Reviewerly/exHarmony huggingface dataset:
TEACHING AND LEARNING ENGLISH AT HIGHER EDUCATIONAL INSTITUTIONS IN UKRAINE THROUGH PANDEMICS AND WARTIME

The study aimed to analyse online English teaching and learning experience in higher education institutions in Ukraine through pandemics and wartime. To answer our research questions, we employed a mixed-method research design, which considered social, methodological and emotional aspects of education. We conducted quantitative research to collect data on the experience of Ukrainian university teachers of English (N=100) and students (N=277) related to the emergency caused by pandemics and wartime. The qualitative data sources included online interviews and written comments of the participants. The main findings were as follows: teachers experienced challenges during the COVID-19 pandemic such as lack of interaction with students, and unawareness of effective teaching methods in the digital environment. The wartime challenges included: air raids and power outages and, consequently, disrupted communication and isolation, stress and sense of insecurity, excessive workload, with students experiencing technical issues, air raids and communication problems as well as large amount of homework, no access to the software, inconvenient schedule, and their own unwillingness. To solve the problems, teachers introduced an online synchronous mode during the COVID-19 pandemic, supported by blended and asynchronous modes which was a transformational period to teaching and learning English during wartime. The most common among the learning platforms and applications were Zoom and Google Classroom, as well as Moodle, MS Teams, YouTube, and Classtime, listed by both university teachers and students. Social networks such as Telegram, Viber, Facebook etc. were widely used too. To conclude, the COVID-19 pandemic caused Ukrainian teachers and students master new tools to use at online lessons of English, during wartime the demand on already familiar tools, as well as the range of them, increased as they provided more interaction. Considering emotional aspects, the study revealed disturbing data on teachers’ well-being: nearly 50% confessed that they were burned out and emotionally devastated, close to 40% were tired and pushed themselves to continue teaching. Despite these challenges, the majority were able to find solutions in conflict situations with students and colleagues, two-thirds were sure that their work was even more important during the wartime and they found ways to provide emotional support to their students needed in the times of war.
SISTEM INFORMASI PENGOLAHAN DATA SISWA BERBASIS WEB PADA RA AN-NASYWA (STUDI KASUS PENGOLAHAN DATA SISWA)

RaudhatulAtfhal An-Nasywa Kindergarten School is an early childhood education school that is 4-6 years old, currently RaudhatulAtfhal An-Nasywa Kindergarten is still doing it manually with books in managing student data and grades. So we need a student data management information system that can increase the speed and accuracy in processing student data. The basic purpose of making a student data processing information system is to make it easier to manage data. Making this student processing information system using system design and database design. The student data processing information system uses PHP MyAdmin as a data storage medium. Student data processing information system that will be used by administrators to enter the data needed to perform data processing. The final result of the research was found that by building an information system for processing student data at the RaudhatulAtfhal An-Nasywa Kindergarten School, it could be more helpful for the school where previously student data and other documents were still in conventional form.
Poster: Designing a Memory Disaggregation System for Cloud

Memory disaggregation is a new datacenter paradigm separating compute and memory nodes. While memory disaggregation improves memory utilization and scalability, it poses challenges for cloud applications, particularly in terms of high tail latency. Existing memory disaggregation systems focus on optimizing the disaggregation stack, but it does not always guarantee excellent application performance. We review existing memory disaggregation and tail-optimized systems and explain their limitations in this context. We also propose two preliminary solutions: asynchronous page fault handling and a faulty request classifier. The emulation result shows that asynchronous page fault handling reduces tail latencies by 50% compared to synchronous handling.
PERANCANGAN SISTEM PAKAR DIAGNOSA PENYAKIT EPILEPSI (STUDI KASUS RUMAH SAKIT RADEN MATTAHER) BERBASIS WEB

The diagnosis of the disease made by an expert has its weaknesses as well as its biological weaknesses. One potential solution is an expert system. An expert system is a system that seeks to bring human knowledge into a computer so that it can solve problems as professionals are accustomed to. By trying to build an expert system for the diagnosis and treatment of epilepsy. There are 8 types of epilepsy, with a total of 92 symptoms. The inference method used is forward chaining. This method was chosen to overcome the problem of determining the type of disease by knowing the symptoms experienced. Web-based expert systems for easy system deployment. System performance is provided in consultation with answering questions posed by the system. This test gives patients what type of epilepsy they suffer from, so we can conclude that this system can be applied.
Dynamic cooperative value-added service strategies of the smart manufacturing platform considering the network effect and altruism preference

Smart manufacturing platform has become an important carrier of digital transformation of manufacturing industry. However, due to the capital and technology constraints of manufacturing enterprises, a cross-border cooperation mode with cloud service providers has emerged. To expand users' scale and enhance competitiveness, the manufacturing enterprise and the cloud service provider have focused on investing in value-added service (VAS) based on their resource endowments. Based on differential game theory and Bellman's continuous dynamic planning theory, this study investigates dynamic cooperative VAS efforts decisions of the platform considering network effect and altruism preference. The results show that the network effect can stimulate the VAS efforts level of both manufacturer and cloud service provider. Moreover, the manufacturer's altruistic preference weakens the double marginal effect, which is conducive to improve the profit of the platform system, the VAS efforts level and the profit of the cloud service provider. However, the manufacturer's effort level is not affected by the manufacturer's altruistic preference, and it only benefits the manufacturer when the manufacturer's altruistic preference coefficient is less than a certain threshold. In addition, the manufacturer derives far less profit growth from altruistic preference than the cloud provider does.
Differences and similarities in false interpretations by AI CAD and radiologists in screening mammography

We aimed to evaluate the false interpretations between artificial intelligence (AI) and radiologists in screening mammography to get a better understanding of how the distribution of diagnostic mistakes might change when moving from entirely radiologist-driven to AI-integrated breast cancer screening.This retrospective case-control study was based on a mammography screening cohort from 2008 to 2015. The final study population included screening examinations for 714 women diagnosed with breast cancer and 8029 randomly selected healthy controls. Oversampling of controls was applied to attain a similar cancer proportion as in the source screening cohort. We examined how false-positive (FP) and false-negative (FN) assessments by AI, the first reader (RAD 1) and the second reader (RAD 2), were associated with age, density, tumor histology and cancer invasiveness in a single- and double-reader setting.For each reader, the FN assessments were distributed between low- and high-density females with 53 (42%) and 72 (58%) for AI; 59 (36%) and 104 (64%) for RAD 1 and 47 (36%) and 84 (64%) for RAD 2. The corresponding numbers for FP assessments were 1820 (47%) and 2016 (53%) for AI; 1568 (46%) and 1834 (54%) for RAD 1 and 1190 (43%) and 1610 (58%) for RAD 2. For ductal cancer, the FN assessments were 79 (77%) for AI CAD; with 120 (83%) for RAD 1 and with 96 (16%) for RAD 2. For the double-reading simulation, the FP assessments were distributed between younger and older females with 2828 (2.5%) and 1554 (1.4%) for RAD 1 + RAD 2; 3850 (3.4%) and 2940 (2.6%) for AI+RAD 1 and 3430 (3%) and 2772 (2.5%) for AI+RAD 2.The most pronounced decrease in FN assessments was noted for females over the age of 55 and for high density-women. In conclusion, AI could have an important complementary role when combined with radiologists to increase sensitivity for high-density and older females.Our results highlight the potential impact of integrating AI in breast cancer screening, particularly to improve interpretation accuracy. The use of AI could enhance screening outcomes for high-density and older females.
Pseudo inverse versus iterated projection: Novel learning approach and its application on broad learning system

Broad learning system (BLS) has attracted widespread attention owing to its concise structure and efficient incremental learning based on ridge regression approximation of pseudo-inverse. However, BLS struggles with expensive computational costs and high memory usage when processing complex problems and training large networks due to the inverse operation of a large matrix. There has been some attempts to address this issue. In this work, we attempt to ameliorate the computational efficiency motivated by the state-of-the-art randomized iterative least squares solver. First, we improve the iteration manner of randomized extended Kaczmarz (REK) and then propose the iterated projection learning for training BLS without pseudo-inverse. The iterated projection learning is suitable for both offline and online learning scenarios and can adjust the model structure by incremental learning. Thanks to its linear convergence of least norm in expectation, the obtained model is expected to have better generalization ability without additional regularization procedure. Our model is found to maintain stable performance during incremental learning. Moreover, instead of grid search, we present an evolutionary bilevel programming (EBP) method with dispersion operator to further optimize the hyperparameters of the network structure. Numerical experiments indicate that the proposed methods can improve the efficiency, robustness, and generalization ability compared with pseudo-inverse and grid search based methods.
Into the Unknown: Assigning Reviewers to Papers with Uncertain Affinities

A successful peer review process requires that qualified and interested reviewers are assigned to each paper. Most automated reviewer assignment approaches estimate a real-valued affinity score for each paper-reviewer pair that acts as a proxy for the quality of the match, and then assign reviewers to maximize the sum of affinity scores. Most affinity score estimation methods are inherently noisy: reviewers can only bid on a small number of papers, and textual similarity models and subject-area matching are inherently noisy estimators. Current paper assignment systems are not designed to rigorously handle noise in the peer-review matching market. In this work, we assume paper-reviewer affinity scores are located in or near a high-probability region called an uncertainty set. We maximize the worst-case sum of scores for a reviewer assignment over the uncertainty set. We demonstrate how to robustly maximize the sum of scores across various classes of uncertainty sets, avoiding potentially serious mistakes in assignment. Our general approach can be used to integrate a large variety of paper-reviewer affinity models into reviewer assignment, opening the door to a much more robust peer review process.
Implementasi Pembelajaran Bahasa Inggris di SDN 1 Cigembor

Reflection in learning is one of the most important activities for both prospective teachers and teachers. The existence of reflection makes an improvement in how he should teach, finds real problems and ways to overcome them, and reviews methods, models, and approaches that are appropriate to the conditions of students. The purpose of this study was to find out the results of reflection on the implementation of teaching practices by researchers as part of the process of implementing English learning courses in elementary schools. The research method used is a qualitative descriptive approach. The results of this study show that the learning process in class IV at SDN 1 Cigembor runs smoothly according to the lesson plans. The implication of this research is that the practice of teaching English in elementary schools can train and hone the abilities of prospective teachers so that they can prepare themselves to become real teachers so that they become real images for subsequent teaching.
Threat of Climate Change

The anthropogenic activities of mankind have led to an unprecedented rise in global greenhouse concentrations since the industrial revolution. The consequence of this unequivocal rise in global surface temperature has been witnessed over the last century and more so after 1970. The global surface temperature in the last twenty years (2001-2020) was 0.9°C higher than what was registered during 1850-1900 (IPCC, 2023). This has also been observed that the number of hot days every year has been on the increase when the temperature around touches 50°C. There are multiple reports which have predicted very hot summers across the globe in the coming days and the report further reveals that the total number of days when the temperature crossed 50°C in different parts of the world was 14 in the years between 1980 and 2009 whereas this rose to 26 days during 2010-2019. The year 2022 witnessed the hottest summer in many western and south-western European countries where the temperature was above 2.3°C above the pre-industrial average and this extraordinary temperature rise in different parts of Europe exacerbated many unimaginable things like a severe forest fire, drought, drying up of many water bodies and heat-related deaths.
Detecting compromised email accounts via login behavior characterization

Abstract The illegal use of compromised email accounts by adversaries can have severe consequences for enterprises and society. Detecting compromised email accounts is more challenging than in the social network field, where email accounts have only a few interaction events (sending and receiving). To address the issue of insufficient features, we propose a novel approach to detecting compromised accounts by combining time zone differences and alternate logins to identify abnormal behavior. Based on this approach, we propose a compromised email account detection framework that relies on widely available and less sensitive login logs and does not require labels. Our framework characterizes login behaviors to identify logins that do not belong to the account owner and outputs a list of account-subnet pairs ranked by their likelihood of having abnormal login relationships. This approach reduces the number of account-subnet pairs that need to be investigated and provides a reference for investigation priority. Our evaluation demonstrates that our method can detect most email accounts that have been accessed by disclosed malicious IP addresses and outperforms similar research. Additionally, our framework has the capability to uncover undisclosed malicious IP addresses.
KNOWLEDGE AND PRACTICES ABOUT PREVENTION AND TREATMENT OF BURNS IN THE HOME ENVIRONMENT

Objectives: To analyze the knowledge and practices of individuals on the prevention and treatment of burns in the home environment and to describe the educational nursing care in the prevention and treatment of burns in the home environment. Method: Convergent Care Research (PCA) carried out virtually, from June to August 2021, with people residing in the city of Macaé/RJ. Results: The survey included 16 participants, 81.25% (n=13) female, aged between 19 and 59 years. In the analysis of the interviews, it was possible to identify mistaken knowledge and practices about the prevention and treatment of burns, to be discussed in this study. Conclusion: Although there is knowledge about the knowledge and practices on the prevention and treatment of burns in the home environment, there was identification of wrong practices that can harm the correct treatment and prevention of injuries. The findings of this study point to the need to build materials and carry out educational practices with this population to reinforce preventive measures for burns.
An empirical evaluation of Q-learning in autonomous mobile robots in static and dynamic environments using simulation

Path planning plays a crucial role in the navigation of mobile robots. Among the various path planning techniques, Q-learning (QL) has gained popularity as a reinforcement learning approach that exhibits the ability to learn without significant prior knowledge of the environment. However, despite the introduction of enhanced versions of Q-learning, specifically distance metric and moving target Q-learning (DMMTQL) and distortion and optimization Q-learning (DOQL), the validation of these algorithms in real-world scenarios is incomplete. In this study, we conduct real-world experiments to assess the performance of DMMTQL and DOQL in two distinct environments: one featuring static and another with dynamic obstacles. Our investigation involves comparing the real-world results obtained from DMMTQL and DOQL with those obtained from QL and contrasting them with simulation results. The findings from our real-world experiments demonstrate that both DMMTQL and DOQL outperform QL in terms of path planning effectiveness. Both improved QL algorithms are able to analyse and decide the optimal path with free collision for the mobile robots. When comparing the improvements achieved by DMMTQL and DOQL with simulation results, we observe similar outcomes in most aspects, with the exception of the time taken and distance travelled metrics.
Ambiguity-aware breast tumor cellularity estimation via self-ensemble label distribution learning

In this work, we address the task of tumor cellularity (TC) estimation with a novel framework based on the label distribution learning (LDL) paradigm. We propose a self-ensemble label distribution learning framework (SLDL) to resolve the challenges of existing LDL-based methods, including difficulties for inter-rater ambiguity exploitation, proper and flexible label distribution generation, and accurate TC value recovery. The proposed SLDL makes four main contributions which have been demonstrated to be quite effective in numerous experiments. First, we propose an expertness-aware conditional VAE for diversified single-rater modeling and an attention-based multi-rater fusion strategy that enables effective inter-rater ambiguity exploitation. Second, we propose a template-based label distribution generation method that is tailored for the TC estimation task and constructs label distributions based on the annotation priors. Third, we propose a novel restricted distribution loss, significantly improving the TC value estimation by effectively regularizing the learning with unimodal loss and regression loss. Fourth, to the best of our knowledge, we are the first to simultaneously leverage inter-rater and intra-rater variability to address the label ambiguity issue in the breast tumor cellularity estimation tasks. The experimental results on the public BreastPathQ dataset demonstrate that the SLDL outperforms the existing methods by a large margin and achieves new state-of-the-art results in the TC estimation task. The code will be available from https://github.com/PerceptionComputingLab/ULTRA.
KSGAN: Knowledge-aware subgraph attention network for scholarly community recommendation

On online scholarly platforms, recommending suitable communities to researchers matters for researchers' communication and collaboration. Previous studies on community recommendation either treat a community as a single item or simply aggregate its member features while ignoring rich user interactions and side information in scholarly communities. Existing knowledge-aware recommenders fail to capture the complicated knowledge graph structures and profile the rich information in scholarly communities, and thus not suitable for the scenario of scholarly community recommendation. In this paper, we propose a knowledge-aware subgraph attention network (KSGAN) for scholarly community recommendation. Specifically, by using a scholarly KG to profile rich information of scholarly communities, we design a biquaternion-based embedding method to capture its multiple relational patterns and hierarchical structures. Then, by profiling a scholarly community as a subgraph, we design a scalable subgraph representation learning module to learn enhanced community representation. Last, we design an attention-based historical community fusion module that captures both global dependencies and target dependencies for recommendation. Extensive experiments on two real-world scholarly datasets show that KSGAN significantly outperforms state-of-the-art baselines for scholarly community recommendation. The proposed KSGAN can find potential practical implementations on scholarly platforms to recommend scholarly communities.
OS-DS tracker: Orientation-variant Siamese 3D tracking with Detection based Sampling

LIDAR point clouds are less affected by the weather and possess more depth of field than 2D images. However, sparsity and disorder of point clouds bring challenges for 3D object tracking due to the difficulty in detecting and encoding. In addition, ineffective candidate proposals increase the phenomenon of the loss of target tracking or wrong target tracking. In this paper, we propose an Orientation-variant Siamese 3D object tracking network that utilizes a Detection based Sampling module to generate candidate proposals (OS-DS tracker). The Detection based Sampling module is used to cut the excessive and useless proposals. And the multivariate Gaussian sampling generates the candidate proposals when the objects are not detected. Concretely, we first pre-train PointRCNN Network to globally detect objects from LIDAR point clouds. Then the 3D detected objects are refined by Candidate Regions Sampling to generate candidate proposals. Meanwhile, to make the feature vectors more discriminative, we design an Orientation-variant Siamese Auto-encoder, i.e., tracking loss regress to the intersection over union (IOU) of the template box and the candidate region boxes. Our method is tested on the KITTI dataset and the SHIP Tracking dataset. Compared with the previous state-of-the-arts, the proposed method outperforms in vessel tracking with 5.2%/2.1% improvement in Success and Precision.