Reviewerly/exHarmony huggingface dataset:
Judge a Sentence by Its Content to Generate Grammatical Errors

Data sparsity is a well-known problem for grammatical error correction (GEC). Generating synthetic training data is one widely proposed solution to this problem, and has allowed models to achieve state-of-the-art (SOTA) performance in recent years. However, these methods often generate unrealistic errors, or aim to generate sentences with only one error. We propose a learning based two stage method for synthetic data generation for GEC that relaxes this constraint on sentences containing only one error. Errors are generated in accordance with sentence merit. We show that a GEC model trained on our synthetically generated corpus outperforms models trained on synthetic data from prior work.
Fuse and Attend: Generalized Embedding Learning for Art and Sketches

While deep Embedding Learning approaches have witnessed widespread success in multiple computer vision tasks, the state-of-the-art methods for representing natural images need not necessarily perform well on images from other domains, such as paintings, cartoons, and sketch. This is because of the huge shift in the distribution of data from across these domains, as compared to natural images. Domains like sketch often contain sparse informative pixels. However, recognizing objects in such domains is crucial, given multiple relevant applications leveraging such data, for instance, sketch to image retrieval. Thus, achieving an Embedding Learning model that could perform well across multiple domains is not only challenging, but plays a pivotal role in computer vision. To this end, in this paper, we propose a novel Embedding Learning approach with the goal of generalizing across different domains. During training, given a query image from a domain, we employ gated fusion and attention to generate a positive example, which carries a broad notion of the semantics of the query object category (from across multiple domains). By virtue of Contrastive Learning, we pull the embeddings of the query and positive, in order to learn a representation which is robust across domains. At the same time, to teach the model to be discriminative against examples from different semantic categories (across domains), we also maintain a pool of negative embeddings (from different categories). We show the prowess of our method using the DomainBed framework, on the popular PACS (Photo, Art painting, Cartoon, and Sketch) dataset.
A Visual Analytics Framework for Composing a Hierarchical Classification for Medieval Illuminations

Annotated data is a requirement for applying supervised machine learning methods, and the quality of annotations is crucial for the result. Especially when working with cultural heritage collections that inhere a manifold of uncertainties, annotating data remains a manual, arduous task to be carried out by domain experts. Our project started with two already annotated sets of medieval manuscript images which however were incomplete and comprised conflicting metadata based on scholarly and linguistic differences. Our aims were to create (1) a uniform set of descriptive labels for the combined data set, and (2) a hierarchical classification of a high quality that can be used as a valuable input for supervised machine learning. To reach these goals, we developed a visual analytics system to enable medievalists to combine, regularize and extend the vocabulary used to describe these data sets. Visual interfaces for word and image embeddings as well as co-occurrences of the annotations across the data sets enable annotating multiple images at the same time, recommend annotation label candidates and support composing a hierarchical classification of labels. Our system itself implements a semi-supervised method as it updates visual representations based on the medievalists' feedback, and a series of usage scenarios document its value for the target community.
Trigger-free Event Detection via Derangement Reading Comprehension

Event detection (ED), aiming to detect events from texts and categorize them, is vital to understanding actual happenings in real life. However, mainstream event detection models require high-quality expert human annotations of triggers, which are often costly and thus deter the application of ED to new domains. Therefore, in this paper, we focus on low-resource ED without triggers and aim to tackle the following formidable challenges: multi-label classification, insufficient clues, and imbalanced events distribution. We propose a novel trigger-free ED method via Derangement mechanism on a machine Reading Comprehension (DRC) framework. More specifically, we treat the input text as Context and concatenate it with all event type tokens that are deemed as Answers with an omitted default question. So we can leverage the self-attention in pre-trained language models to absorb semantic relations between input text and the event types. Moreover, we design a simple yet effective event derangement module (EDM) to prevent major events from being excessively learned so as to yield a more balanced training process. The experiment results show that our proposed trigger-free ED model is remarkably competitive to mainstream trigger-based models, showing its strong performance on low-source event detection.
Finding Emotions in Faces: A Meta-Classifier

Machine learning has been used to recognize emotions in faces, typically by looking for 8 different emotional states (neutral, happy, sad, surprise, fear, disgust, anger and contempt). We consider two approaches: feature recognition based on facial landmarks and deep learning on all pixels; each produced 58% overall accuracy. However, they produced different results on different images and thus we propose a new meta-classifier combining these approaches. It produces far better results with 77% accuracy
YOLOV: Making Still Image Object Detectors Great at Video Object Detection

Video object detection (VID) is challenging because of the high variation of object appearance as well as the diverse deterioration in some frames. On the positive side, the detection in a certain frame of a video, compared with that in a still image, can draw support from other frames. Hence, how to aggregate features across different frames is pivotal to VID problem. Most of existing aggregation algorithms are customized for two-stage detectors. However, these detectors are usually computationally expensive due to their two-stage nature. This work proposes a simple yet effective strategy to address the above concerns, which costs marginal overheads with significant gains in accuracy. Concretely, different from traditional two-stage pipeline, we select important regions after the one-stage detection to avoid processing massive low-quality candidates. Besides, we evaluate the relationship between a target frame and reference frames to guide the aggregation. We conduct extensive experiments and ablation studies to verify the efficacy of our design, and reveal its superiority over other state-of-the-art VID approaches in both effectiveness and efficiency. Our YOLOX-based model can achieve promising performance (\emph{e.g.}, 87.5\% AP50 at over 30 FPS on the ImageNet VID dataset on a single 2080Ti GPU), making it attractive for large-scale or real-time applications. The implementation is simple, we have made the demo codes and models available at \url{https://github.com/YuHengsss/YOLOV}.
MemoNav: Selecting Informative Memories for Visual Navigation

Image-goal navigation is a challenging task, as it requires the agent to navigate to a target indicated by an image in a previously unseen scene. Current methods introduce diverse memory mechanisms which save navigation history to solve this task. However, these methods use all observations in the memory for generating navigation actions without considering which fraction of this memory is informative. To address this limitation, we present the MemoNav, a novel memory mechanism for image-goal navigation, which retains the agent's informative short-term memory and long-term memory to improve the navigation performance on a multi-goal task. The node features on the agent's topological map are stored in the short-term memory, as these features are dynamically updated. To aid the short-term memory, we also generate long-term memory by continuously aggregating the short-term memory via a graph attention module. The MemoNav retains the informative fraction of the short-term memory via a forgetting module based on a Transformer decoder and then incorporates this retained short-term memory and the long-term memory into working memory. Lastly, the agent uses the working memory for action generation. We evaluate our model on a new multi-goal navigation dataset. The experimental results show that the MemoNav outperforms the SoTA methods by a large margin with a smaller fraction of navigation history. The results also empirically show that our model is less likely to be trapped in a deadlock, which further validates that the MemoNav improves the agent's navigation efficiency by reducing redundant steps.
Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features

In computer vision, multi-label recognition are important tasks with many real-world applications, but classifying previously unseen labels remains a significant challenge. In this paper, we propose a novel algorithm, Aligned Dual moDality ClaSsifier (ADDS), which includes a Dual-Modal decoder (DM-decoder) with alignment between visual and textual features, for open-vocabulary multi-label classification tasks. Then we design a simple and yet effective method called Pyramid-Forwarding to enhance the performance for inputs with high resolutions. Moreover, the Selective Language Supervision is applied to further enhance the model performance. Extensive experiments conducted on several standard benchmarks, NUS-WIDE, ImageNet-1k, ImageNet-21k, and MS-COCO, demonstrate that our approach significantly outperforms previous methods and provides state-of-the-art performance for open-vocabulary multi-label classification, conventional multi-label classification and an extreme case called single-to-multi label classification where models trained on single-label datasets (ImageNet-1k, ImageNet-21k) are tested on multi-label ones (MS-COCO and NUS-WIDE).
Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks

Autonomous agents are able to draw on a wide variety of potential sources of task knowledge; however current approaches invariably focus on only one or two. Here we investigate the challenges and impact of exploiting diverse knowledge sources to learn online, in one-shot, new tasks for a simulated office mobile robot. The resulting agent, developed in the Soar cognitive architecture, uses the following sources of domain and task knowledge: interaction with the environment, task execution and search knowledge, human natural language instruction, and responses retrieved from a large language model (GPT-3). We explore the distinct contributions of these knowledge sources and evaluate the performance of different combinations in terms of learning correct task knowledge and human workload. Results show that an agent's online integration of diverse knowledge sources improves one-shot task learning overall, reducing human feedback needed for rapid and reliable task learning.
Minecraft: An Engaging Platform to Learn Programming

Teaching programming effectively is difficult. This paper explores the benefits of using Minecraft Education Edition to teach Python programming. Educators can use the game to teach various programming concepts ranging from fundamental programming concepts, object-oriented programming, event-driven programming, and parallel programming. It has several benefits, including being highly engaging, sharpen creativity and problem-solving skill, motivating the study of mathematics, and making students realizes the importance of programming.
[Re] Differentiable Spatial Planning using Transformers

This report covers our reproduction effort of the paper 'Differentiable Spatial Planning using Transformers' by Chaplot et al. . In this paper, the problem of spatial path planning in a differentiable way is considered. They show that their proposed method of using Spatial Planning Transformers outperforms prior data-driven models and leverages differentiable structures to learn mapping without a ground truth map simultaneously. We verify these claims by reproducing their experiments and testing their method on new data. We also investigate the stability of planning accuracy with maps with increased obstacle complexity. Efforts to investigate and verify the learnings of the Mapper module were met with failure stemming from a paucity of computational resources and unreachable authors.
Generalized Projection Matrices

Projection matrices are necessary for a large portion of rendering computer graphics. There are primarily two different types of projection matrices -- perspective and orthographic -- which are used frequently, and are traditionally treated as mutually incompatible with each other in how they are defined. Here, we bridge the gap between the two different forms of projection matrices to present a single generalized projection matrix that can represent both.
Intersection of Parallels as an Early Stopping Criterion

A common way to avoid overfitting in supervised learning is early stopping, where a held-out set is used for iterative evaluation during training to find a sweet spot in the number of training steps that gives maximum generalization. However, such a method requires a disjoint validation set, thus part of the labeled data from the training set is usually left out for this purpose, which is not ideal when training data is scarce. Furthermore, when the training labels are noisy, the performance of the model over a validation set may not be an accurate proxy for generalization. In this paper, we propose a method to spot an early stopping point in the training iterations without the need for a validation set. We first show that in the overparameterized regime the randomly initialized weights of a linear model converge to the same direction during training. Using this result, we propose to train two parallel instances of a linear model, initialized with different random seeds, and use their intersection as a signal to detect overfitting. In order to detect intersection, we use the cosine distance between the weights of the parallel models during training iterations. Noticing that the final layer of a NN is a linear map of pre-last layer activations to output logits, we build on our criterion for linear models and propose an extension to multi-layer networks, using the new notion of counterfactual weights. We conduct experiments on two areas that early stopping has noticeable impact on preventing overfitting of a NN: (i) learning from noisy labels; and (ii) learning to rank in IR. Our experiments on four widely used datasets confirm the effectiveness of our method for generalization. For a wide range of learning rates, our method, called Cosine-Distance Criterion (CDC), leads to better generalization on average than all the methods that we compare against in almost all of the tested cases.
Pendampingan Pelatihan Seni Baca Al- Quran di Pesantren Darussalam 2 Watukosek Gempol Pasuruan

This mentoring aims to provide knowledge and learning and experience directly to all students in training in the art of reading the alquran at pesantren Darussalam 2 Watukosek Gempol Pasuruan. This assistance uses PAR discharge through an explicit instruction method. The results of this assistance can improve students' understanding both in terms of theory and practice, skills in the art of reading the alquran by memorizing tausyih through explicit instruction. Process improvement is known through student activities in training and implementation of mentoring through activities, while the increase in results can be seen through the learning achievements of students in the practice of reading alquran both individually and in groups
A New Outlook on the Profitability of Rogue Mining Strategies in the Bitcoin Network

Many of the recent works on the profitability of rogue mining strategies hinge on a parameter called $\gamma$ that measures the proportion of the honest network attracted by the attacker to mine on top of his fork. These works, see arXiv:1808.01041 and arXiv.1805.08281, have surmised conclusions based on premises that erroneously treat $\gamma$ to be constant. In this paper, we treat $\gamma$ as a stochastic process and attempt to find its distribution through a Markov analysis. We begin by making strong assumptions on gamma's behaviour and proceed to translate them mathematically in order to apply them in a Markov setting. The aforementioned is executed in two separate occasions for two different models. Furthermore, we model the Bitcoin network and numerically derive a limiting distribution whereby the relative accuracy of our models is tested through a likelihood analysis. Finally, we conclude that even with control of 20% of the total hashrate, honest mining is the strongly dominant strategy.
Free Choice in Modal Inquisitive Logic

Abstract This paper investigates inquisitive extensions of normal modal logic with an existential modal operator taken as primitive. The semantics of the existential modality is generalized to apply to questions, as well as statements. When the generalized existential modality is applied to a question, the result is a statement that roughly expresses that each way of resolving the question is consistent with the available information. I study the resulting logic both from a semantic and from a proof-theoretic point of view. I argue that it can be used for reasoning about a general notion of ignorance, and for reasoning about choice-offering permissions and obligations. The main technical results are sound and complete axiomatizations, both for the class of all Kripke frames, and for any class of frames corresponding to a canonical normal modal logic.
Multi-dimensional Maximal Coherent Subsets Made Easy: Illustration on an Estimation Problem

Fusing uncertain pieces of information to obtain a synthetic estimation when those are inconsistent is a difficult task. A particularly appealing solution to solve such conflict or inconsistency is to look at maximal coherent subsets of sources (MCS), and to concentrate on those. Yet, enumerating MCS is a difficult combinatorial task in general, making the use of MCS limited in practice. In this paper, we are interested in the case where the pieces of information are multi-dimensional sets or polytopes. While the problem remains difficult for general polytopes, we show that it can be solved more efficiently for hyperrectangles. We then illustrate how such an approach could be used to estimate linear models in the presence of outliers or in the presence of misspecified model.
Model Pengembangan PLUG-IN SLiMS Pada Komunitas SLiMS Kudus

This study aims to determine the role of Kudus SLiMS Community in development of the SLiMS plug-in. Researcher uses open-source development model by Ibrahim Haddad and Brian Warner from The Linux Foundation to help researcher describing SLiMS Plug-ins development in Kudus SLiMS Community. This research uses qualitative research method. Method of data collection is using direct observation, non-structured interviews, and documentation technique. The data analysis method is using Miles and Huberman method. The result of this study is that the role of Kudus SLiMS Community in development of the SLiMS plug-in is as a source of inspiration/idea for developing a plug-in, a place for the member of the Kudus SLiMS Community to share their developed plug-ins. Based on the findings of researcher related to the following study, in the future it is necessary to do further research about the development of the SLiMS plug-ins by other local SLiMS Community as a comparison with the development of the SLiMS plug-in by the Kudus SLiMS Community. The researcher hopes that more and more similar study is carried out so that the SLiMS application developer, local SLiMS Communities, and general public get the comprehensive picture about how SLiMS as one of the open-source based applications is developed.
Students’ Satisfaction Level of English Online Learning at SMPN 1 Ampek Angkek

This research was conducted because the researcher found several problems, namely: Some students difficult to understand the material, Limited internet network access, and Most students are looked less enthusiastic about learning online. This study aims to see the students' satisfaction level of English online learning.This type of research is survey research. The population and sample of this study were class VIII SMP N 1 ampek angkek. Where this research uses the incidental technique. The data collection technique is through a questionnaire or questionnaire as an instrument. The data analysis technique is collecting data, tabulate the data, calculate the data to average, and interpret the data. Based on the results of research and data analysis, the research found that students satisfaction in implementing online learning on the learning technologies item, the results showed that the students were quite satisfied with the learning carried out, namely the data obtained (64,92%). Students' satisfaction in the item pedagogical models shows that students were quite satisfied with the data obtained (71,51%). And students' satisfaction in online learning with the instructional strategy item shows the results that students are quite satisfied with the data obtained (64,08%). Based on the results of this study it can be concluded that the students in class eight at SMP N 1 Ampek Angkek are quite satisfied with English online learning that has been done by obtaining 66,83% data which means quite satisfied.
Remote Surveillance System without Privacy Leakage for Contacts in Infectious Disease

Remote surveillance is an effective method in restraining the spread of infectious diseases via monitoring crowds in affected areas. However, the monitoring targets in existing works are crowds, leading to high system cost, and most of them focus on finding close contacts, less considering the privacy protection and suspected treatment issues. To conquer above problems, this paper develops a new remote surveillance system for infectious diseases, which contains the following major contributions: (1) the monitoring targets are the ordinary contacts not all crowds, effectively decreasing the system cost; (2) establish the joint-control mechanism among contacts, health center, and the hospital to facilitate the prediagnosis and in-time treatment for suspected patients; (3) to avoid the privacy leakage of contacts, a double encryption strategy is designed to protect the location information, and a separating database-storage mechanism is used to improve the contact’s data security on the whole. Theoretical security analysis showed that the proposed system and privacy protection methods can effectively fight transmission attacks and avoid privacy leakage during data usage. Based on the created COVID-19 dataset, the simulation experiments were carried out to evaluate the effectiveness and feasibility of the proposed system, on the metrics of the accuracy of prediagnosis, encryption, and decryption time for health and location data. In summary, this work provides a promising way of low-cost remote surveillance system without privacy leakage to control the spreading of infectious disease.