Reviewerly/exHarmony huggingface dataset:
Sikap Kepedulian Mahasiswa Pendidikan Biologi STKIP PGRI Banjarmasin terhadap Pelestarian Alam

Ecological damage in South Kalimantan has increased. The impact of this ecological damage can be seen clearly from the phenomenon of floods that have almost occurred in all areas of South Kalimantan in the last 2 years. Many ways are recommended by environmental experts to reduce environmental damage. This research is a description of the actions that have been taken by students of Biology Education STKIP PGRI Banjarmasin related to environmental conservation attitudes. To find out how the attitudes or actions that have been taken by Biology students related to environmental conservation, a questionnaire will be given to a number of students. The questionnaire distributed was in the form of a structured questionnaire containing 5 questions related to the attitude of nature conservation. The results of the questionnaire will be analyzed descriptively quantitatively.&#x0D; Keywords:&#x0D;
English Language Education Students' Difficulties in Using Inverted Word Order: Inversion

This research discusses the difficulties of English language education students of FKIP ULM in using inverted word order and identifies the factors that mostly cause their difficulties. Inverted word order is one of grammar aspects that has certain area of difficulties since they consist of different types and seldom are used by the students. Thus, students should notice and more aware when using inverted word order in their production. This is descriptive qualitative research where the subjects of this research were English Language Education Study Program batch 2018, in a total of 90 students. Test and open-ended questionnaire were used as instruments of the research. The test is used to find students’ comprehension through 9 types of inverted word order. Furthermore, the questionnaire used to obtain data about their difficulties and factors that mostly causing students’ difficulties in using inverted word order. The finding of this research showed that there are still many difficulties faced by students in using inverted word order especially in three types of inverted word order, namely tense, types of inversion, and in identifying the subject and verb. The finding also showed that there are 3 factors that mostly causing students’ difficulties; students’ background, teaching technique, and students’ environment. It is suggested for the students to be more pay attention when the lecturers give explanation. It is also suggested for the lecturers to use variety of teaching media and activities. Therefore, the students can be curious and feel more interested in learning inverted word order.
Analysis of Literacy Implementation in Class VI Elementary School

Literacy program for students is very important to be implemented in schools. Reading activities need to be cultivated and accustomed to both at school and at home. The literacy program aims to increase interest in reading for students. Not only reading but understanding what he reads. Reading is an interpreting activity to obtain information based on the writing submitted by the author. This research was conducted in class VI SDS Jai Nalanda Jambi City. This study uses a qualitative approach with a descriptive type of research. Data analysis in this study consisted of 3 stages, namely data reduction, data presentation and conclusions. In this study, the researcher acted as an instrument. The results showed that the implementation of literacy in class VI consisted of three stages, namely the habituation stage, the development stage and the learning stage. The factors that influence the implementation of the literacy movement in class VI are divided into 2, namely internal factors and external factors. The internal factor is that students have a low reading interest. External factors are reading materials that are less varied, the influence of technology and family attention.
Investigating Media Usage of Pre-Service Teachers during Microteaching Practices in TEYL Class

This study was carried out to investigate how Elementary School pre-service teachers used instructional media in TEYL class. The investigation was conducted to reveal perspectives of both lecturer and pre-service teachers related to challenges in using instructional media for microteaching practices. The challenges were discussed in the light of pedagogical aspects and non-pedagogical ones. The method of this research was a case study as this research was particularly targeted for the TEYL class of Elementary Teachers Education Program at STKIP PGRI Banjarmasin. The data were obtained through observations and interviews. The results of this study suggested that based on the lecturer's perspective, there were five challenges related to the use of Media in TEYL microteaching practices. These challenges were (1) the lack of understanding regarding the importance of media, (2) low English proficiency, (3) low creativity, (4) low motivation, and (5) limited time allotment. Furthermore, based on pre-service teachers' perspective, four issues related to the usage of media were found. These issues were (1) low English proficiency, (2) high cost in developing media, (3) burden from other subjects, and (4) confidence issues.
Challenges for Security Assessment of Enterprises in the IoT Era

For years, attack graphs have been an important tool for security assessment of enterprise networks, but IoT devices, a new player in the IT world, might threat the reliability of this tool. In this paper, we review the challenges that must be addressed when using attack graphs to model and analyze enterprise networks that include IoT devices. In addition, we propose novel ideas and countermeasures aimed at addressing these challenges.
The Relationship Between Students' Anxiety in Learning English and Their English Achievement

This study was to figure out the correlation between students' anxiety in learning English and their English achievement. This study used a quantitative design that was descriptive and correlational. The instrument of this study was adapted from Park (2012), based on Horwitz, Horwitz, and Cope (1986), and the result of the English academic achievement was taken from the students' mid-semester grades in English class. This study was conducted on 41 students of grade XI in a private high school in North Sulawesi. The results showed that the students' anxiety in learning English was moderate (M=3.17), and their English academic achievement was categorized as failed (M=71.34). Finally, it was found that there was a significant correlation between students' anxiety in learning English and their English academic achievement as the result p = .00 &lt;0.05, r= .98. Therefore, it is recommended that teachers try to find more creative methods and strategies to support and help the students improve their English.&#x0D; Keywords: students, anxiety, learning English, English academic achievement
Improving Uncertainty Calibration via Prior Augmented Data

Neural networks have proven successful at learning from complex data distributions by acting as universal function approximators. However, they are often overconfident in their predictions, which leads to inaccurate and miscalibrated probabilistic predictions. The problem of overconfidence becomes especially apparent in cases where the test-time data distribution differs from that which was seen during training. We propose a solution to this problem by seeking out regions of feature space where the model is unjustifiably overconfident, and conditionally raising the entropy of those predictions towards that of the prior distribution of the labels. Our method results in a better calibrated network and is agnostic to the underlying model structure, so it can be applied to any neural network which produces a probability density as an output. We demonstrate the effectiveness of our method and validate its performance on both classification and regression problems, applying it to recent probabilistic neural network models.
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels

Gaussian processes are powerful models for probabilistic machine learning, but are limited in application by their $O(N^3)$ inference complexity. We propose a method for deriving parametric families of kernel functions with compact spatial support, which yield naturally sparse kernel matrices and enable fast Gaussian process inference via sparse linear algebra. These families generalize known compactly-supported kernel functions, such as the Wendland polynomials. The parameters of this family of kernels can be learned from data using maximum likelihood estimation. Alternatively, we can quickly compute compact approximations of a target kernel using convex optimization. We demonstrate that these approximations incur minimal error over the exact models when modeling data drawn directly from a target GP, and can out-perform the traditional GP kernels on real-world signal reconstruction tasks, while exhibiting sub-quadratic inference complexity.
Mesh Graphormer

We present a graph-convolution-reinforced transformer, named Mesh Graphormer, for 3D human pose and mesh reconstruction from a single image. Recently both transformers and graph convolutional neural networks (GCNNs) have shown promising progress in human mesh reconstruction. Transformer-based approaches are effective in modeling non-local interactions among 3D mesh vertices and body joints, whereas GCNNs are good at exploiting neighborhood vertex interactions based on a pre-specified mesh topology. In this paper, we study how to combine graph convolutions and self-attentions in a transformer to model both local and global interactions. Experimental results show that our proposed method, Mesh Graphormer, significantly outperforms the previous state-of-the-art methods on multiple benchmarks, including Human3.6M, 3DPW, and FreiHAND datasets. Code and pre-trained models are available at https://github.com/microsoft/MeshGraphormer
Predictive distributions that mimic frequencies over a restricted subdomain (expanded preprint version)

A predictive distribution over a sequence of $N+1$ events is said to be "frequency mimicking" whenever the probability for the final event conditioned on the outcome of the first $N$ events equals the relative frequency of successes among them. Infinitely extendible exchangeable distributions that universally inhere this property are known to have several annoying concomitant properties. We motivate frequency mimicking assertions over a limited subdomain in practical problems of finite inference, and we identify their computable coherent implications. We provide some computed examples using reference distributions, and we introduce computational software to generate any specification. The software derives from an inversion of the finite form of the exchangeability representation theorem. Three new theorems delineate the extent of the usefulness of such distributions, and we show why it may not be appropriate to extend the frequency mimicking assertions for a specified value of $N$ to any arbitrary larger size of $N$. The constructive results identify the source and structure of "adherent masses" in the limit of a sequence of finitely additive distributions. Appendices develop a novel geometrical representation of conditional probabilities which illuminate the analysis.
Can a Robot Trust You? A DRL-Based Approach to Trust-Driven Human-Guided Navigation

Humans are known to construct cognitive maps of their everyday surroundings using a variety of perceptual inputs. As such, when a human is asked for directions to a particular location, their wayfinding capability in converting this cognitive map into directional instructions is challenged. Owing to spatial anxiety, the language used in the spoken instructions can be vague and often unclear. To account for this unreliability in navigational guidance, we propose a novel Deep Reinforcement Learning (DRL) based trust-driven robot navigation algorithm that learns humans' trustworthiness to perform a language guided navigation task. Our approach seeks to answer the question as to whether a robot can trust a human's navigational guidance or not. To this end, we look at training a policy that learns to navigate towards a goal location using only trustworthy human guidance, driven by its own robot trust metric. We look at quantifying various affective features from language-based instructions and incorporate them into our policy's observation space in the form of a human trust metric. We utilize both these trust metrics into an optimal cognitive reasoning scheme that decides when and when not to trust the given guidance. Our results show that the learned policy can navigate the environment in an optimal, time-efficient manner as opposed to an explorative approach that performs the same task. We showcase the efficacy of our results both in simulation and a real-world environment.
UTSA NLP at SemEval-2022 Task 4: An Exploration of Simple Ensembles of Transformers, Convolutional, and Recurrent Neural Networks

The act of appearing kind or helpful via the use of but having a feeling of superiority condescending and patronizing language can have have serious mental health implications to those that experience it. Thus, detecting this condescending and patronizing language online can be useful for online moderation systems. Thus, in this manuscript, we describe the system developed by Team UTSA SemEval-2022 Task 4, Detecting Patronizing and Condescending Language. Our approach explores the use of several deep learning architectures including RoBERTa, convolutions neural networks, and Bidirectional Long Short-Term Memory Networks. Furthermore, we explore simple and effective methods to create ensembles of neural network models. Overall, we experimented with several ensemble models and found that the a simple combination of five RoBERTa models achieved an F-score of .6441 on the development dataset and .5745 on the final test dataset. Finally, we also performed a comprehensive error analysis to better understand the limitations of the model and provide ideas for further research.
Learning Co-segmentation by Segment Swapping for Retrieval and Discovery

The goal of this work is to efficiently identify visually similar patterns in images, e.g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts of a night-time photograph visible in its daytime counterpart. Lack of training data is a key challenge for this co-segmentation task. We present a simple yet surprisingly effective approach to overcome this difficulty: we generate synthetic training pairs by selecting segments in an image and copy-pasting them into another image. We then learn to predict the repeated region masks. We find that it is crucial to predict the correspondences as an auxiliary task and to use Poisson blending and style transfer on the training pairs to generalize on real data. We analyse results with two deep architectures relevant to our joint image analysis task: a transformer-based architecture and Sparse Nc-Net, a recent network designed to predict coarse correspondences using 4D convolutions. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset and achieves competitive performance on two place recognition benchmarks, Tokyo247 and Pitts30K. We also demonstrate the potential of our approach for unsupervised image collection analysis by introducing a spectral graph clustering approach to object discovery and demonstrating it on the object discovery dataset of \cite{rubinstein2013unsupervised} and the Brueghel dataset. Our code and data are available at http://imagine.enpc.fr/~shenx/SegSwap/.
Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision

Taxonomies have been widely used in various domains to underpin numerous applications. Specially, product taxonomies serve an essential role in the e-commerce domain for the recommendation, browsing, and query understanding. However, taxonomies need to constantly capture the newly emerged terms or concepts in e-commerce platforms to keep up-to-date, which is expensive and labor-intensive if it relies on manual maintenance and updates. Therefore, we target the taxonomy expansion task to attach new concepts to existing taxonomies automatically. In this paper, we present a self-supervised and user behavior-oriented product taxonomy expansion framework to append new concepts into existing taxonomies. Our framework extracts hyponymy relations that conform to users' intentions and cognition. Specifically, i) to fully exploit user behavioral information, we extract candidate hyponymy relations that match user interests from query-click concepts; ii) to enhance the semantic information of new concepts and better detect hyponymy relations, we model concepts and relations through both user-generated content and structural information in existing taxonomies and user click logs, by leveraging Pre-trained Language Models and Graph Neural Network combined with Contrastive Learning; iii) to reduce the cost of dataset construction and overcome data skews, we construct a high-quality and balanced training dataset from existing taxonomy with no supervision. Extensive experiments on real-world product taxonomies in Meituan Platform, a leading Chinese vertical e-commerce platform to order take-out with more than 70 million daily active users, demonstrate the superiority of our proposed framework over state-of-the-art methods. Notably, our method enlarges the size of real-world product taxonomies from 39,263 to 94,698 relations with 88% precision.
STURE: Spatial-Temporal Mutual Representation Learning for Robust Data Association in Online Multi-Object Tracking

Online multi-object tracking (MOT) is a longstanding task for computer vision and intelligent vehicle platform. At present, the main paradigm is tracking-by-detection, and the main difficulty of this paradigm is how to associate current candidate detections with historical tracklets. However, in the MOT scenarios, each historical tracklet is composed of an object sequence, while each candidate detection is just a flat image, which lacks temporal features of the object sequence. The feature difference between current candidate detections and historical tracklets makes the object association much harder. Therefore, we propose a Spatial-Temporal Mutual Representation Learning (STURE) approach which learns spatial-temporal representations between current candidate detections and historical sequences in a mutual representation space. For historical trackelets, the detection learning network is forced to match the representations of sequence learning network in a mutual representation space. The proposed approach is capable of extracting more distinguishing detection and sequence representations by using various designed losses in object association. As a result, spatial-temporal feature is learned mutually to reinforce the current detection features, and the feature difference can be relieved. To prove the robustness of the STURE, it is applied to the public MOT challenge benchmarks and performs well compared with various state-of-the-art online MOT trackers based on identity-preserving metrics.
WatchDog: Real-time Vehicle Tracking on Geo-distributed Edge Nodes

Vehicle tracking, a core application to smart city video analytics, is becoming more widely deployed than ever before thanks to the increasing number of traffic cameras and recent advances of computer vision and machine learning. Due to the constraints of bandwidth, latency, and privacy concerns, tracking tasks are more preferable to run on edge devices sitting close to the cameras. However, edge devices are provisioned with a fixed amount of compute budget, making them incompetent to adapt to time-varying tracking workloads caused by traffic dynamics. In coping with this challenge, we propose WatchDog, a real-time vehicle tracking system fully utilizes edge nodes across the road network. WatchDog leverages computer vision tasks with different resource-accuracy trade-offs, and decompose and schedule tracking tasks judiciously across edge devices based on the current workload to maximize the number of tasks while ensuring a provable response time bound at each edge device. Extensive evaluations have been conducted using real-world city-wide vehicle trajectory datasets, showing a 100% tracking coverage with real-time guarantee.
Resource Burning for Permissionless Systems

Proof-of-work puzzles and CAPTCHAS consume enormous amounts of energy and time. These techniques are examples of resource burning: verifiable consumption of resources solely to convey information. Can these costs be eliminated? It seems unlikely since resource burning shares similarities with "money burning" and "costly signaling", which are foundational to game theory, biology, and economics. Can these costs be reduced? Yes, research shows we can significantly lower the asymptotic costs of resource burning in many different settings. In this paper, we survey the literature on resource burning; take positions based on predictions of how the tool is likely to evolve; and propose several open problems targeted at the theoretical distributed-computing research community.
Making the most of small Software Engineering datasets with modern machine learning

This paper provides a starting point for Software Engineering (SE) researchers and practitioners faced with the problem of training machine learning models on small datasets. Due to the high costs associated with labeling data, in Software Engineering,there exist many small (< 1 000 samples) and medium-sized (< 100 000 samples) datasets. While deep learning has set the state of the art in many machine learning tasks, it is only recently that it has proven effective on small-sized datasets, primarily thanks to pre-training, a semi-supervised learning technique that leverages abundant unlabelled data alongside scarce labelled data.In this work, we evaluate pre-trained Transformer models on a selection of 13 smaller datasets from the SE literature, covering both,source code and natural language. Our results suggest that pre-trained Transformers are competitive and in some cases superior to previous models, especially for tasks involving natural language; whereas for source code tasks, in particular for very small datasets,traditional machine learning methods often has the edge.In addition, we experiment with several techniques that ought to aid training on small datasets, including active learning, data augmentation, soft labels, self-training and intermediate-task fine-tuning, and issue recommendations on when they are effective. We also release all the data, scripts, and most importantly pre-trained models for the community to reuse on their own datasets.
Reparametrizing gradient descent

In this work, we propose an optimization algorithm which we call norm-adapted gradient descent. This algorithm is similar to other gradient-based optimization algorithms like Adam or Adagrad in that it adapts the learning rate of stochastic gradient descent at each iteration. However, rather than using statistical properties of observed gradients, norm-adapted gradient descent relies on a first-order estimate of the effect of a standard gradient descent update step, much like the Newton-Raphson method in many dimensions. Our algorithm can also be compared to quasi-Newton methods, but we seek roots rather than stationary points. Seeking roots can be justified by the fact that for models with sufficient capacity measured by nonnegative loss functions, roots coincide with global optima. This work presents several experiments where we have used our algorithm; in these results, it appears norm-adapted descent is particularly strong in regression settings but is also capable of training classifiers.