Reviewerly/exHarmony huggingface dataset:
Developing the assesment of online vocational college students’ reading comprehension test rubrics

Reading is one of the four language skills includes reading as one of its abilities. Reading is frequently a challenge for English language learners, particularly in terms of vocabulary acquisition. In addition to this, additional factors impede the reading comprehension of English learners. This research seeks to develop a rubric for assessing the reading skills of English language learners. This study focuses on the VELT questions, which are a specialized English proficiency exam for vocational school students. This research employs the Research and Development methodology. The results of this study indicate that there are four benchmarks for the reading abilities of English language learners: excellent, decent, satisfactory, and poor.
Corporate Governance in the Digital Age: A Comprehensive Review of Blockchain, AI, and Big Data Impacts, Opportunities, and Challenges

This research examines the intersection of disruptive technologies specifically blockchain, AI, and Big Data with corporate governance, synthesizing insights from a diverse range of scholarly works. This research employed a comprehensive literature review methodology. We sourced and analyzed scholarly articles and papers from prominent databases, focusing on key themes such as the impact of these technologies on corporate governance, potential benefits, and associated challenges. We investigate the implications of these technologies on corporate governance, scrutinizing potential benefits and associated challenges. Our findings reveal that blockchain, with its potential to enhance transparency and reduce information asymmetry, promises to transform accounting and auditing practices. Nevertheless, the successful integration of blockchain into mainstream financial systems necessitates addressing technical and regulatory challenges. AI's potential to augment decision-making capabilities offers opportunities for improved auditing efficiency, yet raises ethical concerns and implementation complexities. Lastly, Big Data presents opportunities for driving sustainable innovation and informed decision-making, while posing significant challenges concerning data privacy and security. This study's findings carry implications for practitioners, policy-makers, and academics alike, providing critical insights into the dynamic interplay between disruptive technologies and corporate governance. Future research may extend this work through empirical studies and comparative analyses across industries and regions.
Detection and Tracking of Broiler Flock Movements in The Chicken Coop using YOLO

Observation of the movement of broilers in the chicken coop is done to monitor the welfare and health condition of broilers. Currently, observing broiler flock activity in the chicken coop is generally still done conventionally, with manual observations made by farmers. But on a large scale, this observation method takes a lot of time and manpower, and is subjective. Therefore, an automatic observation system is needed that continuously monitors broiler activity, so as to increase the efficiency of farmer resources and reduce operational costs for observing broiler activity. This study developed an automatic detection and tracking system for broiler chicken movements using You Only Look Once, Version 4 (YOLOv4) as the base model, Yolo Weights as the Transfer Learning Pretrained Model, and Deep Sort as the Tracker Model. For comparison of base models, use Single Shot Multibox Detector (SSD), You Only Look Once, Version 3 (YOLOv3), You Only Look Once, Version 4 - tiny (YOLOv4-tiny). For comparison, the Network Model uses MobileNet and MobileNet v2. For comparison of Transfer Learning Pretrained Model using Caffe Model Weights and Tensorflow Weights. For comparison of Tracker Models using Centroid Tracker, Centroid Kalman Filter Tracker, Simple Online and Realtime Tracking (SORT) and Intersection over Union (IOU). The results showed that Model You Only Look Once, Version 4 (YOLOv4) with Transfer Learning Pretrained Model = Yolo Weights, and Tracker Model = Deep SORT was able to detect and track the most chicken herds in cages compared to others, with the number of broilers detected as many as 17.
Comparison of Security Performance of NTRU and ECC Algorithms For RFID Authentication

The rapid development of the Internet of Things creates information security vulnerabilities due to the unavoidable process of exchanging data. One device that is vulnerable to data security is RFID. One way to increase its security is to embed a cryptosystem in it. The NTRU algorithm can be a solution because of its low computational power. However, ECC is widely used because its computational power requirements are lower than other traditional public key algorithms. This research proposes the implementation and performance analysis of the ECC and NTRU algorithms on RFID devices. Testing is carried out by running 100-400 RFID devices simultaneously. The key generation and ECC decryption processes were faster than NTRU. The NTRU encryption process is faster than ECC. The ECC algorithm is more efficient and suitable for RFID devices. However, ECC is vulnerable to invalid curve attacks that can attack at the recommended security level. In NTRU, there is also a vulnerability to attacks using the LLL algorithm, but these attacks cannot yet attack the recommended security level. For this reason, the NTRU algorithm is more suitable for use on RFID devices, provided that the RFID device must increase its resources for computational needs heavier than ECC.
An Enhanced Real-Time System for Wrong-Way and Over Speed Violation Detection Using Deep Learning

Nowadays in many of the developing countries, traffic rule violation has become a critical issue. Due to the increase in population, the use of vehicles is also increasing exponentially, leading to an increase in road accidents. Major reasons for these accidents are traffic rule violations, such as over speed and wrong direction driving. To monitor these violations, an enhanced real-time system is needed which has the ability of traffic rule violation detections from nearby CCTV cameras. The proposed traffic signal violation detection system performs tasks like speed violation detection and wrong-way direction detection, without human intervention. The proposed system mainly performs three tasks: vehicle detection, vehicle tracking and violation detection. The YOLOv3 deep learning method is used for locating vehicles moving on road, and then the Kalman filter is used for tracking of detected vehicles. After tracking each vehicle is assigned with an unique id, then speed violation detection algorithm and wrong-way direction detection algorithm vehicles are used to identify the vehicles which violated the traffic rules. The system is very efficient and accurate to identify vehicles moving in the wrong direction and over speed.
Histopathology Breast Cancer Classification Using CNN

Breast cancer is the second leading cause of cancer mortality among women, early detection and treatment of breast cancer is very critical for the reduction of mortality rate. Cancer, known for its devastating impact, poses significant challenges in early detection within the medical field. In this study, breast cancer image samples were utilized for investigation purposes from BreakHis dataset. For accurate identification, current deep learning approaches relies on contour information. However, the cancer detection approach which is already existed retrieved only information which is semantic from the first layer and did not offer insights on the semantic levels which are not deep in breast cancer detection. Deep learning is a highly effective technique that possesses the capability to extract and structure significant insights from data, thus eliminating the necessity for domain experts to manually design feature extractors. Convolutional Neural Networks (CNNs), which belong to the category of deep, feed forward networks, have gained significant interest from researchers and industries. The recognition achieved by CNNs can be attributed to their integration in various domains such as transfer learning, speech recognition, natural language processing, signal processing, and object recognition. The proposed framework incorporates an effective and efficient strategy derived from a comprehensive review of commonly used approaches in each category. In the context of cancer screening, the process involves classifying image biopsies as either malignant or benign. Medical professionals like doctors and pathologists highly rely on the analysis of microscopic biopsy images to identify and categorize different abnormalities.
A Novel Fingerprint-Based Age Group Classification Approach Using DWT and DFT Analysis

In recent years, fingerprint analysis has expanded to include the estimation of age information, providing an additional layer of personal identification because of its uniqueness and permanence. In this study, 2D-Discrete Wavelet Transform (DWT) and 2D-Discrete Fourier transform (DFT) are used for fingerprint-based age group classification and their performance is compared against fingerprint dataset which is distributed among various age groups from the age of 10 to 90 including both genders. The face-based age group classification techniques have limited practice for crime scene investigation due to their dependency on the availability of vast global features, that is, complete face image. The research gap identifies that the accuracy between 2D-DWT and 2D-DFT, varied, and this is proved even for the unbalanced age group classes collected in this study’s real-time database and its corresponding results. The three variants of 2D-DWT Level-4, level-3, level-2 and 2D-DFT are considered for this research work and the most accurate was the DWT Level-4 which classified the age group using the fingerprint images with an astonishing accuracy 99.96 which is verified and supported by detailed analysis of all classification metrics.
Cechy charakterystyczne i parametryzacje dochodzącego promieniowania fotosyntetycznie czynnego w środkowym basenie Biebrzy = Characteristics and parametrisations of the photosynthetically active radiation incoming in the Middle Biebrza Basin

The availability of light is one of the most important factors that determine the photosynthesis process. Wave bands of solar radiation from 400 to 700 nm are called photosynthetically active radiation (PAR) because of their ability to stimulate the process of photosynthesis. The limited data availability and the regional and local PAR dependence cause the need for independent research, especially in areas of high natural value. The main goal of the work is to characterize the incoming photosynthetically active radiation in the Middle Biebrza Basin on the basis of a 9-year measurement series (2013-2021) from the Kopytkowo site (53°35'30.8”N, 22°53'32.4"E). The analysis was carried out for both instantaneous values and daily totals. In addition to the basic statistical characteristics, attention was paid to radiation on clear days and to the relationship between the PAR values on the Earth's surface and at the top of the atmosphere. Due to the limited availability of empirical data, simple parameterizations of PAR radiation were proposed for use in environmental studies. The research was carried out in the north-eastern part of Poland on the outskirts of Biebrza National Park in the Middle Biebrza Basin (Fig. 1). The area can be characterised as natural, almost unchanged, and uninhabited wetlands overgrown by swamp vegetation. The measurement site is located around 350 m north of the small village Kopytkowo with only a handful of permanent residents. The PQS1 PAR Quantum Sensor by Kipp &amp; Zonen mounted at height 2.7 m a.g.l. was used to collect data (Fig. 2). The sensor is part of the eddy-covariance system that measures the exchange of greenhouse gases between the ecosystem and the atmosphere. Data were sampled every 10 seconds and then stored in datalogger (CR500) as 5-minute averages. The results are shown as photosynthetic photon flux density (PPFD) with micromole per square metre per second (µmol∙m⁻²∙s⁻¹) as unit and were collected in Central European Time (CET).
Tenzor Methods in Cyber Security

The development of information technology and intensive use of the Internet has revealed problems related to information security. The issues of protecting information transmitted through communication channels and requiring long-term storage become relevant; the need for authentication procedures for users and messages; improving system performance and reliability. Various tasks of modern information security are solved with the help of cryptographic protocols. Various sections of higher mathematics are successfully used in information protection tasks. In this article to increase the effectiveness of information protection means, it is proposed to use such a mathematical apparatus as tensor analysis. The possibility of using tensor methods in solving various problems of cryptographic protection of information is shown. As a result of tensor analysis operations, this article shows the possibility of encrypting messages and decrypting cryptograms. The possibility of using tensor analysis in the construction of hash functions is shown additionally. In order to increase the effectiveness of means of protecting confidential information, it is proposed to encrypt messages using tensor analysis operations. At the same time, there is an increase in the speed of the process of ensuring the protection of confidential information in the implementation of document flow.
How Abstract Is Linguistic Generalization in Large Language Models? Experiments with Argument Structure

Abstract Language models are typically evaluated on their success at predicting the distribution of specific words in specific contexts. Yet linguistic knowledge also encodes relationships between contexts, allowing inferences between word distributions. We investigate the degree to which pre-trained transformer-based large language models (LLMs) represent such relationships, focusing on the domain of argument structure. We find that LLMs perform well in generalizing the distribution of a novel noun argument between related contexts that were seen during pre-training (e.g., the active object and passive subject of the verb spray), succeeding by making use of the semantically organized structure of the embedding space for word embeddings. However, LLMs fail at generalizations between related contexts that have not been observed during pre-training, but which instantiate more abstract, but well-attested structural generalizations (e.g., between the active object and passive subject of an arbitrary verb). Instead, in this case, LLMs show a bias to generalize based on linear order. This finding points to a limitation with current models and points to a reason for which their training is data-intensive.1
U-CORE: A Unified Deep Cluster-wise Contrastive Framework for Open Relation Extraction

Abstract Within Open Relation Extraction (ORE) tasks, the Zero-shot ORE method is to generalize undefined relations from predefined relations, while the Unsupervised ORE method is to extract undefined relations without the need for annotations. However, despite the possibility of overlap between predefined and undefined relations in the training data, a unified framework for both Zero-shot and Unsupervised ORE has yet to be established. To address this gap, we propose U-CORE: A Unified Deep Cluster-wise Contrastive Framework for both Zero-shot and Unsupervised ORE, by leveraging techniques from Contrastive Learning (CL) and Clustering.1 U-CORE overcomes the limitations of CL-based Zero-shot ORE methods by employing Cluster-wise CL that preserves both local smoothness as well as global semantics. Additionally, we employ a deep-cluster-based updater that optimizes the cluster center, thus enhancing the accuracy and efficiency of the model. To increase the stability of the model, we adopt Adaptive Self-paced Learning that effectively addresses the data-shifting problems. Experimental results on three well-known datasets demonstrate that U-CORE significantly improves upon existing methods by showing an average improvement of 7.35% ARI on Zero-shot ORE tasks and 15.24% ARI on Unsupervised ORE tasks.
Learning to Paraphrase Sentences to Different Complexity Levels

Abstract While sentence simplification is an active research topic in NLP, its adjacent tasks of sentence complexification and same-level paraphrasing are not. To train models on all three tasks, we present two new unsupervised datasets. We compare these datasets, one labeled by a weak classifier and the other by a rule-based approach, with a single supervised dataset. Using these three datasets for training, we perform extensive experiments on both multitasking and prompting strategies. Compared to other systems trained on unsupervised parallel data, models trained on our weak classifier labeled dataset achieve state-of-the-art performance on the ASSET simplification benchmark. Our models also outperform previous work on sentence-level targeting. Finally, we establish how a handful of Large Language Models perform on these tasks under a zero-shot setting.
Metainferential Theories of Truth

A central feature of metainferential logics is that they can be expanded with a transparent truth predicate.
Concluding Remarks

The validity of an inference has traditionally been associated with the preservation of truth from premises to conclusions. Mixed logics call against this way of understanding this relation: impure mixed logics cannot be understood as preserving some set of designated values from premises to conclusions. Also, some of them have proved to be extremely interesting, and have multiple applications, despite lacking this feature of designated-values preservation. $$\textbf{ST }$$ , in particular, shares all the inferential validities of classical logic, but, unlike it, can be expanded in a non-trivial way with a transparent truth predicate. Thus, if a logic is just a set of inferential validities, then it is no longer true that classical logic cannot be non-trivially extended with a transparent truth predicate.
Philosophical Reflections: Applications and Discussions

All the facts and results that we have presented and displayed reveal a partial answer to a general concern about this project: why should we care about metainferential logics? Why are they interesting, and worth our time?
Application of Quantum-Based K-Nearest Neighbors Algorithm in Optical Fiber Classification

Due to the various features of fiber-optic cables, the fields of use vary from one type of cable to another. Therefore, identifying the cable is important before its use. Aging or other factors can make cables inaccessible, such as underground cables that can lose their markings or have torn or worn labels. By analyzing the behaviors of a particular fiber type, it is possible to classify using machine learning algorithms such as K-Nearest Neighbors (KNN). Meanwhile, the possibilities of quantum computing are still being explored in terms of advantages and limitations. The idea of making the best use of both machine learning and quantum computing is the foundation for this work. So, the problem statement of the current project was to first implement the classical KNN algorithm and then develop an efficient KNN algorithm with the support of quantum technology. Finally, the classical and quantum-based KNN algorithms are compared in terms of time complexity and prediction accuracy. During the project, it was found that the quantum KNN algorithm can achieve the same accuracy as conventional algorithms, along with reducing the complexity, i.e., classical version has log complexity whereas quantum version has quadratic complexity.
Introduction: Why Modelling?

Models and modelling evoke a myriad of images and ideas, as they can refer to many different things and processes. This chapter first differentiates between four categories of models: physical models, mathematical models, large data-driven models, and conceptual models. It is the latter category that is the focus of this book. The second part of the chapter outlines why they are important and provides an outline of what will be covered in the remainder of the book.
HyResPR: Hybridized Framework for Recommendation of Research Paper Using Semantically Driven Machine Learning Models

The obligation for research paper recommendation is high as scientific document recommendations like research paper recommendation frameworks are not many in number. The knowledge-centric semantically inclined framework for research paper recommendation HyResPR has been proposed. HyResPR is a hybridized research paper recommendation model which is implemented on the RARD II dataset. A hybridized research paper recommendation framework (HyResPR) is proposed. It uses user queries as input to obtain the query words after preprocessing, later these query words are induvial integrated with the domain ontologies. The dataset is subjected to preprocessing to create a synthesized knowledge map with the help of category term mapping and static domain ontology alignment. Features are extracted from the synthesized knowledge map and classified using logistic regression, the extracted query words are processed along with the top 75% of the instances classified. The experimentations are conducted on the RARD II dataset which is classified using the logistic regression classifier. The SemantoSim similarity measure and Cosine similarity measures are used to compute the similarity among the extracted instances from RARD II dataset. The query words obtained from input, and relevant research papers are recommended back to user depending on the value of the semantic similarity. Auxiliary knowledge is incorporated by using static domain ontology, topic modeling has been used experimentations have been computed for 1416 queries on the RARD II dataset. The proposed HyResPR framework achieved the highest average accuracy of 96.43%, recall of 97.05%, with a least FDR value of 0.05 observed.