Reviewerly/exHarmony huggingface dataset:
Prediction of the Extent of Flood Indicators Used from the Slope Map in the River Flow Area in the City of Samarinda, East Kalimantan, Indonesia

Flood disasters in Indonesia and Samarinda City in general are disasters that continue to occur every year. Information on the dangers and risks of flooding is really needed, including the type of flood, the possibility of a flood occurring, the extent of flood inundation, the depth and speed of the flood, and the level of damage (to life, property and economic activity). The aim of the research is to determine the extent of inundation and the potential for flooding in areas in the river basin in terms of slope maps. This research was carried out in the Karang Asam Kecil River Basin, Samarinda City, East Kalimantan Province, Indonesia. Research activities include: literature study, collection of primary and secondary data, data processing and analysis. The results of the research show that: (1) The area of ​​inundation, namely in Air Hitam Subdistrict, is 77 hectares (2.85%), Air Putih Subdistrict is 81 hectares (3.00%), Bukit Pinang Subdistrict and Sidodasi Subdistrict are 1 hectare each. (0.04%), Teluk Lerong Ilir Village covering an area of ​​14 hectares (0.52%), and in Teluk Lerong Ulu Village covering an area of ​​7 hectares (0.26%); and (2) flood-prone areas in the Karang Asam Kecil sub-watershed occur in the Samarinda Ulu sub-district with an inundation area of ​​174 hectares and in the Sungai Kunjang sub-district an inundation area of ​​7 hectares.
On the Origins of Mass

Probability, as manifested through entropy, is presented in this study as one of the most fundamental components of physical reality. It is demonstrated that the quantization of probability allows for the introduction of the mass phenomenon. In simple terms, gaps in probability impose resistance to change in movement, which observers experience as inertial mass. The model presented in the paper builds on two probability fields that are allowed to interact. The resultant probability distribution is quantized, producing discrete probability levels. Finally, a formula is developed that correlates the gaps in probability levels with physical mass. The model allows for the estimation of quark masses. The masses of the proton and neutron are arrived at with an error of under 0.04%. The masses of sigma baryons are calculated with an error between 0.2% and 0.05%. The W boson mass is calculated with an error of under 0.5%. The model explains why proton is stable while other baryons are not, and it gives an explanation of the origins and nature of dark matter. Throughout the text, the article illustrates that the approach required to describe the nature of mass is incompatible with the mathematical framework needed to explain other physical phenomena.
An Efficient and Secure DNA based Image Encryption Technique

In this paper, an efficient and secure DNA based image encryption technique using hyper chaos Lorenz function is proposed. The technique employs SHA-512 hash of gray scale image and primary key to alter the initial seeds of hyper chaos Lorenz function. Initially, chaotic sequences are generated and redesigned. Then, an image is divided into blocks and randomly shuffled according to the primary key. Further perform circular shift operation over the rows of the shuffled image matrix and index based scrambling operation over the columns. After that, perform dynamic encoding and diffusion based DNA algebraic operation over the encoded DNA sequence and DNA based Key sequence using chaotic sequence. Finally, DNA decoding is employed to convert the diffused dynamic matrix into a ciphered image. The dynamicity of the chaotic sequences makes its extraordinary and contributes to the enhanced security and robustness of the technique. Implementation, security and performance analysis demonstrate that the technique has not only an outstanding encryption effect, but also posses large key space, less pixel correlation, low computation time and successfully resilient to the statistical, differential and brute-force attack. Comparative analysis with other references proves its efficiency and practicability for real time applications.
Vision Measurement Method Based on Plate Glass Window Refraction Model in Tunnel Construction

Due to the harsh environment of high humidity and dust in tunnel construction, the vision measurement system needs to be equipped with an explosion-proof glass protective cover. The refractive effect of the plate glass window invalidates the pinhole model. This paper proposes a comprehensive solution for addressing the issue of plane refraction. First, the imaging model for non-parallel plane refraction is established based on dynamic virtual focal length and the Rodriguez formula. Further, due to the failure of the epipolar constraint principle in binocular vision systems caused by plane refraction, this paper proposes the epipolar constraint model for independent refractive plane imaging. Finally, an independent refraction plane triangulation model is proposed to address the issue of triangulation failure caused by plane refraction. The RMSE of the depth of field errors in the independent refraction plane triangulation model is 2.9902 mm before correction and 0.3187 mm after correction. The RMSE of the positioning errors before and after correction are 3.5661 mm and 0.3465 mm, respectively.
LINGUISTIC COMPLEXITY AND SIMPLIFICATION IN TRANSLATION: COGNITIVELY-GROUNDED PHONOLOGICAL METRICS

ABSTRACT&#x0D; &#x0D; Translated text (TT) is characteristically simpler than non-translated (NTT) authentic text in terms of its lexicon, syntax, and style. It is still not clear what causes this phenomenon, and scholars continue to debate the issue. The traditional lexical metrics that are implemented in the simplification literature are often criticised as unreliable and lacking cognitive grounding. Moreover, being predominantly product-oriented, they cannot tell us how (or why) simplification happens. This paper addresses this limitation in the literature and proposes a paradigm that uses complexity-based measures adopted from Phonology and Cognitive Psychology. Calculations are run on a corpus of 100 translated and non-translated article abstracts drawn from five academic disciplines. Statistical analyses reveal significant differences between TTs and NTTs. The paper discusses the implications of these results and concludes that a cognition-informed approach is a key to demystifying simplification.&#x0D; &#x0D; Keywords: linguistic complexity; simplification; phonological complexity; neighbourhood density, phonotactic probability
A multilayer network diffusion-based model for reviewer recommendation

Abstract With the rapid growth of manuscript submissions, finding eligible reviewers for every submission has become a heavy task. Recommender systems are powerful tools developed in computer science and information science to deal with this problem. However, most existing approaches resort to text mining techniques to match manuscripts with potential reviewers, which require high-quality textual information to perform well. In this paper, we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network, with no requirement for textual information. The network incorporates the relationship of scholar-paper pairs, the collaboration among scholars, and the bibliographic coupling among papers. Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing, with improvements of over 7.62% in Recall, 5.66% in Hit Rate, and 47.53% in Ranking Score. Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem, which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.
Errors and the Console

Throughout the book, we’ve consistently relied on the console object to debug and learn more about JavaScript. We’ve also touched upon the fact that JavaScript can throw errors if you do something it doesn’t understand. In this chapter, we’ll dive deeper into how the console works and additional methods available to you to get the most out of it. After that, we’ll look at errors and how to handle them.
Fetching Data, APIs, and Promises

Throughout the book, we’ve made reference to APIs, but we haven’t gone into detail about how they work. Put simply, an API is a way for us to request and receive information from other computers. API, which stands for application programming interface, provides a mechanism for sending and receiving this interaction. If you want to create things like login pages, user accounts, or persistent user data, we usually have to use APIs. The reason we have to use something like APIs for these things is because front-end JavaScript is local to a user’s computer. Meanwhile, SQL databases containing a user’s email or password are usually stored on a server, and it would be impossible to send all of this information in front-end JavaScript due to security problems.
Implementation of IndoNLU Pre-Trained Model for Aspect-Based Sentiment Analysis of Indonesian Stock News

Investors in Indonesia are increasing from year to year, especially mutual fund investors managed by investment managers. News is one of the factors considered by investment managers in making stock investment decisions. Very diverse news sources and different writing styles can make it difficult to retrieve information on each issuer in the news. In this research, the aspect-based sentiment analysis (ABSA) method is implemented to extract news specifically on each aspect (issuer) in the news and evaluate the issuer. The model used is a pre-trained Indonesian Bidirectional Encoder Representations from Transformers (BERT) model, IndoNLU, because the object of research is Indonesian-language stock news. The results show that with a combination of hyperparameters consisting of batch size 8, learning rate 0.00002, and epoch 8 for the IndoNLU model can produce an average evaluation metric value consisting of precision, recall, f1-score, and accuracy of 90%.
EVALUATING THE VALIDITY OF MEARA AND ROGERS’ VOCABULARY APTITUDE TEST: A RASCH MEASUREMENT MODEL ANALYSIS

Language aptitude tests, such as the LLAMA_ B3, developed by Meara and Rogers (2022), may play an important role in vocabulary learning research. Following Bokander &amp; Bylund's (2019) advice, the construct validity of this test is examined here. Data collected from 314 participants were analyzed using Rasch analysis. All the eigenvalues fall below 2, supporting the assumption of uni-dimensionality. Q3 findings help us claim that the assumption of local independence has been met. This study found a .96 and a .84 reliability for the items and the participants, respectively. There was a convergent estimate of the results (Variance-Covariance Matrix: +2.002 and -2 log likelihood=+11.458.300). Accordingly, the test is valid for the present sample of students. As far as the analysis shows, the items represent a single underlying construct, meaning that the LLAMA_ B3 is fundamentally coherent. Psychometrically, a single trait seems to have been identified. It can be concluded that both the items and participants behaved predictably, indicating that the test is certainly worth further investigation and refinement.
Introduction to Objects, Arrays

Now that we understand how the fundamentals of JavaScript work, let’s dive into one of JavaScript’s most important types of data: objects. Objects are a special kind of data in JavaScript. It is the only type of data that is mutable, and it is widely used as a data store in JavaScript.
Hoax News Detection Using Passive Aggressive Classifier And TfidfVectorizer

Indonesia is one of the countries with the highest number of social media users. Million social media users in Indonesia reached 167 million in January 2023. These users are spread, across various social media, including Twitter with 24 million users. The high number of social media users on Twitter makes the information validation process even more neglected. Moreover, the trend of news interest read by social media users is only adjusted to their individual tastes. This phenomenon is evidenced by the large number of fake news (hoaxes) circulating in society which are spread through social media. Therefore, an accurate machine learning model is needed to classify "real" and "hoax" news. This study uses the TfidfVectorizer algorithm and Passive Aggressive Classifier for datasets that are shared through the Kaggle site. The contents of the dataset were sourced via social media Twitter over a span of 5 years, namely 2015-2020. At the preprocessing stage to making the Confusion Matrix, the machine learning model shows that it can work well as expected, namely getting Accuracy, Precision, and Recall scores of 82.44%, 80.66%, and 82.44%. In addition, the results of the confusion matrix show that in the dataset used, there is more "real" news than "hoaxes", that is, the model is able to predict 1059 real news and 211 hoax news, with actual conditions 1106 real news and 164 hoax news.
Modelling and Evaluating Restricted ESNs on Single- and Multi-Timescale Problems

Abstract Reservoir Computing is a computing model ideal for performing computation on varied physical substrates. However, these physical reservoirs can be difficult to scale up. We propose joining various reservoirs together as an approach to solving this problem, simulating physical reservoirs with Echo State Networks (ESNs).We investigate various methods of combining ESNs to form larger reservoirs, including a method that we dub Restricted ESNs. We provide a notation for describing Restricted ESNs, and use it to benchmark a standard ESN against restricted ones. We investigate two methods to keep the weight matrix density consistent when comparing a Restricted ESN to a standard one, which we call overall consistency and patch consistency. We benchmark restricted ESNs on NARMA10 and the sunspot prediction benchmark, and find that restricted ESNs perform similarly to standard ones.We present some application scenarios in which restricted ESNs may offer advantages over standard ESNs. We then test restricted ESNs on a version of the multi-timescale Multiple Superimposed Sines tasks, in order to establish a baseline performance that can be improved upon in further work. We conclude that we can scale up reservoir performance by linking small homogeneous subreservoirs together without significant loss in performance over a single large reservoir,justifying future work on using heterogeneous subreservoirs for greater flexibility.
Manipulating and Interacting with HTML

So far, we’ve been looking at how we can create functions and variables to perform certain calculations in JavaScript. At some point, we’re going to want to convert that into actual user output which people can see on their screens. Since JavaScript’s main purpose has always been to add interactivity to web pages, there are many ways to achieve this.
Recognition of images of blood cells using texture and neural networks to diagnose leukemia

EXPERT ASSESSMENTS IN DECISION MAKING: RISKS AND SAFETY Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-19.
Як “сірі” телеграм-канали поширюють дезінформацію та впливають на настрої на прикладі телеграм каналу “Начштабу”

One of the most well-known principles of spreading disinformation, influencing public sentiment and promoting desired narratives is the so-called “60/40” formula. Since the beginning of the CTO a number of "gray" telegram channels have appeared, and it was difficult to establish the authorship of these telegrams. The information policy of one of the most famous among those channels - @nach_shtabu, its target audience being military personnel - is analyzed. Changes in the channel’s editorial policy over time, examples of information manipulation, reasons for its popularity growth, information-psychological campaigns during the large-scale invasion of Ukraine are reviewed.
Smart Locking System Using AR and IoT

Modern smart door locks are more prone to damage and attacks, which will reduce robustness. Most smart door locks rely on passcode entry or face recognition outside the door which makes it easy to track and more vulnerable to attacks. Extended (XR) is an emerging term used to denote the amalgamation of multiple immersive technologies such as augmented reality (AR), virtual reality (VR), and mixed reality (MR). Recent research revealed that more than 60% of respondents believed XR will be mainstream in the next five years. Considering the features provided by the XR in terms of visualization and human interaction, XR has a wide range of applications. In this article, we propose the integration of XR with IoT devices to create a Smart door locking system that will be operated using smart glasses or mobile devices. The proposed system aims to implement a smart door-locking system to overcome physical attacks, as no part of the lock is physically intractable. MR is used to take the secret code as input from the user through Smart glasses or mobile devices, which is verified by the mobile application and the corresponding control signal to unlock the door is sent to the NodeMCU if the password is correct. The contactless feature of the lock makes it suitable in hospitals to prevent the spread of diseases and prevent users from touching radioactive components in radioactive areas.
Hardware Implementation of MRO-ELM for Online Sequential Learning on FPGA

This paper presents a parallel hardware accelerator for an online variant of the extreme learning machine (ELM) algorithm, called mixed-norm regularized online ELM (MRO-ELM). ELM is a training algorithm for feedforward neural networks that has been widely adopted in the literature. The proposed parallel architecture is implemented on a field-programmable gate array (FPGA) and designed for classification tasks. It is designed to be scalable and reconfigurable for different problem sizes, and can be used for various numbers of hidden neurons. Among the existing studies, the proposed architecture is the first hardware implementation that has norm regularization with parallel processing capability. The implementation results for the proposed hardware accelerator are reported in terms of hardware efficiency and they show that the proposed design has lower resource utilization than existing parallel implementations.
Enhanced Practical Byzantine Fault Tolerance via Dynamic Hierarchy Management and Location-Based Clustering

Blockchain is a distributed database technology that operates in a P2P network and is used in various domains. Depending on its structure, blockchain can be classified into types such as public and private. A consensus algorithm is essential in blockchain, and various consensus algorithms have been applied. In particular, a non-competitive consensus algorithm called PBFT is mainly used in private blockchains. However, there are limitations to scalability. This paper proposes an enhanced PBFT with dynamic hierarchy management and location-based clustering to overcome these problems. The proposed method clusters nodes based on location information and adjusts the dynamic hierarchy to optimize consensus latency. As a result of the experiment, the proposed PBFT showed significant performance improvement compared to the existing typical PBFT and Dynamic Layer Management PBFT (DLM-PBFT). The proposed PBFT method showed a processing performance improvement rate of approximately 107% to 128% compared to PBFT, and 11% to 99% compared to DLM-PBFT.
Artificial Intelligence in the Service of Dynamical Systems Studies

Artificial intelligence (AI) appears with increasing intensity in our common day practice and increases human powers to solve many of the complex problems that labored people so far for a really long time.
Robot Real-time Pedestrian Tracking Algorithm based on Deep Learning

In recent years, deep learning models have greatly improved the performance of pedestrian detection and tracking. Although the accuracy of neural network-based modeling methods is high, they often require large computational and storage resource overheads, which makes them difficult to apply to robots with high resource requirements. Pedestrian tracking for robots still faces great challenges in the problems of occlusion, multi-target, and target loss. In this thesis, we focus on solving the problem of real-time pedestrian tracking by lightweight fusion of deep learning target detection models for robots, firstly, through the lightweight YOLO network, we perform pedestrian detection and feature extraction, and then we propose a Gaussian mixture model based feature matching method to construct the target pedestrian tracker, and finally, we use the PID-based control algorithm for real-time control of the robot's motion, and finally realize the real-time pedestrian Tracking. In this thesis, we validate the feature matching method based on the Gaussian mixture model on the ETH dataset, and at the same time, combined with the motion control algorithm, we carry out the actual validation, and the experimental results show that our proposed method can realize the real-time pedestrian tracking.