Reviewerly/exHarmony huggingface dataset:
Multi-View Three-Dimensional Reconstruction based on a Two-Stage Multi-Level Depth Network for Agriculture Applications

To address the problems appearing in multi-view three-dimensional (3D) reconstruction, such as the improvement of the accuracy and completeness of the 3D reconstructed images, a two-stage multi-level depth network is proposed. In the stage 1 of the proposed network, several convolutional block attention modules (CBAMs) are applied in the lateral connections of the feature pyramid network (FPN). This is targeted to enhance the spatial and channel relativity of the different hierarchical feature maps so as to bring more semantic information. In the stage 2, the obtained multi-scale feature maps in the stage 1 are tackled by a set of cascaded processing procedures, such as adaptive propagation, single-trees transform, and matching cost computation. As a result, a depth map could be generated and then be further refined in the processing. Comparing with other state-of-the-art methods, the subjective and objective experiments based on the DTU dataset show that our method performs better result in completeness meanwhile maintaining a considerable overall metric. The investigation of applying the proposed method for reconstructing agricultural crop images was carried out, which is based on a set of self-collected images. The experiment shows that a suitable human visual perception for the images could be obtained.
Evolution of superoscillations for spinning particles

Superoscillating functions are band-limited functions that can oscillate faster than their fastest Fourier component. These functions appear in various fields of science and technology, in particular they were discovered in quantum mechanics in the context of weak values introduced by Y. Aharonov and collaborators. The evolution problem of superoscillatory functions as initial conditions for the Schrödinger equation is intensively studied nowadays and the supershift property of the solution of Schrödinger equation encodes the persistence of superoscillatory phenomenon during the evolution. In this paper, we prove that the evolution of a superoscillatory initial datum for spinning particles in a magnetic field has the supershift property. Our techniques are based on the exact propagator of spinning particles, the associated infinite order differential operators and their continuity on suitable spaces of entire functions with growth conditions.
An Initial Scholarly AI Taxonomy

Although advances in artificial intelligence (AI) &lt;sup&gt; 1 &lt;/sup&gt; have been unfolding for over decades, the progress in the last six months has come faster than anyone expected. The public release of ChatGPT in November 2022, in particular, has opened up new possibilities and heightened awareness of AI's potential role in various aspects of our work and life.
Speculation-Free Function Table Construction in LLVM IR for Fine-Grained Control Flow Integrity

Securing the operating system kernel is the key to overall system security. Due to developer negligence and the inherent limitations of the code language, kernel data is exposed to various security risks, such as execution flow leakage and privilege hijacking. In binary security, most vulnerabilities are exploited by hijacking the control flow to make the program run according to the attacker’s idea of attack. Control flow integrity is a common defense scheme against control flow hijacking attacks. In this paper, we use speculation-free function tables in LLVM IR to achieve the integrity of fine-grained control flow. The technique enforces CFI policies by making logical judgments on jump instruction stubbing. All jump instructions share a common function table, and the information in the table is not repeatedly stored, reducing the additional memory consumption caused by the function table and achieving fine-grained CFI protection.
Constraint-Based Adversarial Networks for Unsupervised Abstract Text Summarization

Abstract text summarization is a classic sequence-to-sequence natural language generation task. In order to improve the quality of unsupervised abstract text summarization in unsupervised mode, we propose two constraints for training text summarization model, embedding space constraint and information ratio constraint. We construct a generative adversarial network with two discriminators based on these two constraints (TC-SUM-GAN). We use unsupervised and supervised methods to train the model in the experiment. Experimental results show that the ROUGE-1 value of the unsupervised TC-SUM-GAN increases by [Formula: see text] points compared with the basic model and at least 1.96 points compared with other comparative models. The ROUGE scores of the supervised TC-SUM-GAN are also improved. TC-SUM-GAN achieves very competitive results for the metrics of ROUGE-1 and ROUGE-2. In addition, the abstracts generated by our model are closer to those generated manually.
Cryptocurrency May Prove Financial Crime

Despite the rise in Bitcoin scams, there is a lack of thorough research on the subject. Sophisticated scammers and money launderers can take advantage of Bitcoin mixing services and exchanges. Bitcoin mixing services will send more coins to the customer's address after deducting the mixing cost. The frequency and amount of payments/fees may seem unpredictable, but this is the reality. Criminals use bitcoin mixing services to disguise the source of lost profits and pay securely through cryptocurrency exchanges for anonymous use. Bitcoin's normative unpredictability and organizational opacity attract people with shady intentions, such as supporting rebels or buying drugs. The results of this scoping study show that investigations into cryptocurrency fraud are increasing in scope and depth, but are in the early stages of examining future concerns and circumstances.
Data-Driven Learning for Foreign and Second Language Education in Diverse Contexts

More than three decades have passed since the term “data-driven learning (DDL)” was coined by Johns. Throughout the past few decades, the amount and depth of empirical research regarding DDL in language education have been largely increased; meanwhile, DDL has been implemented widely in diverse contexts. The DDL approach has been suggested as extremely influential in language education by numerous researchers and practitioners. This chapter aims to present a comprehensive picture of the development and implementation of DDL in language education in various cultural and grade contexts. The main organization of the chapter is twofold. First, the employment of DDL in culturally different countries is introduced. Second, the employment of DDL at different grade and age levels is presented. With the introduction and comparison of different means of employing DDL in language education, this chapter is expected to serve as a general reference guide for language educators and researchers who would like to incorporate DDL in their own practice.
Public Perception of Awareness and Adaptation of Cryptocurrency in Brunei Darussalam

Cryptocurrency has been around since 2008 when Satoshi Nakamoto issued a white paper. Satoshi also claimed to be the creator and developer of Bitcoin, the first type of cryptocurrency. This research looks at Bruneians' understanding of Crypto-currencies, their perceptions, and their awareness and acceptance of cryptocurrency in Brunei Darussalam. This research used questionnaires as a method of gathering data. Who would have thought that cryptocurrency was known to almost all levels of age, it did not only focus on higher education respondents, and that the fact it was male-dominated? Cryptocurrency in Brunei is not widespread yet. The awareness of cryptocurrency in Brunei is deficient because information is inadequate. Bitcoin is yet to be the most well-known cryptocurrency. Cryptocurrency is seen as an asset instead of currency by the general public. Information primarily obtained is mainly from social media, family, friends, word of mouth, and press releases from AMBD.
Digital Currencies and Their Relationship Towards Economic Instability

The widespread rise in popularity of cryptocurrencies over the past few years has the potential to have sparked new revolutions in the global ecosystem. The investors face both new opportunities and challenges as a result of this ecosystem. The modern era makes it simple for people to make financial decisions regarding investments, payments, and other financial services via the internet. This development in technology makes it easier for inventors to create and process crypto currencies around the world. Decentralization is the fundamental principle of digital currency. Financial regulations and control are virtually non-existent for digital currencies. As a result, investors are increasingly using it as a decentralized financial instrument. Investors can use it to conduct unregulated financial transactions from one location to any location worldwide. Digital currencies also cause financial instability, despite growth and gains. Every nation in the world faces a serious threat as a result of financial instability.
The Emergence of Cryptocurrency in India and Its Implications on Investments

Investing in the unregulated cryptocurrencies has showed a rising tendency since 2020, despite uncertainty surrounding its future in India. There is no ban on the use of cryptocurrencies in India, nor is there any regulation governing their actual use. This uncertainty is disturbing Indian startups developing blockchain-based products. But this hasn't altered investors' positions in investing in cryptocurrencies; they continue to be positive. For longer-term investments, investors have historically employed fundamental analysis. By analyzing the underlying company's operations and the state of its industry or the overall economy, fundamental analysis seeks to find stocks with high growth potential at fair prices. The issue with conducting a fundamental analysis of cryptocurrencies is that they cannot be evaluated using the same criteria as conventional businesses. Therefore, the authors must focus on several frameworks. Finding good metrics is the first step in that approach. This chapter examines the emergence of cryptocurrency in India and its implications on investments.
Forthcoming Issues

In this work, we study cyclic codes of length n over a finite commutative non-chain ring ℛ=Fq[u,v]/〈u2−γu,v2−ϵv,uv−vu〉 where γ,ϵ∈Fq* and we find new and better quantum error-correcting codes than previously known quantum error correcting codes. Then certain constraints are imposed on the generator polynomials of cyclic codes, so these codes become linear complementary dual codes (in short LCD codes). We then verify that the Gray image of linear complementary dual codes of length n over ℛ is a linear complementary dual code of length 4n over Fq by establishing a Gray map.
Stockwell Transform and its Modifications in Signal Processing Courses: Comparison and Features

It is well known that signal transformations are very powerful and popular methods for signal and image processing. Because of its ability to decompose signals, signal analysis on form level is enabled. Therefore, there are many more or less known signal transformations in the scientific community. The most popular ones are Fourier transform, short time Fourier transform, Wavelet transform, Discrete cosine transform and those transforms are commonly mentioned in academic literature. On the other hand, Stockwell transform has self-adjusted window size, while fractional Stockwell transform improves the flexibility of time–frequency analysis and energy of signal spectra. Because of the above, in this paper Stockwell transform advantages and applications in the signal and image processing area are analyzed. Its features and parity to the other transformations is shown. Also, the most commonly used fractional transformations that improve standard transformations, because their ability of rotation, were listed.
Intro basics of modeling user rights management for the university diploma issuing process with the support of the Hyperledger Fabric

The protection and preservation of the privacy of personal data are one of the main requirements when it comes to an application dealing with the processing of such data. It is no different when it comes to information systems that store and process data about students in higher education systems. The public presentation of such data represents a serious threat to the safety of students as well as their status within the higher education system. For this reason, it is necessary to use the possibility of advanced technologies in order to raise data security to the highest level. One such technology that is able to provide transparency, security and data protection at a high level is blockchain technology. In this work, the Hyperledger Fabric distributed ledger private blockchain network was analyzed and its usability in terms of user rights management in higher education system was evaluated. Experimental analysis showed that such a platform has the ability to take advantage of private blockchain technologies in terms of user rights management and to provide security, flexibility and scalability of the system.
Automated Authorship Attribution using CNG Distance on Blog Posts in the Serbian Language

The automated authorship attribution problem is a task of identifying the author of a given text using an objective algorithmic method based on previous texts written by the candidate authors. We are particularly interested in methods that do not rely on any language-specific knowledge or preprocessing, and that are based on a low-level text representation such as a sequence of letters and other characters. The previous work has shown that author profiles consisting of the most frequent character n-grams are effective in the authorship attribution in a number of languages, but not many results are reported on languages with sparse resources, such as the Serbian and related languages. We show that a character n-gram based method has also a very good performance in the Serbian language. Another contribution of this work is a new dataset prepared as a good benchmark for the authorship attribution task, comparable to the previously published similar datasets for English, Greek, and some other languages. This dataset for authorship attribution prepared in this work consists of blog posts published as commentary columns on a news and commentary portal, and as such is a grammatical and well-written language corpus, and a good representative of current normative language. The CNG distance method, which was shown to work well in a number of languages before, shows high accuracy of 94% over 5 authors, and 83% over 10 authors in the authorship attribution for this dataset as well. As expected from the results for other European languages, the highest accuracy is obtained around n-grams of size n =6,7, or a wider range of n =3,…,8, with L parameter from 500 to 9000, although even for the parameters n =2 and L =500 some relatively high accuracies are achieved.
The Use of Virtual Microscopy in the Teaching of Histology

The tissue sample analysis based on the visualization of the entire tissue sample represents an important methodology in clinical diagnostics, research, and education. This process that enables visualization of the entire tissue sample is named whole slide imaging. For clinical purposes, professional and very expensive scanners are used for tissue sample digitization and the creation of whole slide images. In education, it is possible to use whole slide images that do not necessarily follow strict medical standards and regulations. Therefore, the important research task is to propose a methodology for the manual creation of whole slide images and its adequate use in medical education. In this paper, the use of manually created whole slide images in histology classes education was analyzed. In the experimental part of this research, the whole slide images were evaluated very positively in the histological analysis and education process. The results of this research showed that the use of whole slide images in histology classes is in line with the process of digital transformation in education.
Comparison of VMware Workstation, VirtualBox and MS Hyper-V hypervisor performance with MS Windows OS based guests

This paper presents a performance comparison of the Type-2 hypervisor, using the VMware Workstation player, then the VirtualBox platform, and finally MS Hyper-V, as the virtualization role in Windows 10 Pro environment. The tests were performed under the same conditions and the same test method using HD Tune Pro tool. For the guest operating system, Windows 10 Pro was chosen as the basis for all testing phases. The performance of all three virtual platforms are compared for the cases with one, two and three virtual machines in operation. The tests were validated based on the results obtained using the benchmark program.
Image Classification with Transfer Learning Using a Convolutional Neural Network

Paper covers image classification using the Keras API in TensorFlow. The dataset used is a set of labelled images consisting of characters from the Pokémon media franchise. In order to artificially generate additional data, the process of data augmentation has been applied on the initial dataset to reduce overfitting. A comparison between DenseNet-121, DenseNet-169 and DenseNet-201 has been made to observe which of the models scores a greater accuracy. A Graphics Processing Unit (GPU) has been set up to work with TensorFlow in order to efficiently train the model.
Optimization on ITB Metaverse Development

Metaverse is a massive virtual environment that moves in parallel with the original world. In developing Metaverse, collaboration and inter-discipline are needed so that in its development it can see from various points of view. One of the inter-disciplinary collaborations in the development of the metaverse is the ITB Metaverse Ganesha Campus project. In the initial stage of development, this project will focus on conducting digital twins on several iconic ITB buildings such as the East Hall, Plaza, and Main Gate. The project was developed using Unreal Engine 5 and exported to Android and PC platforms. However, based on the test results using various device specifications, showing the highest average FPS value only gets 4 FPS for Android and 10 FPS for PC. In addition, the existing APK installation file is quite large, which is 2 GB with raw files reaching more than 300 GB. For this reason, optimization is carried out by optimizing LOD and asset management to improve the performance and efficiency of the Metaverse. As a result, FPS increased up to 29 FPS for Android and 38 FPS for PC. While the APK file is compressed to 0.4 GB and the raw file becomes 9.3 GB.
Blockchain Technology for Weather Data Management

Obtaining and distributing real-time and localized primary and secondary weather datasets is very challenging. Primary datasets are collected, aggregated, and released from government-controlled weather stations in a complicated manual bureaucratic process. They are prone to maintenance and breakdowns, which lead to missing large data sections, requiring accessible, transparent, and affordable data cleaning. Also, there is no easy way for an independent researcher or a small company to set up a weather station and aggregate it into a broader system. Secondary datasets obtained from primary ones can lead to a heterogeneous and unstandardized marketplace caused by various data formats from different providers. It is necessary to provide a transparent, immutable, accessible, and monetizable distributed weather data system based on blockchain, containing: governance, oracle, data storage, and marketplace layer.
Impact of the Tilt Angle on the Electrical Output, Characteristics and Performance of a Grid-Connected Photovoltaic System

As the demand for sustainable and renewable energy sources grows, the use photovoltaic (PV) systems have seen rise in popularity and recognition. The performance of PV systems is influenced by numerous factors such as solar irradiance, temperature, and the tilt angle of the PV modules. Among these factors, the tilt angle of the PV modules plays a crucial role in determining the amount of energy that can be generated by a PV system. This paper explores the impact of tilt angle on the output and performance of grid-connected PV systems by using the software PVsyst. The study will examine how different tilt angles affect the energy yield, electrical characteristics, and performance ratio of PV system. A study was conducted to compare the performance of a PV system with fixed tilt angle versus seasonal tilt arrangement. The results showed that a seasonal tilt arrangement led to improved performance and increased electricity generation.
Estimation of the Power of Algebraic Geometric Codes Designed to Construct a Post-Quantum Algorithm for Ensuring Information Security of On-board Systems

This paper continues the study of a post-quantum algorithm built on the basis of algebro-geometric codes, begun in previous works, in the interests of ensuring the information security of the Internet of things systems and on-board communication systems under the conditions of a quantum threat. The paper presents similar codes, the power of which lies above the Gilbert–Varshamov bound, and which can withstand known attacks on algorithms built on the basis of error-correcting codes.
Deviation of the 3D solid model from the printed model

Application of 3D measuring techniqes provides great benefits in field of product control. A three-dimensional scanning device is used in many applications in order to collect as much data as possible about product's shape, color and texture, etc. Acquired data can then be used for the construction of digital, three-dimensional models, as well ai in product development. The goal of this research is to obtain a computer-compatible digital object, in order to compare the data gathered by tree-dimensional scanning device with the existing previously created CAD model.
Minimizing Distortion when Companding an Audio Signal in Transmission Channels

Existing channels for transmission and storage of a sound broadcast signal do not provide the ability to preserve the natural dynamic range. To save it, either compander systems are used, including a compressor and an expander that distort the signal shape, or an increase in the bit depth of the signal representation, which sharply increases the volume of the transmitted signal. An algorithm is proposed that ensures companding with maximum preservation of the signal shape while minimizing its volume.
Towards a Human-in-the-Loop System for Authoring Game AI using Behavior Languages

As games get more complicated, Artificially Intelligent (AI) agents need to be better developed to understand and replicate complex, goal-oriented, reactive behaviors. Many existing behavior language approaches do not often make the AI's behavior transparent and readable to the human, making it difficult to debug and update the behavioral model. In this workshop paper, we present a prototype visualization system that uses a tree-based abstraction to present complex agent behavior to a human user, allowing them to understand and edit the agent's behavioral logic in a visual manner.