Reviewerly/exHarmony huggingface dataset:
Sensitivity-based (p, α, k) - Anonymity Privacy Protection Algorithm

Medical data itself has extremely high research value, but how to protect its privacy and security in the process of sharing medical data has attracted widespread attention from researchers. Aiming at the problems of homogeneity attack, background knowledge attack and high-sensitivity similarity attack in data sharing of k -anonymity privacy protection algorithm, a sensitivity-based (p, α, k) -anonymity privacy protection algorithm is proposed. The concept of semantic similarity tree is introduced, which can resist background knowledge attacks. The improved clustering method of equivalence classes can solve homogeneity attacks and high-sensitivity similarity attacks. Thus, the security of medical data sharing can be realized. Experiments show that (p, α, k) - anonymity privacy protection algorithm has the best performance when α is equal to 0.5. In addition, compared with k -anonymity privacy protection algorithm, although (p, α, k) - anonymity privacy protection algorithm has higher execution time and information loss, it effectively solves the problems of k - anonymity algorithm and improves the security of medical data sharing.
Research on the Digital Method of Teaching Resources Based on Raspberry Pi

At present, online teaching resources are mainly produced by some software like Rain classroom, or recording video in professional rooms. Both methods have their own advantages and disadvantages, and put into school level, there are still a large part of schools can not provide students with sufficient and excellent online teaching resources. In order to realize an easier way to digitize teaching resources, this paper uses Raspberry Pi as the core processor, camera module and Python-OpenCV library as the basis, and combines Yolov4-tiny algorithm to realize the digitization of teaching resources, which provides a more economical and convenient way to utilize teaching resources. The digital method could promote the circulation of teaching resources and raise the frequency of information exchange inside and outside the school.
Error-Driven Triplet-Based Online Fine-Tuning for Cross-Background Image Classification

Image classification is a fundamental task in computer vision with numerous real-world applications. However, classification models trained on one set of images may not perform well when tested on another set, especially when the two sets differ in terms of the background environment. To address this challenge, we propose an error-driven triplet-based online fine-tuning approach that leverages misclassified samples to refine the classification model at intervals when enough misclassified samples are collected. Our approach builds on the triplet network architecture, which learns to represent images in a low-dimensional space where images with the same label are clustered together. We use a pre-trained classification model that was trained on a collection of 180 types of images from one background scene. However, when we apply the model to a new background scene with additional types of images, its performance is compromised due to the domain shift. Our proposed approach leverages the misclassified samples by contrast them with positive and negative samples as triplet data to fine-tune the model in new background scene. We use a loss function that combines the triplet loss and the classification loss to update the model weights. We evaluate our approach on two challenging image classification datasets with different background environments. The experimental results demonstrate that our approach achieves significant improvements in accuracy compared to the baseline classification model without fine-tuning. Overall, our error-driven triplet-based online fine-tuning approach shows promising results for adapting classification models to changing background with additionally new classification types, where the performance of pre-trained models is limited.
Optimal off-grid photovoltaic production in developing countries during critical months by using optimum tilt

The study focuses on determining the optimal monthly inclination angle of the photovoltaic panels with the aim of maximizing the solar irradiance captured each month of the year. This data, together with the knowledge of the necessary monthly electricity consumption, will help to optimally select the generator power or make a correct location of the panels. The use of off-grid photovoltaic systems in latitudes between ±37° is increasingly used as the main method of electricity supply in many developing countries. The aim is to find simple equations to relate the monthly optimal inclination of the solar panels and latitude in the mentioned range of latitudes. These equations can be very useful when irradiance data and advanced computer tools are not available to provide the monthly optimal inclination in that site. By optimizing the solar capture in the critical month and designing the system to guarantee the electricity demand that month, there will be enough photovoltaic production in the rest of the months.
PV Power Production and Consumption Estimation with Uncertainty bounds in Smart Energy Grids

For efficient energy exchanges in smart energy grids under the presence of renewables, predictions of energy production and consumption are required. For robust energy scheduling, prediction of uncertainty bounds of Photovoltaic (PV) power production and consumption is essential. In this paper, we apply several Machine Learning (ML) models that can predict the power generation of PV and consumption of households in a smart energy grid, while also assessing the uncertainty of their predictions by providing quantile values as uncertainty bounds. We evaluate our algorithms on a dataset from Swedish households having PV installations and battery storage. Our findings reveal that a Mean Absolute Error (MAE) of 16.12W for power production and 16.34W for consumption for a residential installation can be achieved with uncertainty bounds having quantile loss values below 5W. Furthermore, we show that the accuracy of the ML models can be affected by the characteristics of the household being studied. Different households may have different data distributions, which can cause prediction models to perform poorly when applied to untrained households. However, our study found that models built directly for individual homes, even when trained with smaller datasets, offer the best outcomes. This suggests that the development of personalized ML models may be a promising avenue for improving the accuracy of predictions in the future.
LD-ABR: An Adaptive Bitrate Algorithm for Video Transmission in Wireless Network

The popularity of mobile terminals has made video transmission in wireless scenarios important. However, achieving high viewing quality video transmission between base and mobile terminals is a difficult problem. The unpredictable fading and noise generated in wireless scenarios cause drastic fluctuations, making existing adaptive bit rate algorithms unable to adapt to the rapidly changing volatility and long tail problems in the network. In this paper, we introduce LSTM-D3QN Adaptive Bitrate Algorithm (LD-ABR), a new reinforcement learning Adaptive Bitrate Algorithm. LD-ABR uses long and short-term memory (LSTM) to predict throughput, and uses video bit rate, bit rate switching frequency, network speed and video pause time for bit rate selection to better cope with the complex changes in wireless networks. Finally, LD-ABR is compared with Comyco and Pensieve algorithms and the results show that LD-ABR has better performance in wireless network environment. Under the worst network conditions, Pensieve mode has a 16% chance of stopping, Comyco has a 7% chance, and LD-ABR mode has only a 1% chance, and its QoE index is more than 30% higher than Pensieve.
Phishy? Detecting Phishing Emails Using ML and NLP

Phishing emails, a type of cyberattack using fake emails, are difficult to recognize due to sophisticated techniques employed by attackers. In this paper, we use a natural language processing (NLP) and machine learning (ML) based approach for detecting phishing emails. We compare the efficacy of six different ML algorithms for the purpose. An empirical evaluation on two public datasets demonstrates that our approach detects phishing emails with high accuracy, precision, and recall. The findings from this work are useful in devising more efficient techniques for recognizing and preventing phishing attacks.
Applications of Causality and Causal Inference in Software Engineering

Causal inference is a study of causal relationships between events and the statistical study of inferring these relationships through interventions and other statistical techniques. Causal reasoning is any line of work toward determining causal relationships, including causal inference. This paper explores the relationship between causal reasoning and various fields of software engineering. This paper aims to uncover which software engineering fields are currently benefiting from the study of causal inference and causal reasoning, as well as which aspects of various problems are best addressed using this methodology. With this information, this paper also aims to find future subjects and fields that would benefit from this form of reasoning and to provide that information to future researchers. This paper follows a systematic literature review, including; the formulation of a search query, inclusion and exclusion criteria of the search results, clarifying questions answered by the found literature, and synthesizing the results from the literature review. Through close examination of the 45 found papers relevant to the research questions, it was revealed that the majority of causal reasoning as related to software engineering is related to testing through root cause localization. Furthermore, most causal reasoning is done informally through an exploratory process of forming a Causality Graph as opposed to strict statistical analysis or introduction of interventions. Finally, causal reasoning is also used as a justification for many tools intended to make the software more human-readable by providing additional causal information to logging processes or modeling languages.
Performance of GAN-Based Denoising and Restoration Techniques for Adversarial Face Images

Facial recognition (FR) systems are employed to identify and authenticate individuals. There has been a rise in privacy concerns regarding mass surveillance and unauthorized usages. As a result, one viable approach is adding adversarial noise to distort user profile images so that FR technology can be bypassed. Nonetheless, such approaches could be used by adversaries to avoid detection in surveillance footage and therefore evade identification. To combat this threat, a line of research efforts focuses on generative adversarial network (GAN)-based Denoising and Restoration to remove adversarial noise. In this paper, GAN-based methods are investigated experimentally for assessing their effectiveness. Particularly, three GAN-based approaches, i.e., Blind Face Restoration, Blur and Restore, and Image-to-image Translation, are extensively examined with several representative classification approaches. Our evaluation results show that GAN denoising schemes could improve image visual quality, but are ineffective to remove perturbations for privacy protection attached by Fawkes or Lowkey. We further discuss some future research directions on image transformation-based approaches, which can potentially improve the effectiveness.
TIPICAL - Type Inference for Python In Critical Accuracy Level

Type inference methods based on deep learning are becoming increasingly popular as they aim to compensate for the drawbacks of static and dynamic analysis approaches, such as high uncertainty. However, their practical application is still debatable due to several intrinsic issues such as code from different software domains will involve data types that are unknown to the type inference system.In order to overcome these problems and gain high-confidence predictions, we thus present TIPICAL, a method that combines deep similarity learning with novelty detection. We show that our method can better predict data types in high confidence by successfully filtering out unknown and inaccurate predicted data types and achieving higher F1 scores to the state-of-the-art type inference method Type4Py. Additionally, we investigate how different software domains and data type frequencies may affect the results of our method.
P‐8.6: A Progressive Single Image Super‐Resolution Algorithm

The existing single‐image super‐resolution convolutional neural network usually elevates the resolution to the specified scale in one step in the final image reconstruction part. However, this may lead to the incomplete reconstruction of some detailed features. Therefore, we propose a super‐resolution reconstruction algorithm based on progressive architecture. For large‐scale scaling, high‐quality images can be gradually generated from coarse to fine in progressive reconstruction. An adaptive high‐frequency residual module is designed to learn the image features through two branches of low frequency and high frequency, respectively, and the output is adaptive fusion. This process is embedded in a Features cut Block to refine feature extraction during reconstruction, capture the dependency between the LR‐HR image pairs, and introduce an efficient channel and spatial attention mechanism to fuse the image information. Experimental results show that this algorithm has good performance and can enhance the reconstruction ability of images.
57.1: Research on Moving Picture Response Time of Organic Light‐emitting Diode

The motion image quality of organic light‐emitting diode (OLED) degrades by motion blur due to the hold‐type display. Moving picture response time (MPRT) is a representative index to evaluate the performance of the moving image. A method based on high speed camera is introduced to evaluate the motion blur of OLED display. We explored the influence of exposure time of high speed camera on MPRT evaluation.
P‐8.2: CNN for Dual‐layer LCDs

Low contrast ratio (CR) is one big drawback for LCD, which is caused by light leakage of LC cell. Light leakage can be greatly reduced by bonding two LC panels to form a dual‐layer LCD. However, with the certain distance between the two LC panels, the ghost may appear when viewing off‐axis. This paper proposes a convolutional neural network‐based method to improve the display quality of a dual‐layer LCD. The network consists of 8 convolution layers, in which the kernel of the layer 4 and layer 8 is 1×1 to eliminate the redundant features and accelerate the processing speed. The kernel of the other layers is 3×3. Concat1 and Concat2 are included to fuse the features from the convolution layer 1‐3 and 5‐7. The loss function is especially defined with the consideration of the total differences between the reconstructed images at the different viewing angles and the input image. The simulation results show that compared with the existing methods, the proposed method has the merits of higher display quality and shorter processing time, which presents high practicability.
Digital literacy of teachers for using electronic textbooks in the teaching class of nature and society

The goal of this study is identification of teachers assessment about digital literacy the have for using electronic textbooks in the teaching class of Nature and society. In this research, we work from general assumption that electronic textbooks are important in teaching Nature and society, but not all teachers are equally digitally competent for using electronic textbooks, then also effects of their usage in Nature and society are different. It also deals with special assumptions that in the research will reveal that there are differences in teachers assessments of the digital competence they have for usage of electronic in Nature and society depending on their years in service in school, their educational level and the ways they improve their knowledge. Research has been conducted in the first half of 2022/2023. school year, in the area of Zlatibor district. Data collected by Scalar - DCT-UeTCNS ( = .924) and processed with descriptive statistics, analysis of variance and Man-Vitney U test have shown there is statistically significant difference in the estimates of teacher about their digital competences they have for using electronic textbooks the class of Nature and society in depends of their years in service in school, their educational level and the ways they improve their knowledge. The research results point to the need of the efficient implementation of electronic textbooks in the class of Nature and society, improving digital competences of students in elementary school on all levels of teaching and the need of continual professional development of teachers, especially in the field of improving digital competences directed on creative usage of electronic textbooks in teaching practice.
The congruence and currency of Comenius's understanding of the theory and practice of preschool and family education

The aim of this paper is to consider the currency of Comenius's understandings of preschool and family education, and their congruence with the modern theory and practice of preschool and family education. In this context, a comparative research was conducted, utilizing the method of theoretical analysis, and the technique of content analysis. The comparative analysis implies that many of Comenius's ideas are still relevant, and congruent with contemporary theoretical and empirical knowledge about preschool and family education to a great extent. The conformity of attitudes within the pedagogical system was recorded, despite the fact that Comenius's attitudes regarding children's nature and childhood, goals, importance and content of early childhood education and family environment, as well as the role of parents in that process were presented and developed in unique historical and sociopolitical circumstances. Although the comparative analysis carried out has certain limitations (selective choice of primary and secondary sources of analysis, difficulties in establishing equivalence), this work can be a stimulus for further elaboration of Comenius' essential understandings of preschool and family education in conditions of increasingly complex crises in education and the family.
Emergency education as a catalyst for teacher change: Extent and correlates of the class and subject teachers' growth of competences

The aim of this study was to explore the possibility that the emergency education during the Covid-19 pandemic served as a catalyst for teacher development. We investigated the growth of teachers' competences that were highlighted by the online teaching, and looked into per sonal and school factors that stimulated or restricted this growth. Four domains of expected teacher growth were chosen: general digital competence, pedagogical-digital competence, competence for stu dent assessment, and competence for conducting inclusive practices. A total of 314 teachers filled out an online questionnaire and rated their pre-pandemic and current levels of 18 items describing skills, beliefs, and attitudes of the four selected domains. The instrument also included scales for measuring school-related (leadership, teacher autonomy, collaboration) and teacher-related variables (teacher self-efficacy, growth mindset, reflective practice). The results of the paired samples t-test showed that improvements of the competences were significant in all four domains, and the highest in the domain of pedagogical-digital competence. Partial correlations revealed that teacher-level variables were more important factors of the growth than school-related variables. Differences between the class and subject teachers were found. Recommendations are given in the direction of supporting the emerged good practices and maintaining of the developed competences in the post-pandemic era.
Modern approach to management as a factor of effective functioning of kindergarten

The provision of open opportunities, the acquisition of experiences where it is possible to develop the capacities of the child's personality and their potential in the period of early age are elements on which his future activities, opportunities and functioning as an active member of society will depend. Through communication, children of pre-school age gain freedom of speech, are independent, learn to navigate different situations, are creative, inquisitive, curious, and this way contributes to the development of dispositions for active action and learning. Quality preschool upbringing and education should be based on this development of preschool children, on the competence of educators, active participation of parents of children in the organization of work and life of their children, and other systems with the aim of progressive changes. All these systems condition the presence of managers in all educational institutions, including kindergartens. We see the development plan and program as an opportunity to raise awareness of the value of the system of organizing educational work in kindergartens, as well as the joint activities of all factors that participate in the process of raising and educating preschool children, through the prism of management in kindergartens.
Specificity and Context Dependent Preferences in Argumentation Systems

Dung and Son [6] argue that specificity as a criterion for resolving conflicts between arguments, is context dependent. They propose to use arguments to address the context dependency of specificity in combination with a new special argumentation semantics. Unfortunately, their solution is restricted to argumentation systems without undercutting arguments. This paper presents a more general solution which allows for undercutting arguments and allows for any argumentation semantics. Moreover, the solution is applicable to any form a context dependent preferences.
Evaluate AMR Graph Similarity via Self-supervised Learning

In work on AMR (Abstract Meaning Representation), similarity metrics are crucial as they are used to evaluate AMR systems such as AMR parsers.Current AMR metrics are all based on nodes or triples matching without considering the entire structures of AMR graphs.To address this problem, and inspired by learned similarity evaluation on plain text, we propose AMRSim, an automatic AMR graph similarity evaluation metric.To overcome the high cost of collecting human-annotated data, AMRSim automatically generates silver AMR graphs and utilizes self-supervised learning methods.We evaluated AMRSim on various datasets and found that AMRSim significantly improves the correlations with human semantic scores and remains robust under diverse challenges.We also discuss how AMRSim can be extended to multilingual cases. 1