Reviewerly/exHarmony huggingface dataset:
The Realization of Formulaic Competence in the Classroom Interactions among Learners in Kampung Inggris Pare

A critical component in communicative competence is formulaic competence, enabling learners to create natural and fluent spoken and written texts. This competence refers to recurrent fixed expressions used by native speakers to communicate in daily life, such as collocations, idioms, lexical bundles, and insert/routines. This study aimed to investigate the formulaic expression by exposing Celce-Murcia (2007) and Biber et al.'s (1999) theory in the classroom interactions among learners in Kampung Inggris Pare. In addition, it used Lennon's (1990) theory to investigate the influence of formulaic language on the learners' fluency. A qualitative approach, exposing conversational analysis, was adopted to analyze the gathered data to fulfill the research objectives. The data were collected by observing the learners' interactions, including recording during the conversation. Thus, the current study reveals that Kampung Inggris, Pare learners produced all types of formulaic expressions. However, due to a lack of collocational awareness, they made simple combinations and tended to avoid more complex words with more than two particles. In addition, the learners who produced many formulaic expressions have high exclusive rates implying that the more formulaic expressions are used, the higher the fluency level will be obtained.
The Influence of The Teachers' Reading Habit On Their Teaching Practice: A Narrative Inquiry

We consider the substantial relationship between teachers' reading habits and teaching practices in the Indonesian context. This issue has piqued the interest of research. Therefore, this study investigates how teachers' reading habits influence their teaching practice. This study employs narrative inquiry by Clandinin and Connelly (2000) as a qualitative research design, allowing us to collect rich data through the unique stories of the participants. This study involves three Indonesian English teachers at one of the vocational high schools in Indonesia, namely SMK Negeri 1 Pringapus. Specifically, this study uses a narrative inquiry framework by Clandinin and Conelly (2002). We collected the data through observation, questionnaires, interviews Creswell (2012); transcribing, and coding based on Bailey (2008). Additionally, to obtain trustworthiness, we use the theory of Guba and Lincoln (1982). The findings show that teachers' reading habits have vital roles in their teaching practice. The findings also clearly demonstrate a solid and lasting relationship between teachers' reading habits and teaching practice. This study suggests that educators, in particular, should improve reading habits both for professional needs as teachers and personal needs. It is also necessary to continue to improve the quality of teaching practice by strengthening reading habits.&#x0D; Key Words: teachers' reading habit, narrative inquiry, teaching practice
GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection

With the rapid development of deep generative models (such as Generative Adversarial Networks and Diffusion models), AI-synthesized images are now of such high quality that humans can hardly distinguish them from pristine ones. Although existing detection methods have shown high performance in specific evaluation settings, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , on images from seen models or on images without real-world post-processing, they tend to suffer serious performance degradation in real-world scenarios where testing images can be generated by more powerful generation models or combined with various post-processing operations. To address this issue, we propose a Global and Local Feature Fusion (GLFF) framework to learn rich and discriminative representations by combining multi-scale global features from the whole image with refined local features from informative patches for AI-synthesized image detection. GLFF fuses information from two branches: the global branch to extract multi-scale semantic features and the local branch to select informative patches for detailed local artifacts extraction. Due to the lack of a synthesized image dataset simulating real-world applications for evaluation, we further create a challenging fake image dataset, named DeepFakeFaceForensics ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$DF^{3}$</tex-math></inline-formula> ), which contains 6 state-of-the-art generation models and a variety of post-processing techniques to approach the real-world scenarios. Experimental results demonstrate the superiority of our method to the state-of-the-art methods on the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$DF^{3}$</tex-math></inline-formula> dataset and three other open-source datasets.
Indoor Campus Navigation Using Augmented Reality

Abstract—Augmented Reality can now be used for a variety of things including educational purposes, businesses, tourism, museum illustrations, manufacturing and marketing as well. One of the parts Augmented Reality is highly exercised and researched, is the Indoor Navigation, Indoor Navigation can be applied in campuses, malls, multiplexes, offices etc., basically where the area is huge, and we need to locate a particular place in that huge indoors. Because in these places what happens is a lot of new visitors enter who are not familiar with the place, and so they keep on searching for their desired location and waste a lot of time in it. So that is the problem that is solved through AR Navigation System. Indoor Campus Navigation Using Augmented reality, which provides people with essential AR direction and Drop-down buttons which consist of destination names. The aim for our project is to direct user to different areas of the campus and alert user about the areas they are passing through with a mini-map. For indoor navigation augmented reality uses inbuilt characteristics to determine a person's position and provide the necessary navigation directives. The project makes use of technology like UNITY, AR CORE, AR FOUNDATION, and NAVMESH etc. Keywords: Indoor campus Navigation system, Augmented Reality(AR),Navmesh, AR Foundation, Unity
Unified Learning-Based Lossy and Lossless Jpeg Recompression

JPEG is still the most widely used image compression algorithm. Most image compression algorithms only consider uncompressed original image, while ignoring a large number of already existing JPEG images. Recently, JPEG recompression approaches have been proposed to further reduce the size of JPEG files. However, those methods only consider JPEG lossless recompression, which is just a special case of the rate-distortion theorem. In this paper, we propose a unified lossly and lossless JPEG recompression framework, which consists of learned quantization table and Markovian hierarchical variational autoencoders. Experiments show that our method can achieve arbitrarily low distortion when the bitrate is close to the upper bound, namely the bitrate of the lossless compression model. To the best of our knowledge, this is the first learned method that bridges the gap between lossy and lossless recompression of JPEG images.
Dodging the Double Descent in Deep Neural Networks

Finding the optimal size of deep learning models is very actual and of broad impact, especially in energy-saving schemes. Very recently, an unexpected phenomenon, the "double descent", has caught the attention of the deep learning community. As the model’s size grows, the performance gets first worse and then goes back to improving. It raises serious questions about the optimal model’s size to maintain high generalization: the model needs to be sufficiently over-parametrized, but adding too many parameters wastes training resources. Is it possible to find, in an efficient way, the best trade-off?Our work shows that the double descent phenomenon is potentially avoidable with proper conditioning of the learning problem, but a final answer is yet to be found. We empirically observe that there is hope to dodge the double descent in complex scenarios with proper regularization, as a simple ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> regularization is already positively contributing to such a perspective.
Blackbox Face Reconstruction from Deep Facial Embeddings Using A Different Face Recognition Model

Face recognition systems generally store features (called embeddings) extracted from each face image during the enrollment stage, and then compare the extracted embeddings with the stored embeddings during the recognition stage. In this paper, we focus on the blackbox face reconstruction from facial embeddings stored in the face recognition database. We use a convolutional neural network (CNN) to reconstruct face images and train our network with a multi-term loss function. In particular, we use a different feature extractor trained for face recognition (which the adversary has the whitebox knowledge of it) to minimize the distance of embeddings extracted from the original and reconstructed face images. We evaluate our method in blackbox attacks against five state-of-the-art face recognition models on the MOBIO and LFW datasets. Our experimental results show that our proposed method outperforms previous face reconstruction methods in the literature. The source code of our experiments is publicly available to facilitate the reproducibility of our work.
Software Requirement Risk Prediction Using Enhanced Fuzzy Induction Models

The development of most modern software systems is accompanied by a significant level of uncertainty, which can be attributed to the unanticipated activities that may occur throughout the software development process. As these modern software systems become more complex and drawn out, escalating software project failure rates have become a critical concern. These unforeseeable uncertainties are known as software risks, and they emerge from many risk factors inherent to the numerous activities comprising the software development lifecycle (SDLC). Consequently, these software risks have resulted in massive revenue losses for software organizations. Hence, it is imperative to address these software risks, to curb future software system failures. The subjective risk assessment (SRM) method is regarded as a viable solution to software risk problems. However, it is inherently reliant on humans and, therefore, in certain situations, imprecise, due to its dependence on an expert’s knowledge and experience. In addition, the SRM does not allow repeatability, as expertise is not easily exchanged across the different units working on a software project. Developing intelligent modelling methods that may offer more unbiased, reproducible, and explainable decision-making assistance in risk management is crucial. Hence, this research proposes enhanced fuzzy induction models for software requirement risk prediction. Specifically, the fuzzy unordered rule induction algorithm (FURIA), and its enhanced variants based on nested subset selection dichotomies, are developed for software requirement risk prediction. The suggested fuzzy induction models are based on the use of effective rule-stretching methods for the prediction process. Additionally, the proposed FURIA method is enhanced through the introduction of nested subset selection dichotomy concepts into its prediction process. The prediction performances of the proposed models are evaluated using a benchmark dataset, and are then compared with existing machine learning (ML)-based and rule-based software risk prediction models. From the experimental results, it was observed that the FURIA performed comparably, in most cases, to the rule-based and ML-based models. However, the FURIA nested dichotomy variants were superior in performance to the conventional FURIA method, and rule-based and ML-based methods, with the least accuracy, area under the curve (AUC), and Mathew’s correlation coefficient (MCC), with values of approximately 98%.
Textisms and textese: A systematic review.

The aim of this article is to find out what scientific production exists on digital communication and the use of textisms between 2009 and 2022, which will allow us to identify which are the lines of research most worked on in this field, as well as to find the current state of the question. To this end, the systematic review methodology PRISMA (2021) has been used. After conducting the search and applying the exclusion criteria, a total of 62 articles were finally obtained from two databases: Web of Science and Scopus, after which the results were analysed. First, a quantitative analysis was carried out to find out how the published works were distributed in the selected years, in which journals they appeared the most frequently and to which countries and universities the authors with the most publications on the subject belonged. Second, a qualitative analysis has been carried out on the thematic lines they deal with and possible future lines of research. The results show a great disparity between the different articles, especially those dealing with the relationship between literacy and the use of textisms, which in turn show discrepancies in their conclusions on the relationship between the two variables.
Implementation of Numerical Methods for Solving Differential Equations using Python

Numerical analysis is a branch of mathematics that deals with the development, analysis, and implementation of numerical algorithms for solving mathematical problems. In particular, it focuses on finding approximate solutions to problems that cannot be solved analytically, often using computers and other numerical methods. The numerical analysis involves the use of various mathematical techniques, including linear algebra, calculus, optimization, and statistics, to develop numerical algorithms that can solve a wide range of problems in fields such as engineering, physics, finance, and computer science. These problems may involve the calculation of derivatives and integrals, the solution of differential equations, the optimization of functions, and the approximation of functions. The goal of numerical analysis is to provide accurate and efficient solutions to mathematical problems that are difficult or impossible to solve using traditional analytical methods. This field has become increasingly important in modern science and engineering, as it enables researchers and practitioners to simulate complex systems, design new products, and solve challenging mathematical problems.
The Assistance of Eye Blink Detection for Two- Factor Authentication

This paper discusses the implementation of a blink detection method using 68 facial markers and the eye aspect ratio (EAR) to provide strong protection for access systems. It investigates the importance of 68 facial markers and explores how to use eye landmarks to calculate the eye aspect ratio. Access systems, which should have good security measures and be difficult to decipher, are typically safeguarded by passwords or multi-factor verification, such as passwords combined with facial recognition. However, these methods have inherent weaknesses, including the risk of shoulder surfing with passwords and the potential to be deceived by fake face images with facial recognition. To address these issues, a two-factor authentication method by using password with eye blink recognition is proposed as an effective solution for access control. By incorporating real-time eye blinking action, the system can avoid the use of fake images and prevent shoulder spoofing. To demonstrate the practical application of eye blink detection for enhanced two-factor authentication, a web application called "Eblink" is introduced. Functional tests have been conducted to validate the application's core features.
Intermodulation Distortion in a Josephson Traveling-Wave Parametric Amplifier

Josephson traveling-wave parametric amplifiers (TWPAs) enable the amplification of weak microwave signals close to the quantum limit with large bandwidth, which has a broad range of applications in superconducting quantum computing and in the operation of single-photon detectors. While the large bandwidth allows for their use in frequency-multiplexed detection architectures, an increased number of readout tones per amplifier puts more stringent requirements on the dynamic range to avoid saturation. Here, we characterize the undesired mixing processes between the different frequency-multiplexed tones applied to a Josephson TWPA, a phenomenon also known as intermodulation distortion. The effect becomes particularly significant when the amplifier is operated close to its saturation power. Furthermore, we demonstrate that intermodulation distortion can lead to significant crosstalk and reduction of fidelity for multiplexed readout of superconducting qubits. We suggest using large detunings between the pump and signal frequencies to mitigate crosstalk. Our work provides insights into the limitations of current Josephson TWPAs and highlights the importance of performing further research on these devices.
CToMP: A Cycle-task-oriented Memory Protection Scheme for Unmanned Systems

Memory corruption attacks (MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and performance. MCAs in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms, we found that MCAs are mainly categorized into two types--return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection (CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CToMP resists the ROP attack, a novel variant of return2libc attacks. Finally, we implement CToMP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CToMP is resilient to various MCAs in unmanned systems with low footprints and system overhead.
Tutorial on Large Language Models for Recommendation

Foundation Models such as Large Language Models (LLMs) have significantly advanced many research areas. In particular, LLMs offer significant advantages for recommender systems, making them valuable tools for personalized recommendations. For example, by formulating various recommendation tasks such as rating prediction, sequential recommendation, straightforward recommendation, and explanation generation into language instructions, LLMs make it possible to build universal recommendation engines that can handle different recommendation tasks. Additionally, LLMs have a remarkable capacity for understanding natural language, enabling them to comprehend user preferences, item descriptions, and contextual information to generate more accurate and relevant recommendations, leading to improved user satisfaction and engagement. This tutorial introduces Foundation Models such as LLMs for recommendation. We will introduce how recommender system advanced from shallow models to deep models and to large models, how LLMs enable generative recommendation in contrast to traditional discriminative recommendation, and how to build LLM-based recommender systems. We will cover multiple perspectives of LLM-based recommendation, including data preparation, model design, model pre-training, fine-tuning and prompting, multi-modality and multi-task learning, as well as trustworthy perspectives of LLM-based recommender systems such as fairness and transparency.
Demo: ISOTTA - A Slow Exploration of Power Relations in Writing with Language Models

This paper presents ISOTTA (Intelligent System for Organic Tweeting and Thoughtful Artistry), an unconventional artifact aimed at investigating creative writing with Large Language Models. ISOTTA embodies slow technology principles to highlight the productivity disparities between humans and computers and foster a discussion on the resulting implications. ISOTTA provokes critical reflections along the continuum between collaboration and competition in Human-AI interaction in creative endeavors and beyond. These insights mainly address power dynamics regulating the sharing of resources between humans and machines.
Navigating the Quantum Computing Threat Landscape for Blockchains: A Comprehensive Survey

&lt;p&gt;Quantum computers pose a significant threat to blockchain technology’s security, which heavily relies on public-key cryptography and hash functions. The cryptographic algorithms used in blockchains, based on large odd prime numbers and discrete logarithms, can be easily compromised by quantum computing algorithms like Shor’s algorithm and its future qubit variations. This survey paper comprehensively examines the impact of quantum computers on blockchain security and explores potential mitigation strategies. We begin by surveying the existing literature on blockchains and quantum computing, providing insights into the current state of research. We then present an overview of blockchain, highlighting its key components and functionalities. We delve into the preliminaries and key definitions of quantum computing, establishing a foundation for understanding the implications on blockchain security. The application of blockchains in cybersecurity is explored, considering their strengths and vulnerabilities in light of evolving quantum computing capabilities. The survey focuses on the quantum security of blockchain’s fundamental building blocks, including digital signatures, hash functions, consensus algorithms, and smart contracts. We analyze the vulnerabilities introduced by quantum computers and discuss potential countermeasures and enhancements to ensure the integrity and confidentiality of blockchain systems. Furthermore, we investigate the quantum attack surface of blockchains, identifying potential avenues for exploiting quantum computing to strengthen existing attacks. We emphasize the need for developing quantum-resistant defenses and explore solutions for mitigating the threat of quantum computers to blockchains, including the adoption of quantum and post-quantum blockchain architectures. By examining vulnerabilities and discussing mitigation strategies, we aim to guide researchers, practitioners, and policymakers in developing robust and secure blockchain systems capable of withstanding advancements in quantum computing technology.&lt;/p&gt;