Reviewerly/exHarmony huggingface dataset:
Fully-Dynamic Decision Trees

We develop the first fully dynamic algorithm that maintains a decision tree over an arbitrary sequence of insertions and deletions of labeled examples. Given $\epsilon > 0$ our algorithm guarantees that, at every point in time, every node of the decision tree uses a split with Gini gain within an additive $\epsilon$ of the optimum. For real-valued features the algorithm has an amortized running time per insertion/deletion of $O\big(\frac{d \log^3 n}{\epsilon^2}\big)$, which improves to $O\big(\frac{d \log^2 n}{\epsilon}\big)$ for binary or categorical features, while it uses space $O(n d)$, where $n$ is the maximum number of examples at any point in time and $d$ is the number of features. Our algorithm is nearly optimal, as we show that any algorithm with similar guarantees uses amortized running time $\Omega(d)$ and space $\tilde{\Omega} (n d)$. We complement our theoretical results with an extensive experimental evaluation on real-world data, showing the effectiveness of our algorithm.
Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs

We tackle open-world semantic segmentation, which aims at learning to segment arbitrary visual concepts in images, by using only image-text pairs without dense annotations. Existing open-world segmentation methods have shown impressive advances by employing contrastive learning (CL) to learn diverse visual concepts and transferring the learned image-level understanding to the segmentation task. However, these CL-based methods suffer from a train-test discrepancy, since it only considers image-text alignment during training, whereas segmentation requires region-text alignment during testing. In this paper, we proposed a novel Text-grounded Contrastive Learning (TCL) framework that enables a model to directly learn region-text alignment. Our method generates a segmentation mask for a given text, extracts text-grounded image embedding from the masked region, and aligns it with text embedding via TCL. By learning region-text alignment directly, our framework encourages a model to directly improve the quality of generated segmentation masks. In addition, for a rigorous and fair comparison, we present a unified evaluation protocol with widely used 8 semantic segmentation datasets. TCL achieves state-of-the-art zero-shot segmentation performances with large margins in all datasets. Code is available at https://github.com/kakaobrain/tcl.
Analysis Of Policy Implementation In Tanjungpinang Flood Disaster Risk Reduction

Disaster management is an early awareness to reduce the risk of potential disasters, especially floods in the community. This study aims to analyze implementing policies in Tanjungpinang Flood Disaster Risk Reduction. The research object was chosen because Tanjungpinang, on a land geographical location, is smaller than the sea area and can potentially be a disaster threat in an archipelagic area. Analysis of Policy implementation using Edward III Model. The research method is descriptive qualitative with a case study approach—the data obtained using secondary data with analysis of relevant training techniques. The study results indicate that four factors in the Edward III model have several weaknesses in the Tanjungpinang Flood Disaster Risk Reduction findings. The factors in question are the lack of cooperation in the bureaucratic structure on a large scale. The resources used have not met the standardization with the minimal involvement of resources in improving the quality of both technical and non-technical. Communication still has a good intensity between the stakeholders involved, so the participating actors are still running fast following the flood disaster management program in Tanjungpinang City. Furthermore, lastly, the attitude of the implementer or disposition has their respective ego-sectoral so that the participation between stakeholders in its implementation is less effective. Researchers provide additional recommendations or alternatives that need to strengthen policy implementation with a community empowerment approach so that people gain insight into disaster awareness involved in disaster management programs, primarily floods in Tanjungpinang City, for the future on a sustainable basis.
The development of an android-based e-module to increase student's critical thinking skills: A comprehensive needs analysis

Education in the 21st century becomes important to ensure students have the skills to learn and innovate, the skills to use technology and information media, and can work, and survive by using the skills to live. In an era of increasingly sophisticated technological developments, it would be nice if these technological developments were used to create interesting and fun innovations during the learning process, such as using teaching materials based on information technology to support the learning process. The purpose of this research is to analyze the need to develop an Android-based mobile E-Module. The method used was descriptive qualitative research method, namely by conducting a needs analysis. Research data were collected through observation and interviews with fifth grade teachers and 30 students. The results of observational research indicated that students’ critical thinking attitudes are still very low and learning uses textbooks, and they still rarely use electronic media such as computers or androids to teach. Dealing with the pre-research conducted that teachers and students had desire that the teaching materials used in the learning process were developed so that they varied and facilitated to understand the material.
Perturb Initial Features: Generalization of Neural Networks Under Sparse Features for Semi-supervised Node Classification

Graph neural networks (GNNs) are commonly used in semi-supervised settings. Previous research has primarily focused on finding appropriate graph filters (e.g. aggregation methods) to perform well on both homophilic and heterophilic graphs. While these methods are effective, they can still suffer from the sparsity of node features, where the initial data contain few non-zero elements. This can lead to overfitting in certain dimensions in the first projection matrix, as training samples may not cover the entire range of graph filters (hyperplanes). To address this, we propose a novel data augmentation strategy. Specifically, by flipping both the initial features and hyperplane, we create additional space for training, which leads to more precise updates of the learnable parameters and improved robustness for unseen features during inference. To the best of our knowledge, this is the first attempt to mitigate the overfitting caused by the initial features. Extensive experiments on real-world datasets show that our proposed technique increases node classification accuracy by up to 46.5% relatively.
SISTEM INFORMASI PENGADUAN MAHASISWA POLITEKNIK BAJA TEGAL BERBASIS WEB

Information technology is one thing that has an important role in the current era, especially during the pandemic. Complaints or submission of aspirations and student complaints are important in an institution or college, because with the delivery of these aspirations a university can easily improve and improve its quality. Building student confidence in the services and facilities and infrastructure carried out by the organizers is an activity that must be carried out with the expectations and demands of students regarding service improvement. This student complaint system is a website-based system that is useful for assisting Tegal Steel Polytechnic students in submitting complaints to universities. Services regarding receiving complaints and responding to complaints are still manual. Students must come to the secretariat to fill out the form directly. Therefore, with the development of technology, it encourages authors to create a website-based information system that can help students who want to make complaints. Therefore, researchers build a website-based system that makes it easier for students to make complaints and see how far their complaints have been processed by the university.
Methodologies for Resolving Data Security and Privacy Protection Issues in Cloud Computing Technology

Because of its accessibility and flexibility, cloud technology is among the most notable innovations in today's world. Having many service platforms, such as GoogleApps by Google, Amazon, Apple, and so on, is well accepted by large enterprises. Distributed cloud computing is a concept for enabling every-time, convenient, on-demand network access to processing resources including servers, storage devices, networks, and services that may be mutually configured. The major security risks for cloud computing as identified by the Cloud security alliance (CSA) have been examined in this study. Also, methods for resolving issues with cloud computing technology's data security and privacy protection were systematically examined.
Mitigating the Integrity Issues in Cloud Computing Utilizing Cryptography Algorithms

The cloud can be created, monitored, and disseminated with slight disruption or service provider involvement. Among the most rapidly evolving phenomenon, cloud computing provides users with a variety of low-cost solutions. By putting the ideas of confidentiality, authentication, encryption techniques, non-repudiation, intrusion prevention, and effectiveness into practice, the challenge of cloud information security for computers and cloud storage security has been resolved in its totality. As cloud security has become a growing problem, cloud technology is prominent throughout many emerging disciplines of study in which a significant amount of research is conducted in this field. Each of these efforts uses a cryptography approach. Current solutions to these issues have certain important drawbacks. To protect sensitive information stored in the cloud, one needs to design programs that implement hybrid cryptographic mechanisms using challenging encryption algorithms. This research elaborates on an examination of using cryptographic techniques to mitigate the integrity problems in cloud computing.
Sara Alert: An automated symptom monitoring tool for COVID-19 in 11 jurisdictions in the United States, June – August, 2021

Health department personnel conduct daily active symptom monitoring for persons potentially exposed to SARS-CoV-2. This can be resource-intensive. Automation and digital tools can improve efficiency. We describe use of a digital tool, Sara Alert, for automated daily symptom monitoring across multiple public health jurisdictions.Eleven of the 20 U.S. public health jurisdictions using Sara Alert provided average daily activity data during June 29 to August 30, 2021. Data elements included demographics, communication preferences, timeliness of symptom monitoring initiation, responsiveness to daily messages, and reports of symptoms.Participating jurisdictions served a U.S. population of over 22 million persons. Health department personnel used this digital tool to monitor more than 12,000 persons per day on average for COVID-19 symptoms. On average, monitoring began 3.9 days following last exposure and was conducted for an average of 5.7 days. Monitored persons were frequently < 18 years old (45%, 5,474/12,450) and preferred communication via text message (47%). Seventy-four percent of monitored persons responded to at least one daily automated symptom message.In our geographically diverse sample, we found that use of an automated digital tool might improve public health capacity for daily symptom monitoring, allowing staff to focus their time on interventions for persons most at risk or in need of support. Future work should include identifying jurisdictional successes and challenges implementing digital tools; the effectiveness of digital tools in identifying symptomatic individuals, ensuring appropriate isolation, and testing to disrupt transmission; and impact on public health staff efficiency and program costs.
Evolutionary Random Graph for Bitcoin Overlay and Blockchain Mining Networks

The world economy is experiencing the novel adoption of distributed currencies that are free from the control of central banks. Distributed currencies suffer from extreme volatility, and this can lead to catastrophic implications during future economic crisis. Understanding the dynamics of this new type of currencies is vital for empowering supervisory bodies from current reactive and manual incident responders to more proactive and well-informed planners. Bitcoin, the first and dominant distributed cryptocurrency, is still notoriously vague, especially for a financial instrument with market value exceeding 1 trillion. Modeling of bitcoin overlay network poses a number of important theoretical and methodological challenges. Current measuring approaches, for example, fail to identify the real network size of bitcoin miners. This drastically undermines the ability to predict forks, the suitable mining difficulty and most importantly the resilience of the network supporting bitcoin. In this work, we developed Evolutionary Random Graph, a theoretical model that describes the network of bitcoin miners. The correctness of this model has been validated using simulated and measure real bitcoin data. We then predicted forking, optimal mining difficulty, network size and consequently the network's inability to stand a drastic drop in bitcoin price using the current mining configuration.
Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models

Most machine learning-based regressors extract information from data collected via past observations of limited length to make predictions in the future. Consequently, when input to these trained models is data with significantly different statistical properties from data used for training, there is no guarantee of accurate prediction. Consequently, using these models on out-of-distribution input data may result in a completely different predicted outcome from the desired one, which is not only erroneous but can also be hazardous in some cases. Successful deployment of these machine learning models in any system requires a detection system, which should be able to distinguish between out-of-distribution and in-distribution data (i.e. similar to training data). In this paper, we introduce a novel approach for this detection process using a Reduced Robust Random Cut Forest (RRRCF) data structure, which can be used on both small and large data sets. Similar to the Robust Random Cut Forest (RRCF), RRRCF is a structured, but a reduced representation of the training data sub-space in form of cut trees. Empirical results of this method on both low and high-dimensional data showed that inference about data being in/out of training distribution can be made efficiently and the model is easy to train with no difficult hyper-parameter tuning. The paper discusses two different use-cases for testing and validating results.
Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data

Domain adaptive text classification is a challenging problem for the large-scale pretrained language models because they often require expensive additional labeled data to adapt to new domains. Existing works usually fails to leverage the implicit relationships among words across domains. In this paper, we propose a novel method, called Domain Adaptation with Structured Knowledge (DASK), to enhance domain adaptation by exploiting word-level semantic relationships. DASK first builds a knowledge graph to capture the relationship between pivot terms (domain-independent words) and non-pivot terms in the target domain. Then during training, DASK injects pivot-related knowledge graph information into source domain texts. For the downstream task, these knowledge-injected texts are fed into a BERT variant capable of processing knowledge-injected textual data. Thanks to the knowledge injection, our model learns domain-invariant features for non-pivots according to their relationships with pivots. DASK ensures the pivots to have domain-invariant behaviors by dynamically inferring via the polarity scores of candidate pivots during training with pseudo-labels. We validate DASK on a wide range of cross-domain sentiment classification tasks and observe up to 2.9% absolute performance improvement over baselines for 20 different domain pairs. Code will be made available at https://github.com/hikaru-nara/DASK.
On the Capacity Region of Bipartite and Tripartite Entanglement Switching

We study a quantum entanglement distribution switch serving a set of users in a star topology with equal-length links. The quantum switch, much like a quantum repeater, can perform entanglement swapping to extend entanglement across longer distances. Additionally, the switch is equipped with entanglement switching logic, enabling it to implement switching policies to better serve the needs of the network. In this work, the function of the switch is to create bipartite or tripartite entangled states among users at the highest possible rates at a fixed ratio. Using Markov chains, we model a set of randomized switching policies. Discovering that some are better than others, we present analytical results for the case where the switch stores one qubit per user, and find that the best policies outperform a time division multiplexing (TDM) policy for sharing the switch between bipartite and tripartite state generation. This performance improvement decreases as the number of users grows. The model is easily augmented to study the capacity region in the presence of quantum state decoherence and associated cut-off times for qubit storage, obtaining similar results. Moreover, decoherence-associated quantum storage cut-off times appear to have little effect on capacity in our identical-link system. We also study a smaller class of policies when the switch stores two qubits per user.
A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection

The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spatial and temporal saliency fusion by solely considering the current consecutive limited frames. However, the short-term methodology has one critical limitation, which conflicts with the real mechanism of our visual system -- a typical long-term methodology. As a result, failure cases keep showing up in the results of the current SOTA models, and the short-term methodology becomes the major technical bottleneck. To solve this problem, this paper proposes a novel VSOD approach, which performs VSOD in a complete long-term way. Our approach converts the sequential VSOD, a sequential task, to a data mining problem, i.e., decomposing the input video sequence to object proposals in advance and then mining salient object proposals as much as possible in an easy-to-hard way. Since all object proposals are simultaneously available, the proposed approach is a complete long-term approach, which can alleviate some difficulties rooted in conventional short-term approaches. In addition, we devised an online updating scheme that can grasp the most representative and trustworthy pattern profile of the salient objects, outputting framewise saliency maps with rich details and smoothing both spatially and temporally. The proposed approach outperforms almost all SOTA models on five widely used benchmark datasets.
On a model with two scalar leptoquarks -- $R_2$ and $S_3$

We discuss a model that can accommodate the $B$-physics anomalies, based on combining two scalar leptoquarks, $R_2$ and $S_3$, of mass $\mathcal{O}(1\,\mathrm{TeV})$, and that we proposed in our previous paper. We update the analysis of its parameter space and show that a model remains viable and consistent with a number of low energy and high energy flavor physics constraints. Since the model predicts a non-zero New Physics phase, we discuss the possibility to test its contribution to the neutron electric dipole moment and to the angular distributions of the exclusive $b\to c\tau \bar \nu$ decays. We find that the model can provide a significant enhancement to $\mathcal{B}(B\to K^{(\ast)}\nu\nu)$ and provides both the upper and lower bounds to $\mathcal{B}(B\to K^{(\ast)}\mu\tau)$.
Peningkatan Motivasi Belajar Peserta Didik SMPN 1 Garut Dalam Pembelajaran IPS Melalui Through Guessing Games

In the current situation, in the midst of the spread of the Covid-19 pandemic, which is one of the challenges for students, especially in the educational aspect and society in general in social activities. Where student must carry out the learning with the limitations of supporting technology and the understanding of students in using the learning technology. Apart from these challenges, especially in social studies learning which in considered by students as a subject that is less acttrative and less motivated to learn social studies. So that students tend to be less enthusiastic when participating in learning, especially in distance learning. The purpose of this study to describe social studies learning innovations for students based on fun educational games for students. This research method is a qualitative method using case study techniques directly on students and literature students. The results of this study are; (1) social studies learning strategies based on fun educational games releted to simple social studies materials, but packaged in interesting patterns for students so that they can increase students motivation and interest in learning, especially in social studies leraning; (2) Increasing the optimalization of students in understanding social studies materials with the concept of strengthening reflection in social studies learning, especially in the introduction of regions and cultures in Indonesia.
Interaksi Sosial di Kalangan Peserta Didik Pada Saat Pembelajaran Online

This article was made with the title Social Interaction Among Students During Online Learning with the aim of being able to inform the wider community how important social interaction among students during online learning is both in terms of education and in terms of family. The method used is descriptive qualitative by using various supporting sources so that it can be seen that in terms of education students become lazy, have difficulty understanding the material, do not continue school, some work or even get married while online learning is applied and in terms of family, children and parents will be able to understand each other because with the implementation of online learning, children and parents have a lot of time to be together so that social interaction or communication occurs more often and better
Hubungan Pembelajaran Secara Daring Dengan Minat Belajar Di Masa Pandemi Covid 19 di Universitas Kristen Palangka Raya

The purpose of this study was to determine the relationship between online learning and interest in learning during the covid 19 pandemic at Palangka Raya Christian University. The respondents in this study were students at the Christian University of Palangka Raya. The data collection technique used in this study was a questionnaire using the google form application. The analytical method used is simple linear regression analysis. The results of this study indicate that online learning has a significant influence on student interest in learning at Palangka Raya Christian University, the relationship between online learning and student interest in learning at Palangka Raya Christian University is positive and strong and the relative contribution of online learning to learning interest is 38.1%.