Reviewerly/exHarmony huggingface dataset:
Development E-Book Learning Media Based on Kvisoft Flipbook Maker

The main purpose of this research is to produce a kvisoft flipbook maker-based ebook learning media product on root material, rank, and logarithms. The research carried out is development research (Development Research) with the ADDIE model. The sample in this study was a student of class X SMK Avicena Rajeg Tangerang Regency in 2021. The data in the study was collected using questionnaires, which consist of expert validation questionnaires, and student response questionnaires. Based on the results of the analysis of research data conducted obtained a value of 76% for media experts and 75.75% for material experts with good categories. The next stage is a practicality test and questionnaire on a small scale and obtained a score of 75.25% with good and practical categories. So it was concluded that the kvisoft flipbook maker-based ebook learning medium is feasible and practically used in math learning.Tujuan utama penelitian ini adalah untuk menghasilkan produk media pembelajaran ebook berbasis kvisoft flipbook maker pada materi akar, pangkat, dan logaritma. Penelitian yang dilaksanakan adalah penelitian pengembangan (Development Research) dengan model ADDIE. Sampel pada penelitian ini adalah siswa kelas X SMK Avicena Rajeg Kabupaten Tangerang tahun 2021. Data dalam penelitian ini dikumpulkan dengan menggunakan angket, yang terdiri dari angket validasi ahli, dan angket respon siswa. Berdasarkan hasil analisis data penelitian yang dilakukan diperoleh nilai 76% untuk ahli media dan 75,75% untuk ahli materi dengan kategori yang baik. Tahap berikutnya adalah tes kepraktisan dan kuesioner dengan skala kecil dan memperoleh nilai 75,25% dengan kategori baik dan praktis. Jadi disimpulkan bahwa media pembelajaran ebook berbasis kvisoft flipbook maker layak dan praktis digunakan dalam pembelajaran matematika.
Multi-initialization Optimization Network for Accurate 3D Human Pose and Shape Estimation

3D human pose and shape recovery from a monocular RGB image is a challenging task. Existing learning based methods highly depend on weak supervision signals, e.g. 2D and 3D joint location, due to the lack of in-the-wild paired 3D supervision. However, considering the 2D-to-3D ambiguities existed in these weak supervision labels, the network is easy to get stuck in local optima when trained with such labels. In this paper, we reduce the ambituity by optimizing multiple initializations. Specifically, we propose a three-stage framework named Multi-Initialization Optimization Network (MION). In the first stage, we strategically select different coarse 3D reconstruction candidates which are compatible with the 2D keypoints of input sample. Each coarse reconstruction can be regarded as an initialization leads to one optimization branch. In the second stage, we design a mesh refinement transformer (MRT) to respectively refine each coarse reconstruction result via a self-attention mechanism. Finally, a Consistency Estimation Network (CEN) is proposed to find the best result from mutiple candidates by evaluating if the visual evidence in RGB image matches a given 3D reconstruction. Experiments demonstrate that our Multi-Initialization Optimization Network outperforms existing 3D mesh based methods on multiple public benchmarks.
EFEKTIVITAS PEMANFAATAN MEDIA PEMBELAJARAN TEKA-TEKI SILANG ONLINE TERHADAP PENINGKATAN MINAT BELAJAR SISWA

&#x0D; This research is a quantitative research which aims to determine the effectiveness of the use of online crossword puzzle learning media in increasing student interest in learning. The population in this study were all students of SMA Widya Manggala. The sample in this study was selected using purposive sampling based on topic considerations. The research design was pre non test and post non test experimental control design. The learning interest questionnaire instrument used in this study was validated by expert validators. The results of the normality test for the Shapiro-Wilk approach show that the significance value (Sig.) Is 0.101. If associated with the normality criteria 0.101 is greater than 0.05 (0.101&gt; 0.05). This means that the research data is normally distributed and can be analyzed using parametic statistics, in this case the one-sample t test. The normality test carried out in this study is a hypothesis test using SPSS 24, the significance value (Sig.) Of 0.000 is smaller than 0.05 (0.000 &lt;0.05), so Ho is rejected and Ha is accepted. Hypothesis test results obtained that further gain test is carried out to determine how much increase in student learning outcomes. The result of the gain test is 0.336 with the medium category.
Generalized Wasserstein Dice Loss, Test-Time Augmentation, and Transformers for the BraTS 2021 Challenge

Brain tumor segmentation from multiple Magnetic Resonance Imaging (MRI) modalities is a challenging task in medical image computation. The main challenges lie in the generalizability to a variety of scanners and imaging protocols. In this paper, we explore strategies to increase model robustness without increasing inference time. Towards this aim, we explore finding a robust ensemble from models trained using different losses, optimizers, and train-validation data split. Importantly, we explore the inclusion of a transformer in the bottleneck of the U-Net architecture. While we find transformer in the bottleneck performs slightly worse than the baseline U-Net in average, the generalized Wasserstein Dice loss consistently produces superior results. Further, we adopt an efficient test time augmentation strategy for faster and robust inference. Our final ensemble of seven 3D U-Nets with test-time augmentation produces an average dice score of 89.4% and an average Hausdorff 95% distance of 10.0 mm when evaluated on the BraTS 2021 testing dataset. Our code and trained models are publicly available at https://github.com/LucasFidon/TRABIT_BraTS2021 .
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation

Availability of large, diverse, and multi-national datasets is crucial for the development of effective and clinically applicable AI systems in the medical imaging domain. However, forming a global model by bringing these datasets together at a central location, comes along with various data privacy and ownership problems. To alleviate these problems, several recent studies focus on the federated learning paradigm, a distributed learning approach for decentralized data. Federated learning leverages all the available data without any need for sharing collaborators’ data with each other or collecting them on a central server. Studies show that federated learning can provide competitive performance with conventional central training, while having a good generalization capability. In this work, we have investigated several federated learning approaches on the brain tumor segmentation problem. We explore different strategies for faster convergence and better performance which can also work on strong Non-IID cases.
Neighbor Correspondence Matching for Flow-based Video Frame Synthesis

Video frame synthesis, which consists of interpolation and extrapolation, is an essential video processing technique that can be applied to various scenarios. However, most existing methods cannot handle small objects or large motion well, especially in high-resolution videos such as 4K videos. To eliminate such limitations, we introduce a neighbor correspondence matching (NCM) algorithm for flow-based frame synthesis. Since the current frame is not available in video frame synthesis, NCM is performed in a current-frame-agnostic fashion to establish multi-scale correspondences in the spatial-temporal neighborhoods of each pixel. Based on the powerful motion representation capability of NCM, we further propose to estimate intermediate flows for frame synthesis in a heterogeneous coarse-to-fine scheme. Specifically, the coarse-scale module is designed to leverage neighbor correspondences to capture large motion, while the fine-scale module is more computationally efficient to speed up the estimation process. Both modules are trained progressively to eliminate the resolution gap between training dataset and real-world videos. Experimental results show that NCM achieves state-of-the-art performance on several benchmarks. In addition, NCM can be applied to various practical scenarios such as video compression to achieve better performance.
Detecting Out-Of-Context Objects Using Graph Contextual Reasoning Network

This paper presents an approach for detecting out-of-context (OOC) objects in images. Given an image with a set of objects, our goal is to determine if an object is inconsistent with the contextual relations and detect the OOC object with a bounding box. In this work, we consider common contextual relations such as co-occurrence relations, the relative size of an object with respect to other objects, and the position of the object in the scene. We posit that contextual cues are useful to determine object labels for in-context objects and inconsistent context cues are detrimental to determining object labels for out-of-context objects. To realize this hypothesis, we propose a graph contextual reasoning network (GCRN) to detect OOC objects. GCRN consists of two separate graphs to predict object labels based on the contextual cues in the image: 1) a representation graph to learn object features based on the neighboring objects and 2) a context graph to explicitly capture contextual cues from the neighboring objects. GCRN explicitly captures the contextual cues to improve the detection of in-context objects and identify objects that violate contextual relations. In order to evaluate our approach, we create a large-scale dataset by adding OOC object instances to the COCO images. We also evaluate on recent OCD benchmark. Our results show that GCRN outperforms competitive baselines in detecting OOC objects and correctly detecting in-context objects. Code and data: https://nusci.csl.sri.com/project/trinity-ooc
UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translation

Most translation tasks among languages belong to the zero-resource translation problem where parallel corpora are unavailable. Multilingual neural machine translation (MNMT) enables one-pass translation using shared semantic space for all languages compared to the two-pass pivot translation but often underperforms the pivot-based method. In this paper, we propose a novel method, named as Unified Multilingual Multiple teacher-student Model for NMT (UM4). Our method unifies source-teacher, target-teacher, and pivot-teacher models to guide the student model for the zero-resource translation. The source teacher and target teacher force the student to learn the direct source to target translation by the distilled knowledge on both source and target sides. The monolingual corpus is further leveraged by the pivot-teacher model to enhance the student model. Experimental results demonstrate that our model of 72 directions significantly outperforms previous methods on the WMT benchmark.
Survey of Control-flow Integrity Techniques for Real-time Embedded Systems

Computing systems, including real-time embedded systems, are becoming increasingly connected to allow for more advanced and safer operation. Such embedded systems are also often resource-constrained, for example, with lower processing capabilities compared to general-purpose computing systems like desktops or servers. With the advent of paradigms such as internet-of-things (IoT), embedded systems in both commercial and industrial contexts are being increasingly interconnected and exposed to the external networks to improve automation and efficiency of operation. However, allowing external interfaces to such embedded systems increases their exposure to attackers. With an increase in attacks against embedded systems ranging from home appliances to industrial control systems operating critical equipment that have real-time requirements, it is imperative that defense mechanisms be created that explicitly consider such resource and real-time constraints. Control-flow integrity (CFI) is a family of defense mechanisms that prevent attackers from modifying the flow of execution. We survey CFI techniques, ranging from the basic to state of the art, that are built for embedded systems and real-time embedded systems and find that there is a dearth, especially for real-time embedded systems, of CFI mechanisms. We then present open challenges to the community to help drive future research in this domain.
SAFARI: shape analysis for AI-segmented images

Abstract Background Recent developments to segment and characterize the regions of interest (ROI) within medical images have led to promising shape analysis studies. However, the procedures to analyze the ROI are arbitrary and vary by study. A tool to translate the ROI to analyzable shape representations and features is greatly needed. Results We developed SAFARI (shape analysis for AI-segmented images), an open-source package with a user-friendly online tool kit for ROI labelling and shape feature extraction of segmented maps, provided by AI-algorithms or manual segmentation. We demonstrated that half of the shape features extracted by SAFARI were significantly associated with survival outcomes in a case study on 143 consecutive patients with stage I–IV lung cancer and another case study on 61 glioblastoma patients. Conclusions SAFARI is an efficient and easy-to-use toolkit for segmenting and analyzing ROI in medical images. It can be downloaded from the comprehensive R archive network (CRAN) and accessed at https://lce.biohpc.swmed.edu/safari/ .
Privacy-preserving Federated Learning based on Multi-key Homomorphic  Encryption

With the advance of machine learning and the internet of things (IoT), security and privacy have become key concerns in mobile services and networks. Transferring data to a central unit violates privacy as well as protection of sensitive data while increasing bandwidth demands.Federated learning mitigates this need to transfer local data by sharing model updates only. However, data leakage still remains an issue. In this paper, we propose xMK-CKKS, a multi-key homomorphic encryption protocol to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, collaboration between all participating devices is required. This scheme prevents privacy leakage from publicly shared information in federated learning, and is robust to collusion between $k<N-1$ participating devices and the server. Our experimental evaluation demonstrates that the scheme preserves model accuracy against traditional federated learning as well as secure federated learning with homomorphic encryption (MK-CKKS, Paillier) and reduces computational cost compared to Paillier based federated learning. The average energy consumption is 2.4 Watts, so that it is suited to IoT scenarios.
Proposition-Level Clustering for Multi-Document Summarization

Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition.Particularly, clusters were leveraged to indicate information saliency as well as to avoid redundancy.Such prior methods focused on clustering sentences, even though closely related sentences usually contain also non-aligned parts.In this work, we revisit the clustering approach, grouping together sub-sentential propositions, aiming at more precise information alignment.Specifically, our method detects salient propositions, clusters them into paraphrastic clusters, and generates a representative sentence for each cluster via text fusion.Our summarization method improves over the previous state-ofthe-art MDS method in the DUC 2004 and TAC 2011 datasets, both in automatic ROUGE scores and human preference.
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog

A typical end-to-end task-oriented dialog system transfers context into dialog state, and upon which generates a response, which usually faces the problem of error propagation from both previously generated inaccurate dialog states and responses, especially in low-resource scenarios.To alleviate these issues, we propose BORT, a back and denoising reconstruction approach for end-to-end task-oriented dialog system.Squarely, to improve the accuracy of dialog states, back reconstruction is used to reconstruct the original input context from the generated dialog states since inaccurate dialog states cannot recover the corresponding input context.To enhance the denoising capability of the model to reduce the impact of error propagation, denoising reconstruction is used to reconstruct the corrupted dialog state and response.Extensive experiments conducted on MultiWOZ 2.0 and CamRest676 show the effectiveness of BORT.Furthermore, BORT demonstrates its advanced capabilities in the zero-shot domain and low-resource scenarios 1 .
NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension

NER has been traditionally formulated as a sequence labeling task.However, there has been recent trend in posing NER as a machine reading comprehension task (Wang et al., 2020;Mengge et al., 2020), where entity name (or other information) is considered as a question, text as the context and entity value in text as answer snippet.These works consider MRC based on a single question (entity) at a time.We propose posing NER as a multi-question MRC task, where multiple questions (one question per entity) are considered at the same time for a single text.We propose a novel BERT-based multi-question MRC (NER-MQMRC) architecture for this formulation.NER-MQMRC architecture considers all entities as input to BERT for learning token embeddings with selfattention and leverages BERT-based entity representation for further improving these token embeddings for NER task.Evaluation on three NER datasets show that our proposed architecture leads to average 2.5 times faster training and 2.3 times faster inference as compared to NER-SQMRC framework based models by considering all entities together in a single pass.Further, we show that our model performance does not degrade compared to single-question based MRC (NER-SQMRC) (Devlin et al., 2019) leading to F1 gain of +0.41%, +0.32% and +0.27% for AE-Pub, Ecommerce5PT and Twitter datasets respectively.We propose this architecture primarily to solve large scale ecommerce attribute (or entity) extraction from unstructured text of a magnitude of 50k+ attributes to be extracted on a scalable production environment with high performance and optimised training and inference runtimes.
DISARM: Detecting the Victims Targeted by Harmful Memes

Internet memes have emerged as an increasingly popular means of communication on the Web.Although typically intended to elicit humour, they have been increasingly used to spread hatred, trolling, and cyberbullying, as well as to target specific individuals, communities, or society on political, socio-cultural, and psychological grounds.While previous work has focused on detecting harmful, hateful, and offensive memes, identifying whom they attack remains a challenging and underexplored area.Here we aim to bridge this gap.In particular, we create a dataset where we annotate each meme with its victim(s) such as the name of the targeted person(s), organization(s), and community(ies).We then propose DISARM (Detecting vIctimS targeted by hARmful Memes), a framework that uses named entity recognition and person identification to detect all entities a meme is referring to, and then, incorporates a novel contextualized multimodal deep neural network to classify whether the meme intends to harm these entities.We perform several systematic experiments on three test setups, corresponding to entities that are (a) all seen while training, (b) not seen as a harmful target on training, and (c) not seen at all on training.The evaluation results show that DISARM significantly outperforms ten unimodal and multimodal systems.Finally, we show that DISARM is interpretable and comparatively more generalizable and that it can reduce the relative error rate for harmful target identification by up to 9 points absolute over several strong multimodal rivals.
Digital Experiential Learning for Sustainable Horticulture and Landscape Management Education

During the COVID-19 pandemic, horticulture and landscape management programmes in higher education experienced a huge drawback because of the impossibility of organising field studies and conducting site research. To pursue a more sustainable method of teaching, immersive technology such as augmented reality (AR) and virtual reality (VR) has been increasingly adopted as an effective approach for multimodal experiential learning. This study examines student perceptions on the use of digital technology in team-based hybrid learning to achieve sustainability in tree management using data collected from students of horticulture and landscape management in a higher education institute in Hong Kong. Key theoretical principles on Kolb’s experiential learning cycle as an interactive process are discussed, followed by an empirical analysis of student survey results. This research deepens the understanding of how immersive technology enhances both environmental sustainability and learning innovation. The results demonstrate that innovative ideas in instructional methods such as ARVR simulation can enhance the environmental sustainability of how tree management can be conducted, promoting a more environmentally conscious, experiential, collaborative and digital learning experience in higher education.
Dynamic Multiscale Fusion Generative Adversarial Network for Radar Image Extrapolation

Typhoons, a kind of devastating natural disaster, have caused incalculable damages worldwide. The meteorological radar image is essential for weather forecasting, especially typhoons. The weather nowcasting (future 0-6 hours) can be implemented via extrapolating radar images without using the primary weather forecasting method &#x2013; the Numerical Weather Prediction model. However, the existing related techniques based on statistics or Artificial Intelligence were not efficient enough. In this paper, a novel radar image extrapolation algorithm named Dynamic Multi-Scale Fusion-Generative Adversarial Network (DMSF-GAN) was proposed. DMSF-GAN captures the future radar image distribution based on current radar images through modifying the GAN. In the generative module of the GAN, an auto-encoder consisting of Dynamic Inception-3D and Feature Connection blocks extracts significant features from current radar images. The feasibility of the proposed model was verified on a real radar image dataset, and experimental results proved that the proposed algorithm could effectively capture the location and pattern of the future radar echo, especially for typhoon weather systems. Compared with mainstream methods of radar image extrapolation such as Optical-Flow and Recurrent Neural Network (RNN)-based models, DMSF-GAN has a more superior and robust performance, which is also suitable for running on low configuration machines.