Reviewerly/exHarmony huggingface dataset:
Multi-hop teleportation of unknown qutrit state based on the GHZ channels

The combination of quantum communication technology and wireless networks brings a flexible and secure communication method, which adapts to a more complex network environment. In this paper, a new multi-hop teleportation scheme is investigated for transferring an unknown qutrit state between distant parties. The intermediate nodes are introduced and linked with each other via partially entangled Greenberger-Horne-Zeilinger (GHZ) states as quantum channels. In this multi-hop teleportation protocol, the source node and all the intermediate nodes can perform the entanglement measurement and transmit the measurement results simultaneously. Furthermore, we find the relationship between the measurement results and the receiver’s state, which is given as the matrix form. Compared with previous protocols for multi-hop transmission, our scheme can reduce the transmission delay and save the transmission overhead.
Elliptic curve cryptography

Number theory is a very important cornerstone of cryptography. Modern public key cryptography has been applied to many fields due to its security and flexibility. This paper will focus on elliptic curve cryptography. The paper discusses the formula derivation and encryption method of elliptic curve cryptosystem, and then uses the code to implement the elliptic curve cryptosystem, simulates the whole process of encryption and decryption, and proves the security performance of the elliptic curve cryptosystem.
Searching for Technical Debt - An Empirical, Exploratory, and Descriptive Case Study

Commonly, Technical Debt (TD) is used as metaphor to describe &#x201C;technical compromises that are expedient in the short term, but that create a technical context that increases complexity and cost in the long term&#x201D; [1]. Since TD is a metaphor, there does not exist a uniform understanding of what concretely such &#x201C;technical compromises&#x201D; are. Practitioners, researchers, and tools all subsume and consider widely different concepts as TD. In this paper, we set out to empirically and exploratorily, identify potential &#x201C;technical compromises&#x201D; that increase cost and complexity of modifications of two open-source database systems (Apache Cassandra and GCHQ Gaffer). In a manual investigation of 217 commits that are associated to 40 of the most costly and complex issues, we find that refactorings in the sense of Ur-TD [2] are often related to high complexity of modifications and that high cost is due to organization and coordination of work. Other than that, we cannot identify any &#x201C;technical compromises&#x201D; that can explain high cost and complexity of the studied contributions.
Construction and Implementation of College Student Management Evaluation System from Perspective of Big Data

Big data technology is an attractive topic of current research. The big data technology based on the construction of an evaluation model allows the acquisition of data preprocessing. Through the data analysis, the evaluation model is obtained. In the pedagogy education of colleges and universities, student management plays an important role in the construction of the evaluation system. SVM is a binary classification model. Its algorithm maps sample data into a high-dimensional space to find hyperplanes that accurately separate the samples of binary data and maximize the interval. This article aims to solve the problem of the construction of a university students' management evaluation system under big data in combination with the SVM algorithm. On the basis of large data processing and analysis by using analytic hierarchy, the main level factor are identified to compare with each other and establish the main factors comparison matrix. Comparison matrix feature vector for different evaluation index weights is obtained to establish the evaluation system. Evaluation results and implementation requirements were verified that the accuracy of the evaluation model was 82.27%, the recall rate was 83.55%, and F1 value was 0.8198. The values of the three evaluation parameters were close to 1, indicating that the model had good reliability and applicability.
Multi-resolution Parallel Aggregation Network for Single Image Deraining

Single image deraining is an important preprocessing task, as rain streaks awfully reduce the image quality and hinder the subsequence outdoor multimedia issues. In this paper, we explore the multi-resolution representation for rain streaks through parallel hierarchical structure and multi-scale feature extraction and fusion, termed Multi-resolution Parallel Aggregation Network (MPA-Net) in end-to-end manner. Specially, considering the significant role of multi-resolution, we employ the first stage to capture the high-resolution features, progressively introduce high-to-low resolution streams to produce more stages, and then connect all stages in parallel. In each stage, Densely Connected Residual (DCR) block is involved to guide the feature extraction. Besides, Cross-Scale Feature Fusion (CSFF) is first introduced to receive and consolidate the correlated features from different scales followed with Squeeze-and-Excitation (SE) blocks, leading to rich the resolution representations. Extensive experiments demonstrate that our method outperforms the recent comparing approaches on the frequent-use synthetic and real-world datasets.
Construction and Application of a Cloud Platform for English Teaching Supported by Computer Network Technology

With the rapid development of network technology, network users have higher and higher requirements for understanding and using the network. At present, the wide application of cloud technology provides the best solution for people. Network is the main carrier of information exchange and interpersonal communication and a new way for human beings to obtain information. The education community, like other industries, is increasingly recognizing the importance of cloud computing and constantly using these new capabilities in education and instruction. Cloud service group and WeChat group are the new mobile applications. This article introduces cloud computing technology into college English classroom, aiming to explore new methods of college English by using the new Internet technology. At the same time, it also discusses some problems existing in the current college English classroom, providing new ideas for the reform of college English classroom. This paper adopts J2EE technology in college English classroom teaching cloud platform application. Based on B/S model, a cloud university English teaching platform is established. The cloud platform English classroom teaching system covers three modules of English listening, English reading, and English writing, covering various fields of education and management. It has laid a solid foundation for the development of education informatization and network.
Reversible integer approximation of color space transforms for lossless compression of big color raster data

Reversible integer transforms are or great importance for lossless compression algorithms. To perform reversible decorrelation of color channels we propose an algorithm for calculating parameters of a reversible integer transform, which approximates such continuous mappings as a discrete Karunen-Loeve transform. We propose a method for estimating the approximation errors, which makes it possible to choose an optimal approximation of the original transform that minimizes these errors. Using the MRG file format, intended for storing large amounts of integer raster data, as an example, we show that after applying decorrelation, it is possible to increase the compression ratio of multichannel raster images using lossless compression algorithms.
A Lean Case Study On Managing Interlibrary Loan and Document Delivery Service in an Academic Library

Interlibrary loan and document delivery (ILL/DD) is an essential service provided by academic libraries to support research activities, publication and academic programmes. ILL/DD service capitalises on resource sharing or library networking by providing library materials that are not available at the host library. The Lean Six Sigma method was applied to identify the root problems. A construction of a web-based system branded as “Request Book and Article System (RBAS)” soon followed as an intervention. A retrospective study was conducted from March 2020 to August 2021 (16 months) to evaluate the key indicators of the service. Customer feedback on service quality was also surveyed. The majority of request was for articles, 649 (87.5%), followed by book request 84 (11.3%) and ebook, 9 (1.2%). Turnaround time fell within 24 hours for the majority of requests, 494 (68.4%). The highest number was from article request category, 460 (71.8%). A high proportion of users (78%) deemed the service as “high quality”. The use of a lean approach and the development of a homegrown system have improved the ILL/DD service at our library. Here we share lean practices in ILL/DD management, focusing on transforming a manual method to a digital homegrown system development and its advantages.
Correction to: Galois hulls of constacyclic codes over finite fields

A Correction to this paper has been published: https://doi.org/10.1007/s12095-022-00602-6
Reader’s Dot

In this mini project, a website that allows parents and instructors to monito their children’s concentration levels while reading books, which is especially important during the pandemic situations. Technologies used in this mini project are, for frontend like HTML, CSS and JavaScript and, we are using Bootstrap for Framework. The mentor come to know whether the students are physically and mentally present or not. There are many reading strategies to improve reading comprehension and inferences, including improving one's vocabulary, critical text analysis (inter textually, actual events vs. narration of events, etc.) and practicing deep reading. Ability to comprehend text is influenced by readers' skills and their ability to process information. If word recognition is difficult, students use too much of their processing capacity to read individual words, which interferes with their ability to comprehend hat is read. People learn comprehension skills through education or instruction and some learn by direct experiences. Proficient reading depends on the ability to recognize words quickly and effortlessly. It is also determined by an individual's cognitive development, which is "the construction of thought processes."
Kavi-Kannada Natural Language Processing System

Southwest Indian state of Karnataka is home to the majority of speakers of the ancient Dravidian language known as Kannada. The word models embeddings are employed in natural language processing for upcoming Indic NLP work. We convert Kannada voice data to Kannada text data using machine learning techniques and a specific analogy and similarity challenge, and then access our models for the subsequent task of text classification.
Object and subject of historical research in the coordinate system of traditional logic

The purpose of the article is to clarify the logical basis of the substantive distinction between the object and the subject of historical research.&#x0D; Research methods: analysis, synthesis, abstraction, definition of concepts, limitations and generalizations, hermeneutic, comparative.&#x0D; The main results: the problems of determining the content of the concepts of the object and subject of knowledge, historical science and specific historical research, as well as the substantive distinction between the object and subject of historical research are defined; the state of solving the problem by scientists at this stage is criticized; the concept of history and the object of historical knowledge are meaningfully connected; the logical content of the object and subject of historical research is determined; logical relationship between the object and the subject of historical research is established; three types of relations between the object and the subject of historical research are revealed; the necessity to apply the methods and rules of traditional logic in determining the object and subject of historical research is proved; some specific features of historical science as a kind of historical knowledge are highlighted.&#x0D; Practical significance: the results of the research can be used in understanding the theoretical and methodological foundations of specific historical research, especially in dissertations, as well as in the development of appropriate educational and methodological courses at historical faculties and during preparation of students’ research papers.&#x0D; Originality: the work is completely original, it contains a critique of a number of works on the definition of the object and subject of research; it has elements of scientific reasoning and logical analysis.&#x0D; Scientific novelty: the author’s interpretation of the meaningful distinction between the object and the subject of historical research is offered.&#x0D; Type of article: theoretical and methodological, analytical.
A Practice of Physics Education for Systematic and Deep Learning Using Electronic Books and LMS

We have developed a practice of physics education to enable students to systematically and deeply learn physics through differential and integral calculus and mechanics, using interactive electronic books and an LMS (Learning Management System), for third year students in national colleges of maritime technology. Using the gallery function of electronic books, we teach a graphical understanding and systematic learning for physical quantities and the laws that connected by differential and integral calculus. As a class exercise, the students took a three-step online examination using the LMS : general concepts of physics (first-step), systematic understandings for physical quantities and the laws (second-step), and fundamental calculations (third-step). From the results of a student questionnaire given after the practice, about 73％ of the students found the electronic books to be helpful. And about 84-89％ of them found the three-step online examinations in the LMS to be useful.
Magnet-škole i različiti aspekti njihovog delovanja

Starting from the position that innovations in education are becoming an imperative of educational systems, this paper deals with magnet schools that, although repre- senting the American model of public, government schools, attract the attention of researchers and educators outside the American continent. The aim of this paper is to analyze the basic characteristics of magnet schools in the context of global trends and modern educational poli- cies. This goal is conceptualized within three tasks: (1) investigating global trends in education with special emphasis on the question of school choice, (2) presenting the emergence of magnet schools and analyzing their basic characteristics, (3) examining the effects of magnet schools. Based on theoretical analysis, it is concluded that magnet schools, as schools of choice that attract students and parents interested in certain contents or approaches to education, have the potential to act towards equal opportunities for quality education, by introducing innovations in educational practice, they strive to simultaneously promote the quality of education, equality and social justice.
Framework for IoT ecosystems based on distributed ledger technologies and decentralized identifiers

Blockchains are the first Distributed Ledger Technologies (DLT), technologies based on decentralized networks of devices which share data on a distributed ledger. They possess qualities such as resilience, reliability, and traceability of sensitive data. The low scalability and frequent inefficiency of Blockchain result in ineffective integration with Internet of Things (IoT) systems. Other DLTs such as DAGs (Directed Acyclic Graph DLTs) possess benefits comparable to those of Blockchains without presenting most of the limitations that prevent their application in the IoT domain. In particular, the objective of this work was to design a framework for IoT systems based on DLT with focus on security, reliability, traceability, and decentralization. The benefit obtained in terms of security, scalability, traceability, and resilience is bound to the architecture utilized. This makes our solution usable for most IoT applications that do not need to process external data in real-time. Nevertheless, a hybrid edge-DLT architecture would be sufficient to extend the system capabilities to any IoT application.
Graph Ontology Based IAFCM Model for Itemset Mining in Transactional Database: An Optimized Fuzzy Framework

HUIM (High Utility Itemset Mining) is one of the most analyzed data mining activities.Product suggestion, e-learning, bioinformatics, text mining, market basket analysis, and web click stream analysis are few of the areas where it can be used.Cost savings, greater competitive advantage, and increased revenue are the advantages gained by pattern analysis.However, because HUIM approaches do not examine the correlation of retrieved patterns, they may uncover false patterns.As a result, a number of technique for mining related HUIs have been presented.These algorithms still have issues with computational cost, both in conditions of period and memory usage.As a result, a method for mining weighted temporal patterns is proposed.The suggested method begins by preprocessing time series-based data into fuzzy itemsets.These are fed into the Improved Adaptive Fuzzy C Means (IAFCM) technique, which is a hybrid of the FCM clustering method and the Graph based Ant Colony Optimization (GACO) technique.The proposed IAFCM technique accomplishes two goals: IAFCM clustering and data reduction in FCM clusters, and ii) optimal itemet placement in clusters using GACO.Using GACO, the suggested technique produces high-quality clusters.On these clusters, weighted sequential pattern mining is used to find the most effective sequential patterns, which take into account knowledge of patterns with low frequency and high weight in a repository that is updated over time.The results of this method show that when compared to other traditional methodologies, the IAFCM with GACO improves execution time.Furthermore, it improves the data representation process by increasing accuracy while using less memory.
Emojis to Conversion on Social Media: Insights into Online Consumer Engagement and Reactions

Emojis have become popular in online marketing communications. Marketers use emojis to humanize brand voice and elicit an emotional response from target audiences. However, little is known about how emojis are perceived and what kind of reactions they evoke. The aim of this study was to examine whether exposure to emojis leads to an increased intention to purchase, and whether the use of emojis impacts campaign effectiveness. Surprisingly, it found that the use of emojis had a negative effect on purchase intention, while the effect was positive when mediated by positive affect. Emojis increased the effectiveness of marketing campaigns for hedonic products and had a strong impact on the return on advertising spend. The findings of this study have both theoretical and practical implications in the observed domains, particularly in relation to the type of products emojis are most effective in promoting, gender differences, and real-life consumer behavior.
Nanoparticle Localization Using Gabor Filters

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Strategy Synthesis for Zero-sum Neuro-symbolic Concurrent Stochastic  Games (Extended Version)

Neuro-symbolic approaches to artificial intelligence, which combine neural networks with classical symbolic techniques, are growing in prominence, necessitating formal approaches to reason about their correctness. We propose a novel modelling formalism called neuro-symbolic concurrent stochastic games (NS-CSGs), which comprise probabilistic finite-state agents interacting in a shared continuous-state environment, observed through perception mechanisms implemented as neural networks. Since the environment state space is continuous, we focus on the class of NS-CSGs with Borel state spaces. We consider the problem of zero-sum discounted cumulative rewards and prove the existence of the value of NS-CSGs under Borel measurability and piecewise-constant restrictions on the components of the model. From an algorithmic perspective, existing methods to compute values and optimal strategies for CSGs focus on finite state spaces. We present, for the first time, implementable value iteration and policy iteration algorithms to solve a class of uncountable state space CSGs, namely NS-CSGs, and prove their convergence. Our approach works by exploiting the underlying game structures and then formulating piecewise linear or constant representations of the value functions and strategies of NS-CSGs. We illustrate our approach by applying a prototype implementation of value iteration to a dynamic vehicle parking case study.
Micro Level Determinants of Private Investment Performance: Focus in Soro Woreda in Hadiya Zone: Southern Ethiopia

The study set out to make empirical analysis on factors influencing private investment performance in Soro Woreda.In doing that, the study collected primary data from investors Registered Private Investment in Soro Woreda in year 2018 through administering a questionnaire to respondents.The study used both qualitative and quantitative approaches and covered domestic private investment.The study reveals that the variables: political factors, location factors and market factors were significant with positive correlation with private investment in bombe town.Hence, have predictive powers over private investment in bombe.The results show that investment facilitation services have positive and significant predictive power as a factor to attract more investment to Bombe town.For example size of the market, government law and regulation, incentive given from town investment facilities provided in the town.This is portrayed by the probability of 0.034, 0.085, 0.054 and 0.007 which is significant at 5%, 10% and 1 per cent level respectively.The rest of the variables like political factors, government rules and regulations, raw material availability, political stability and however, are not significant and hence have no predictive power as attraction of investment to Bombe town.
Robust Localization with Bounded Noise: Creating a Superset of the  Possible Target Positions via Linear-Fractional Representations

Locating a target is key in many applications, namely in high-stakes real-world scenarios, like detecting humans or obstacles in vehicular networks. In scenarios where precise statistics of the measurement noise are unavailable, applications require localization methods that assume minimal knowledge on the noise distribution. We present a scalable algorithm delimiting a tight superset of all possible target locations, assuming range measurements to known landmarks, contaminated with bounded noise and unknown distributions. This superset is of primary interest in robust statistics since it is a tight majorizer of the set of Maximum-Likelihood (ML) estimates parametrized by noise densities respecting two main assumptions: (1) the noise distribution is supported on a ellipsoidal uncertainty region and (2) the measurements are non-negative with probability one. We create the superset through convex relaxations that use Linear Fractional Representations (LFRs), a well-known technique in robust control. For low noise regimes the supersets created by our method double the accuracy of a standard semidefinite relaxation. For moderate to high noise regimes our method still improves the benchmark but the benefit tends to be less significant, as both supersets tend to have the same size (area).