Reviewerly/exHarmony huggingface dataset:
Usability Enhanced Thumbnail-Preserving Encryption Based on Data Hiding for JPEG Images

As an increasing number of people tend to upload personal images to cloud platforms such as iCloud, Google Drive, and Baidu Cloud, the issue of image privacy protection in clouds has attracted widespread attention. Thumbnail-preserving encryption (TPE) is a newly proposed technology especially for balancing usability and privacy of images stored in clouds. The TPE-encrypted image does not reveal anything other than the thumbnail and size of the original one. Although many well-designed TPE schemes have been put forward, they almost all operate in the spatial domain and are not compatible with JPEG images. The only two schemes that can be applied in the frequency domain were proposed by Wright et al. and Marohn et al., but both of them have disadvantages and do not consider non-visual usability. In this paper, we propose a usability enhanced TPE scheme especially for JPEG images by utilizing reversible data hiding and image encryption in the frequency domain. We first transform the original image into the frequency domain and select some specific locations for extra information recording in every block. The original coefficient values at the selected locations are then collected and embedded into the rest of the block before encryption. Finally, the extra information is expected to be recorded into the selected locations of every block for non-visual usability. Experiments confirm that the proposed scheme achieves the target of balancing enhanced usability and privacy.
Fusing sentiment knowledge and inter-aspect dependency based on gated mechanism for aspect-level sentiment classification

Aspect level sentiment classification is a fine-grained sentiment analysis task that aims to identify the sentiment polarity of one or more given aspects in a sentence. In natural language, words frequently carry certain sentimental tendencies, which can be beneficial in obtaining the features between aspects and contexts. On the other hand, the dependencies between different aspects in a sentence can provide sufficient information for the sentiment polarity discrimination of a target aspect. However, existing models tend to focus on sentiment knowledge or aspect interactions individually without leveraging their converged information. Therefore, we propose a model based on Gated Mechanism Fusing Sentiment Knowledge and Inter-Aspect dependency (GMF-SKIA) for Aspect-level Sentiment Classification in this paper, aiming to dynamically fuse sentiment knowledge information of words and inter-aspect dependency. Specifically, the model uses the SenticNet sentiment dictionary to add sentiment knowledge information to words during dependency tree construction, and then we introduce a graph convolutional network to obtain sentiment information of dependency tree. We utilize an aspect-related multiheaded self-attention mechanism to model the inter-aspect interactions. Moreover, we design an information gate based on gated mechanism to fuse sentiment knowledge and inter-aspect features. We performed experiments on four publicly available datasets, our model outperforms the best benchmark model by an average of 2.1% and achieves the highest accuracy of 91.56% on the Rest16 dataset.
Effective Educational Ukrainian Practices of the Formation of Media Literacy

The article touches on the problem of forming the younger generation ’s media literacy. It is shown that in order to solve it, it is necessary to: develop the critical thinking of young people regarding the consumption, verification, and evaluation of media products; to form the ability to resist manipulation, destructive influences, and hostile propaganda; to develop a culture of interaction in the Internet space and the ability to protect one’s own information space. A workshop developed by a team of authors and the methodology of the practicum are described: the logical sequence of classes (motivation, encouragement to study; teaching practical techniques; checking the results; reflection); forms and methods of learning (quizzes, competitions, training, master classes). It is shown that the following are important for the successful implementation of the workshop: interactivity, game methods, learning techniques for emotional relief, learning by example, and real media products. The effectiveness of the workshop has been confirmed. The dynamics of achievements have shown significant positive changes in the ability to distinguish facts from judgments and to identify inaccurate information. The number of students who realized the responsibility for spreading unverified information and the importance of creating an adequate own information space also increased.
Information Extraction from Security-Related Datasets

There are various approaches to executing security breaches which are nowadays massively occurring in electronic communication environments, and phishing attacks are one of the most applied ones. A vast majority of phishing attacks are initiated using electronic messages, which attackers utilize to direct users to harmful or fake websites, to infect computers or to obtain personal or sensitive data for malicious purposes. Consequently, it is necessary to identify phishing messages in order to provide suitable user protection. Research and numerous studies have included machine learning algorithms and techniques from the field of artificial intelligence which predominantly depend on language-specific datasets and characteristics of phishing messages, and which have demonstrated to be effective for extracting critical information and for data-driven decision making. However, phishing datasets exist mainly for the English language. The aim of this paper is to present an information extraction pipeline that encompasses phases, such as corpus pre-processing, generating predictions of phishing messages using selected machine learning algorithms, along with a basic analysis, confusion matrices and evaluation scores for Croatian phishing messages. This type of key information can be used for teaching in higher education, e.g. in security-related courses or subjects that deal with artificial intelligence, machine learning, big data analysis, computational linguistics etc. This is essential as it can provide deeper insights into phishing attack strategies and potential countermeasures.
Deep Neural Network Based Multi-Object Detection for Real-time Aerial Surveillance

Aerial surveillance is one of the widely used modern days surveillance methodologies, finding applications in many important fields including military and civilian. This article presents a comprehensive study of Deep Neural Network (DNN) based solutions for real-time object tracking from Unmanned Aerial Vehicle (UAV) using a modified version of the state-of-the-art object detection algorithm YOLOv5 model. The modified YOLOv5 architecture is achieved by changing the activation function to Rectified Linear Unit (ReLU) and fine-tuning the network’s hyperparameter. A comparative analysis was then done on a subset of the AU-AIR dataset by comparing the different YOLOv5 models based on the network depth to determine the improvements in training speed and accuracy. The modified network was also compared in terms of mean average precision (mAP) to the original paper, a performance gain of almost 2.9 times was achieved in the best-case scenario.
Towards Online Anomaly Detection in Steel Manufacturing Process

Data generated by manufacturing processes can often be represented as a data stream. The main characteristics of these data are that it is not possible to store all the data in memory, the data are generated continuously at high speeds, and it may evolve over time. These characteristics of the data make it impossible to use ordinary machine learning techniques. Specially crafted methods are necessary to deal with these problems, which are capable of assimilation of new data and dynamic adjustment of the model. In this work, we consider a cold rolling mill, which is one of the steps in steel strip manufacturing, and apply data stream methods to predict distribution of rolling forces based on the input process parameters. The model is then used for the purpose of anomaly detection during online production. Three different machine learning scenarios are tested to determine an optimal solution that fits the characteristics of cold rolling. The results have shown that for our use case the performance of the model trained offline deteriorates over time, and additional learning is required after deployment. The best performance was achieved when the batch learning model was re-trained using a data buffer upon concept drift detection. We plan to use the results of this investigation as a starting point for future research, which will involve more advanced learning methods and a broader scope in relation to the cold rolling process.
Using Large Language Models to Provide Formative Feedback in Intelligent Textbooks

As intelligent textbooks become more ubiquitous in classrooms and educational settings, the need arises to automatically provide formative feedback to written responses provided by students in response to readings. This study develops models to automatically provide feedback to student summaries written at the end of intelligent textbook sections. The study builds on Botarleanu et al. (2022), who used the Longformer Large Language Model, a transformer Neural Network, to build a summary grading model. Their model explains around 55% of holistic summary score variance when compared to scores assigned by human raters on an analytic rubric. This study uses a principal component analysis to distill scores from the analytic rubric into two principal components – content and wording. When training the models on the summaries and the sources using these principal components, we explained 79% and 66% of the score variance for content and wording, respectively. The developed models are freely available on HuggingFace and will allow formative feedback to users of intelligent textbooks to assess reading comprehension through summarization in real-time. The models can also be used for other summarization applications in learning systems.
Ensuring Fairness of Human- and AI-Generated Test Items

Large language models (LLMs) have been a catalyst for the increased use of AI for automatic item generation on high-stakes assessments. Standard human review processes applied to human-generated content are also important for AI-generated content because AI-generated content can reflect human biases. However, human reviewers have implicit biases and gaps in cultural knowledge which may emerge where the test population is diverse. Quantitative analyses of item responses via differential item functioning (DIF) can help to identify these unknown biases. In this paper, we present DIF results based on item responses from a high-stakes English language assessment (Duolingo English Test - DET). We find that human- and AI-generated content, both of which were reviewed for fairness and bias by humans, show similar amounts of DIF overall but varying amounts by certain test-taker groups. This finding suggests that humans are unable to identify all biases beforehand, regardless of how item content is generated. To mitigate this problem, we recommend that assessment developers employ human reviewers which represent the diversity of the test-taking population. This practice may lead to more equitable use of AI in high-stakes educational assessment.
Decomposed Prompting to Answer Questions on a Course Discussion Board

We propose and evaluate a question-answering system that uses decomposed prompting to classify and answer student questions on a course discussion board. Our system uses a large language model (LLM) to classify questions into one of four types: conceptual, homework, logistics, and not answerable. This enables us to employ a different strategy for answering questions that fall under different types. Using a variant of GPT-3, we achieve 81% classification accuracy. We discuss our system's performance on answering conceptual questions from a machine learning course and various failure modes.
Service Mechanism for the Cloud–Edge Collaboration System Considering Quality of Experience in the Digital Economy Era: An Evolutionary Game Approach

In the digital economy era, cloud–edge collaboration technology provides the necessary technical support for the digital transformation of enterprises, which can improve the quality of services (QoS), and it attracts extensive attention from scholars and entrepreneurs from all fields. Under the bounded-rationality hypothesis, this paper investigates the service mechanism for the cloud–edge collaboration system considering the quality of experience (QoE) and presents a dynamic evolutionary game model between cloud service providers and edge operators by applying the evolutionary game theory. Then, this paper analyzes the equilibrium and stability conditions for the decision-making of both parties involved to guarantee the QoE reaches the ideal state. In addition, we investigate the factors that influence the stable cooperation between the two evolutionary stable strategies and validate the theoretical analytical results with numerical simulations. The research results show that the final evolution of the cloud–edge collaboration system depends on the benefits and costs of the game matrix between the two parties and the initial state values of the system. Under a specific condition, the cloud–edge collaboration system can eventually be driven to be an ideal state by reducing the collaboration cost and improving the collaboration benefit. The more both parties focus on the QoE, the more conducive it will be for the formation of a cloud–edge collaboration, thus effectively promoting long-term stability and better serving enterprises’ digital transformation.
Going beyond general competencies in teachers' technological knowledge: describing and assessing pre-service physics teachers' competencies regarding the use of digital data acquisition systems and their relation to general technological knowledge

The use of digital technologies and media in physics classrooms has pedagogical potential. In addition to everyday common technologies (e. g., presenters or computers), highly subject-specific media and technologies (e. g., simulation and digital data acquisition systems) are now available for these purposes. As the diversity of these technologies/media increases, so do the required competencies on the part of the (pre-service) physics teachers who must be able to exploit the given potential. Corresponding competency frameworks and related evaluation instruments exist to describe and assess the corresponding competencies. These frameworks and scales are characterized by their generality and do not reflect the use of highly subject-specific technologies. Thus, it is not clear how relevant they are for describing competencies in highly subject-specific technological situations, such as working with digital data acquisition systems in educational lab work settings. Against this background, two studies are presented. Study 1 identifies empirically 15 subject-specific competencies for handling digital data acquisition systems in lab work settings based on a literary review of lab manuals and thinking aloud. In Study 2, based on the 15 identified competencies, an abbreviated content- and construct-validated self-efficacy scale for handling digital data acquisition systems is provided. We show that general technological-specific self-efficacy is only moderately related to the highly subject-specific self-efficacy of handling digital data acquisition systems. The results suggest that specific competency frameworks and measurement scales are needed to design and evaluate specific teaching and learning situations.
Path planning of autonomous UAVs using reinforcement learning

Abstract Autonomous BVLOS Unmanned Aerial Vehicles (UAVs) are gradually gaining their share in the drone market. Together with the demand for extended levels of autonomy comes the necessity for high-performance obstacle avoidance and navigation algorithms that will allow autonomous drones to operate with minimum or no human intervention. Traditional AI algorithms have been extensively used in the literature for finding the shortest path in 2-D or 3-D environments and navigating the drones successfully through a known and stable environment. However, the situation can become much more complicated when the environment is changing or not known in advance. In this work, we explore the use of advanced artificial intelligence techniques, such as reinforcement learning, to successfully navigate a drone within unspecified environments. We compare our approach against traditional AI algoriths in a set of validation experiments on a simulation environment, and the results show that using only a couple of low-cost distance sensors it is possible to successfully navigate the drone beyond the obstacles.
QAOA.jl: Toolkit for the Quantum and Mean-Field Approximate Optimization Algorithms

Quantum algorithms are an area of intensive research thanks to their potential for speeding up certain specific tasks exponentially.However, for the time being, high error rates on the existing hardware realizations preclude the application of many algorithms that are based on the assumption of fault-tolerant quantum computation.On such noisy intermediatescale quantum (NISQ) devices (Preskill, 2018), the exploration of the potential of heuristic quantum algorithms has attracted much interest.A leading candidate for solving combinatorial optimization problems is the so-called Quantum Approximate Optimization Algorithm (QAOA) (Farhi et al., 2014).QAOA.jl is a Julia package (Bezanson et al., 2017) that implements the mean-field Approximate Optimization Algorithm (mean-field AOA) (Misra-Spieldenner et al., 2023) -a quantum-inspired classical algorithm derived from the QAOA via the mean-field approximation.This novel algorithm is useful in assisting the search for quantum advantage by providing a tool to discriminate (combinatorial) optimization problems that can be solved classically from those that cannot.Note that QAOA.jl has already been used during the research leading to Misra-Spieldenner et al. (2023).
An end-to-end screen shooting resilient blind watermarking scheme for medical images

The rapid progress in artificial intelligence and network communication has led to the development of advanced regional medical systems, revolutionizing the way hospitals and patients connect. However, the security of these systems is threatened by the potential breach of medical data, especially sensitive medical images. Existing watermarking schemes have focused on optimizing either imperceptibility or robustness, creating a fundamental trade-off between these two aspects that poses a significant challenge in the field. To address this challenge and ensure the confidentiality and integrity of medical data, this paper proposes an end-to-end screen shooting resistant blind watermarking scheme for medical images. The proposed method offers a robust and reliable solution for the secure transmission and storage of medical images, particularly in the face of screen shooting attacks. To enhance both security and imperceptibility, the scheme employs a novel encryption technique based on BCH codes, resulting in a trinary sequence for watermark images. These watermarks are then embedded in the spatial domain, effectively protecting against unauthorized access while preserving the integrity of the original image. To further enhance robustness, the method introduces a neural network structure specifically designed to resilient screen shooting attacks. By simulating the screen shooting process and incorporating various distortions, this network combines the strengths of residual networks and generative adversarial networks. Moreover, the proposed method introduces a novel loss function that exploits the unique characteristics of the image transformation domain, optimizing both imperceptibility and robustness. In the subsequent experiment, the watermarked images achieved satisfactory results with the highest PSNR and SSIM values reaching 62.563 dB and 0.9999, respectively. The proposed scheme provides an effective solution to protect the privacy and copyright of medical images, effectively addressing a critical security concern prevalent in regional healthcare systems.
A comprehensive research framework for Bitcoin’s energy use: Fundamentals, economic rationale, and a pinch of thermodynamics

The energy use of Bitcoin is fiercely debated among academics, practitioners, and the general public. This debate is often biased and characterized by a lack of understanding. Therefore, I start this paper with a discussion of the fundamentals of Bitcoin, which includes the clarification of widely held misconceptions. Next, I illustrate how Bitcoin is related to energy and describe the underlying incentive mechanism. In the main body of the paper I discuss various components of Bitcoin’s energy use, including the amount, composition, and geographical distribution of the energy, as well as emerging positive and negative effects. These components are then combined into a comprehensive framework that provides a solid foundation for future academic research and presents practitioners with the big picture of how and why Bitcoin requires energy and whether this can be justified from an environmental point of view.
FPGA Accelerator for Meta-Recognition Anomaly Detection: Case of Burned Area Detection

Optical remote sensing instruments accumulate abundant data from across all of the earth's land surfaces, making it possible both to understand the effects of climate change and to monitor, investigate, and manage ground-level events in detail.Processing data using resources located near on-board satellite sensors can bring major benefits in terms of minimizing analysis time and quickly initiating active actions in critical situations.In satellite missions, long-term production on-board algorithms may encounter unexplored samples, i.e., abnormal ground-level events, and need to be able to discriminate and take the correct action.In this matter, the authors present a field programmable gate array (FPGA)-based solution for natural anomaly detection in multispectral imagery using deep convolutional neural networks.The effects of weather-induced hazards and natural disasters, considered anomalies in this sense, are discovered by modeling an anomaly detector on a hybrid system that is hardware efficient.The proposed approach is assembled on a Xilinx Zynq UltraScale+ XCZU9EG multiprocessor system-on-chip (MPSoC) device, where a deep convolutional model is scaled into the FPGA logic, followed by a downstream statistical meta-recognition predictor.The proposed anomaly detection accelerator has produced notable results in identifying a contemporary natural hazard, i.e., burned areas, in scenes acquired by Sentinel-2 over Europe, i.e., Spain and France.The implemented algorithm achieved on the FPGA accelerator an equivalent speedup of 4.46× and 4.5× lower power consumption than the equivalent implementation on the Tesla K80 GPU.