPy, Scikit-learn, TensorFlow. This chapter introduces some of the most important as well as popular ML libraries through the implementation of a series of example cases.
Database Programming with Python

The rapid advances of computing systems over the past decades have led to significant improvements in terms of data storage capacity, as well as the algorithms and programs associated with data management and organisation. In the scope of Computer Science and Information Technology these elements are commonly referred to as database systems. A notable and widely used form of database systems is that of the Relational Database. This form of database allows the user to create, modify, and delete database structures through the Data Definition Language (DDL), modify the database content through the Data Manipulation Language (DML), and filter and manage data using specific programming commands called Queries. The various different elements of such a system are usually managed through a unified platform or interface commonly known as a Database Management System (DBMS). This chapter focuses on the use of Python and MySQL, a popular DBMS, in order to perform a number of fundamental Relational Database tasks. The chapter starts with the introduction of the various different components of MySQL as well as the necessary steps one has to follow in order to establish the foundation for the data manipulation work presented later on. Once this foundation is laid down, the chapter moves on to present fundamental Relational Database tasks of increasing complexity through the use of Python scripts and SQL Queries. Ultimately, the chapter can be viewed as a walk-through for readers with little or no prior database programming experience or as a handy reference guide for those with general experience in databases but who would like to get exposed to workflows specifically involving the Python platform.
Application Development with Python

Seldom an application is single-functional. Almost always it provides multiple functions and quite often even these are multi-faceted. Programming languages, like Python, provide a number of tools to allow the organization and access of these multiple functions through some form of menus and common API (Application Programming Interfaces). This chapter builds on the previous two, i.e., object-oriented programming and graphical user interface programming, to demonstrate how the various types of menus and APIs available in Python may be used to create such an application.
Introduction to Programming with Python

Traditional programming languages are utilizing the concepts of sequence, selection, and iteration, widely known as the basic principles of computer programming, as well as the concepts of variables and data types and fundamental divide and conquer functions/methods. These elements are presented in this chapter in a comprehensive, thorough, and detailed manner that aims at helping the novice programmer become comfortable with the basic ideas of computational thinking and coding. The programming language of choice is Python and, in particular, the Anaconda/Jupyter Notebook platform. At the end of the chapter, the reader can use the associated case study and list of exercises to practice and further explore the ideas presented in the chapter.
IoT device identification method based on CNN+SVM

The Internet of Things allows everything to be interconnected, but there is also the risk of malicious devices or vulnerable devices causing damage to the network. In order to avoid this risk, how to accurately identify which devices are connected to the network in a resource-limited IoT environment becomes an urgent problem to be solved. Identification by extracting network traffic features as device fingerprints is a more effective and low-interference method today. This paper proposes a device identification method based on device traffic fingerprints, which utilizes convolutional neural networks (CNN) to extract features from device network traffic, and then uses SVM for device classification. The experimental results show that, compared with the CNN method and the method of manually extracting features, the identification accuracy and stability of the method proposed in this paper are significantly improved.
The Exploration of the experiences of Play-Centered Teaching Demonstrations of Pre-Service Early Childhood Teachers

Objectives The purposes of this study were to explore the understanding of the Nuri Curriculum and experiences in the curriculum integration activities in the play-centered teaching demonstration of pre-service early childhood teachers. Methods The participants in this study were 20 pre-service early childhood teachers who attended early childhood teacher education courses at K University in Gyeonggi. The research data were collected three times and qualitatively analyzed the reflective journals on the experiences while performing play-centered activities in connection with the Nuri course in the teaching demonstration process. Results The results of the study are that first, pre-service early childhood teachers tried to understand the Nuri Curriculum for early childhood play-centered classes, to integrate it with the subject, and to find the educational meaning. Second, it was found that pre-service early childhood teachers, as supporters of early childhood play, organize and implement activities in the course of teaching demonstrations, develop reflective thinking skills, and improve their professionalism as cooperators for child-led play support. Conclusions Based on the results of this study, it was discussed how to improve the understanding of the Nuri Curriculum and the professionalism of the integrated curriculum.
AGK: the Adaptive Grid K-means algorithm

As the most widely used clustering algorithm nowadays, the K-means algorithm has been applied in diverse fields. It is characterized with a fast calculation speed and a simple algorithm. It is, however, nonetheless beset by a number of problems. One difficulty is that choosing the starting center at random will have a negative influence on the clustering result. Second, outliers are susceptible to the K-means algorithm. Third, such an algorithm still has a significant time cost. To address these concerns, this work introduces the AGK Adaptive-GK method, which takes use of the grid clustering technique's benefits. Our AGK can accurately identify an initial center, increase the accuracy of the standard K-means method, and lower the algorithm's computation complexity. We did a thorough study of our AGK on a variety of data sets, and the findings show that it is accurate and efficient.
A survey of contrastive learning in NLP

Contrastive learning (CL) is building example pairs and computing loss to make models more robust in processing samples by reducing the distance between positive samples and amplifying the distance between negative samples. Recently, CL has attracted interest in natural language processing (NLP) where it has worked well and is mainly applied to sentence embedding and text classification tasks. To our knowledge, no study has reviewed the application of CL in NLP. In this paper, we describe two types of contexts with CL and present the methods to compute different losses. And then, we introduce some classic models which are significant. Finally, we discuss the current challenges and possible future directions of the CL.
Comparison on long-term tracking: methods and benchmarks

While dealing with transient target absence or sudden position change, short-term visual object tracking is not capable of continuously spotting the target. Therefore, long-term tracking methods are indispensable for handling these issues. It is necessary to summarize these long-term tracking methods. In this work, we propose to demonstrate some long-term tracking methods including LTMU, ST Transformer and GlobalTrack and three benchmarks (OxUvA, TLP, and CDTB), and we aim to compare these methods based on various benchmarks. All those methods achieve distinguishing performance on those datasets. Furthermore, we are going to present some prospects of the long-term tracking.
Analysis and research on electricity consumption characteristics of massive users based on fuzzy C-means clustering

With the progress of informatization and intelligent construction of power grid enterprises, it is possible to obtain large data information on electricity consumption of massive users. Based on blockchain sharing and integrating external data such as social economy, industry development, environmental protection energy consumption, industrial and commercial taxation, and based on the electricity consumption data of massive enterprise users, this paper innovatively proposes a fuzzy C-means clustering of electricity consumption characteristics for massive users. Class analysis method, and at the same time, according to the common electricity consumption patterns of users, 9 types of typical electricity consumption patterns of users are divided, and clustering methods are used to aggregate the cluster centers of these 9 types of electricity consumption patterns. Users carry out multi-dimensional and refined analysis, and divide these users into the best aggregation mode, which lays the foundation for the subsequent identification of external influencing factors and the construction of prediction models.
Cross-domain network video quality assessment based on video bitstream

Accurate and efficient video quality assessment (VQA) methods are important guidelines for optimizing network video quality, improving video compression performance, and recommending compression coding parameters. No-reference VQA methods are mainly divided into bitstream-based methods, which are less time-consuming but less accurate, and pixel-based methods, which are more accurate but more time-consuming and require high hardware requirements. Both methods are not applicable to the quality assessment of a large number of network videos. To address the above problem, we propose an efficient and accurate cross-domain no-reference network video quality assessment method (CDNRVQA) based on bitstream. CDNRVQA takes I-frames as representative frames and uses deep neural network to obtain video high semantic features and distortion features from the representative frames, then obtains temporal features and some spatial features of the video based on the macroblock and motion vector in the compressed videos, and uses feature fusion strategy to fuse features from pixel domain and compressed domain, and finally obtains comprehensive cross-domain features which can represent the video distortion brought by content, motion and compression. CDNRVQA performs well on large VQA datasets. CDNRVQA achieves similar performance to the state-of-the-art VQA models, but greatly improves prediction time, and greatly reduces hardware consumption on the device. CDNRVQA makes it possible to apply VQA methods to video platforms.
An effective anime character generation method using DCGAN

Face generation of anime characters, this fairy belongs to the category of image generation, given a specific anime character. We use generative models to further refine the picture. In addition, we do our best to require our projects to be the most authentic and detailed restoration of the original characteristics of the cartoon characters. For online animation face generation, first we apply a large amount of datasets and materials from DCGAN offline mobile phones and obtain an offline training model, so we can model the overall facial features. During the online process, input a specified cartoon character and fine-tune their characteristics to adapt to the data of different faces. Consequently, we compared offline and online generated data, and obtained the effectiveness of our magic through the results.
A Class Case Study on the Influence of a Mindfulness-based Course in University Students upon Resilience

The purpose of this study is to figure out the learning performance and the applicability of a mindfulness-based course at a university through analyzing the influence of mindfulness upon resilience and a meaning of experiencing a mindfulness-based class. For the research, an ‘empathy’ class was progressed among the mindfulness-based compulsory liberal arts subjects at K university. And then, a questionnaire was conducted targeting 265 learners. For the qualitative research, the materials were collected through review, observation log, and university’s e-class. As a result of the research, the variables of the mindfulness significantly influencing the resilience appeared to include ‘current awarness,’ ‘attention concentration,’ ‘postcentralism.’ As for the learners, the experience of an empathy class was indicated the learning outcomes such as prosociality, positive property, healing, emotion control, the understanding of oneself, and so on. This research result can be considered to show the necessity of expanding a mindfulness-based course of the university teaching in order to enhance the future talent capabilities such as mindfulness and resilience.