Reviewerly/exHarmony huggingface dataset:
Multistep Two-Copy Distillation of Squeezed States via Two-Photon Subtraction

Squeezed states are nonclassical resources of quantum cryptography and photonic quantum computing. The higher the squeeze factor, the greater the quantum advantage. Limitations are set by the effective nonlinearity of the pumped medium and energy loss on the squeezed states produced. Here, we experimentally analyze for the first time the multistep distillation of squeezed states that in the ideal case can approach an infinite squeeze factor. Heralded by the probabilistic subtraction of two photons, the first step increased our squeezing from 2.4 to 2.8 dB. The second step was a two-copy Gaussification, which we emulated. For this, we simultaneously measured orthogonal quadratures of the distilled state and found by probabilistic postprocessing an enhancement from 2.8 to 3.4 dB. Our new approach is able to increase the squeeze factor beyond the limit set by the effective nonlinearity of the pumped medium.
CCECGP: causal consistency model of edge–cloud collaborative based on grouping protocol

At present, most causal consistency models based on cloud storage can no longer meet the needs of delay-sensitive applications. Moreover, the overhead of data synchronization between replicas is too high. This paper proposes a causal consistency model of edge–cloud collaborative based on grouping protocol. The model is based on the edge–cloud collaboration architecture, partitions cloud data centers and groups edge nodes by distributed hash tables, and stores a subset of the complete data set in nodes located at the edge of the network, thereby realizing partial geo-replication in edge–cloud collaboration environment. At the same time, we design a group synchronization algorithm called Imp_Paxos, so that the update only needs to be synchronized to the main group, which reduces the visibility delay of the update and decreases the data synchronization overhead. Besides, a sort timestamp is proposed in this paper, which generates different timestamps according to the type of update to track causality, keeping the amount of metadata managed in a relatively stable and low state. Therefore, the proposed model reduces the overhead of metadata for system management and improves throughput quantity of system. Experiments show that our model performs well in terms of throughput, operation latency, and update visibility latency compared with existing causal consistency models.
A Model Proposal for Smart Campus Investment Prioritization by Using Data Envelopment Analysis

Smart campus (SC), one of the most highlighted concepts in the education system, is the utilization and combination of smart physical-digital spaces to provide improved, intelligent, and responsive services in a sustainable, productive, and creative environment for universities. Considering its multi-dimensional structure, investment in this area is a crucial strategic decision with many conflicting aspects. On the other hand, Data envelopment analysis (DEA) is a linear programming (LP) technique that quantifies the relative efficiency of decision-making unit (DMU) inputs into their outputs through a trial-and-error model. Through that, in this paper, by using the capability of the DEA model, an SC investment efficiency problem is considered in multiple ways. According to the literature review and experts’ evaluations, four cost-based inputs are determined for the SC ecosystem. Based on the six outcomes, an application for the SC investment problem is carried out. Based on the results obtained, investments prioritized with the spending on classes and online teaching fulfilled the efficient rate input have the efficiency rate with one for all scenarios. Moreover, a scenario with equally distributed investments has the desired efficient rate equal to one.
Real-Time Algorithm Recommendation Using Meta-Learning

As the field of Machine Learning evolves, the number of available learning algorithms and their parameters continues to grow. On the one hand, this is positive as it allows for the finding of potentially more accurate models. On the other hand, however, it also makes the process of finding the right model more complex, given the number of possible configurations. Traditionally, data scientists rely on trial-and-error or brute force procedures, which are costly, or on their own intuition or expertise, which is hard to acquire. In this paper we propose an approach for algorithm recommendation based on meta-learning. The approach can be used in real-time to predict the best n algorithms (based on a selected performance metric) and their configuration, for a given ML problem. We evaluate it through cross-validation, and by comparing it against an Auto ML approach, in terms of accuracy and time. Results show that the proposed approach recommends algorithms that are similar to those of traditional approaches, in terms of performance, in just a fraction of the time.
Discovering Respects for Visual Similarity

Similarity is a fundamental concept in artificial intelligence and cognitive sciences. Despite all the efforts made to study similarity, defining and measuring it between concepts or images remains challenging. Fortunately, measuring similarity is comparable to answering “why”/“in which respects” two stimuli are similar. While most related works done in computer sciences try to measure the similarity, we propose to analyze it from a different angle and retrospectively find such respects. In this paper, we provide a pipeline allowing us to find, for a given dataset of image pairs, what are the different concepts generally compared and why. As an indirect evaluation, we propose generating an automatic explanation of clusters of image pairs found using a model pre-trained on texts/images. An experimental study highlights encouraging results toward a better comprehension of visual similarity.
Rotation Distance for Rank Bounded Trees

Computing the rotation distance between two binary trees with n internal nodes efficiently (in $$\textsf {poly}(n)$$ time) is a long standing open question in the study of height balancing in tree data structures. In this paper, we initiate the study of this problem bounding the rank of the trees given at the input (defined in [6] in the context of decision trees). We define the rank-bounded rotation distance between two given binary trees $$T_1$$ and $$T_2$$ (with n internal nodes) of rank at most r, denoted by $$d_r(T_1,T_2)$$ , as the length of the shortest sequence of rotations that transforms $$T_1$$ to $$T_2$$ with the restriction that the intermediate trees must be of rank at most r. We show that the rotation distance problem reduces in polynomial time to the rank bounded rotation distance problem. This motivates the study of the problem in the combinatorial and algorithmic frontiers. Observing that trees with rank 1 coincide exactly with skew trees (binary trees where every internal node has at least one leaf as a child), we show the following results in this frontier: En route our proof of the above theorems, we associate binary trees to permutations and relate the rotation operation on trees to transpositions in the corresponding permutations. We give exact combinatorial characterizations of permutations that correspond to binary trees and skew binary trees under this association. We also precisely characterize the transpositions that correspond to the set of rotations in binary trees.
Research on the Prediction Method of Disease Classification Based on Imaging Features

The medical imaging diagnosis report records the imaging signs and diagnostic conclusions in the medical imaging service in detail. The diagnostic conclusion is the conclusive opinion made by the doctor based on the imaging signs and his own experience, and generally includes the corresponding disease category. Using imaging signs to intelligently predict the corresponding disease categories can provide effective support for reducing doctors’ conclusion errors and improving the quality of medical students’ diagnostic reports. Aiming at the text characteristics of medical imaging diagnosis reports, this paper proposes a multi-dimensional feature fusion capsule network model based on structural domain dictionary to predict disease categories for imaging signs. Our model first uses the medical imaging domain dictionary constructed based on our own dataset to improve the accuracy of text representation, and then constructs a low-dimensional feature fusion layer to mix and extract the local features and contextual semantic features of words to realize the multi-level semantic modeling of imaging signs, and finally uses the capsule network to obtain location information and perform feature clustering, which can capture high-level semantic features to predict diagnostic conclusions. Experiments show that the model proposed in this paper outperforms baselines on the imaging diagnosis report dataset, the precision of the model reaches 97.30%, and F1 is 2.49% higher than the best performing baseline.
Optimal Sequence Alignment to ED-Strings

Partial Order Alignment (POA) was introduced by Lee et al. in 2002 to allow the alignment of a string to a graph-like structure representing a set of aligned strings (a Multiple Sequence Alignment, MSA). However, the POA edit transcript (the sequence of edit operations that describe the alignment) does not reflect the possible elasticity of the MSA (different gaps sizes in the aligned string), leaving room for possible misalignment and its propagation in progressive MSA. Elastic-Degenerate Strings (ED-strings) are strings that can represent the outcome of an MSA by highlighting gaps and variants as a list of strings that can differ in size and that can possibly include the empty string. In this paper, we define a method that optimally aligns a string to an ED-string, the latter compactly representing an MSA, overcoming the ambiguity in the POA edit transcript while maintaining its time and space complexity.
An Autoencoding Method for Detecting Counterfeit Coins

We use coins in our daily life to pay for bus, metro tickets, vending machines, etc. However, the market for antique and historical coins is another place, where the quality of coins and their genuinity play a significant role. Hence, researchers have considered different methods in coin detection studies. In recent years 2-D and 3-D image processing approaches have been widely used in image-based coin detection. In this paper, we propose a method to detect counterfeit coins based on image content. We employed SIFT, SURF, and MSER to determine the similarity degree of our datasets. Then, we evaluate those descriptors by statistical analysis to see which one is the most effective criterion for counterfeit coin detection. According to experiments, SIFT was selected as the most reliable algorithm for the Danish coin image dataset. Then, we train an autoencoder to find anomalies in the coin images. The trained autoencoder receives a coin image as input and generates a new image. The output image is compared with a basic image using the selected criterion. If the similarity between these two images meets a threshold then the coin is genuine. Most counterfeit coin detection methods require fake data for training. This can be eliminated by our autoencoding-based anomaly method.
String Rearrangement Inequalities and a Total Order Between Primitive Words

We study the following rearrangement problem: Given n words, rearrange and concatenate them so that the obtained string is lexicographically smallest (or largest, respectively). We show that this problem reduces to sorting the given words so that their repeating strings are non-decreasing (or non-increasing, respectively), where the repeating string of a word A refers to the infinite string $$AAA\ldots $$ . Moreover, for fixed size alphabet $$\varSigma $$ , we design an O(L) time sorting algorithm of the words (in the mentioned orders), where L denotes the total length of the input words. Hence we obtain an O(L) time algorithm for the rearrangement problem. Finally, we point out that comparing primitive words via comparing their repeating strings leads to a total order, which can further be extended to a total order on the finite words (or all words).
Introduction to GANs

This chapter introduces the basic concepts of generative adversarial networks (GANs), a neural network that can generate synthetic data. The next chapters demonstrate how to use R and Python to use GANs to generate synthetic data for specific applications; this chapter provides crucial context for the R and Python chapters on generating synthetic data.
Integration of Industry and Education in Higher Vocational Education under Ecological Metaphor Methodology, Systematic Construction

China puts forward higher requirements for vocational education reform, the transformation and development of colleges and universities, and the training of applied talents. This paper to return to the ecological metaphor of higher vocational education fusion system, based on the "analogy source selection-metaphor mapping" metaphor process perspective, tracing the theory of logic starting point, for higher vocational education fusion system components deconstruction and the construction of higher vocational education fusion system.
Role of e-learning environments in training applicants for higher education in the realities of large-scale military aggression

Electronic educational environments in the conditions of quarantine restrictions of COVID-19 have become a common phenomenon for the organization of distance educational activities. The purpose of the article is to analyze the role of electronic educational environments in the process of training applicants for higher education. General scientific, pedagogical methods were used. In the results, the general properties of the Internet educational platforms common in Ukraine, the peculiarities of using the Moodle and Prometheus platforms were discussed. The conclusions state that the military actions drew the attention of universities in Ukraine to the formation of their own educational platforms.
APLIKASI SIG BERBASISA WEB PEMETAAN LOKASI TOKO OLEH-OLEH DI KOTA BATAM

The development of technology today is very undoubted so that information is easy to access anytime and anywhere. The ease of accessing information gives new enthusiasm for humans in developing information technology or commonly referred to as Geographic Information System (GIS). The definition of GIS itself is a special information system that manages data that has special information, or it can also be interpreted as information about the place or location where the geographical location is located. Batam city is one of the cities in the Riau archipelego province. Batam is famous as a location visited by foreign and domestic tourists. Tourists that visit Batam city have a habit of bringing food as souvenirs from Batam. However, tourists and local people sometimes have difficulty in finding the location of stores that sell food souvenirs. This study aims to develop a Geographic Information System application that maps the location of gift shops in Batam. The result of this study is web-based GIS applications, which uses Google Maps API to display maps and retrieve store location coordinates. Through the application, the tourist can be helped in finding the location of souvenir shops in the city of Batam.
A Preliminary Study on Job Competency Scale for Special Educational Teachers for Students with Severe and Multiple Disabilities

This study was conducted to improve the quality of education in special education in accordance with the trend of using the basis for job competency as part of improving the expertise of teachers in the school. Due to the recent increase in the proportion of students with severe and multiple disabilities, special teachers have had difficulties in keeping up; this is because the definition of students with severe and multiple disabilities is ambiguous, and the necessary support for them has not been clearly identified. Institutional support for students with severe and multiple disabilities is improved, but schools still have not established specific guidelines for education and support, and opinions on medical support are still divided. Therefore, the purpose of this study is to develop a special teacher job competency scale necessary for the education of students with severe and multiple disabilities. To this, through the Delphi survey, the domains, factors, and items of the special teacher competency scale for students with severe and multiple disabilities were examined. As a result of the study, five domains, 31 factors, and 90 items were finally derived as a special teacher competency scale for students with severe and multiple disabilities. This study is meaningful in that it presented specific education and support guides for special teachers in the education of students with severe and multiple disabilities and became basic data for enhancing the professionalism and quality of education for special teachers.
The structural relationship among cognitive presence, social presence, self-directed learning ability, learning satisfaction and creativity confluence competency in non face-to-face learning environment

본 연구의 목적은 비대면 교육환경에서 인지적 실재감, 사회적 실재감, 자기주도학습능력, 학습만족도, 창의융합역량 간의 관계를 탐색하는 것이다. 최근 창의융합역량의 중요성이 강조되고 있다. 교육적 처방으로 협력적 학습상황에서 이루어지는 집단 활동을 통해 고차원적 사고가 발현되길 기대하고 있다. 하지만 비대면 교육으로의 전환이 개별학습형태로 인식됨에 따라 교육의 방향성에 대한 고민과 더불어 자기주도학습능력과 학습의 만족도에 관심을 가지게 되었다. 이러한 교육환경에서 창의융합역량에 영향을 미치는 요인으로 학습자가 인식하는 인지적 실재감과 사회적 실재감 그리고 자기주도학습능력과 학습만족도의 관계가 어떠한지 살펴볼 필요가 있었다. 본 연구에서는 비대면 교육에 참여한 A대학교 재학생 300명을 연구 대상으로 삼았으며, 287명의 자료를 분석에 활용하였다. 본 연구에서 설정한 가설모형의 적합도 지수는 양호한 것으로 나타났다. 구체적인 연구결과는 첫째, 인지적 실재감과 사회적 실재감 모두 자기주도학습능력과 학습만족도에 유의미한 영향을 미치는 것으로 나타났다. 둘째, 인지적 실재감과 사회적 실재감은 창의융합역량에 직접적인 영향을 주지 않지만 자기주도학습능력과 학습만족도를 매개로 유의미한 영향을 줄 수 있다는 것이 확인되었다. 연구결과를 바탕으로 비대면 교육환경에서 수업설계 시 교수자가 고려해야 할 사항들을 제시하였다.The purpose of this study is to explore the relationship between cognitive presence, social presence, self-directed learning ability, learning satisfaction, and creativity confluence competency in a non-face-to-face learning environment. Recently, the importance of creativity confluence competency has been emphasized. As an educational prescription, it is expected that high-level thinking will be expressed through group activities in a collaborative learning situation. However, as the transition to non-face-to-face education was recognized as an individual learning form, concerns about the direction of education and interest in self-directed learning ability and learning satisfaction. In this educational environment, it was necessary to examine the relationship between cognitive presence and social presence perceived by learners, self-directed learning ability, and learning satisfaction as factors affecting creativity confluence competency. In this study, 300 students at A University who participated in non-face-to-face learning were selected as the study subjects, and data from 287 students were used for analysis. The fitness index of the hypothesis model established in this study was found to be good. The specific research results showed that, first, both cognitive presence and social presence had a significant effect on self-directed learning ability and learning satisfaction. Second, it was confirmed that cognitive presence and social presence do not directly affect creativity confluence competency, but can have a significant effect through self-directed learning ability and learning satisfaction. Based on the research results, the matters that instructors should consider when instructional systems design in a non-face-to-face learning environment are presented.
The Effect of Information Technology Misuse, Learning Motivation and Level of Religiosity on Academic Fraud Behavior of Accounting Students as Prospective Accountants (Studies at Islamic-Based and National Private Universities)

This study aims to determine the effect of misuse of information technology, learning motivation and level of religiosity on the academic cheating behavior of accounting students as prospective accountants at Islamic-Based and National Private Universities. This study uses quantitative methods with primary data, by distributing questionnaires. The population in this study was students of accounting study program at Muhammadiyah University of Sidoarjo as an Islamic-based university and at Hayam Wuruk Perbanas University Surabaya as a national private-based university. The sampling technique used purposive sampling with multiple linear regression analysis, t test, independent sample t-test as a data analysis technique. The results of this study are the misuse of information technology and the level of religiosity affect the academic cheating behavior of accounting students as prospective accountants, learning motivation has no effect on the academic cheating behavior of accounting students as prospective accountants, there are differences in academic cheating behavior that occurs in Islamic-based and National Private Universities.
Load Redistribution-based Reliability Enhancement for Storage Area Networks

Storage area networks (SANs) are one of the prevalent reliable data storage solutions. However, cascading failures triggered by data overloading have become a major threat to SANs, preventing the desired quality of service from being delivered to users. Based on our preliminary works on studying the impacts of data loading on the reliability performance of SANs, this paper advances the state of the art by implementing node degree-based load redistribution strategies to enhance the SAN reliability, thus mitigating or even preventing the occurrence of cascading failures during the mission time. Load-based and reliability-based node selection rules are considered, which choose nodes with the highest load level and the lowest reliability for load redistribution, respectively. The relationship between data loading and reliability of an individual SAN component is modeled using the accelerated failure-time model with the power law. The SAN reliability is assessed using a combinatorial decision diagram-based approach. The application and effectiveness of the proposed load redistribution strategies are demonstrated and compared through a case study of an SAN with the mesh topology.
Introduction

Quantum information science is the intersection of physics and information science that expands the understanding of information science by processing information using the principles of quantum mechanics [1]. For example, the quantum computer, first proposed by Feynman [2], is considered the game-changer of the current cryptography and information processing due to its promising computing power and the potential to tackle complex problems [3, 4].
Intelligent Appliances for AAL Environments

Healthcare projects intend to decrease the economic and social costs of the ageing population phenomenon through the relocation of healthcare services delivery and management to the home. The VirtualECare project was one of those Ambient Assisted Living environments in which a step forward was taken with the introduction of proactive techniques for better adapting to its user’s needs, namely elderly or chronic patients, by learning through interaction based on contexts. The VirtualECare2 project aimed to extend the VirtualECare platform by extending its usage from the user’s home to institutions like elderly care facilities and hospitals and providing real-time information to caregivers and relatives. All this learning and information sharing, however, requires data that must comply with current and future GDPR and be locally and remotely accessible by the different modules and deployments of the project. This document exposes the initial approach of the VirtualECare2 project to the creation of an Appliance, both physical and virtual, that will allow more granular control of all this data.