Reviewerly/exHarmony huggingface dataset:
Optimization Approaches of Bucket Brigade Quantum RAM architecture

Abstract The need for a Quantum RAM (QRAM) is in high demand in the field of quantum computing research. The Bucket-Brigade architecture is the most conventional among the current proposed architectures. However, this architecture has a high circuit depth as well as a high complexity which makes this architecture susceptible to noise and errors. In order to address this, we present 3 approaches to modify the existing architecture. In these approaches, we will be attempting to reduce the T-Count in the circuit using ZX-Calculus. The second approach is by modifying the routing qubits quantum gates using Fredkin Gates. We will also be introducing dynamic circuits and their properties of mid-circuit measurement and feed-forward mechanism in order to further decrease the circuit depth and T-Count. Using ZX-Calculus, we observed a 15% reduction in T-Count and a 50% reduction in T-Count when employing dynamic circuits, despite the trade-offs involving the increase in the number of Clifford gates or latency in dynamic circuits.
Adoption of Two-Factor Authentication in a Pre-Existing Heterogeneous System

Hacker attacks aimed at stealing credentials or encrypting sensitive user data are constantly increasing. For this reason, cybersecurity has become an ever more popular topic in recent years, and one on which both public administration and private companies are progressively convinced to make investments in both time and money. One of the most fragile points in security is undoubtedly authentication, which is mostly still based on a single-factor mode, especially in the case of access to virtual machines or physical computing devices. A change in access mode, however, is far from easy to enforce. In this paper we will see how it is possible to adopt two-factor authentication for system access, in a company with a considerable historical background and an heterogeneous and high amount of systems to be adapted.
The impact of an online technology course on pre-service teachers’ technological knowledge: Strategies and design

This study investigated how pre-service teachers’ technology integration knowledge changed as a result of participating in an online educational technology course for a semester in the Fall of 2020 and the Spring of 2021. The pre-service teachers’ technology integration knowledge development was assessed through their self-reported technological pedagogical content knowledge (TPACK). A total of 194 participants completed a validated TPACK pre- and post-test survey. The results of multilevel modeling analysis suggested that the participants had significant gains in all TPACK domains. Strategies used in designing the online educational technology course were examined for their contribution to pre-service teachers’ TPACK development. Educational technology-based course included activities that facilitated the integration of the components of TPACK framework through peer interactions, lesson planning, and peer feedback. The findings have implications for future research and provide guidance for the design of effective online learning environments to improve pre-service teachers’ technology integration knowledge.
Beyond animal detection: a benchmark for detecting animal age group

There are numerous practical applications for animal age estimation, such as age simulation, population structure estimation, wildlife conservation, biometrics, and more. Most of the current methods either concentrate solely on animal detection or animal age estimation. However, animal detection provides limited information, which is not adequate for more sophisticated computer vision tasks. On the other hand, existing methods for estimating animal age are based primarily on facial images, but capturing an accurate image of an animal’s face in a wild environment is extremely difficult and impractical for animals without discriminative facial features. In this paper, we propose a novel task, called Animal Age Group Detection. This task is a combination of animal detection and animal age estimation, with the primary goal of obtaining more information beyond the location of the animal by also providing crude age information, which is simple to accomplish with computer vision methods in a variety of open-world applications. To facilitate the study of this challenging, we present a benchmark for Animal Age Group Detection (AAGD), which contains a diverse range of 22 animal types, comprising a total of 4,778 images. These images are well-annotated with axis-aligned bounding boxes and include information about the age groups of the animals. In addition, we establish a baseline detector for this task, called AG-YOLOF. The release of AAGD aims to facilitate future research on the identification of animal age groups and enhance awareness of this significant area of study.
Research on intelligent combat decision making based on deep reinforcement learning

With the operational advantages of unmanned combat platforms in modern war gradually appearing, the research of unmanned combat platforms has become the focus of all circles. In order to realize intelligent and autonomous unmanned operation in a real sense, a combat mission computer based on AI development board was proposed to be built as the control core of unmanned vehicles, simulate the operational mobility situation diagram of unmanned vehicles, and use the deep reinforcement learning network DQN to establish angle and distance decision-making network, so as to realize intelligent mobility decision-making of unmanned vehicles. The experiment verified that the unmanned vehicle can maneuver to the target area autonomously, which proved that the deep reinforcement learning network can realize the feasibility of platform autonomous and intelligent decision-making, and provided a feasible technical approach and theoretical support for the construction of combat mission computer to realize intelligent, autonomous and unmanned combat in a real sense.
A Data-Driven Exploration of a New Islamic Fatwas Dataset for Arabic NLP Tasks

Islamic content is a broad and diverse domain that encompasses various sources, topics, and perspectives. However, there is a lack of comprehensive and reliable datasets that can facilitate conducting studies on Islamic content. In this paper, we present fatwaset, the first public Arabic dataset of Islamic fatwas. It contains Islamic fatwas that we collected from various trusted and authenticated sources in the Islamic fatwa domain, such as agencies, religious scholars, and websites. Fatwaset is a rich resource as it does not only contain fatwas but also includes a considerable set of their surrounding metadata. It can be used for many natural language processing (NLP) tasks, such as language modeling, question answering, author attribution, topic identification, text classification, and text summarization. It can also support other domains that are related to Islamic culture, such as philosophy and language art. We describe the methodology and criteria we used to select the content, as well as the challenges and limitations we faced. Additionally, we perform an Exploratory Data Analysis (EDA), which investigates the dataset from different perspectives. The results of the EDA reveal important information that greatly benefits researchers in this area.
Extraction of Virtual Machine Execution Traces

Debugging virtual machines can be challenging. Advanced debugging techniques using execution trace analysis can simplify debugging, but they often show only the execution of the virtual machine (in terms of machine instructions) and not the execution of the guest program (in terms of VM instructions). Ideally, the virtual machine as well as the guest program should be inspectable simultaneously to quickly locate the bug.
Black and White Image Colourisation Using Deep Learning Techniques

Image colourisation is a challenging task for machine learning that aims to convert grayscale images into their matching colour equivalents. This paper provides a thorough examination of various methodologies and strategies used for image colourisation. The research looks at how auto-encoders and Generative Adversarial Networks (GANs) can be used to achieve accurate and visually appealing colourisation outcomes. To measure the quality and similarity of the colourised images, quantitative assessment metrics such as Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) are used. A qualitative examination is also performed using human perceptual evaluation to assess the visual accuracy of the resulting colourisations. The outcomes of this study provide important insights into the efficacy and limitations of various colourisation techniques.
Counterfactual Adversarial Learning for Recommendation

Long-term user responses, i.e., clicks or purchases on e-commerce platforms, are crucial for sequential recommender systems. Recent off-policy evaluation methods involve these responses by simultaneously maximizing expected cumulative rewards. However, two aspects of these methods require further consideration. Firstly, from the system's point of view, candidates with various values are interchangeable, which may result in contradictory future recommendations despite having the same interaction history. Secondly, rewards are manually designed, which necessitates a trial-and-error approach to strike a balance between training stabilization and reward distinction. To address these issues, we propose a new sequential recommender system called NCM4Rec. Specifically, for the distinction problem, NCM4Rec achieves counterfactual consistency via a neural causal model, which is learnable yet equally expressive as classic structural causal models. Such consistency is maintained by a Gumbel-Max design. For the representing problem, NCM4Rec encodes different types of responses as one-hot vectors and captures the long-term preference via adversarial learning. As a consequence, NCM4Rec is both adaptive and identifiable. Both theoretical analyses of the consistency and empirical studies over two real-world datasets demonstrate the effectiveness of our method.
A study on the status and types of Military Targets terrorism using the Global Terrorism Database

At a time of heightened terrorism threats from terrorism organizations and increasingly realistic threats such as North Korea's drone incursions, few studies have focused on the military, despite the fact that it is one of the biggest targets of terrorism. Therefore, this study utilized the Global Terrorism Database (GTD) to conduct an in-depth analysis of terrorism incidents and military-related terrorism damage data over the past 50 years to face the reality and risk of terrorism against military facilities and to propose alternatives.&#x0D; The analysis revealed that the number of military victims and the percentage of victims are increasing, the number of deaths per incident is higher in military-related terrorism incidents than in general terrorism incidents, and the type of attack in military-related terrorism incidents is becoming more complex, while the type of attack in general terrorism incidents is becoming more monolithic with explosions. As a result of this analysis, it is necessary to adjust the priority of counter-terrorism alternatives according to the proportion of targeted facilities, and establish defense measures for each targeted facility. We hope that this study will serve as a basis for the military's future terrorism prevention measures and provide a foundation for further research.
Research Trends in the Ill-Structured Problem-Solving of Mathematics Education in Korea

In mathematics education, there has been substantial interest in mathematical problem-solving, which has led to the development of competencies of problem-solving based on the relevance and applicability of mathematical knowledge learned in school to problem-solving in one’s everyday life. This study revisits the development of ill-structured problem-solving and its pedagogical implications with the aim of providing students with a transferable problem-solving experience in which the knowledge they learn in school can be meaningfully employed in a real-world context. To examine the overall trend of research focusing on ill-structured problems or ill-structured problem-solving, this study statistically analyzed the papers published in the field of mathematics education in Korea according to the research period, topic, research method, and mathematics content strands to provide useful insights as descriptive research. More specifically, the ill-structuredness of textbook problems, the process of ill-structured problem-solving and the collaborative problem solving that emerges from ill-structured problem-solving were analyzed more closely. Based on the results, the researchers suggested the directions and implications for mathematical problem-solving in terms of ill-structured problems or problem-solving in mathematics education.
Informatics of the Distribution of Endings Appearance in Korean Textbooks

The purpose of this study is to find and discuss linguistics implications that can be found in the distribution of endings in Korean textbooks through quantitative analysis of listening and reading passages in Korean textbooks. Through this work, we will have more meaningful information about the distributional characteristics of the appearance of endings in Korean textbooks, and it is expected that this information can be used as an objective basis for seeking improvements in Korean textbooks. This study used Korean textbooks published by Seoul National University as analysis data. Python was used as an analysis tool. The kkma morpheme analyzer included in the konlpy package was used. For analysis, functions such as ‘kkma.sentences()’, ‘kkma.morphos()’, ‘kkma.pos()’, and ‘len()’ were applied. I went through the process of configuring it as a data frame using the pandas library. Through this process, it was possible to know the characteristics of the type frequency and appearance frequency of final endings, pre-final endings, conjunctive endings, and function-converting endings in listening and reading passages, respectively. In addition, the necessary items for the kkma morpheme analyzer to perform more appropriate endings analysis were also mentioned.
Research on path planning techniques based on deep reinforcement learning

Path planning problem stands for a sort of problem that find a secure path from the departure position to the destination in the unique application area without conflicts with other objects under the premise of minimum time or distance cost. Path planning has many applications in real life. This paper summarizes and classifies the current main research results on path planning, which are classified according to conventional techniques, single-agent path planning techniques and multi-agent path planning techniques. Conventional techniques are classified into three different algorithms: Traditional techniques, Graphics techniques and Intelligent bionics techniques. For the single-agent path planning techniques, it is primarily broken down into two categories: value-based and strategy-based. For multi-agent techniques, according to the improvement techniques, it is split in three categories: expert demonstration type, improved communication type and task decomposition type. Based on above classification, the mechanisms, advantages, and disadvantages of the existing algorithms are summarized and compared, and the future work in this field is prospected.
Predicting Z Boson Decay Modes: Evaluating the Performance of Machine Learning and Deep Learning Techniques in Particle Physics

This study presents an application of machine learning and deep learning techniques to the problem of classifying the decay modes of the Z boson, a fundamental particle in the Standard Model of particle physics. We focus on two specific decay modes: Zee and Zmumu. Our dataset comprises simulated events with variables such as transverse momentum, pseudorapidity, and phi angle of the decay leptons. The data is preprocessed via a mapping method for categorical data and a MinMaxScaler for numerical data. We compare the performance of several classification models, including K-Nearest Neighbors (KNN), Weighted KNN, Random Forest Classifier, KDTree KNN, XGBoost, Multi Layer Perceptron, Support Vector Machine, Decision Tree Classifier, and a Deep Learning model based on a sequential neural network architecture. Our results indicate that the XGBoost provides the highest accuracy (81.42%) among the tested models. However, the Deep Learning model exhibits superior precision (84.14%), which is crucial in reducing false positives in the classification task. The study emphasizes the potential of machine learning and deep learning in high-energy physics and suggests directions for future research to further improve the classification performance.
A Review of the Effects of Automated Writing Evaluation on Learners English Writing Ability

With the aid of advanced technology and the popularity of electronic devices, more and more English learners use automated writing evaluation (AWE) tools as an aid in their English writing. AWE tools score written works and analyze them, which provide immediate feedback and personal suggestions on how to correct errors in terms of grammar, punctuation, and other aspects. Using AWE tools for scoring papers offer privacy and convenience for students, and teachers in high schools and universities widely use these AWE tools to assist their courses. Therefore, a lot of research based on the usage, effects, and advantages of AWE tools is done. This paper critically reviews 20 papers related to AWE tools in three different aspectsstudents test performance, learners revision, and their writing self-efficacy. These papers include a comparison of students test results (both before and after using AWE tools), interviews about students opinions on AWE tools, and an analysis of how AWE tools help English learners enhance their writing ability. The authors believe that great benefits from different aspects of improving students overall writing skills are shown when using AWE tools. However, the degree of influence varies in students English proficiency.
Climate and Air Quality Indices for the European Social Survey

Knowledge of how personal experience with climate and air quality influence personal attitudes, concerns, and actions about environmental issues is increasingly important. A solid foundation for such studies is to combine interview or survey data on respondents&amp;rsquo; attitudes and beliefs with indices created from independent meteorological or environmental monitoring data matched to the respondents location. In this project, &amp;nbsp;indicators of climate and air pollution were integrated with data from the European Social Survey for a selection of large European urban regions. A prototype provenance description application was also developed for describing the workflow for creating indicators and integrating data. Our main focus was on creating indicators that represent regional anomalies in local air quality and weather for a range of time windows up-to-and-including the dates of the interviews. The goal is to facilitate investigation of relationships between urban citizen&amp;rsquo;s attitudes and behaviors as represented in the survey responses and the conditions in their local environment. &amp;nbsp;
Temporal Differential Privacy for Human Activity Recognition

Differential privacy (DP) is a method to protect individual privacy when the data is used for downstream analytical tasks. The core ability of DP to quantity privacy numerically separates it from other privacy-preserving methods. In human activity recognition (HAR), differential privacy can protect users’ privacy who contribute their data to train machine learning algorithms. While some methods are developed for privacy protection in such cases, no method quantifies privacy and seamlessly integrates into machine learning frameworks like DP. The paper proposes a DP framework called TEMPDIFF (short for temporal differential privacy), which guarantees privacy preserving human activity recognition for wearable time-series data with competitive classification performance and works with any machine-learning/deep-learning methods. TEMPDIFF capitalizes on the temporal characteristics of wearable sensor data to improve the modelling task, which enhances the privacy-utility tradeoff. TEMPDIFF uses ensembling and a novel temporal partitioning algorithm for time-series data to ensure optimal training of ensemble models. In TEMPDIFF, consensus through ensembling and the addition of controlled Laplacian noise obscures sensitive information used to train the models, guaranteeing strict levels of differential privacy. The proposed method is evaluated on two popular HAR datasets. It outperforms the classification accuracy and privacy budget for both datasets compared to the state-of-the-art approaches.
Enabling Privacy-preserving Multidimensional Network Telemetry with Autoencoders

Network telemetry systems are essential for monitoring network traffic and informing management decisions. However, increasing privacy concerns make user data access and analysis challenging for operators. We introduce PrvTel, a privacy-preserving telemetry system that uses an AutoEncoder model to encode user traffic data and preserve telemetry query ability with differential privacy guarantees. PrvTel features a lightweight model to be stored, facilitates quick training, and executes queries with minimal delay.
Examining IoT-Based Smart Campus Adoption Model: An Investigation Using Two-Stage Analysis Comprising Structural Equation Modelling and Artificial Neural Network

The progress and evolution of technology have been rapidly transforming various aspects of our society and daily lives, including colleges and campuses into smarter environments compared to the past. Despite the numerous advantages offered by cutting-edge technologies, such as IoT-based smart campuses, academic research on their implementation suffers from a significant lack of comprehensive information necessary to deliver efficient smart campus solutions. Therefore, the focus of this study is to investigate the significance of IoT-based smart campus adoption from 14 proposed hypotheses. The researchers collected data from stakeholders affiliated with universities in Iraq, resulting in a dataset of 442 observations. To analyze this data, a two-stage approach was employed, consisting of structural equation modeling (SEM) and reevaluated with the artificial neural networks (ANN) method. The findings provide evidence supporting the significance of various constructs. In particular, the model demonstrates satisfactory predictive relevance, indicating its effectiveness in making accurate predictions or forecasts. The ANN analysis suggests that the model has predictive capabilities. Moreover, the study findings support the importance of perceived usefulness in technology-specific factors, facilitating conditions, and propagation in organizational-specific factors, government support, social influence, and external pressure in environmental-specific factors, as well as privacy concerns, self-efficacy, satisfaction, and domain-specific knowledge in end-user-specific factors. Four hypotheses related to perceived ease of use, service collaboration, habit, and innovativeness were rejected. Notably, the study identifies propagation as the most significant predictor in the ANN analysis. The conclusions of this study can be beneficial for university administrators, manufacturers, and policymakers in understanding the essential components of smart campuses to enhance the adoption and maximize the effectiveness of smart solutions.