Reviewerly/exHarmony huggingface dataset:
Learning continuous dynamic network representation with transformer-based temporal graph neural network

Continuous dynamic graph neural network (DGNN) methods have attracted increasing attention owing to their ability to learn fine-grained temporal representations. Real-world network events not only involve newly emerged edges but also affect larger regions owing to changes in the overall structure. However, most continuous DGNN methods only aggregate local neighborhood information and ignore the importance of global information, leading to the loss of structural and semantic information that is not aggregated during the evolution of networks. Therefore, we propose a continuous dynamic network representation learning model called the Transformer-based Temporal Graph Neural Network (T-TGNN) to aggregate global event-driven information in continuous dynamic networks. Specifically, we first adopted ordinary neural differential equations to model continuous temporal changes in dynamic networks. Subsequently, we used transformer mechanisms to aggregate the temporal and structural information. To better utilize transformers to aggregate the impacts of large regional changes, we designed a spatial event coefficient based on an attention mechanism to describe the global range of event dependencies in dynamic networks. Finally, extensive experiments were conducted on dynamic network benchmark datasets to demonstrate the effectiveness of our model. The T-TGNN achieved the best performance over state-of-the-art baselines of network embedding.
Compression of Qubit Circuits: Mapping to Mixed-Dimensional Quantum Systems

Quantum computers are becoming a reality thanks to the accomplishments made in recent years. The quantum computers available today offer hundreds of qubits but are still limited in the number of operations they can perform before errors accumulate and the quantum state decays. In regard to the error accumulation, non-local operations such as CX or CZ are main contributors. One promising solution to reduce the number of required non-local operations is to make a more efficient use of the quantum hardware by exploiting the inherent high-dimensional capabilities of quantum systems. In a process called circuit compression, non-local operations between qubits are mapped to local operations in qudits, i.e., higher-dimensional systems. In this work, we present a strategy for enabling quantum circuit compression with the aim of mapping clusters of qubits in a given circuit to the mixed-dimensional qudits of the target hardware. Further, we discuss the principles of circuit compression as well as the physical structure of qubits and qudits, before introducing a new representation that captures the essence of quantum operations, affecting the different logical levels in the quantum states in nodes and edges of a graph. Based on this, we propose an automated approach for mapping qubit circuits of arbitrary gate sets to mixed-dimensional quantum systems, lowering the number of non-local operations. Empirical evaluations confirm the effectiveness of the proposed approach reducing the number of non-local operations by up to 50% for almost half of the cases. Finally, the corresponding source code is available freely at github.com/cda-tdum/qudit-compression.
A Genetic Optimization Method for Spatial Layout of Cameras in Video Sensor Networks

Video sensor networks consist of cameras to monitor targets and play an important role in monitoring and analysis of agriculture, security management and emergency rescue, etc. However, traditional methods for camera placement result in little effective monitoring area and low utilization rate of cameras. To solve these problems, we propose a method for optimizing the spatial layout of cameras in video sensor networks based on the genetic algorithm. First, candidate layout positions of cameras were selected according to the oblique photogrammetry 3D model. Secondly, we calculated and selected the best orientation of cameras and encoded cameras. Then, the visible area of all cameras was calculated. Finally, the best video sensor network with genetic algorithm optimization was formed. The simulation results show the algorithm effectively improves the visibility of cameras, achieves larger area with fewer cameras and reduces cost while increasing the utilization of cameras.
Application of Chaotic Cat Swarm Optimization in Cloud Computing Multi Objective Task Scheduling

In cloud computing Computer multitasking, a large number of tasks require the system to have excellent task scheduling capabilities to ensure stable and efficient operation of the system. However, the integration and scheduling of multi task resources in cloud computing directly affect the effectiveness of cloud computing services. Traditional cloud computing task scheduling has low efficiency and single objective optimization, which cannot meet the requirements of cloud computing tasks. In this regard, cat swarm optimization model is used to construct a multi-objective task scheduling model for cloud computing, achieving cloud computing tasks optimization. Cloud computing task objectives were analyzed, and a multi-objective task scheduling model was constructed with execution time and system load as scheduling objectives. Considering multi-objective task scheduling complexity, cat swarm optimization model was introduced for solution. Cat swarm optimization model can easily fall into local optimization. This model was improved by adjusting weight factor and fitness. In algorithm model performance analysis, Ackley function was used for optimization testing. The new model tends to converge after 445 iterations. At this moment, its optimal optimization value is 0.506, which is superior to other two optimization models. In cloud computing instances analysis, the cloud computing execution time was tested. This proposed model tends to converge after 102 iterations, with a task execution time of 6.23 seconds. The traditional optimization model has a task execution time of 6.51 seconds. The particle swarm optimization model has a task execution time of 6.96 seconds. In task cost analysis, this proposed model has a task execution cost of 3326 yuan when tasks number is 500, which is lower than other two models. From this, it can be seen that the proposed multi-objective task scheduling model has excellent application effects, which can effectively optimize and improve traditional cloud computing multi-objective scheduling, and ensure the stability of system operation. The research content provides important technical support for the management and scheduling optimization of cloud computing resources.
ANALISIS FAKTOR PENGHAMBAT SKRIPSI MAHASISWA JURUSAN PENDIDIKAN GURU SEKOLAH DASAR UNIVERSITAS WIJAYA KUSUMA SURABAYA

This study aimed to describe the inhibiting factors for the completion of Elementary Education Teacher studentsâ€™ bachelor thesis (hereinafter will be called PGSD Students) in terms of internal and external factors. This research is qualitative descriptive using mixed method approach. The data of this research were collected using questionnaire and interview. The data analysis was performed by counting the percentage from frequent number that emerge in the questionnaires, then describes. The collected data were grouped and presented so that it can be easy top read, then analyzed and concluded using percentage technique. The result of this study showed that (1) The highest obstacle experienced by students in completing a thesis is psychological barrier with cognitive sub-indicator, it has 62 voters with percentage 67,39%; (2) The lack of books or references with 61 voters with a percentage of 66.30%; (3) physical barriers with pain sub-indicator were chosen by 60 voters with a percentage of 65.21%; (4) psychological barriers with affective sub indicator has the number of voters as many as 57 with a percentage of 61.95%; (5) counseling problems with a total of 55 voters with a percentage of 59.78; (6) meeting with supervisors problem were chosen by a total of 52 voters with a percentage of 56.52%; (7) Psychic or mental obstacle problems with psychomotor sub-indicators with 50 voters with a percentage of 54.34%; (8) the costs / funds problems with a total of 49 voters with a percentage of 53.26%; (9) on-campus activities with a total of 46 voters with a percentage of 50%; (10) problems with off-campus activities and the influence of parents with a total of 39 voters with a percentage of 42.39%; (11) pregnancy problems with 37 voters with a percentage of 40.21%; (12) family problems with 27 voters with a percentage of 29.34%. Based on the results of the analysis above, it is expected to be an evaluation material
Motivation, Strategy Use, and Comprehension in Foreign Language Reading: The Case of Saudi EFL Learners at the Preparatory Year

This study explored the relationships among reading motivation, reading strategy use and, reading comprehension in EFL reading. A cohort of 308 male and female EFL students at the preparatory year of an emerging Saudi university completed a reading motivation questionnaire, a reading strategy questionnaire, and a reading comprehension test. Independent samples t-test results revealed that students with high reading motivation (extrinsic, intrinsic, and efficacy) used three reading strategy categories (pre, while, and post-reading strategies) more frequently than students with moderate reading motivation. Similarly, students with high reading motivation were found to have better reading comprehension. Reading strategy use was also found to induce significant differences in students’ reading comprehension. Results of multiple regression revealed that extrinsic motivation was the best predictor of students’ reading strategy use and reading comprehension. Of all strategy categories, while-reading strategies were the best predictor of students’ reading comprehension. Of all motivation and strategy factors, while-reading strategies, reading efficacy and extrinsic motivation significantly predicted reading comprehension, with while-reading strategies being the strongest predictor. Implications for reading instruction and suggestions for further research are offered.
The pandas Library—An Introduction

This chapter gets into the heart of this book: the pandas library. This fantastic Python library is a perfect tool for anyone who wants to perform data analysis using Python as a programming language.
Segmentation of Breast Cancer on Ultrasound Images using Attention U-Net Model

Breast cancer (BC) is one of the most prevailing and life-threatening types of cancer impacting women worldwide. Early detection and accurate diagnosis are crucial for effective treatment and improved patient outcomes. Deep learning techniques have shown remarkable promise in medical image analysis tasks, particularly segmentation. This research leverages the Breast Ultrasound Images BUSI dataset to develop two variations of a segmentation model using the Attention U-Net architecture. In this study, we trained the Attention3 U-Net and the Attention4 U-net on the BUSI dataset, consisting of normal, benign, and malignant breast lesions. We evaluated the model's performance based on standard segmentation metrics such as the Dice coefficient and Intersection over Union (IoU). The results demonstrate the effectiveness of the Attention U-Net in accurately segmenting breast lesions, with high overall performance, indicating agreement between predicted and ground truth masks. The successful application of the Attention U-Net to the BUSI dataset holds promise for improving breast cancer diagnosis and treatment. It highlights the potential of deep learning in medical image analysis, paving the way for more efficient and reliable diagnostic tools in breast cancer management.
Design and Application of an Automatic Scoring System for English Composition Based on Artificial Intelligence Technology

The automatic grading of English compositions involves utilizing natural language processing, statistics, artificial intelligence (AI), and other techniques to evaluate and score compositions. This approach is objective, fair, and resource-efficient. The current widely used evaluation system for English compositions falls short in off-topic assessment, as subjective factors in manual marking lead to inconsistent scoring standards, which affects objectivity and fairness. Hence, researching and implementing an AI-based automatic scoring system for English compositions holds significant importance. This paper examines various composition evaluation factors, such as vocabulary usage, sentence structure, errors, development, word frequency, and examples. These factors are classified, quantified, and analysed using methods such as standardization, cluster analysis, and TF word frequency. Scores are assigned to each feature factor based on fuzzy clustering analysis and the information entropy principle of rough set theory. The system can flexibly identify composition themes in batches and rapidly score English compositions, offering more objective and impartial quality control. The goal of the proposed system is to address existing issues in teacher corrections and evaluations, as well as low self-efficacy in students' writing learning. The test results demonstrate that the system expands the learning material collections, enhances the identification of weak points, optimizes the marking engine performance with the text matching degree, reduces the marking time, and ensures efficient and high-quality assessments. Overall, this system shows great potential for widespread adoption.
Asymmetric Key Cryptosystem for Image Encryption by Elliptic Curve over Galois Field

Protecting the integrity and secrecy of digital data transmitted through the internet is a growing problem. In this paper, we introduce an asymmetric key algorithm for specifically processing images with larger bit values. To overcome the separate flaws of elliptic curve cryptography (ECC) and the Hill cipher (HC), we present an approach to picture encryption by combining these two encryption approaches. In addition, to strengthen our scheme, the group laws are defined over the rational points of a given elliptic curve (EC) over a Galois field (GF). The exclusive-or (XOR) function is used instead of matrix multiplication to encrypt and decrypt the data which also refutes the need for the inverse of the key matrix. By integrating the inverse function on the pixels of the image, we have improved system security and have a wider key space. Furthermore, through comprehensive analysis of the proposed scheme with different available analyses and standard attacks, it is confirmed that our proposed scheme provides improved speed, security, and efficiency.
Feature independent Filter Pruning by Successive Layers analysis

Convolutional neural networks (CNNs) have become deeper and wider over time. However due to low computational power, mobile devices or embedded systems cannot use very deep models. Filter pruning solves this by eliminating redundant filters. Pruning can be performed in a feature dependent or independent manner. Feature dependent methods require extensive time to determine the filter importance as these methods require generation and processing of feature maps for each example. Additionally, in iterative pruning, filter importance is computed several times based on the current state. This increases algorithm execution time further. However, existing feature independent methods are fast, but they perform poor as they compute importance using only current layer filter weights. However, our analysis suggests that both the current and succeeding layer filters are crucial to determine filter importance. We propose ‘Filter Pruning by Successive Layers analysis’ (FPSL), a novel feature independent algorithm, that considers the effect of pruning a filter on the generation of feature maps for the first time. Moreover, FPSL does not require layer-wise retraining, rigorous hyperparameter search for fine-tuning, or human intervention to set the pruning percentage per layer. These make FPSL extremely fast, efficient, and adaptive. Thus it follows iterative pruning and retraining. FPSL outperforms the state-of-the-art (SOTA) methods on extensive experiments with different datasets (CIFAR, ImageNet) and architectures (VGG, ResNet, MobileNet). It decreases the computational burden of VGG16 by half but improves CIFAR10 and CIFAR100 accuracy. Even for ImageNet, FPSL reduces 42.7% floating point operations (FLOPs) while maintaining top-1 accuracy for ResNet50.
Analisis Kemampuan Literasi Sosial Siswa Kelas IV Sekolah Dasar

The purpose of this study was to describe students' social literacy abilities in terms of social literacy indicators, namely understanding oneself, understanding and managing emotions, understanding social situations, and establishing relationships. This research is a descriptive research, by analyzing student test results. The research subjects consisted of 30 fourth grade students at SDN 1 Mangkujayan. The results of the study showed that the social literacy skills of fourth grade students at SDN 1 Mangkujayan regarding kingdoms in the archipelago were still in the very low category, namely getting a percentage of 51%. Special teaching materials are needed for students who can increase students' social sensitivity so that they can increase social literacy in students.&#x0D; &#x0D; Keyword: social literacy, socil education, history, local wisdom
Smart health technologies used to support physical activity and nutritional intake in fall prevention among older adults: A scoping review

Falls are the second leading cause of accidental injury deaths globally. Older age is a key risk factors for falls. Besides older age, physical inactivity and malnutrition are identified risk factors for falls. Smart health technologies might offer a sustainable solution to prevent falls by supporting physical activity and nutritional status. The aim is to identify, describe, and synthesize knowledge, and identify knowledge gaps on the use of existing smart health technologies to support health behaviour in relation to physical activity and nutrition, among older (65+) in risk of falling. A scoping review was conducted following the PRISMA-ScR. Searches were carried out in PubMed, Scopus, and Embase using search strings on the themes; smart health technology, physical activity, nutrition, behaviour, falls and older. Identified literature was screened. Data from the included studies was extracted and synthesized. 2948 studies were obtained through searches. 18 studies were included. Various smart health technologies are used for fall prevention to support physical activity among older, including software and applications for smart phones, TV, and tablet. Three gaps were identified: use of smart health technologies to support nutrition in fall prevention. Inclusion of relevant stakeholders and fall prevention in low-and middle-income countries. Smart health technology can offer sustainable and cost-effective fall prevention in the future. More knowledge is needed on the use of smart health technologies to support nutritional status for fall prevention, and studies involving older with physical and cognitive conditions, and studies on measures for fall prevention in low- and middle-income countries is needed.
Preprocessing Techniques of Solar Irradiation Data

Solar energy being abundant, non-exhaustive, environmentally friendly attracts the people attention towards the alternate renewable energy. High-quality time series data is essential for producing an accurate estimate of solar power generation. In most cases, the plethora of information hidden in time series data cannot be accessed. Common issues with time series include outliers, noise, missing data, and a lack of order in the timestamps itself that impair forecasting accuracy. So, preprocessing of the input data is a mandate in order to achieve a precise and dependable forecast. This study proposes various pre-processing techniques to improve the performance of the forecasting accuracy. The different ways to handle the missing values and outliers detection by sliding window method and box plots are presented in this study. The solar irradiation data collected from solar center Alice Springs, Australia used for validation of the preprocessing results. The efficacy of the proposed method in detecting the missing values and outliers is effective from the obtained results.
In Search for Optimal Methods: New Insights About Meta-Induction

Abstract In this paper, the contributions to the account of meta-induction (Schurz 2019) collected in this volume are critically discussed and thereby, new insights are developed. How broad and expandable the program of meta-induction is can be learned from Ortner’s contribution. New insights about the transition from the a priori justification of meta-induction to the a posteriori justification of object-induction emerge from the reflection of Shogenji’s paper. How meta-induction may be applied also to religious prophecies and that their meta-inductive justification does not fail for a priori reasons but because of missing evidence for predictive success is learned from the discussion of Pitts’ contribution. That meta-induction does not rely on a particular prior distribution, while the no free lunch theorem depends implicitly on a uniform prior, is the major conclusion drawn from the discussion of Wolpert’s article. How the problem of induction is treated in different versions of the Bayesian account is learned from the discussion of Willliamson’s paper. That meta-induction can also be employed for abduction, and that abductive theory-revision can offer meta-inductive aggregation methods is a new insight emerging from the reflection of Aliseda’s contribution.
Analysis of Social Engineering Awareness among Students and Lecturers

The massive technological progress and wide use of Information Technology have increased cyber security threats. Social engineering attacks are a common type of cyber security threat that faces everyone. It uses several methods, such as pretexting using Artificial Intelligence or phishing, to attack users’ valuable data due to human error. The risks of data attacks have increased, especially in the institutions sector, as the use of digital technologies become easier around the users. This paper investigates the awareness of social engineering attacks and cyber-security threats at the University of Sulaimani. The University of Sulaimani, based in the Kurdistan Region of Iraq, has a large number of students and staff; due to the increase of social engineering threats and lack of knowledge of cyber securities, the internet users at the University of Sulaimani put their confidential data at risk. This research has employed a quantitative approach, using a self-report questionnaire to gather primary data from participants. The online survey has been launched at the University of Sulaimani to provide a measurement of social engineering attacks on students and staff. The results show a variety of factors impacting participants’ awareness of their data. The objective of this study is to evaluate the participants’ knowledge of cyber-security and analyze their awareness of social engineering data breaches.
Combining Identity Features and Artifact Analysis for Differential Morphing Attack Detection

Due to the importance of the Morphing Attack, the development of new and accurate Morphing Attack Detection (MAD) systems is urgently needed by private and public institutions. In this context, D-MAD methods, i.e. detectors fed with a trusted live image and a probe tend to show better performance with respect to S-MAD approaches, that are based on a single input image. However, D-MAD methods usually leverage the identity of the two input face images only, and then present two main drawbacks: they lose performance when the two subjects look alike, and they do not consider potential artifacts left by the morphing procedure (which are instead typically exploited by S-MAD approaches). Therefore, in this paper, we investigate the combined use of D-MAD and S-MAD to improve detection performance through the fusion of the features produced by these two MAD approaches.