Reviewerly/exHarmony huggingface dataset:
Gradient Leakage Attack Resilient Deep Learning

Gradient leakage attacks are considered one of the wickedest privacy threats in deep learning as attackers covertly spy gradient updates during iterative training without compromising model training quality, and yet secretly reconstruct sensitive training data using leaked gradients with high attack success rate. Although deep learning with differential privacy is a defacto standard for publishing deep learning models with differential privacy guarantee, we show that differentially private algorithms with fixed privacy parameters are vulnerable against gradient leakage attacks. This paper investigates alternative approaches to gradient leakage resilient deep learning with differential privacy (DP). First, we analyze existing implementation of deep learning with differential privacy, which use fixed noise variance to injects constant noise to the gradients in all layers using fixed privacy parameters. Despite the DP guarantee provided, the method suffers from low accuracy and is vulnerable to gradient leakage attacks. Second, we present a gradient leakage resilient deep learning approach with differential privacy guarantee by using dynamic privacy parameters. Unlike fixed-parameter strategies that result in constant noise variance, different dynamic parameter strategies present alternative techniques to introduce adaptive noise variance and adaptive noise injection which are closely aligned to the trend of gradient updates during differentially private model training. Finally, we describe four complementary metrics to evaluate and compare alternative approaches.
GRAMMATICAL ERROR ANALYSIS ON THE THESIS ABSTRACT OF THE MASTER MANAGEMENT PROGRAM'S STUDENTS, FACULTY OF ECONOMICS, DIPONEGORO UNIVERSITY

Abstrak&#x0D; Dalam menulis bahasa Inggris, pembelajar Bahasa kedua sering membuat kesalahan dikarenakan kurangnya menguasai bahasa Inggris diakibatkan sedikitnya pengetahuan mengenai bahasa Inggris, Tujuan penelitian ini adalah untuk melihat variasi kesalahan tata Bahasa yang dilakukanoleh pembelajar Bahasa kedua. Dalam penelitian ini, data utama yang digunakan adalah sampel dari 50 abstrak tesis mahasiswa program magister, fakultas ekonomi dari Universitas Diponegoro., yang diambil penulis secara acak. Data tersebut dianalisa menggunakan Sruface Structure Taxonomy, diantaranya omission, addition, misformation dan misordering, dan menggunakan pendapat dari Brook yang mengatakan ada 4 alasan umum kesalahan tersebut dilakukan oleh pembelajar Bahasa kedua. Hasil penelitian ini menunjukan bahwa kesalahan terbesar yang terjadi adalah misformation, terutama misformation dari to be, kesalahan berikutnya secara berurutan adalah omission, addition dan misordering. Sebagai tambahan, kesalahan yang terjadi kebanyakan karena pengaruh Bahasa pertama dan mengeneralisasikan kata-kata; kedua hal inilah yang disebutkan oleh Brook.&#x0D; &#x0D; Kata Kunci: Pembelajar Bahasa kedua, Kesalahan tata Bahasa, &#x0D; &#x0D; Abstract&#x0D; In writing English, second language learners often make errors due to the lack of mastering English or insufficient knowledge about English. The purpose of the research is to look at varieties of second language learners' grammatical errors. In this research, the primary data used are samples from 50 theses abstracts that come from the students of Economics Faculty, magisterial management program of Diponegoro University, which the writer took randomly. The data is analyzed using the surface structure taxonomy, namely omission, addition, misformation, and misordering. They are using Brook's opinion, saying that there are four common reasons for the error made by second language learners. The research shows that the most significant number of mistakes is concerned with misformation. The other errors belong to omission, addition, and misordering, respectively. Besides, their errors are caused mainly by first language interference and overgeneralization; Brook mentions these two reasons. (Ellis, 1993: 159.)&#x0D; &#x0D; Keywords: Second language learner, Grammatical error, Surface Structure Taxonomy
A Visible Video Data Hiding Scheme Based on Fade-In and Fade-Out Effects Utilizing Barcodes

Data hiding techniques are usually applied into digital watermarking or digital fingerprinting, which is used to protect intellectual property rights or to avoid illegal copies of the original works. It has been pointed out that data hiding can be utilized as a communication medium. In conventional digital watermarking frameworks, it is required that the difference between the cover objects and the stego objects are quite small, such that the difference cannot be recognized by human sensory systems. On the other hand, the authors have proposed a ‘hearable’ data hiding technique for audio signals that can carry secret messages and can be naturally recognized as a musical piece by human ears. In this study, we extend the idea of the hearable data hiding into video signals by utilizing the visual effects. As visual effects, we employ fade-in and fade-out effects which can be used as a kind of visual rendering for scene transitions. In the proposed schemes, secret messages are generated as one-dimensional barcodes which are used for fade-in or fade-out effects. The present paper shows that the proposed schemes have the high accuracy in extracting the embedded messages even from the video signals captured by smartphones or tablets. It is also shown that the video signals conveying the embedded messages can be naturally recognized by human visual systems through subjective evaluation experiments.
A Scientometric Review of Syntactic Complexity in L2 writing based on Web of Science (2010-2022)

As an important construct in the field of second language teaching and assessment, syntactic complexity is closely related to the language proficiency and language development process of L2 learners. Using the visualization software of CiteSpace, this study conducts an in-depth scientometric analysis of 140 articles on written syntactic complexity published over the past 10 years (2010-2022), thus uncovering the current development and challenges faced by relevant studies. Specifically, a frequency analysis was firstly administrated to describe the overall development in written syntactic complexity research. Furthermore, the current study conducted a Document Co-Citation Analysis (DCA), which enables researchers to conduct a network of co-cited references to identify the underlying research hotpots and future trends. The results indicate that the study concerning automatic essay scoring is the most prominent cluster active from 2010 to 2021. In addition, Norris &amp; Ortega (2009) is the most cited paper, followed by Ortega (2003) and Biber et al. (2011). Meanwhile, the bursts of detected papers demonstrate that McNamara et al. (2012) and Grant &amp; Ginther (2000) generated the strongest citation burst with a burst strength of 3.14 and 3.09, respectively. The findings of the study would have implications for subsequent research on written syntactic complexity in the field of language teaching and language learning.
The Bayesian Inferential Paradigm in Archaeology

Archaeologists often use data and quantitative statistical methods to evaluate their ideas. Although there are various statistical frameworks for decision-making in archaeology and science in general, in this chapter, we provide a simple explanation of Bayesian statistics. To contextualize the Bayesian statistical framework, we briefly compare it to the more widespread null hypothesis significance testing (NHST) approach. We also provide a simple example to illustrate how archaeologists use data and the Bayesian framework to compare hypotheses and evaluate their uncertainty. We then review how archaeologists have applied Bayesian statistics to solve research problems related to radiocarbon dating and chronology, lithic, ceramic, zooarchaeological, bioarchaeological, and spatial analyses. Because recent work has reviewed Bayesian applications in archaeology from the 1990s up to 2017, this work considers the relevant literature published since 2017.
Improved Hemodynamic Status of Head Injured Patients in the Emergency Unit (ER): Literature Review

Background: Head injury is a serious problem that can lead to death and even death. Handling of head injuries starts from protecting the brain with blood flow to the brain so that hypoxia or brain ischemia does not occur. Hemodynamics is the result of measuring systolic and diastolic blood pressure, pulse rate, and respiratory rate.&#x0D; Objectives: This literature review aims to find out what interventions can be done when there is an increase in hemodynamic status in head injury patients&#x0D; Methods: This database search was conducted by searching on google scholars with the keywords head injury, hemodynamics. The inclusion criteria of this literature review are articles that were researched within the last 5 years with the year published 2015-2020, full text, using the Indonesian language, the article that used is the article.&#x0D; Results: Interventions that can be done when there is an increase in hemodynamic status in head injury patients are giving oxygen and increasing 30o, giving oxygen through a simple mask and head position 30o, giving head-up position 30o compared to 15o position, giving nasal prong oxygenation therapy and murotal therapy Al-Qur'an for 30 minutes 3 times/day.&#x0D; Conclusion: All interventions resulting from this literature review were in the form of giving oxygen and increasing the head 30o, giving oxygen through a simple mask and head position 30o, giving the head position 30o compared to 15o position, giving nasal branch oxygenation therapy, and murotal Al-Qur'an therapy for 30 minutes 3 times/day.
Service Level Management of Information Technology and System in ITB STIKOM Bali

ITB STIKOM Bali is a technology-based university so that all business processes, especially related to academics, student affairs, and finance, utilize information technology. Management of the information system and technology is carried out by the Computer and Network Center unit (Pusat Komputer dan Jaringan - PUSKOMJAR). In the management of Information Technology (IT) services, an agreement is needed in the form of Service Level Management (SLM) which contains the level of service, quality of service, and complaints handling, so users’ needs are aligned with the system developer. Meanwhile, in managing the internal system of ITB STIKOM Bali, the agreement between PUSKOMJAR and related units is only in the form of submitting a form for making or changing the system. Therefore, it raises a problem if there is a mismatch in the manufacture or development of the system because there is no contract document that regulates in detail about the system. Hence, designing an SLM of IT and system services at ITB STIKOM Bali to overcome these problems is necessary. The research presents a case study by analyzing and designing the SLM of service system and IT in ITB STIKOM Bali. The applied research method is literature study and observation, analysis of existing SLM, designing new SLM, and conclusions. The result of the research is a document of SLM for internal IT and system services at ITB STIKOM Bali. Based on the analysis and design of SLM, the SLM document can help the PUSKOMJAR in documenting IT service information and making it easier for users to get information related to the provided IT services.
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training

Deep neural networks have achieved remarkable success in a wide variety of natural image and medical image computing tasks. However, these achievements indispensably rely on accurately annotated training data. If encountering some noisy-labeled images, the network training procedure would suffer from difficulties, leading to a sub-optimal classifier. This problem is even more severe in the medical image analysis field, as the annotation quality of medical images heavily relies on the expertise and experience of annotators. In this paper, we propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification from noisy-labeled data to combat the lack of high quality annotated medical data. Specifically, we employ the self-ensemble model with a noisy label filter to efficiently select the clean and noisy samples. Then, the clean samples are trained by a collaborative training strategy to eliminate the disturbance from imperfect labeled samples. Notably, we further design a novel global and local representation learning scheme to implicitly regularize the networks to utilize noisy samples in a self-supervised manner. We evaluated our proposed robust learning strategy on four public medical image classification datasets with three types of label noise,ie,random noise, computer-generated label noise, and inter-observer variability noise. Our method outperforms other learning from noisy label methods and we also conducted extensive experiments to analyze each component of our method.
The Influencing Legal and Factors of Migrant Children’s Educational Integration Based on Convolutional Neural Network

This research aims to analyze the influencing factors of migrant children's education integration based on the convolutional neural network (CNN) algorithm. The attention mechanism, LSTM, and GRU are introduced based on the CNN algorithm, to establish an ALGCNN model for text classification. Film and television review data set (MR), Stanford sentiment data set (SST), and news opinion data set (MPQA) are used to analyze the classification accuracy, loss value, Hamming loss (HL), precision (Pre), recall (Re), and micro-F1 (F1) of the ALGCNN model. Then, on the big data platform, data in the Comprehensive Management System of Floating Population and Rental Housing, Student Status Information Management System, and Student Information Management System of Beijing city are taken as samples. The ALGCNN model is used to classify and compare related data. It is found that in the MR, STT, and MPQA data sets, the classification accuracy and loss value of the ALGCNN model are better than other algorithms. HL is the lowest (15.2 ± 1.38%), the Pre is second only to the BERT algorithm, and the Re and F1 are both higher than other algorithms. From 2015 to 2019, the number of migrant children in different grades of elementary school shows a gradual increase. Among migrant children, the number of migrant children from other counties in this province is evidently higher than the number of migrant children from other provinces. Among children of migrant workers, the number of immigrants from other counties in this province is also notably higher than the number of immigrants from other provinces. With the gradual increase in the years, the proportion of township-level expenses shows a gradual decrease, whereas the proportion of district and county-level expenses shows a gradual increase. Moreover, the accuracy of the ALGCNN model in migrant children and local children data classification is 98.6 and 98.9%, respectively. The proportion of migrant children in the first and second grades of a primary school in Beijing city is obviously higher than that of local children (p < 0.05). The average final score of local children was greatly higher than that of migrant children (p < 0.05), whereas the scores of migrant children's listening methods, learning skills, and learning environment adaptability are lower, which shows that an effective text classification model (ALGCNN) is established based on the CNN algorithm. In short, the children's education costs, listening methods, learning skills, and learning environment adaptability are the main factors affecting migrant children's educational integration, and this work provides a reference for the analysis of migrant children's educational integration.
THE ROLE OF COMPUTER MODELING IN THE FORMATION OF SYSTEMIC THINKING OF SENIOR STUDENTS

The article analyzes the pedagogical research on the essence and content of the concepts of systems approach and systems thinking, substantiates the main features of the formation of systems thinking. It is noted that systematic thinking, in contrast to linear, is focused on seeing the integrity of an object or phenomenon, to identify not things but connections between them, not instantaneous states, but patterns of change. Systems thinking is needed to distinguish the structures that form the basis of complex situations. The implementation of integrative connections during the educational process is a necessary condition for the formation of a scientific worldview and competencies. It is determined that the formation of an environment for the effective formation of systems thinking is possible through the implementation of a competency approach and the introduction of active, activity forms of learning, in particular with the use of computer models. It is noted that the use of computer models in the educational process provides a high degree of clarity, and their interactivity allows to directly influence the course of the experiment, change the conditions of its conduct, which promotes motivation, interest and desire to experiment, conduct independent research in science, and thus promotes the development of creative skills and critical thinking. It is emphasized that individual research tasks based on computer simulations are an effective tool for implementing the activity approach. Students have the opportunity to discover something new, unknown to them, to observe the patterns of processes and phenomena, to "discover" a known law in physics by measuring physical quantities.
Contextualization of Personal Data Discovery and Anonymization Tools

Today for most of the companies, the volume of data continues to increase and in an incredible way. Many of them do not automatically ask customers for permission to use their data, which is illegal. In an increasingly connected and digitized world, it is necessary to establish a precise and strict legal framework so that each piece of data is used wisely and protected. The legal framework is called GDPR (General Data Protection Regulation). The objective is to reassure and protect the user when using internet particularly social networks and ecommerce applications. Anonymization is a technique that irreversibly prevents the identification of an individual's personal data. Our work is about how to contextualize the personal data discovery tools to improve the anonymization process adopted by a company to protect its corporate applications and those of its international subsidiaries. The anonymization is reinforced by different contextualization of connected data groups through user practices added to well-established procedures. For instance, some contextual elements about personal information (Customer Bank Account, zip codes, name of streets and cities, etc.) can be used to change the anonymization rules to mask data differently from one subsidiary to the other. We use a contextual graphs formalism to improve sensitive personal data discovery and anonymization tool.
EdTech: Why the project-based approach must change in order to contribute to system resilience

This viewpoint article argues that there is an urgent need to reform the project-based EdTech approach in order to allow EdTech to contribute to the resilience of education systems in the aftermath of Covid-19. Looking at the contrast between the multiplication of EdTech pilot projects presented as a necessary step in a process that will eventually lead to scaled solutions and the lack of solutions that actually scale, the article highlights those long-standing issues perceived as most pressing by the actors involved in project-based EdTech initiatives. Their perspective and statements allow one to grasp how the EdTech project approach favors the setup of EdTech projects that are by design unscalable, driven by a utopian perception of scalability and instrumentalized in the name of a goal that is de facto only a branding. As a result, and despite the mobilization of tremendous resources, the EdTech project-based approach cannot be system-transformative.
Improving the quality of strategic decision-making process in universities through employing expert systems: A case study from a developing country

Strategic decisions represent the fundamental core of the strategic planning process and strategic management in universities and they are essential in shaping the universities' policies and achieving their strategic goals. Without those strategic decisions, the universities stand unable to achieve their strategic goals and mission; therefore, specialists realized the critical importance of improving the quality of strategic decision-making in the current complex fast-changing environment that its dynamism continuously increases and which is based on the use of cutting-edge information and communications technology (ICT). Undoubtedly strategic decision-making process requires processing a huge amount of information with different robust smart methods and the extensive use of experts knowledge. There are many discussions about the uses and applications of expert systems (ESs), which are evolving rapidly in solving real problems in many fields that require experienced experts with deep sound experiences, and despite these many applications in many different fields and domains. Literature reveals that there is a scarcity of scientific research on how to employ expert systems to raise the quality of strategic decision-making processes in universities. Thus the purpose of the research is to fill this research gap by investigating how expert systems will enhance the quality of the strategic decision-making process in universities. The research design is a case study applied in Ain Shams University as a model of public universities in a developing country. This research makes a new research contribution by suggesting a futuristic proposal for improving the quality of the strategic decision-making process in universities through employing expert systems that are based on the theoretical framework of the research and the results of the field study.
Switching: understanding the class-reversed sampling in tail sample memorization

Long-tailed visual recognition poses significant challenges to traditional machine learning and emerging deep networks due to its inherent class imbalance. Existing reweighting and re-sampling methods, although effective, lack a fundamental theory while leaving the paradoxical effects of long tail unsolved, where network failing with head classes under-represented and tail classes overfitted. In this paper, we investigate long-tailed recognition from a memorization-generalization point of view, which not only unravels the whys of previous methods, but also derives a new principled solution. Specifically, we first empirically identify the regularity of classes under long-tailed distributions, finding that long-tailed challenge is essentially a trade-off between the representation of high-regularity head classes and generalization to low-regularity tail classes. To memorize tail samples without seriously damaging the representation of head samples, we propose a simple yet effective sampling strategy for ordinary mini-batch SGD optimization process, Switching, which switches from instance-balanced sampling to class-reversed sampling for only once at small learning rate. By theoretical analysis, we show that the upper bound on the generalization error of the proposed sampling strategy is lower than instance-balanced sampling conditionally. In our experiments, the proposed method can reach feasible performance more efficiently than current methods. Further experiments validate the superiority of the proposed Switching strategy, implying that the long-tailed learning trade-off could be parsimoniously tackled only in the memorization stage with a small learning rate and over-exposure of tail samples.