Reviewerly/exHarmony huggingface dataset:
Towards Time-Optimal Any-Angle Path Planning With Dynamic Obstacles

Path finding is a well-studied problem in AI, which is often framed as graph search. Any-angle path finding is a technique that augments the initial graph with additional edges to build shorter paths to the goal. Indeed, optimal algorithms for any-angle path finding in static environments exist. However, when dynamic obstacles are present and time is the objective to be minimized, these algorithms can no longer guarantee optimality. In this work, we elaborate on why this is the case and what techniques can be used to solve the problem optimally. We present two algorithms, grounded in the same idea, that can obtain provably optimal solutions to the considered problem. One of them is a naive algorithm and the other one is much more involved. We conduct a thorough empirical evaluation showing that, in certain setups, the latter algorithm might be as fast as the previously-known greedy non-optimal solver while providing solutions of better quality. In some (rare) cases, the difference in cost is up to 76%, while on average it is lower than one percent (the same cost difference is typically observed between optimal and greedy any-angle solvers in static environments).
OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification

Recent studies on neural networks with pretrained weights (i.e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located.In this work, we propose a new approach, called OoMMix, to finding and regularizing the remainder of the space, referred to as out-ofmanifold, which cannot be accessed through the words.Specifically, we synthesize the outof-manifold embeddings based on two embeddings obtained from actually-observed words, to utilize them for fine-tuning the network.A discriminator is trained to detect whether an input embedding is located inside the manifold or not, and simultaneously, a generator is optimized to produce new embeddings that can be easily identified as out-of-manifold by the discriminator.These two modules successfully collaborate in a unified and end-to-end manner for regularizing the out-of-manifold.Our extensive evaluation on various text classification benchmarks demonstrates the effectiveness of our approach, as well as its good compatibility with existing data augmentation techniques which aim to enhance the manifold.
To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text

Abstractive neural summarization models have seen great improvements in recent years, as shown by ROUGE scores of the generated summaries.But despite these improved metrics, there is limited understanding of the strategies different models employ, and how those strategies relate their understanding of language.To understand this better, we run several experiments to characterize how one popular abstractive model, the pointer-generator model of See et al. ( 2017), uses its explicit copy/generation switch to control its level of abstraction (generation) vs extraction (copying).On an extractive-biased dataset, the model utilizes syntactic boundaries to truncate sentences that are otherwise often copied verbatim.When we modify the copy/generation switch and force the model to generate, only simple paraphrasing abilities are revealed alongside factual inaccuracies and hallucinations.On an abstractivebiased dataset, the model copies infrequently but shows similarly limited abstractive abilities.In line with previous research, these results suggest that abstractive summarization models lack the semantic understanding necessary to generate paraphrases that are both abstractive and faithful to the source document.
What's in a Face? Metric Learning for Face Characterization

Abstract We present a method for determining which facial parts (mouth, nose, etc.) best characterize an individual, given a set of that individual's portraits. We introduce a novel distinctiveness analysis of a set of portraits, which leverages the deep features extracted by a pre‐trained face recognition CNN and a hair segmentation FCN, in the context of a weakly supervised metric learning scheme. Our analysis enables the generation of a polarized class activation map (PCAM) for an individual's portrait via a transformation that localizes and amplifies the discriminative regions of the deep feature maps extracted by the aforementioned networks. A user study that we conducted shows that there is a surprisingly good agreement between the face parts that users indicate as characteristic and the face parts automatically selected by our method. We demonstrate a few applications of our method, including determining the most and the least representative portraits among a set of portraits of an individual, and the creation of facial hybrids : portraits that combine the characteristic recognizable facial features of two individuals. Our face characterization analysis is also effective for ranking portraits in order to find an individual's look‐alikes (Doppelgängers).
Cyberbullying Sentiment Analysis with Word2Vec and One-Against-All Support Vector Machine

Depression and social anxiety are the two main negative impacts of cyberbullying. Unfortunately, a survey conducted by UNICEF on 3rd September 2019 showed that 1 in 3 young people in 30 countries had been victims of cyberbullying. Sentiment analysis research will be conducted to detect a comment that contains cyberbullying. Dataset of cyberbullying is obtained from the Kaggle website, named, Toxic Comment Classification Challenge. The pre-processing process consists of 4 stages, namely comment generalization (convert text into lowercase and remove punctuation), tokenization, stop words removal, and lemmatization. Word Embedding will be used to conduct sentiment analysis by implementing Word2Vec. After that, One-Against-All (OAA) method with the Support Vector Machine (SVM) model will be used to make predictions in the form of multi labelling. The SVM model will go through a hyperparameter tuning process using Randomized Search CV. Then, evaluation will be carried out using Micro Averaged F1 Score to assess the prediction accuracy and Hamming Loss to assess the numbers of pairs of sample and label that are incorrectly classified. Implementation result of Word2Vec and OAA SVM model provide the best result for the data undergoing the process of pre-processing using comment generalization, tokenization, stop words removal, and lemmatization which is stored into 100 features in Word2Vec model. Micro Averaged F1 and Hamming Loss percentage that is produced by the tuned model is 83.40% and 15.13% respectively.&#x0D; &#x0D; Index Terms— Sentiment Analysis; Word Embedding; Word2Vec; One-Against-All; Support Vector Machine; Toxic Comment Classification Challenge; Multi Labelling
PERANCANGAN SISTEM INFORMASI MANAJEMEN KEGIATAN MAHASISWA BERBASIS WEB

This article discusses about management of student activities in the process of submitting activity proposals, reviewing and reporting activities. Carried out by student activity units at the State University of Padang which aims to minimize the time, energy and cost of implementing procedures for submitting proposals and reporting activities. In meeting Head of Students Affairs, WR III (interest and talent section) and KaBag (treasurer section). Submission of proposals and activity reports should be in accordance with technological developments at this time to effective and efficient. In designing this information system using the method waterfall method and the Object Oriented Programming (OOP) method. This system also uses the Model View Controller (MVC) technique. This system is design usimg Yii2 Framework with the Hypertext Preprocessor (PHP) and MySQL Database Management System (DBMS) programming languages. Designing a student activity management information system produces a system that can facilitate student activity units at State University of Padang in submitting proposals and activity reports and assisting the Head of Student Affairs in managing information about students activities. Keywords:Information Systems, College Student Activity, SQL, MVC, Framework Yii2.
The Segmentation of Road Scenes Based on Improved ESPNet Model

Image segmentation is an important research in image processing and machine vision in which automated driving can be seen the main application scene of image segmentation algorithms. Due to the many constraints of power supply and communication in in-vehicle systems, the vast majority of current image segmentation algorithms are implemented based on the deep learning model. Despite the ultrahigh segmentation accuracy, the problem of mesh artifacts and segmentation being too severe is obvious, and the high cost, computational, and power consumption devices required are difficult to apply in real-world scenarios. It is the focus of this paper to construct a road scene segmentation model with simple structure and no need of large computing power under the premise of certain accuracy. In this paper, the ESPNet (Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation) model is introduced in detail. On this basis, an improved ESPNet model is proposed based on ESPNet. Firstly, the network structure of the ESPNet model is optimized, and then, the model is optimized by using a small amount of weakly labeled and unlabeled scene sample data. Finally, the new model is applied to video image segmentation based on dash cam. It is verified on Cityscape, PASCAL VOC 2012, and other datasets that the algorithm proposed in this paper is faster, and the amount of parameters required is less than 1% of other algorithms, so it is suitable for mobile terminals.
Learning to Sample Replacements for ELECTRA Pre-Training

ELECTRA (Clark et al., 2020a) pretrains a discriminator to detect replaced tokens, where the replacements are sampled from a generator trained with masked language modeling.Despite the compelling performance, ELEC-TRA suffers from the following two issues.First, there is no direct feedback loop from discriminator to generator, which renders replacement sampling inefficient.Second, the generator's prediction tends to be over-confident along with training, making replacements biased to correct tokens.In this paper, we propose two methods to improve replacement sampling for ELECTRA pre-training.Specifically, we augment sampling with a hardness prediction mechanism, so that the generator can encourage the discriminator to learn what it has not acquired.We also prove that the efficient sampling reduces the training variance of the discriminator.Moreover, we propose to use a focal loss for the generator in order to relieve oversampling correct tokens as replacements.Experimental results show that our method improves ELECTRA pre-training on various downstream tasks.Our code and pre-trained models will be released at:
Pelatihan Daring Penulisan Artikel Ilmiah Bagi Guru Sekolah Menengah Kejuruan Negeri 1 Sidoarjo

As a professional teacher must have various abilities, one of the ability to write scientific papers in the form of scientific articles. By writing scientific papers, in addition to getting a promotion, position and class, will be used as a benchmark of success and to demonstrate professionalism. Departing from the problems at the partner school, the purpose of this training activity is to improve teachers' understanding and ability to write scientific articles that are in accordance with the journal template. The target of this activity is teachers at SMK N 1 Sidoarjo. The methods used in this training are lecture, question and answer, mentoring to write scientific articles, and citing references. Based on the results of the pre-test and post-test conducted during the training showed that there was an increase in knowledge about writing scientific articles. Increasing the percentage of knowledge about the article component by 46%, increasing the ethical knowledge of writing articles by 58%, and increasing knowledge about uploading articles in national journals by 54%.
ESA-Stream: Efficient Self-Adaptive Online Data Stream Clustering (Extended Abstract)

With ever-increasing data streams from various applications such as smart phones, network monitoring, Internet of Things (IoT), etc., unsupervised clustering of data streams has become an important problem for machine learning and big data analysis. As data streams are data-intensive, temporally ordered, and rapidly evolving, efficiently and effectively online clustering of data streams presents a challenging problem [1] .
FDPHI: Fast Deep Packet Header Inspection for Data Traffic Classification and Management

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc.Most systems of network traffic identification are based on features.These features may be static signatures, port numbers, statistical characteristics, and so on.Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc.Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational characteristics of traffic flow types; by considering only the position of the selected bits from the packet header.The proposal a learning approach based on deep packet inspection which integrates both feature extraction and classification phases into one system.The results show that the FDPHI works very well on the applications of feature learning.Also, it presents powerful adequate traffic classification results in terms of energy consumption (70% less power CPU utilization around 48% less), and processing time (310% for IPv4 and 595% for IPv6).
A Statistical Evaluation of The Depth of Inheritance Tree Metric for Open-Source Applications Developed in Java

Abstract The Depth of Inheritance Tree (DIT) metric, along with other ones, is used for estimating some quality indicators of software systems, including open-source applications (apps). In cases involving multiple inheritances, at a class level, the DIT metric is the maximum length from the node to the root of the tree. At an application (app) level, this metric defines the corresponding average length per class. It is known, at a class level, a DIT value between 2 and 5 is good. At an app level, similar recommended values for the DIT metric are not known. To find the recommended values for the DIT mean of an app we have proposed to use the confidence and prediction intervals. A DIT mean value of an app from the confidence interval is good since this interval indicates how reliable the estimate is for the DIT mean values of all apps used for estimating the interval. A DIT mean value higher than an upper bound of prediction interval may indicate that some classes have a large number of the inheritance levels from the object hierarchy top. What constitutes greater app design complexity as more classes are involved. We have estimated the confidence and prediction intervals of the DIT mean using normalizing transformations for the data sample from 101 open-source apps developed in Java hosted on GitHub for the 0.05 significance level.
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs

Counting and uniform sampling of directed acyclic graphs (DAGs) from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper, we show that these tasks can be performed in polynomial time, solving a long-standing open problem in this area. Our algorithms are effective and easily implementable. Experimental results show that the algorithms significantly outperform state-of-the-art methods.
ADVERSARIAL DISCRIMINATIVE DOMAIN ADAPTATION FOR DEFORESTATION DETECTION

Abstract. Although very efficient in a number of application fields, deep learning based models are known to demand large amounts of labeled data for training. Particularly for remote sensing applications, responding to that demand is generally expensive and time consuming. Moreover, supervised training methods tend to perform poorly when they are tested with a set of samples that does not match the general characteristics of the training set. Domain adaptation methods can be used to mitigate those problems, especially in applications where labeled data is only available for a particular region or epoch, i.e., for a source domain, but not for a target domain on which the model should be tested. In this work we introduce a domain adaptation approach based on representation matching for the deforestation detection task. The approach follows the Adversarial Discriminative Domain Adaptation (ADDA) framework, and we introduce a margin-based regularization constraint in the learning process that promotes a better convergence of the model parameters during training. The approach is evaluated using three different domains, which represent sites in different forest biomes. The experimental results show that the approach is successful in the adaptation of most of the domain combination scenarios, usually with considerable gains in relation to the baselines.
LCollision: Fast Generation of Collision-Free Human Poses using Learned Non-Penetration Constraints

We present LCollision, a learning-based method that synthesizes collision-free 3D human poses. At the crux of our approach is a novel deep architecture that simultaneously decodes new human poses from the latent space and predicts colliding body parts. These two components of our architecture are used as the objective function and surrogate hard constraints in a constrained optimization for collision-free human pose generation. A novel aspect of our approach is the use of a bilevel autoencoder that decomposes whole-body collisions into groups of collisions between localized body parts. By solving the constrained optimizations, we show that a significant amount of collision artifacts can be resolved. Furthermore, in a large test set of 2.5 × 10 6 randomized poses from SCAPE, our architecture achieves a collision-prediction accuracy of 94.1% with 80× speedup over exact collision detection algorithms. To the best of our knowledge, LCollision is the ﬁrst approach that accelerates collision detection and resolves penetrations using a neural network.
BERTGen: Multi-task Generation through BERT

We present BERTGEN, a novel generative, decoder-only model which extends BERT by fusing multimodal and multilingual pretrained models VL-BERT and M-BERT, respectively.BERTGEN is auto-regressively trained for language generation tasks, namely image captioning, machine translation and multimodal machine translation, under a multitask setting.With a comprehensive set of evaluations, we show that BERTGEN outperforms many strong baselines across the tasks explored.We also show BERTGEN's ability for zero-shot language generation, where it exhibits competitive performance to supervised counterparts.Finally, we conduct ablation studies which demonstrate that BERTGEN substantially benefits from multi-tasking and effectively transfers relevant inductive biases from the pre-trained models.
Self-Attention Networks Can Process Bounded Hierarchical Languages

Despite their impressive performance in NLP, self-attention networks were recently proved to be limited for processing formal languages with hierarchical structure, such as Dyck k , the language consisting of well-nested parentheses of k types.This suggested that natural language can be approximated well with models that are too weak for formal languages, or that the role of hierarchy and recursion in natural language might be limited.We qualify this implication by proving that self-attention networks can process Dyck k,D , the subset of Dyck k with depth bounded by D, which arguably better captures the bounded hierarchical structure of natural language.Specifically, we construct a hard-attention network with D + 1 layers and O(log k) memory size (per token per layer) that recognizes Dyck k,D , and a soft-attention network with two layers and O(log k) memory size that generates Dyck k,D .Experiments show that self-attention networks trained on Dyck k,D generalize to longer inputs with near-perfect accuracy, and also verify the theoretical memory advantage of self-attention networks over recurrent networks. 1
Session Logical Relations for Noninterference

Information flow control type systems statically restrict the propagation of sensitive data to ensure end-to-end confidentiality. The property to be shown is noninterference, asserting that an attacker cannot infer any secrets from made observations. Session types delimit the kinds of observations that can be made along a communication channel by imposing a protocol of message exchange. These protocols govern the exchange along a single channel and leave unconstrained the propagation along adjacent channels. This paper contributes an information flow control type system for linear session types. The type system stands in close correspondence with intuitionistic linear logic. Intuitionistic linear logic typing ensures that process configurations form a tree such that client processes are parent nodes and provider processes child nodes. To control the propagation of secret messages, the type system is enriched with secrecy levels and arranges these levels to be aligned with the configuration tree. Two levels are associated with every process: the maximal secrecy denoting the process' security clearance and the running secrecy denoting the highest level of secret information obtained so far. The computational semantics naturally stratifies process configurations such that higher-secrecy processes are parents of lower-secrecy ones, an invariant enforced by typing. Noninterference is stated in terms of a logical relation that is indexed by the secrecy-level-enriched session types. The logical relation contributes a novel development of logical relations for session typed languages as it considers open configurations, allowing for a more nuanced equivalence statement.
A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow

In this digital age, online users expect personalized content.To cater to diverse group of audiences across online platforms it is necessary to generate multiple variants of same content with differing degree of characteristics (sentiment, style, formality, etc.).Though text-style transfer is a well explored related area, it focuses on flipping the style attribute polarity instead of regulating a fine-grained attribute transfer.In this paper we propose a hierarchical architecture for finer control over the attribute, preserving content using attribute disentanglement.We demonstrate the effectiveness of the generative process for two different attributes with varied complexity, namely sentiment and formality.With extensive experiments and human evaluation on five real-world datasets, we show that the framework can generate natural looking sentences with finer degree of control of intensity of a given attribute.
On the minimum number of minimal codewords

We study the minimum number of minimal codewords in linear codes from the point of view of projective geometry. We derive bounds and in some cases determine the exact values. We also present an extension to minimal subcode supports.