Reviewerly/exHarmony huggingface dataset:
Professional and Educational Culture of a Student as an Object of Pedagogical Monitoring at the Higher Educational Institution

The article is devoted to the study of the professional and educational culture of a university student in the context of pedagogical monitoring. Vocational education culture is an important aspect of a student's development that determines his professional and social competencies. Pedagogical monitoring, in turn, is a tool for systematic observation, evaluation and improvement of the quality of the educational process. On the example of Kyrgyzstan, the article analyzes the objects of pedagogical monitoring in the context of a student’s professional and educational culture. The study is based on an analysis of current literature, legislation and the experience of universities in Kyrgyzstan. Particular attention is paid to the definition of key components of a professional educational culture, such as professional values, ethics, communication skills, creative thinking and self-development. The article also considers the methods and tools used in the pedagogical monitoring of a student’s professional and educational culture. She discusses the possibilities of using questionnaires, observation, interviews and other tools to collect data on the professional and educational culture of university students. An important aspect of the article is the description of the problems faced by the universities of Kyrgyzstan in the implementation of pedagogical monitoring of the student’s professional and educational culture. This includes limited resources, the lack of a unified methodology, difficulties in collecting and analyzing data, and the need for teacher training. In conclusion, the article emphasizes the importance of pedagogical monitoring of the professional and educational culture of a university student in Kyrgyzstan. The results of such monitoring can be useful for evaluating the effectiveness of educational programs, identifying problem areas and developing measures to improve them. The article offers some recommendations and strategies for the development of pedagogical monitoring of a student’s professional and educational culture. This includes the development of a unified methodology and standards that will ensure the uniformity and comparability of data, as well as the training of teaching staff in monitoring skills. In addition, the article emphasizes the importance of students’ participation in the process of monitoring their professional and educational culture. Students should be actively involved in the assessment and self-assessment of their competencies, be aware of the importance of professional values and develop the skills necessary for a successful career. In general, the article calls for further research and development of pedagogical monitoring of the professional and educational culture of a university student in Kyrgyzstan. This will improve the quality of education, adapt programs to the requirements of the labor market and contribute to the successful professional adaptation of students.
Tackling Long-tailed Distribution Issue in Graph Neural Networks via Normalization

Graph Neural Networks (GNNs) have attracted much attention due to their superior learning capability. Despite the successful applications of GNNs in many areas, their performance suffers heavily from the long-tailed node degree distribution. Most prior studies tackle this issue by devising sophisticated model architectures. In this paper, we aim to improve the performance of tail nodes (low-degree or hard-to-classify nodes) via a generic and light normalization method. In detail, we propose a novel normalization method for GNNs, termed as ResNorm, which <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Res</b> hapes a long-tailed distribution into a normal-like distribution via <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Norm</b> alization. The ResNorm includes two operators. Firstly, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scale</i> operator reshapes the distribution of the node-wise standard deviation (NStd) so as to improve the accuracy of tail nodes. Secondly, the analysis of the behavior of the standard shift indicates that the standard shift serves as a preconditioner on the weight matrix, increasing the risk of over-smoothing. To address this issue, we design a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">shift</i> operator for ResNorm, which simulates the degree-specific parameter strategy in a low-cost manner. Extensive experiments on various node classification benchmark datasets have validated the effectiveness of ResNorm in improving the performance of tail nodes as well as the overall performance.
Hierarchical Optimization-Derived Learning

In recent years, by utilizing optimization techniques to formulate the propagation of deep model, a variety of so-called Optimization-Derived Learning (ODL) approaches have been proposed to address diverse learning and vision tasks. Although having achieved relatively satisfying practical performance, there still exist fundamental issues in existing ODL methods. In particular, current ODL methods tend to consider model constructing and learning as two separate phases, and thus fail to formulate their underlying coupling and depending relationship. In this work, we first establish a new framework, named Hierarchical ODL (HODL), to simultaneously investigate the intrinsic behaviors of optimization-derived model construction and its corresponding learning process. Then we rigorously prove the joint convergence of these two sub-tasks, from the perspectives of both approximation quality and stationary analysis. To our best knowledge, this is the first theoretical guarantee for these two coupled ODL components: optimization and learning. We further demonstrate the flexibility of our framework by applying HODL to challenging learning tasks, which have not been properly addressed by existing ODL methods. Finally, we conduct extensive experiments on both synthetic data and real applications in vision and other learning tasks to verify the theoretical properties and practical performance of HODL in various application scenarios.
Teaching with Integrity: English Teachers’ Awareness on the Ethical Practices on the Use of Online Materials

This study focused on determining the awareness of English teachers on the ethical guidelines on the use of online materials. Specifically, the study sought to look into information on the demographic profile of the respondents as to sex, age, highest educational attainment, number of years in teaching, grade level taught and the number of hours spent using the internet per day. Significant relationship between the demographic profile and the level of awareness on the three components were also looked into. Further, crafting of intervention program that can serve as guide for teachers and students into safe and responsible use of online materials was also made possible. The study made use of descriptive-quantitative research design wherein the researcher utilizes a researcher-made survey questionnaire consisting of two parts: (1) demographic profile, (2) statements to determine awareness of teachers on the ethical guidelines on the use of online materials. The data collected were analyzed and interpreted using the following statistical tools; Frequency Count and Percentage, Mean and Standard Deviation, Pearson r, Spearman Rank Correlation, and Point Biserial Correlation. The study revealed the following findings; majority of the English teachers in San Jose, Dinagat Islands are young female who have finished only with their bachelor’s degree, and spent a minimal time online. It was also noted that the respondents are “very much aware” on the ethical practices in using online materials in terms of authenticity, textual resource, and audio-visual resource. Moreover, data of the study also revealed that the educational attainment has significant impact on the level of awareness on ethical guidelines in using online materials. Hence, an intervention program needs to be crafted and to be implemented to sustain their awareness.
Adaptive Filter in Single Image SRGAN

Abstract: SRGAN is a generative adversarial network for super-resolution of a single picture. In this paper an adaptive filter in single image SRGAN which is a Super-Resolution Generative Adversarial Network (SRGAN), is a deep learning-based approach that is used to generate high-resolution images from low-resolution ones. The SRGAN model is based on the Generative Adversarial Network (GAN) architecture, which consists of two deep neural networks: a generator network and a discriminator network. The generator network takes a low-resolution image as input and tries to generate a high-resolution image that is similar to the original high-resolution image. The discriminator network, on the other hand, tries to distinguish between the high-resolution images generated by the generator network and the original high-resolution images. The SRGAN model has been shown to be very effective in generating high-quality, realistic images from low-resolution inputs, and it has been applied in various applications, including image and video upscaling, medical imaging, and satellite imaging
The Implementation of “Kampus Mengajar” Program to Enhance Students Literacy and Numeracy at SDN 100209 TN Sukaramai

This study aims to determine the implementation of the “Kampus Mengajar” Program as an effort to improve the students’ quality of literacy and numeracy. The method used for this research is a descriptive qualitative approach. Data collection is done by observation, interviews, and documentation. The result shows that the implementation of “kampus mengajar” program has contributed to enhance students’ literacy, numeracy through three aspects of implementation, namely: conformity between the program and the target group (SDN 100209 TN Sukaramai), conformity between the program and implementing the organization, and conformity between implementing the program of “kampus mengajar” program with the target group.
A Research of Digitalization Status and Database Construction on Chinese Language Textbooks Published in Korea During the Japanese Colonial Period

This study looked at the current status of digitization of Chinese language textbooks published in Korea during the Japanese colonial era and discussed the establishment of a database of original texts and translated texts, focusing on 14 types of research materials. Now, more than ten years have passed since the study of Chinese learning books during the Japanese colonial period began, not only domestic scholars but also foreign scholars are interested in these research materials, and related studies are actively being conducted, such as publishing English editions. It is thought to be an important resource for research in various fields such as the transition of Korean and Chinese languages in the modern and contemporary transitional period, the history of Chinese education, and social and cultural exchanges. This study designed a modern translation database to help the public as well as related researchers to understand the original texts containing information on the original Chinese texts, pronouns, and double texts of the 14 types of data that have been intersected so far. In addition, data input principles were established for accurate data processing, and data were constructed in TSV and JSON formats. In the future, if data from the same period are gathered together to design and build a database to produce digital humanities research results, it will be able to contribute to the development of related humanities research.
Fingerprinting Bitcoin entities using money flow representation learning

Abstract Deanonymization is one of the major research challenges in the Bitcoin blockchain, as entities are pseudonymous and cannot be identified from the on-chain data. Various approaches exist to identify multiple addresses of the same entity, i.e., address clustering. But it is known that these approaches tend to find several clusters for the same actor. In this work, we propose to assign a fingerprint to entities based on the dynamic graph of the taint flow of money originating from them, with the idea that we could identify multiple clusters of addresses belonging to the same entity as having similar fingerprints. We experiment with different configurations to generate substructure patterns from taint flows before embedding them using representation learning models. To evaluate our method, we train classification models to identify entities from their fingerprints. Experiments show that our approach can accurately classify entities on three datasets. We compare different fingerprint strategies and show that including the temporality of transactions improves classification accuracy and that following the flow for too long impairs performance. Our work demonstrates that out-flow fingerprinting is a valid approach for recognizing multiple clusters of the same entity.
Algorithmic Background for Machine Learning

This chapter provides a detailed discussion of the machine learning models typically deployed on (extreme) edge devices. This is necessary to understand the rest of the book, which delves into the hardware-algorithm co-optimization and hardware acceleration of these models. The first part presents the basic concepts of one of the classical machine learning models, i.e., support vector machines (SVMs), followed by a discussion on a sub-field of machine learning (ML), called deep learning (DL), and the models used in it. Rather than giving a detailed overview of the wide range of models used in machine learning, these sections focus on the models extensively used in the subsequent chapters of this book. Finally, the chapter presents some of the standard feature extraction algorithms used in the machine learning pipeline for the applications accelerated in this book.
Occamy: Memory-efficient GPU Compiler for DNN Inference

This work proposes Occamy, a new memory-efficient DNN compiler that reduces the memory usage of a DNN model without affecting its accuracy. For each DNN operation, Occamy analyzes the dimensions of input and output tensors, and their liveness within the operation. Across all the operations, Occamy analyzes liveness of all the tensors, generates a memory pool after calculating the maximum required memory size, and schedules when and where to place each tensor in the memory pool. Compared to PyTorch, on an integrated embedded GPU for six DNNs, Occamy reduces the memory usage by 34.6% and achieves a geometric mean speedup of 1.25×.
Research on Intelligent Scheduling Method of Multi Cloud Collaborative Computing Network Fusion Resources

Conventional scheduling methods mainly focus on shortening the scheduling time, and the network resources cannot be balanced, resulting in poor scheduling results. Therefore, an intelligent scheduling method for multi cloud collaborative computing network fusion resources is designed. Construct a global Nash equilibrium of computing network integration resources, balance the resources after computing network integration, and avoid the problem of local optimal scheduling. Based on multi cloud collaboration, the resource scheduling model of computing network cloud computing is constructed to find the optimal scheduling value of resource data after computing network integration. Verify the resource scheduling consistency of the multi cloud platform, the hash function of the fixed scheduling model, and analyze the integrity of the resource scheduling data. By means of simulation experiments, it is verified that the intelligent scheduling effect of this scheduling method is more stable and can be applied to real life.
The Relationship between Reading Attitude and Student Grade Point Average

Reading is crucial for individuals to achieve optimal functioning, as it facilitates the development of cognitive processes such as critical thinking and decision-making, which foster the ability to conceptualize and solve problems. This research study is significant to conduct because there is a dier need for a scholarly inquiry more focused on establishing the relationship between students’ attitudes toward reading and their academic performance while considering the influence of study skills across all levels of education. The main aim of the study was to examin the relation between the reading attitudes of students and their academic acheivment by gainning higher grades in the class. Thus, the main research question the study was; What is the relationship between reading attitude and students’ grade point average? The main research tool used in thse study was quantitative research method. The study was conducted in King Saud University for Health and Sciences (KSUHS) in Riyadh Saudi Arabia. Random sampling technique was used to select the sample. Sample size of the study was 146 students enrolled in first-year at KSUHS. The Pearson correlation value of E1, E2, E3, G1, E10, E13, E16, E21, G25, E1, E2, E22, E12, E7, E8, E14 lie between +1 and -1 which showed that variables have a significant relationship with each other. The hypothesis test values were (t=4.797, p&lt;.01) which supported the hypothesis that a positive reading attitude contributes to higher grade point averages in the classroom due to which H1 was accepted. Correlation among the variables showed that grade point average is significantly correlated with Reading attitudes toward the English language.
Optimal Synthesis of Multi-Controlled Qudit Gates

We propose a linear-size synthesis of the multi-controlled Toffoli gate on qudits with at most one borrowed ancilla. This one ancilla can even be saved when the qudit dimension is odd. Our synthesis leads to improvements in various quantum algorithms implemented on qudits. In particular, we obtain (i) a linear-size and one-clean-ancilla synthesis of multi-controlled qudit gates; (ii) an optimal-size and one-clean-ancilla synthesis of unitaries on qudits; (iii) a near-optimal-size and ancilla-free/one-borrowed-ancilla implementation of classical reversible functions as qudit gates.
A Dropout-Tolerated Privacy-Preserving Method for Decentralized Crowdsourced Federated Learning

Mobile crowdsourcing federated learning (FL-MCS) allows a requester to outsource its model-training tasks to other workers who have the desired data as well as strong computing power. FL-MCS can thereby overcome the limitations of computing capability as well as the data availability of participants. However, FL-MCS still faces the problem of workers’ data privacy leakage when diverse malicious attacks (e.g., gradient inference attacks) are launched. To address these problems, some privacy-preserving FL-MCS (PPFL-MCS) schemes are proposed to aggregate local models at a central server. Unfortunately, these schemes are vulnerable to single-point-of-failure and other malicious attacks at the central server. Meanwhile, the workers may drop from the online task due to the erratic communication network in PPFL-MCS schemes, thereby resulting in the failure of the entire model aggregation. To solve these issues, we propose a novel dropout-tolerated and privacy-preserving decentralized FL-MCS scheme, namely DTPP-DFL-MCS based on blockchain. Specifically, we define a novel cryptographic primitive, i.e., ID-based Aggregated Decryptable Broadcast Encryption (AD-IBBE) based on traditional ID-based broadcast encryption. In AD-IBBE, the senders’ ciphertexts can only be decrypted by themselves while the aggregated ciphertexts can be decrypted by all receivers in the broadcast group. Then, we design a homomorphic AD-IBBE algorithm, which is formally proved to be semantically secure. We next devise the decentralized PPFL-MCS scheme to guarantee the confidentiality of model gradients against internal and external adversaries. Moreover, we design a dropout-tolerated aggregation method to ensure the robustness of our decentralized PPFL-MCS scheme even if some workers lose connection. Extensive experimental results on different models and datasets demonstrate that the proposed scheme guarantees a close model accuracy to the non-dropout case. Even when some workers are offline, our scheme still performs more efficiently than existing schemes in terms of dropout aggregation overhead.
Verifiable Key-Aggregate Searchable Encryption With a Designated Server in Multi-Owner Setting

Key-aggregate searchable encryption (KASE) schemes support selective data sharing and keyword-based ciphertext searching by using the constant-size shared key and trapdoor, making these schemes attractive for resource-constrained users to store, share, and search encrypted data in public clouds. However, most previously proposed KASE schemes suffer from our proposed “off-line keyword guessing attack (KGA)” and some other weaknesses. Consequently, they fail to gain the keyword ciphertext indistinguishability and trapdoor indistinguishability, which are vital security goals of searchable encryption. Inspired by the relationship of public key encryption with keyword search (PEKS) and KASE, we design a new KASE scheme called key-aggregate searchable encryption with a designated server (dKASE). The dKASE scheme achieves our proposed keyword ciphertext indistinguishability against chosen keyword attack (KC-IND-CKA) and keyword trapdoor indistinguishability against keyword guessing attack (KT-IND-KGA) security models, where the latter model captures off-line KGA. Then, we extend the dKASE scheme to verifiable dKASE in multi-owner setting (dVKASEM) scheme. With dVKASEM, when multiple data owners authorize a user to access data, the user merely needs to store his single key and generate a single trapdoor to query these owners’ data. Besides, the adoption of the aggregate signature significantly reduces the overhead of verifying whether data has been tampered with. Performance analysis illustrates that our schemes are efficient.
Postdigital Nature 2.0

Postdigital nature is the consumption and prosumption of nature through postdigital technologies. Individuals are increasingly co-creating with nature as postdigital technologies become more prevalent. Where interactions with nature used to be between humans and the nonhuman (animals, plants, water, soil etc.), postdigital nature also includes the interactions between these entities and postdigital technologies. Postdigital nature is creating new assemblages and entanglements which have implications for humans, the nonhuman, and postdigital technologies.
CEDA: Learned Cardinality Estimation with Domain Adaptation

Cardinality Estimation (CE) is a fundamental but critical problem in DBMS query optimization, while deep learning techniques have made significant breakthroughs in the research of CE. However, apart from requiring sufficiently large training data to cover all possible query regions for accurate estimation, current query-driven CE methods also suffer from workload drifts. In fact, retraining or fine-tuning needs cardinality labels as ground truth and obtaining the labels through DBMS is also expensive. Therefore, we propose CEDA, a novel domain-adaptive CE system. CEDA can achieve more accurate estimations by automatically generating workloads as training data according to the data distribution in the database, and incorporating histogram information into an attention-based cardinality estimator. To solve the problem of workload drifts in real-world environments, CEDA adopts a domain adaptation strategy, making the model more robust and perform well on an unlabeled workload with a large difference from the feature distribution of the training set.