Reviewerly/exHarmony huggingface dataset:
Object-Oriented Java

Object-oriented programs consist of many different pieces of code that all work together in unison. Rather than write a program that contains a long list of statements and commands, an object-oriented philosophy is to break functionality into separate organized objects. Each of the objects contains functionality that pertains to it, and as the objects are pieced together, they can be used to develop sophisticated solutions. In this chapter, we touch on some of the key object-oriented features of the Java language. From the basic recipes covering access modifiers to the advanced recipes that deal with inner classes, this chapter contains recipes that help you understand Java’s object-oriented methodologies.
A First Step Towards Even More Sparse Encodings of Probability Distributions

Real world scenarios can be captured with lifted probability distributions. However, distributions are usually encoded in a table or list, requiring an exponential number of values. Hence, we propose a method for extracting first-order formulas from probability distributions that require significantly less values by reducing the number of values in a distribution and then extracting, for each value, a logical formula to be further minimized. This reduction and minimization allows for increasing the sparsity in the encoding while also generalizing a given distribution. Our evaluation shows that sparsity can increase immensely by extracting a small set of short formulas while preserving core information.
Application Of Dempster Shafer Method To Diagnose Disease In Sugarcane Plant

Sugarcane is a very important and agrarian plantation product in Indonesia, because sugar cane is a producer of sugar. However, in its cultivation, several problems often occur, such as diseases in sugarcane plants where when sugarcane plants are attacked by disease and are not handled, farmers will experience crop failure. Based on this, a tool is needed in the form of an expert system to diagnose diseases in sugarcane plants. The method used in this study is the Dempster Shafer method which processes data by providing evidence based on the belief funcation and plausible value of each symptom. This study treats 4 types of disease with 13 symptoms. The results showed that the dempster shafer method can be used as a tool in diagnosing sugarcane plant diseases so that it is expected to help farmers in handling diseases that attack sugarcane plants quickly and precisely and can increase profits from sugarcane harvests for farmers.
Role of Blockchain in the Music Industry

In recent years, most data on users is available on the internet, which can be the user’s credentials, the transactions they make, or any message they send to one another. The security of this data is a major concern. In 2008, Satoshi Nakamoto invented the blockchain to manage the cryptocurrency bitcoin. Blockchain technology was used to secure and streamline the transactions that occurred online. Blockchain, being a recent technology, has many domains of application, such as risk management, anti-money laundering tracking systems, secure sharing of medical data, security of personal identity, supply chain and logistics monitoring, real-time IoT operating systems, and so on. This chapter aims to focus on the use of blockchain technology and how it would help the music industry, saving them billions of rupees by modernizing the rights and royalties process. A blockchain-based platform for the Indian music industry is also proposed in this chapter.
Face Tracking

The TrueDepth front-facing camera is the most potent in augmented reality. The camera has an infrared emitter capable of projecting over 30,000 invisible dots to create your face’s face mesh and infrared image representation. Apple’s state-of-the-art TrueDepth camera system is capable of mapping the geometry of your face accurately. You’ll delve into how you can make the most out of the TrueDepth front-facing camera with ARKit.
“What a beautiful world. But is it the world we live in?”

Abstract The present two-phased study set out to identify simplified and non-simplified sources of input popular among learners of English as a foreign language ( EFL ) and to compare and contrast the manifestations of impoliteness strategies and responses to them in such resources. First, a rather comprehensive survey conducted among 250 adult EFL learners revealed that English TV series and movies of comedy and crime genres were the most popular non-simplified sources of input, while the popular simplified sources were American English File and Interchange ELT textbooks. Then, the results of our in-depth qualitative-quantitative, comparative, macro- and micro-analysis (adopting a variety of stringent incivility frameworks) indicated there were noticeable quantitative and/or qualitative differences in (re-)presenting face-threatening acts between the two sources. Generally, while EFL learners are not judiciously familiarized with various impoliteness strategies and their responses in the textbooks, they are almost bombarded with them through their favourite media. Underlining the inherent instructional and motivational impact of both language input sources and the possible detrimental bearing of underrepresentation and/or overrepresentation of impoliteness for the learners’ pragmatic competence, the study concludes with a discussion of our findings’ implications for stake-holders including language learners and education specialists.
Chinese Language and Literature Relational Database Mining Based on Association Rules Algorithm

With the ever-increasing demand for high-quality and high-quality teachers in society, the professional certification of teacher training aimed at ensuring the professionalism of teacher education has been fully implemented in our country. This article mainly studies the application of association rule algorithm in the reform of Chinese language and literature majors in colleges and universities. The collection of student performance data information is a process of collecting information such as student ID, name, academic performance of each subject, and learning evaluation. Data extraction is to select the data needed in our mining tasks from various relational databases of our school, integrate it into the data warehouse, and prepare the data for our future mining. This article selects some courses closely related to Chinese language and literature for excavation, trying to find out the relevance of these courses and the support of these courses for obtaining vocational qualification certificates, so as to provide us with theories for strengthening professional skills training, professional construction and curriculum reform in accordance with. The scores of general education courses in the five normal universities fluctuate between 23.84% and 31.23%, and the average score is 28.4%. The results show that the association rule algorithm provides theoretical support and practical guidance for the reform of Chinese language and literature majors in colleges and universities.
Application of “Blockchain + 5G” Technology in the Transformation of Financial Digital Intelligence

Blockchain technology is one of the representative technologies of technological innovation in the new era. It has the advantages of distributed ledgers, collective maintenance, reliable databases, non-tamperable, and traceable, and has gradually integrated into various industries in the economic field. Finance is the business language of social economy. The combination of “blockchain + 5G” technology applied in the financial field can promote the transformation of finance to digital intelligence. This article mainly aims at the application research of “blockchain + 5G” technology in the transformation of financial digital intelligence. This article analyzes the application of “blockchain + 5G” technology in the intelligent storage of financial statement data, corporate financial internal supervision and auditing, and capital management. This paper uses a case study to analyze the application effect of the group's “blockchain + 5G” technology application financial system. Data shows that M Group built and started to use a financial analysis platform based on “blockchain + 5G” technology in March 2017. The order tracking rate increased by 7.46%, the after-sales collection rate increased by 3.34%, and the cost accounting rate increased by 9.68%. It can be seen that the application of “blockchain + 5G” technology in the financial field has a good effect.
Mobile Displays for Cross-Reality Interactions between Virtual and Physical Realities

We present two use cases of mobile displays in cross-reality interactions between users immersed in Virtual Reality (VR) and users present in the Physical Reality (PR) by using the mobile display to show select artefacts of interest. The first use case is the “Substitutional Display” where a display serves as a passive haptic for an artefact. Both VR and PR users can then move the artefact by physically moving the display. The second use case is a “Virtual Artefact Handover” which allows the VR user to pass artefacts onto the PR user’s display. We envision this handover as a natural interaction where the VR user moves the artefact onto a virtual proxy of the display the PR user is holding, after which the artefact is displayed for the PR user to see.
Applying Interactive Technology to Construct a Popular-Science Teaching Aid System for Protecting Cetaceans along Sea Coasts

Wang, C.-M. and Chen, I-T., 2022. Applying interactive technology to construct a popular-science teaching aid system for protecting cetaceans along sea coasts. Journal of Coastal Research, 38(2), 389–413. Coconut Creek (Florida), ISSN 0749-0208. Cetaceans, such as whales and dolphins, are often found stranded at sea coasts. Teaching the public to rescue such endangered animals in emergency cases is important for educational learning of environmental conservation. Based on the interactive technology, a teaching aid system for popular-science cetacean rescue education is proposed. After a review of related literature, theories of interactive design, tangible interfacing, and science communication are adopted to derive the principles for designing the system. Accordingly, a dolphin model and a series of graphics were designed to construct the teaching aid scheme of the system in the form of a five-level game with a smart phone or pad as the tangible interface. Implemented by a pseudo-code algorithm, the game-play process is designed according to a rule of “Three Musts and Four Nos” advocated by a cetacean conservation society, including the five operation steps of rescue requesting, cetacean identification, holding upright, moisturizing, and recording. The system was exhibited in a public space to invite visitors to play the game. A questionnaire was conducted to collect 124 users' opinions, which were then analyzed, using the software packages of SPSS and AMOS, to evaluate the effectiveness of the proposed system with the aim of cetacean rescue education, leading to the following findings: (1) the interactive popular-science teaching aid provided by the proposed system helps spread cetacean conservation concepts; (2) the interactive game-play process provided by the system teaches correct scientific knowledge; (3) the teaching aid of the system in the form of interactive game holds users' attention; (4) the users enjoyed operating the interactive teaching aid system and wanted to know more about the issue of cetacean rescues; and (5) the interactive popular-science teaching content cannot build some of the users’ confidence to face real cetacean rescue situations.
ThermoQuest - A Wearable Head Mounted Display to Augment Realities with Thermal Feedback

We present ThermoQuest, a self-contained wearable head-mounted display system for enhancing Virtual Reality experiences with temperature feedback. It’s constructed with commodity hardware elements, featuring 6 Peltier elements on the rim of the headset touching the users face. We explain the design and implementation of an affordable wearable thermal VR prototype build with commodity hardware. In a user study with 15 participants, we show evidence for a significant difference in reported presence in VR between thermal VR and control conditions (p > 0.017) in a counterbalanced experimental setup. We end with a discussion and use cases of the presented VR prototype and similar systems.
Two-Linker Mofs-Based Chitosanized Glass Fiber Paper Monolithic Adsorbent for Atmospheric Water Harvesting in Arid Climates

With the increasing demand for freshwater resources, the sustainably and effectively utilization of atmospheric water has become a critical issue. Herein, we customized an efficient and stable monolithic water-adsorbent designed via chitosanized glass fiber paper loaded with two-linker metal-organic frameworks (CGF-mixed-MOFs(Al)). Firstly, a chitosan (CTS) polyelectrolyte layer is constructed on glass fiber paper (GF) to improve the MOFs loading capacity (306.71%) through electrostatic interaction and hydrophilic interaction. Subsequently, a series of two-linker metal-organic frameworks (mixed-MOFs (Al)) composed of MIL-160(Al) and Al-fumarate were tailor-made on CGF surface using a single aluminum source (Al3+) and tunable double ligands of 2, 5-furandicarboxylic acid and fumaric acid. The resultant mixed-MOFs (Al) monolith has better water harvesting ability and faster adsorption kinetics at low humidity because of its more active adsorption sites. Different from CGF-MIL-160(Al) and CGF-Al-fumarate, the water sorption of CGF-mixed-MOFs(Al)5 at 30% RH, 25% RH and 20% RH reached 0.37 g/g, 0.31 g/g and 0.25 g/g, respectively. The advanced nature of the as-prepared CGF-mixed-MOFs(Al)5 could also be evidenced by its low thermo-desorption temperature (35 ℃), sensitive thermal-response capability and long-term operational stability, which guarantees its working reliability and sustainable benefits. Therefore, our customized water adsorption system provides a simple, long-effective, economical and advanced strategy to capture more fresh water from atmosphere in arid climates.
Research on EHR Storage and Sharing Scheme Based on Consortium Blockchain

In order to realize the safe storage and sharing of medical data, this paper proposes a medical data storage model based on blockchain. Firstly, all medical institutions are divided by regions, and multiple medical institutions alliances are generated, and the consortium blockchain is formed respectively. The medical metadata is stored on the blockchain, and the medical data block is stored in InterPlanetary File System (IPFS). In each consortium blockchain, a small number of authoritative organizations are selected to form a main chain, which is responsible for the cross chain query function among the consortium blockchains. In the query process, the proxy re encryption technology is used to realize the user's access to the patient's medical information. The experimental results show that the model ensures the tamper proof and security of medical data, It realizes the safe sharing of medical data and improves the query efficiency of medical data, When 78.5% of the total number of queries in the same alliance, the cross chain scheme is better than the ordinary scheme.
On the Bottleneck Complexity of MPC with Correlated Randomness

At ICALP 2018, Boyle et al. introduced the notion of the bottleneck complexity of a secure multi-party computation (MPC) protocol. This measures the maximum communication complexity of any one party in the protocol, aiming to improve load-balancing among the parties. In this work, we study the bottleneck complexity of MPC in the preprocessing model, where parties are given correlated randomness ahead of time. We present two constructions of bottleneck-efficient MPC protocols, whose bottleneck complexity is independent of the number of parties: Compared with previous bottleneck-efficient constructions, our protocols can be based on a wider range of assumptions, and avoid the use of fully homomorphic encryption.
Visual Analysis of Electronic Invoices to Identify Suspicious Cases of Tax Frauds

Electronic invoices are important documents that allow the State to control and collect taxes resultant from the commercialization of items, goods and services. The large amount of invoices that are daily generated makes auditing tasks unfeasible, time consuming and error prone when attempting to retrieve invoices that present inconsistent taxes in relation to the product characteristics. Considering that it is difficult to obtain labels to design a classification task for fraud detection and the exploratory fashion of the fraud inspection process, the use of multidimensional visualization emerges as a potential approach. This paper introduces a method for visualizing electronic invoices using point placement strategies to support tax inspectors on the detection of suspicious cases of tax frauds. Experimental results were conducted to validate the proposed method according to the visualizations’ quality and a case study simulating the discovery of suspicious invoices considering a subset of selected products. The visual analysis of the graphical representations (layouts) alongside with interactive mechanisms enabled to identify similar invoices regarding product description, but presenting different tax categories, demanding further investigation by the tax inspectors.
DEVELOPMENT OF A SIMPLIFIED ESTIMATION METHOD FOR SPECTRAL GLOBAL SOLAR IRRADIANCE INCLUDING ULTRAVIOLET, VISIBLE, AND NEAR-INFRARED IRRADIANCE

In order to evaluate energy saving performance of multi-coated glazings and photovoltaic systems in buildings, spectral solar irradiance data are required. Although broadband solar irradiance data are included in existing weather data for building energy simulation, spectral solar irradiance data are not included. In this paper, a simplified regression formula that estimates spectral global solar irradiance between 0.3 and 2.5 μm under all weather conditions from broadband global solar irradiance, air temperature, and humidity, is presented. Furthermore, three kinds of correction equations to correct the regression formula are created using clearness index, air-mass, and precipitable water.
Privacy-Preserving Cluster Validity

In this paper, we proposed a new privacy-preserving clustering framework. We proposed two different types of data transformation methods on clustering solution vectors and clustering ensemble consensus matrix in a unified way. The first one is encryption, which includes cryptography-based methods and hashing functions. The other one is the perturbation, which includes data swapping, spatial transformation, and randomized perturbation. The related transformed clustering pairs or the consensus matrix pairs are evaluated them using 19 popular pair-counting similarity measures. The original evaluation results and those of privacy-preserving methods are discussed according to different privacy-preserving methods and different pair-counting similarity measures. We found that 1) for clustering similarity, the information loss of clustering similarity in cryptography-based methods and hashing functions is generally large and not applicable to the clustering ensemble case, while the information loss of data swapping, spatial transformation, and randomized perturbation are small and applicable to a wide range. 2) Through experimental validation, we found that different privacy-preserving methods and different pair-counting similarity measures show certain adaptability, where five pair-counting similarity measures are stable for all types of privacy protection methods. 3) Cryptography and hashing functions have high security and too much complexity, while data swapping, spatial transformation, and randomized perturbation have low complexity but low security. The above work can be used as an original investigation in this field and as a reference for subsequent related future work.
Self-supervised 3D Out-of-Distribution Detection via Pseudoanomaly Generation

Recent studies on anomaly detection have achieved great success in data analysis, yet the application of out-of-distribution detection in medical imaging remains an underdeveloped area of study. In this paper, we propose a 3D fully self-supervised learning method for volumetric medical image data. Inspired by recent advancements in representation learning for out-of-distribution detection, we propose a training method for pseudoanomaly generation with copy-paste. The training uses contrasts of the normal image with the pseudoanomaly image that is generated from the normal image. Through this scheme, a representation is learned to detect an abnormal image and to localize the anomaly area. In addition, we use a 3D patch as an input to provide the spatial information of the third dimension from volumetric image data. The proposed approach was tested in the 2021 MICCAI MOOD challenge, and it ranked the first place in both sample-level and pixel-level tasks.
Open To Support Accessibility: The Use of Open Books by SQU Faculty

&#x0D; &#x0D; &#x0D; &#x0D; With Sultan Qaboos University (SQU)’s growing strategic priorities to improve teaching and research performance while increasing their competitive edge and reducing costs, Open Educational Resources (OER) appear as a possible option where e-books are often cited to help streamline the SQU’s resources and services. Similarly, the Instructional and Learning Technologies Program in the College of Education has an interest in the use of open books as a method of increasing accessibility to textbook materials for faculty and students. This descriptive study employed a survey to investigate SQU faculty uses and perceptions of open books to identify the advantages of increasing its accessibility. The analysis of the findings indicates that the faculty at SQU have access to open books and encourage positive experiences using them in their classrooms. That implicates a number of pedagogical benefits that can be supported by the use of open books. Therefore, it is important to grow the awareness among the policy makers and faculty about the benefits of encouraging and granting students access to other OER resources that might lead to more student learning.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
HyperNetVec: Fast and Scalable Hierarchical Embedding for Hypergraphs

Many problems such as node classification and link prediction in network data can be solved using graph embeddings. However, it is difficult to use graphs to capture non-binary relations such as communities of nodes. These kinds of complex relations are expressed more naturally as hypergraphs. While hypergraphs are a generalization of graphs, state-of-the-art graph embedding techniques are not adequate for solving prediction and classification tasks on large hypergraphs accurately in reasonable time. In this paper, we introduce HyperNetVec, a novel hierarchical framework for scalable unsupervised hypergraph embedding. HyperNetVec exploits shared-memory parallelism and is capable of generating high quality embeddings for real-world hypergraphs with millions of nodes and hyperedges in only a couple of minutes while existing hypergraph systems either fail for such large hypergraphs or may take days to produce the embeddings.
Retardation-dependent decaying modes of a single excitation in an atomic array coupled to a one-dimensional waveguide

We investigate the effects of retardation on the collective emissions from a linear array of $N\ensuremath{\ge}2$ atoms interacting with a one-dimensional field continuum. Decaying modes consisting of a single excitation in a photon-atom superposition with definite decay rates and frequency shifts are determined as a function of a retardation parameter proportional to the separation between atoms. In particular, the structures of these decaying modes are examined in terms of their photonic and atomic components. In addition to modes that resemble Dicke states in the Markovian limit, higher modes that decay much faster than Dicke superradiance exist. We find that these modes are dominantly photonic with field excitation localized near the edges of the array.