Reviewerly/exHarmony huggingface dataset:
Comparing hand-crafted and learned key-point feature extraction for co-location of sequential low-altitude UAS video frames

The ability to accurately monitor the position of an object or group of objects in some video sequence can be of high value when making decisions with respect to security, maneuverability, ecology, and infrastructure. Efforts from our previous work<sup>1, 2</sup> provided evidence that in overhead video, a system’s ability to perform this tracking could be greatly enhanced through effective and segmented calculation of frame-to-frame spatial correspondences. We now continue the investigations began by this previous work (wherein frames and their detections are co-mapped) through a case study wherein hand-crafted and machine learning (ML) approaches for co-registration of video frames are reviewed and compared. First, the merits and shortcomings of hand-crafted algorithms, ML models, and hybrid approaches are discussed for key-point detection and key-point description. Modifications to feature matching and homography estimation are discussed as well, and following this, a more recently published class of co-registration involving “detector-less” correspondence is outlined. These approaches are applied and evaluated on four overhead image sequences with ground-truth corresponding to object detection locations only. Because of this lack of ground control points for evaluation, co-mapped centroids of stationary objects are used to generate an accuracy metric for the various mapping approaches. Further, given the value of mapping and tracking in real-time contexts, this notion of accuracy is compared to computation time so that trade-offs in temporal and spatial performance can be better understood.
Crowd counting using Yolov5 and KCF

Crowd detection has various applications nowadays.However, detecting humans in crowded circumstances is difficult because the features of different objects conflict, making cross-state detection impossible.Detectors in the overlapping zone may therefore overreact.In this paper, real-time people counting is proposed using a proposed model of the YOLOv5 (You Only Look Once) algorithm and KCF (kernel correlation filter) algorithm.The YOLOv5 algorithm was used because it is considered one of the most accurate algorithms for detecting people in real time.Despite the high accuracy of the YOLOv5 algorithm in detecting the people in the image, video or real-time camera capturing, it needs an increase in speed.For this reason, the YOLOv5 algorithm was combined with the KCF tracking algorithm.Where the YOLOv5 algorithm identifies people to be tracked by the KCF.The YOLOv5 algorithm was trained on a database of people, and the system's accuracy reached 98%.The speed of the proposed system was increased after adding the KCF.
STUDI KOMPARASI ADSORPSI TIGA JENIS ZAT WARNA REMAZOL PADA PERMUKAAN KARBON AKTIF

Remazol is a reactive dye that is widely used in the textile industry with functional groups that can be well covalently bonded to textile fibres, but difficult to decompose, carcinogenic, and harmful to the health and ecosystems. This study specifically aims to analyse the adsorption power of activated carbon on three types of Remazol dyes using batch experiment method as an effective way to reduce the concentration of these dyes in textile industry wastewater. Using activated carbon with the same treatment, Remazol Red dye had the best interaction and was adsorbed in the highest amount on the activated carbon surface (125.16 mg/g), followed by Remazol Blue (16.23 mg/g) and finally Remazol Black dye (11.07 mg/g). The three adsorption processes that occur are in accordance with the Langmuir adsorption isotherm model, with color removal percentage of 74.4 % (Remazol Red), 49.5 % (Remazol Blue), and 48.1 % (Remazol Black). These results are in accordance with the structural studies conducted on the three types of Remazol synthetic dyes, in which Remazol Black with the bulkiest structure has the weakest physical interaction with carbon atoms on the activated carbon surface thus absorbed in the least amount on the adsorbent surface.
E-SeniArca on IoT Platform in Education of Sculpture Arts

Purpose: In education, by using any media method is one of the most important elements of an educational institution, especially in order to make a consistent learning session. It also can give the two ways communication between teachers and students. Based on E-SeniArca learning tool, it will provide a new way to deliver the information with more effectively. By the lack of references material of sculpture arts topics, it was difficult for students to learn and perform their achievement. In fact, students can purpose to design, transfer, construct and test the effectiveness of the E-SeniArca learning tool in sculpture topics.&#x0D; Design: The methods are used to the production of E-SeniArca learning tool on technology are the ADDIE Model.&#x0D; Findings: The actual image depiction of the sculpture arts through the images is shown to clearly explain the shape of arc that have in sculpture concepts.&#x0D; Research limitations: This study has developed an application of 12 flip cards for scanning by E-SeniArca learning tools to generate 3D images on each of the different picture cards.&#x0D; Practical implications: Much of the software used to produce this learning tool has been able to attract students to learn the basics of sculpture arts.&#x0D; Paper type: International Journal of Entrepreneurship and Business Development (IJEBD)
Good, but not always Fair: An Evaluation of Gender Bias for three commercial Machine Translation Systems

Machine Translation (MT) continues to make significant strides in quality and is increasingly adopted on a larger scale. Consequently, analyses have been redirected to more nuanced aspects, intricate phenomena, as well as potential risks that may arise from the widespread use of MT tools. Along this line, this paper offers a meticulous assessment of three commercial MT systems - Google Translate, DeepL, and Modern MT - with a specific focus on gender translation and bias. For three language pairs (English/Spanish, English/Italian, and English/French), we scrutinize the behavior of such systems at several levels of granularity and on a variety of naturally occurring gender phenomena in translation. Our study takes stock of the current state of online MT tools, by revealing significant discrepancies in the gender translation of the three systems, with each system displaying varying degrees of bias despite their overall translation quality.
Hubungan Self-efficacy dan Kemampuan Literasi Numerasi Siswa: Ditinjau Berdasarkan Gender

Students with high self-efficacy mean having high motivation, awareness and self-regulation so that it is expected to have a positive effect on learning outcomes including numeracy skills, self-efficacy can be influenced by internal factors such as gender. This study aims to analyze differences in self-efficacy students' and numeracy literacy skills based on gender, the relationship between self-efficacy and students' numeracy literacy skills based on gender and the effect of self-efficacy on students' numeracy literacy skills. The method used is descriptive method. The sample of this study was 88 students consisting of 44 male students and 44 female students of VIII SMP from four schools in North Aceh and Lhokseumawe who were participants in the 2021 national assessment. Data collection used questionnaire instrument self-efficacy containing 45 statement items and 36 numeracy literacy questions that are already valid and in accordance with the AN question grid. The results showed that the significance value was 0.288 &gt; 0.005 indicating that there was no significant difference between the self-efficacy of male and female students, (2) the significance value was 0.652 &gt; 0.05, meaning that there was no significant difference between literacy abilities numeracy men and female (3) self-efficacy and literacy numeracy students have a relationship with the degree of correlation is high and positively related (4) the coefficient of determination (R 2 ) of 0.489 explains that the influence of self-efficacy with literacy numeracy student is 48.9%.
Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?

Recent advances in natural language processing (NLP) have led to the development of large language models (LLMs) such as ChatGPT. This paper proposes a methodology for developing and evaluating ChatGPT detectors for French text, with a focus on investigating their robustness on out-of-domain data and against common attack schemes. The proposed method involves translating an English dataset into French and training a classifier on the translated data. Results show that the detectors can effectively detect ChatGPT-generated text, with a degree of robustness against basic attack techniques in in-domain settings. However, vulnerabilities are evident in out-of-domain contexts, highlighting the challenge of detecting adversarial text. The study emphasizes caution when applying in-domain testing results to a wider variety of content. We provide our translated datasets and models as open-source resources. https://gitlab.inria.fr/wantoun/robust-chatgpt-detection
Continual Construction of Adaptive Learning Model for English Vocabulary Using Machine Learning and Virtual Reality

An adaptive learning model for English vocabulary through a machine learning is proposed in this paper.The four main types of user information, including basic student information, quiz information, course video viewing information, and forum interaction information, are processed through feature engineering, and a better model on sparse data is proposed through comparison on different models, and the prediction accuracy of the model is improved through natural language processing techniques, to achieve feedback on user learning efficiency through user data and provide teachers and students with the corresponding teaching and learning suggestions for teachers and students.It is found that the quiz information has more influence than the course video viewing information, and the accuracy is improved by about 3% compared with TF-IDF after introducing word embedding.The use of mobile for English learners to learn to read in a fragmented learning context enables targeted training in weak areas of English reading, thus improving different aspects of learners' reading skills.
Usama gate: A State-Preserving Circuit

&lt;p&gt; This research introduces a pioneering approach in quantum computing that harnesses the fundamental principles of the Mobius strip to store and preserve the state of qubits. Leveraging the capabilities of quantum gates and entanglement, a custom quantum circuit is designed and implemented using the Qiskit framework. Through rigorous experimentation, the circuit successfully emulates the behavior of a Mobius strip, ensuring the enduring preservation of stored information. The devised methodology encompasses the creation of a quantum circuit comprising an input qubit and an output qubit. The initial state of the input qubit is determined by user input, setting the foundation for subsequent operations. By skillfully applying specialized gates and entanglement operations, the circuit faithfully represents the unique characteristics of the Mobius strip. Crucially, the output qubit’s state is measured, unveiling the resultant information encoded within. The experimental findings unequivocally establish the circuit’s unrivaled ability to steadfastly preserve information, irrespective of the number of iterations. Each execution yields the desired output, substantiating the efficacy of the bespoke gate arrangement in storing and upholding qubit states. This seminal research not only underscores the immense potential of integrating non-trivial mathematical concepts, exemplified by the Mobius strip, into quantum computing but also opens up unprecedented vistas for information storage and manipulation. The preservation of qubit states within the circuit paves the way for cutting-edge advancements in diverse quantum algorithms and applications. By seamlessly embodying the principles of scientific inquiry, this study serves as a trailblazing foundation for future explorations in the burgeoning field of quantum computing. &lt;/p&gt;
Speaker Embeddings as Individuality Proxy for Voice Stress Detection

Since the mental states of the speaker modulate speech, stress introduced by cognitive or physical loads could be detected in the voice. The existing voice stress detection benchmark has shown that the audio embeddings extracted from the Hybrid BYOL-S self-supervised model perform well. However, the benchmark only evaluates performance separately on each dataset, but does not evaluate performance across the different types of stress and different languages. Moreover, previous studies found strong individual differences in stress susceptibility. This paper presents the design and development of voice stress detection, trained on more than 100 speakers from 9 language groups and five different types of stress. We address individual variabilities in voice stress analysis by adding speaker embeddings to the hybrid BYOL-S features. The proposed method significantly improves voice stress detection performance with an input audio length of only 3-5 seconds.
BALSA para sa PAGBASA 30 MINUTES PUROK- BASED READING REMEDIAL SA BAGONG NORMAL: INTERVENTION FOR IMPROVING READING PERFORMANCE LEVEL OF GRADE – FIVE PUPILS STRUGGLING READERS

This action research was conducted to enhance the level of reading performance of pupils of Grade –Five struggling readers of Maasim Central Elementary School using the Balsa Para sa Pagbasa 30-minute Purok-based reading remedial program. This study employed the explanatory sequential mixed methods (single-experimental and phenomenological) design. Using Schutte Self-Report Emotional Intelligence Test, the EI level of the learners were identified while their experiences were captured through online interview using the validated interview guide. Frequency, mean, percentage and paired sample t-test were applied in treating the quantitative data and thematic analysis following Colaizzis method in analyzing the qualitative inputs. Robert Ebels Criterion of Mastery was used to determine the level of performance of Grade –Five Copper pupils struggling readers. Result showed that there is SIGNIFICANCE DIFFERENCE in the level of reading performance based on the result in the t – test. The increase in the pupils reading performance shows that using Balsa Para sa Pagbasa 30-minute Purok-Based program was effective in enhancing the reading performance of Grade V –pupil. This was supported by the participants narratives revealing the balsa para sa pagbasa purok-based remedial program was beneficial experience. They reported an opportunity to experience reading with purok chairman, a relieved confidence booster for them and a bond of friendship developed through reading. Thus, Balsa Para sa Pagbasa Purok-Based Reading Remedial Program should be institutionalized in elementary schools particularly at Maasim Central Elementary School and similar studies be conducted involving triangulation.
The Security Issues in The Analysis of Information Management Systems

The security issue in the design of information management systems is considered one of the vital issues, in the era of information technology and the information revolution, securing information has become a very necessary requirement. In this paper, the issue of analyzing information management systems was discussed with an emphasis on the security aspect of the analysis process. It is not possible to build and design information management systems without analyzing the full requirements of these systems, including security requirements. The analysis phase is useful to developers in studying and analyzing all system requirements to avoid potential risks, including security risks. It is necessary to try to avoid serious information breaches. If this happens, it is important for the system to have a specific mechanism to detect them and reduce the damage that resulted from them, as well as knowing the reason for their occurrence. Developing a layer of analysis of the security requirements at each stage of the analysis and developing a specific mechanism to detect violations that may occur to the system will be sufficient to increase the reliability of these systems.
Recipe Networks and the Principles of Healthy Food on the Web

People increasingly use the Internet to make food-related choices, prompting research on food recommendation systems. Recently, works that incorporate nutritional constraints into the recommendation process have been proposed to promote healthier recipes. Ingredient substitution is also used, particularly by people motivated to reduce the intake of a specific nutrient or in order to avoid a particular category of ingredients due for instance to allergies. This study takes a complementary approach towards empowering people to make healthier food choices by simplifying the process of identifying plausible recipe substitutions. To achieve this goal, this work constructs a large-scale network of similar recipes, and analyzes this network to reveal interesting properties that have important implications to the development of food recommendation systems.
Language Models Can Learn Exceptions to Syntactic Rules

Artificial neural networks can generalize productively to novel contexts. Can they also learn exceptions to those productive rules? We explore this question using the case of restrictions on English passivization (e.g., the fact that "The vacation lasted five days" is grammatical, but "*Five days was lasted by the vacation" is not). We collect human acceptability judgments for passive sentences with a range of verbs, and show that the probability distribution defined by GPT-2, a language model, matches the human judgments with high correlation. We also show that the relative acceptability of a verb in the active vs. passive voice is positively correlated with the relative frequency of its occurrence in those voices. These results provide preliminary support for the entrenchment hypothesis, according to which learners track and uses the distributional properties of their input to learn negative exceptions to rules. At the same time, this hypothesis fails to explain the magnitude of unpassivizability demonstrated by certain individual verbs, suggesting that other cues to exceptionality are available in the linguistic input.
Correction to: Representation Learning for Natural Language Processing

In the original version of the book, the following belated correction has been incorporated in both Preface and Chapter 8. In the Preface, a new reference citation has been added in Chapter 8. The original version of Chapter 8 was revised with a new paragraph replacing the first paragraph in section 8.3. The chapter and the book have been updated with the changes.
Overcoming Adversarial Attacks for Human-in-the-Loop Applications

Including human analysis has the potential to positively affect the robustness of Deep Neural Networks and is relatively unexplored in the Adversarial Machine Learning literature. Neural network visual explanation maps have been shown to be prone to adversarial attacks. Further research is needed in order to select robust visualizations of explanations for the image analyst to evaluate a given model. These factors greatly impact Human-In-The-Loop (HITL) evaluation tools due to their reliance on adversarial images, including explanation maps and measurements of robustness. We believe models of human visual attention may improve interpretability and robustness of human-machine imagery analysis systems. Our challenge remains, how can HITL evaluation be robust in this adversarial landscape?
Enhancing cloud security through the integration of deep learning and data mining techniques: A comprehensive review

Cloud computing is crucial in all areas of data storage and online service delivery.It adds various benefits to the conventional storage and sharing system, such as simple access, on-demand storage, scalability, and cost savings.The employment of its rapidly expanding technologies may give several benefits in protecting the Internet of Things (IoT) and physical cyber systems (CPS) from various cyber threats, with IoT and CPS providing facilities for people in their everyday lives.Because malware (malware) is on the rise and there is no well-known strategy for malware detection, leveraging the cloud environment to identify malware might be a viable way forward.To avoid detection, a new kind of malware employs complex jamming and packing methods.Because of this, it is very hard to identify sophisticated malware using typical detection methods.The article presents a detailed assessment of cloud-based malware detection technologies, as well as insight into understanding the cloud's use in protecting the Internet of Things and critical infrastructure from intrusions.This study examines the benefits and drawbacks of cloud environments in malware detection, as well as presents a methodology for detecting cloud-based malware using deep learning and data extraction and highlights new research on the issues of propagating existing malware.Finally, similarities and variations across detection approaches will be exposed, as well as detection technique flaws.The findings of this work may be utilized to highlight the current issue being tackled in malware research in the future.
Higher Education in the COVID-19 Era: Challenges and Opportunities of Using of Distance Education

The coronavirus (COVID-19) pandemic has caused a major health crisis and quarantined half of the planetary population, and as a result, it has caused a momentary rupture or even, in the invisibility of the magnitude of this virus total suspension of higher education. It is in this perspective that the treatment of a subject related to this Pandemic is, for us, very important, and especially to study its impact on higher education. Moreover, especially that there is little or no research that has addressed this issue in the case of Tunisia. The objective of this contribution is to report on research that we conducted at the Higher Institute of Technology Studies of Kairouan in Tunisia (HITS of Kairouan), with the aim of studying the experience feedback from the practice of Distance Education, through the Virtual University of Tunis (UVT) platform, in the COVID-19 confinement period (for the years 2020 and 2021). In this context, we will divide our research into three parts. The first will be reserved for the presentation of the general and current context of the COVID-19 phenomenon. Next, we will explain the extent and impact of this virus on higher education around the world. Finally, a third empirical part will be devoted to the presentation of our research, while demonstrating the impact of COVID-19 on teaching within the HITS of Kairouan, and also the opportunities and constraints of the use of distance education.