Reviewerly/exHarmony huggingface dataset:
Developing English learning material for grade 4 students

Background: A task of a teacher or an educator is to create a pleasant learning atmosphere or situation for students. This situation can be realized by doing fun learning by compiling fun teaching materials as well.&#x0D; Purpose: To overcome these problems, it is deemed necessary to renew the development of appropriate teaching materials.&#x0D; Design and methods: This research method is Research and Development (R&amp;D). This research was conducted at SDN Ciomas 3 Bogor. The developing teaching materials for the English language module uses the research and development method of 4D learning tools which consists of 4 stages, namely define, design, develop, and disseminate. Data analysis techniques in this study used quantitative data and qualitative data. Qualitative data were obtained based on input, suggestions, assessments carried out by two experts, namely material and media experts, English teachers, and students. Then the quantitative data is based on a closed-ended questionnaire that has been compiled to determine the quality of the developed product, namely the English Language Module teaching materials.&#x0D; Results: The results of the product trial design were supervisors, material experts and media experts and the subjects tried 3 teachers and 38 students. the results of this study are the English Language Module for class IV which contains 5 material and has obtained very good quality based on the validation value of the material experts scored 69 or 92% from maximum value of 75, validating media experts with a score of 70 or 93% from a maximum value of 75 assessment by 3 teachers with a total score of 445 or 98.8% from a maximum value of 450, and the actual field trial for students with a total score of 1854 or 97.57% from a maximum value of 1900 with the results explained, the English module teaching materials are very suitable to be used as a supporting book for learning English in Grade IV Elementary School.
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure

Code pre-trained models (CodePTMs) have recently demonstrated significant success in code intelligence. To interpret these models, some probing methods have been applied. However, these methods fail to consider the inherent characteristics of codes. In this paper, to address the problem, we propose a novel probing method CAT-probing to quantitatively interpret how CodePTMs attend code structure. We first denoise the input code sequences based on the token types pre-defined by the compilers to filter those tokens whose attention scores are too small. After that, we define a new metric CAT-score to measure the commonality between the token-level attention scores generated in CodePTMs and the pair-wise distances between corresponding AST nodes. The higher the CAT-score, the stronger the ability of CodePTMs to capture code structure. We conduct extensive experiments to integrate CAT-probing with representative CodePTMs for different programming languages. Experimental results show the effectiveness of CAT-probing in CodePTM interpretation. Our codes and data are publicly available at https://github.com/nchen909/CodeAttention.
Variational Quantum Non-Orthogonal Optimization

Current universal quantum computers have a limited number of noisy qubits. Because of this, it is difficult to use them to solve large-scale complex optimization problems. In this paper we tackle this issue by proposing a quantum optimization scheme where discrete classical variables are encoded in non-orthogonal states of the quantum system. We develop the case of non-orthogonal qubit states, with individual qubits on the quantum computer handling more than one bit classical variable. Combining this idea with Variational Quantum Eigensolvers (VQE) and quantum state tomography, we show that it is possible to significantly reduce the number of qubits required by quantum hardware to solve complex optimization problems. We benchmark our algorithm by successfully optimizing a polynomial of degree 8 and 15 variables using only 15 qubits. Our proposal opens the path towards solving real-life useful optimization problems in today's limited quantum hardware.
Readability Controllable Biomedical Document Summarization

Different from general documents, it is recognised that the ease with which people can understand a biomedical text is eminently varied, owing to the highly technical nature of biomedical documents and the variance of readers' domain knowledge. However, existing biomedical document summarization systems have paid little attention to readability control, leaving users with summaries that are incompatible with their levels of expertise. In recognition of this urgent demand, we introduce a new task of readability controllable summarization for biomedical documents, which aims to recognise users' readability demands and generate summaries that better suit their needs: technical summaries for experts and plain language summaries (PLS) for laymen. To establish this task, we construct a corpus consisting of biomedical papers with technical summaries and PLSs written by the authors, and benchmark multiple advanced controllable abstractive and extractive summarization models based on pre-trained language models (PLMs) with prevalent controlling and generation techniques. Moreover, we propose a novel masked language model (MLM) based metric and its variant to effectively evaluate the readability discrepancy between lay and technical summaries. Experimental results from automated and human evaluations show that though current control techniques allow for a certain degree of readability adjustment during generation, the performance of existing controllable summarization methods is far from desirable in this task.
A two-stage approach for table extraction in invoices

The automated analysis of administrative documents is an important field in document recognition that is studied for decades. Invoices are key documents among these huge amounts of documents available in companies and public services. Invoices contain most of the time data that are presented in tables that should be clearly identified to extract suitable information. In this paper, we propose an approach that combines an image processing based estimation of the shape of the tables with a graph-based representation of the document, which is used to identify complex tables precisely. We propose an experimental evaluation using a real case application.
PyHopper -- Hyperparameter optimization

Hyperparameter tuning is a fundamental aspect of machine learning research. Setting up the infrastructure for systematic optimization of hyperparameters can take a significant amount of time. Here, we present PyHopper, a black-box optimization platform designed to streamline the hyperparameter tuning workflow of machine learning researchers. PyHopper's goal is to integrate with existing code with minimal effort and run the optimization process with minimal necessary manual oversight. With simplicity as the primary theme, PyHopper is powered by a single robust Markov-chain Monte-Carlo optimization algorithm that scales to millions of dimensions. Compared to existing tuning packages, focusing on a single algorithm frees the user from having to decide between several algorithms and makes PyHopper easily customizable. PyHopper is publicly available under the Apache-2.0 license at https://github.com/PyHopper/PyHopper.
mPSAuth: Privacy-Preserving and Scalable Authentication for Mobile Web Applications

As nowadays most web application requests originate from mobile devices, authentication of mobile users is essential in terms of security considerations. To this end, recent approaches rely on machine learning techniques to analyze various aspects of user behavior as a basis for authentication decisions. These approaches face two challenges: first, examining behavioral data raises significant privacy concerns, and second, approaches must scale to support a large number of users. Existing approaches do not address these challenges sufficiently. We propose mPSAuth, an approach for continuously tracking various data sources reflecting user behavior (e.g., touchscreen interactions, sensor data) and estimating the likelihood of the current user being legitimate based on machine learning techniques. With mPSAuth, both the authentication protocol and the machine learning models operate on homomorphically encrypted data to ensure the users' privacy. Furthermore, the number of machine learning models used by mPSAuth is independent of the number of users, thus providing adequate scalability. In an extensive evaluation based on real-world data from a mobile application, we illustrate that mPSAuth can provide high accuracy with low encryption and communication overhead, while the effort for the inference is increased to a tolerable extent.
Efficient NTK using Dimensionality Reduction

Recently, neural tangent kernel (NTK) has been used to explain the dynamics of learning parameters of neural networks, at the large width limit. Quantitative analyses of NTK give rise to network widths that are often impractical and incur high costs in time and energy in both training and deployment. Using a matrix factorization technique, we show how to obtain similar guarantees to those obtained by a prior analysis while reducing training and inference resource costs. The importance of our result further increases when the input points' data dimension is in the same order as the number of input points. More generally, our work suggests how to analyze large width networks in which dense linear layers are replaced with a low complexity factorization, thus reducing the heavy dependence on the large width.
Ensemble Learning using Transformers and Convolutional Networks for Masked Face Recognition

Wearing a face mask is one of the adjustments we had to follow to reduce the spread of the coronavirus. Having our faces covered by masks constantly has driven the need to understand and investigate how this behavior affects the recognition capability of face recognition systems. Current face recognition systems have extremely high accuracy when dealing with unconstrained general face recognition cases but do not generalize well with occluded masked faces. In this work, we propose a system for masked face recognition. The proposed system comprises two Convolutional Neural Network (CNN) models and two Transformer models. The CNN models have been fine-tuned on FaceNet pre-trained model. We ensemble the predictions of the four models using the majority voting technique to identify the person with the mask. The proposed system has been evaluated on a synthetically masked LFW dataset created in this work. The best accuracy is obtained using the ensembled models with an accuracy of 92%. This recognition rate outperformed the accuracy of other models and it shows the correctness and robustness of the proposed model for recognizing masked faces. The code and data are available at https://github.com/Hamzah-Luqman/MFR
Generating image captions with external encyclopedic knowledge

Accurately reporting what objects are depicted in an image is largely a solved problem in automatic caption generation. The next big challenge on the way to truly humanlike captioning is being able to incorporate the context of the image and related real world knowledge. We tackle this challenge by creating an end-to-end caption generation system that makes extensive use of image-specific encyclopedic data. Our approach includes a novel way of using image location to identify relevant open-domain facts in an external knowledge base, with their subsequent integration into the captioning pipeline at both the encoding and decoding stages. Our system is trained and tested on a new dataset with naturally produced knowledge-rich captions, and achieves significant improvements over multiple baselines. We empirically demonstrate that our approach is effective for generating contextualized captions with encyclopedic knowledge that is both factually accurate and relevant to the image.
4D Unsupervised Object Discovery

Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. Therefore, discovering objects without annotations has great significance. However, this task seems impractical on still-image or point cloud alone due to the lack of discriminative information. Previous studies underlook the crucial temporal information and constraints naturally behind multi-modal inputs. In this paper, we propose 4D unsupervised object discovery, jointly discovering objects from 4D data -- 3D point clouds and 2D RGB images with temporal information. We present the first practical approach for this task by proposing a ClusterNet on 3D point clouds, which is jointly iteratively optimized with a 2D localization network. Extensive experiments on the large-scale Waymo Open Dataset suggest that the localization network and ClusterNet achieve competitive performance on both class-agnostic 2D object detection and 3D instance segmentation, bridging the gap between unsupervised methods and full supervised ones. Codes and models will be made available at https://github.com/Robertwyq/LSMOL.
Hierarchical3D Adapters for Long Video-to-text Summarization

In this paper, we focus on video-to-text summarization and investigate how to best utilize multimodal information for summarizing long inputs (e.g., an hour-long TV show) into long outputs (e.g., a multi-sentence summary). We extend SummScreen (Chen et al., 2021), a dialogue summarization dataset consisting of transcripts of TV episodes with reference summaries, and create a multimodal variant by collecting corresponding full-length videos. We incorporate multimodal information into a pre-trained textual summarizer efficiently using adapter modules augmented with a hierarchical structure while tuning only 3.8\% of model parameters. Our experiments demonstrate that multimodal information offers superior performance over more memory-heavy and fully fine-tuned textual summarization methods.
Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset

Previous work on Neural Referring Expression Generation (REG) all uses WebNLG, an English dataset that has been shown to reflect a very limited range of referring expression (RE) use. To tackle this issue, we build a dataset based on the OntoNotes corpus that contains a broader range of RE use in both English and Chinese (a language that uses zero pronouns). We build neural Referential Form Selection (RFS) models accordingly, assess them on the dataset and conduct probing experiments. The experiments suggest that, compared to WebNLG, OntoNotes is better for assessing REG/RFS models. We compare English and Chinese RFS and confirm that, in line with linguistic theories, Chinese RFS depends more on discourse context than English.
Layer Ensembles

Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the prediction of multiple neural networks by collecting votes from each network and computing the difference in those predictions. In this paper, we introduce a method for uncertainty estimation that considers a set of independent categorical distributions for each layer of the network, giving many more possible samples with overlapped layers than in the regular Deep Ensembles. We further introduce an optimized inference procedure that reuses common layer outputs, achieving up to 19x speed up and reducing memory usage quadratically. We also show that the method can be further improved by ranking samples, resulting in models that require less memory and time to run while achieving higher uncertainty quality than Deep Ensembles.
Transformer-based Localization from Embodied Dialog with Large-scale Pre-training

We address the challenging task of Localization via Embodied Dialog (LED). Given a dialog from two agents, an Observer navigating through an unknown environment and a Locator who is attempting to identify the Observer's location, the goal is to predict the Observer's final location in a map. We develop a novel LED-Bert architecture and present an effective pretraining strategy. We show that a graph-based scene representation is more effective than the top-down 2D maps used in prior works. Our approach outperforms previous baselines.
CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation

Counterfactual data augmentation (CDA) -- i.e., adding minimally perturbed inputs during training -- helps reduce model reliance on spurious correlations and improves generalization to out-of-distribution (OOD) data. Prior work on generating counterfactuals only considered restricted classes of perturbations, limiting their effectiveness. We present COunterfactual Generation via Retrieval and Editing (CORE), a retrieval-augmented generation framework for creating diverse counterfactual perturbations for CDA. For each training example, CORE first performs a dense retrieval over a task-related unlabeled text corpus using a learned bi-encoder and extracts relevant counterfactual excerpts. CORE then incorporates these into prompts to a large language model with few-shot learning capabilities, for counterfactual editing. Conditioning language model edits on naturally occurring data results in diverse perturbations. Experiments on natural language inference and sentiment analysis benchmarks show that CORE counterfactuals are more effective at improving generalization to OOD data compared to other DA approaches. We also show that the CORE retrieval framework can be used to encourage diversity in manually authored perturbations
DNA Extraction

The Next Generation Science Standards (NGSS) were proposed in 2013 in the United States for K–12 learning to emphasize the importance of science and engineering practices in the three dimensions of science learning. In this study, we show that DNA extraction experiments, which are familiar in the United States, can be used not only as demonstration experiments but also as educational material for science practices. We could visualize extracted DNA using an enzyme treatment with DNase and RNase and agarose gel electrophoresis. We conducted a science practice in which a DNA extraction experiment was used as an extracurricular club activity in Japan. The students made predictions, conducted experiments, obtained results, and reflected on their findings. Although there are still points needing improvement, our results indicate that the DNA extraction experiment can be used as a new teaching material for NGSS science practices.
Pelatihan Pemanfaatan Free Platform untuk Mengembangkan Penilaian dalam Pembelajaran Daring

The free platform is a free application that can be used to evaluate online learning. The problem is that teachers are hampered by using modern free platforms. Teachers still apply conventional assessments, such as printing paper, taking photos of student's work, and sending them via WhatsApp groups. This assistance is intended to describe the results of training on using free platforms to develop assessments in online learning for elementary schools in Kebumen Regency. Servants collaborated with several partners, namely Public Elementary School 4 Kedawung, Public Elementary School 2 Kewayuhan, Public Elementary School 1 Kewayuhan, and Public Elementary School 3 Kewayuhan. The first training was held on June 24, 2021. The service results showed that the preparation aspect had reached a sufficient percentage of 75.4%, while the implementation aspect had a percentage result of 80.1% in the high category. Moreover, the processing aspect reached a sufficient percentage of 75.4 %, and the utilization aspect reached 79% with a good category. From these results, it can be concluded that teachers can follow the procedures for using free platforms (Google Forms and Quizizz) to develop assessments quite well.
DeepScale: Online Frame Size Adaptation for Multi-object Tracking on Smart Cameras and Edge Servers

In surveillance and search and rescue applications, it is important to perform multi-target tracking (MOT) in real-time on low-end devices. Today's MOT solutions employ deep neural networks, which tend to have high computation complexity. Recognizing the effects of frame sizes on tracking performance, we propose DeepScale, a model agnostic frame size selection approach that operates on top of existing fully convolutional network-based trackers to accelerate tracking throughput. In the training stage, we incorporate detectability scores into a one-shot tracker architecture so that DeepScale can learn representation estimations for different frame sizes in a self-supervised manner. During inference, it can adapt frame sizes according to the complexity of visual contents based on user-controlled parameters. To leverage computation resources on edge servers, we propose two computation partition schemes tailored for MOT, namely, edge server only with adaptive frame-size transmission and edge server-assisted tracking. Extensive experiments and benchmark tests on MOT datasets demonstrate the effectiveness and flexibility of DeepScale. Compared to a state-of-the-art tracker, DeepScale++, a variant of DeepScale achieves 1.57X accelerated with only moderate degradation ~2.3\ in tracking accuracy on the MOT15 dataset in one configuration. We have implemented and evaluated DeepScale++ and the proposed computation partition schemes on a small-scale testbed consisting of an NVIDIA Jetson TX2 board and a GPU server. The experiments reveal non-trivial trade-offs between tracking performance and latency compared to server-only or smart camera-only solutions.
Phish What You Wish

IT professionals have no simple tool to create phishing websites and raise the awareness of users. We developed a prototype that can dynamically mimic websites by using enriched screenshots, which requires no additional programming experience and is simple to set up. The generated websites are functional and remain up-to-date. We found that 98% of the hyperlinks in mimicked websites are functional with our tool, compared to 43% with the best competitor, and only two participants suspected phishing attempts at the time they were performing tasks with our prototype. This work intends to raise awareness for phishing attempts especially with local websites by providing an easy to use prototype to set up such phishing sites.
A Proposed Framework for Face Recognition using Enhanced Local Binary Pattern Algorithm with Chinese Remainder Theorem

One of the biometric methods that have recently gained attention across the globe is Face Recognition. This was due to the availability of practicable technologies, including movable results. Several studies have been carried out on face recognition for decades, but the problem is still largely unsolved. Significant progress has been made recently in this area as a result of advancements in face modeling and analysis techniques. While a system has been developed for face recognition, the problem of high processing time is still largely unresolved. This research framework proposed an Enhanced Local Binary Pattern (ELBP) algorithm for face recognition. The Local Binary Pattern (LBP) algorithm is a method used in facial feature dimensionality reduction. Standard LBP had challenges of computational complexity. Therefore, the LBP will be enhanced with the Chinese Remainder Theorem (CRT) and will be used for feature extraction to reduce computational time, Chicken Swarm Optimization (CSO) will be used for feature selection and classification will be done using a Support Vector Machine (SVM). Performance Evaluation of the system will be done by comparing the computation time result obtained from the combination of LBP-CSO and ELBP-CSO. The ELBP-CSO is expected to have a lower computation recognition time than the LBP-CSO.