Reviewerly/exHarmony huggingface dataset:
Examining convolutional feature extraction using Maximum Entropy (ME) and Signal-to-Noise Ratio (SNR) for image classification

Convolutional Neural Networks (CNNs) specialize in feature extraction rather than function mapping. In doing so they form complex internal hierarchical feature representations, the complexity of which gradually increases with a corresponding increment in neural network depth. In this paper, we examine the feature extraction capabilities of CNNs using Maximum Entropy (ME) and Signal-to-Noise Ratio (SNR) to validate the idea that, CNN models should be tailored for a given task and complexity of the input data. SNR and ME measures are used as they can accurately determine in the input dataset, the relative amount of signal information to the random noise and the maximum amount of information respectively.We use two well known benchmarking datasets, MNIST and CIFAR-10 to examine the information extraction and abstraction capabilities of CNNs. Through our experiments, we examine convolutional feature extraction and abstraction capabilities in CNNs and show that the classification accuracy or performance of CNNs is greatly dependent on the amount, complexity and quality of the signal information present in the input data. Furthermore, we show the effect of information overflow and underflow on CNN classification accuracies. Our hypothesis is that the feature extraction and abstraction capabilities of convolutional layers are limited and therefore, CNN models should be tailored to the input data by using appropriately sized CNNs based on the SNR and ME measures of the input dataset.
Translation Quality Estimation by Jointly Learning to Score and Rank

The translation quality estimation (QE) task, particularly the QE as a Metric task, aims to evaluate the general quality of a translation based on the translation and the source sentence without using reference translations.Supervised learning of this QE task requires human evaluation of translation quality as training data.Human evaluation of translation quality can be performed in different ways, including assigning an absolute score to a translation or ranking different translations.In order to make use of different types of human evaluation data for supervised learning, we present a multi-task learning QE model that jointly learns two tasks: score a translation and rank two translations.Our QE model exploits crosslingual sentence embeddings from pretrained multilingual language models.We obtain new state-of-the-art results on the WMT 2019 QE as a Metric task and outperform sentBLEU on the WMT 2019 Metrics task.
Multiparty quantum random access codes

Random access code (RAC), a primitive for many information processing protocols, enables one party to encode a n-bit string into one bit of message such that another party can retrieve partial information of that string. We introduce the multiparty version of RAC in which the n-bit string is distributed among many parties. For this task, we consider two distinct quantum communication scenarios: one allows shared quantum entanglement among the parties with classical communication, and the other allows communication through a quantum channel. We present several multiparty quantum RAC protocols that outclass its classical counterpart in both the aforementioned scenarios.
Wasserstein Distances for Stereo Disparity Estimation

Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values. This leads to inaccurate results when the true depth or disparity does not match any of these values. The fact that this distribution is usually learned indirectly through a regression loss causes further problems in ambiguous regions around object boundaries. We address these issues using a new neural network architecture that is capable of outputting arbitrary depth values, and a new loss function that is derived from the Wasserstein distance between the true and the predicted distributions. We validate our approach on a variety of tasks, including stereo disparity and depth estimation, and the downstream 3D object detection. Our approach drastically reduces the error in ambiguous regions, especially around object boundaries that greatly affect the localization of objects in 3D, achieving the state-of-the-art in 3D object detection for autonomous driving.
A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

A fundamental issue of the software engineering economics is productivity. In this regard, one measure of software productivity is delivery speed. Software productivity prediction is useful to determine corrective activities, as well as to identify improvement alternatives. A type of software maintenance is enhancement. In this paper, we propose a data analytics-based software engineering algorithm called search method based on feature construction (SMFC) for predicting the delivery speed of software enhancement projects. The SMFC belongs to the minimalist machine learning paradigm, and as such it always generates a two-dimensional model. Unlike the usual data analytics methods, SMFC includes an original algorithmic training procedure, in which both the independent and dependent variables are considered for transformation. SMFC prediction performance is compared to those of statistical regression, neural networks, support vector regression, and fuzzy regression. To do this, seven datasets of software enhancement projects obtained from the International Software Benchmarking Standards Group (ISBSG) Release 2017 were used. The validation method is leave-one-out cross validation, whereas absolute residuals have been chosen as the performance measure. The results indicate that the SMFC is statistically better than statistical regression. This fact represents an obvious advantage in favor of SMFC, because the other two methods are not statistically better than SMFC.
Generalizable and Explainable Dialogue Generation via Explicit Action Learning

Response generation for task-oriented dialogues implicitly optimizes two objectives at the same time: task completion and language quality.Conditioned response generation serves as an effective approach to separately and better optimize these two objectives.Such an approach relies on system action annotations which are expensive to obtain.To alleviate the need of action annotations, latent action learning is introduced to map each utterance to a latent representation.However, this approach is prone to over-dependence on the training data, and the generalization capability is thus restricted.To address this issue, we propose to learn natural language actions that represent utterances as a span of words.This explicit action representation promotes generalization via the compositional structure of language.It also enables an explainable generation process.Our proposed unsupervised approach learns a memory component to summarize system utterances into a short span of words.To further promote a compact action representation, we propose an auxiliary task that restores state annotations as the summarized dialogue context using the memory component.Our proposed approach outperforms latent action baselines on MultiWOZ, a benchmark multi-domain dataset.
Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data

Data annotation is an essential stage in supervised learning. However, the annotation process is exhaustive and time consuming, specially for large datasets. Activities of Daily Living (ADL) recognition is an example of systems that exploit very large raw sensor data readings. In such systems, sensor readings are collected from activity-monitoring sensors in a 24/7 manner. The size of the generated dataset is so huge that it is almost impossible for a human annotator to give a certain label to every single instance in the dataset. This results in annotation gaps in the input data to the adopting supervised learning system. The performance of the recognition system is negatively affected by these gaps. In this work, we propose and investigate three different paradigms to handle these gaps. In the first paradigm, the gaps are taken out by dropping all unlabeled readings. A single "Unknown" or "Do-Nothing" label is given to the unlabeled readings within the operation of the second paradigm. The last paradigm handles these gaps by giving every one of them a unique label identifying the encapsulating deterministic labels. Also, we propose a semantic preprocessing method of annotation gaps by constructing a hybrid combination of some of these paradigms for further performance improvement. The performance of the proposed three paradigms and their hybrid combination is evaluated using an ADL benchmark dataset containing more than $2.5\times 10^6$ sensor readings that had been collected over more than nine months. The evaluation results emphasize the performance contrast under the operation of each paradigm and support a specific gap handling approach for better performance.
Beyond X.509: Token-based authentication and authorization in practice

One of the key challenges identified by the HEP R&amp;D roadmap for software and computing is the ability to integrate heterogeneous resources in support of the computing needs of HL-LHC. In order to meet this objective, a flexible Authentication and Authorization Infrastructure (AAI) has to be in place, to allow the secure composition of computing and storage resources provisioned across heterogeneous providers (e.g., Grid, private and commercial Clouds, HPC centers). At CHEP 2018, we presented how a flexible AAI based on modern, standard Web technologies (OpenID Connect, OAuth and JSON Web Tokens) and centered on the INDIGO Identity and Access Management (IAM) service could support the transition of the WLCG infrastructure to a token-based AAI. In the meanwhile, INDIGO IAM has been selected by the WLCG Management Board as the solution that will be adopted by LHC experiments, and is also at the core of the AAI envisioned to support the computing needs of the ESCAPE project. In this contribution, which represents a follow up to last-year plenary talk, we describe the work done recently on the IAM service to support WLCG requirements.
Q-learning with Language Model for Edit-based Unsupervised Summarization

Unsupervised methods are promising for abstractive textsummarization in that the parallel corpora is not required.However, their performance is still far from being satisfied, therefore research on promising solutions is on-going.In this paper, we propose a new approach based on Q-learning with an edit-based summarization.The method combines two key modules to form an Editorial Agent and Language Model converter (EALM).The agent predicts edit actions (e.t., delete, keep, and replace), and then the LM converter deterministically generates a summary on the basis of the action signals.Qlearning is leveraged to train the agent to produce proper edit actions.Experimental results show that EALM delivered competitive performance compared with the previous encoderdecoder-based methods, even with truly zero paired data (i.e., no validation set).Defining the task as Q-learning enables us not only to develop a competitive method but also to make the latest techniques in reinforcement learning available for unsupervised summarization.We also conduct qualitative analysis, providing insights into future study on unsupervised summarizers. 1
Markov process simulation on a real quantum computer

This paper shows a novel way of simulating a Markov process by a quantum computer. The main purpose of the paper is to show a particular application of quantum computing in the field of stochastic processes analysis. Using a Quantum computer, the process could be superposed, where the random variables of the Markov chain are represented by entangled qubit states, which gives the great opportunity of having all the possible scenarios simultaneously.
Smart Mobile Application for Decision Support Systems on Determination of Resident in Dormitory

The dormitory is one of the facilities provided with the aim of helping students to get a place to live because some of them come from distant places. The Trunojoyo Madura University dormitory has regulations that serve as a place for the process of character education, spiritual deepening, moral improvement. With these regulations, plus the boarding house is a conducive, economical, and strategic place to stay making many students interested in being able to live there. The problem is that each semester change is carried out by the selection of dorm residents and so far it is still done manually by way of discussion of each individual. Therefore, the purpose of this research is to help the board by building a decision support system in determining residents who are still eligible to live in a dormitory and provide opportunities for other students to live in a dormitory. We develop systems based on mobile applications. TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) is implemented as a multi criteria decision making method with four main criteria including routine absence, non-routine absence, violation and notes. The final results of this decision support system are ranks and colors that indicate the status of boarding residents. Ranking starts from the top (1) to the bottom (216) accompanied by a status of green to red. The color status is divided into 3 namely green (safe), yellow (vulnerable), and red (issued). From 216 boarders we took 10 samples of dormitory data for testing. The results of trials with 10 data samples by applying the TOPSIS method obtained an accuracy of 90%.
Pembangunan Aplikasi E-Magang Perguruan Tinggi dengan Memanfaatkan SDLC SCRUM pada Agile Project Management

Higher Education facilitates the process of Internship students through several stages, which can be taken by students after fulfilling the requirements. The Process of Internship, starting with making an official letter of submission to the company where the internship is accompanied by the submission of proposals internships until the implementation of Internship Work in the company is completed. After the internship process is completed students then make a report Internship. Readiness of Study Program to the Faculty in managing the process of implementation of internship work vary. Especially readiness when the Study Program enrolls students until obtaining the scheduling of the seminar report Internship. &#x0D; Universities that have fewer students may not be a problem. But if universities have a large number of Faculties and Study Programs, so the number of active students owned is also a lot, this can be the first step of the complexity of a system. The problems that arise are conflicting schedules of supervisors, examiners and rooms.&#x0D; To facilitate the implementation of the process, e-Magang application is made to help the application process of Internship. The e-Internship application is built with the phase flow of the Scrum SDLC method in Project Management.
Developing Non-Empirical Metrics and Tools for Ontology Visualizations Evaluation and Comparing

There are numerous ontology visualization systems, however, the choice of a visualization system is non-trivial, as there is no method for evaluation and comparing them, except for empirical experiments, that are subjective and costly.In this research, we aim to develop non-empirical metrics for ontology visualizations evaluation and comparing.First, we propose several half-formal metrics that require expert evaluation.These metrics are completeness, semanticity, and conservativeness.We apply the proposed metrics to evaluate and compare VOWL, Graphol and Logic Graphs visualization systems.And second, we develop a completely computable measure for the complexity of ontology visualizations, based on graph theory and information theory.In particular, ontology visualizations are considered as hypergraphs and the information measure is derived from the Hartley function.The usage of the proposed information measure is exemplified by the evaluation of visualizations of the sample of axioms from the DoCO ontology in Logic Graphs and Graphol.These results can be practically applied for choosing ontology visualization systems in general and regarding a particular ontology.The application for ontology visualization evaluation and comparing based on the formal metrics is provided.
Accurate polyglot semantic parsing with DAG grammars

Semantic parses are directed acyclic graphs (DAGs), but in practice most parsers treat them as strings or trees, mainly because models that predict graphs are far less understood.This simplification, however, comes at a cost: there is no guarantee that the output is a well-formed graph.A recent work by Fancellu et al. (2019) addressed this problem by proposing a graphaware sequence model that utilizes a DAG grammar to guide graph generation.We significantly improve upon this work, by proposing a simpler architecture as well as more efficient training and inference algorithms that can always guarantee the well-formedness of the generated graphs.Importantly, unlike Fancellu et al., our model does not require language-specific features, and hence can harness the inherent ability of DAG-grammar parsing in multilingual settings.We perform monolingual as well as multilingual experiments on the Parallel Meaning Bank (Abzianidze et al., 2017).Our parser outperforms previous graph-aware models by a large margin, and closes the performance gap between string-based and DAG-grammar parsing.
Determining Question-Answer Plausibility in Crowdsourced Datasets Using Multi-Task Learning

Datasets extracted from social networks and online forums are often prone to the pitfalls of natural language, namely the presence of unstructured and noisy data.In this work, we seek to enable the collection of high-quality question-answer datasets from social media by proposing a novel task for automated quality analysis and data cleaning: question-answer (QA) plausibility.Given a machine or usergenerated question and a crowd-sourced response from a social media user, we determine if the question and response are valid; if so, we identify the answer within the free-form response.We design BERT-based models to perform the QA plausibility task, and we evaluate the ability of our models to generate a clean, usable question-answer dataset.Our highestperforming approach consists of a singletask model which determines the plausibility of the question, followed by a multitask model which evaluates the plausibility of the response as well as extracts answers (Question Plausibility AUROC=0.75,
Cydex: Neural Search Infrastructure for the Scholarly Literature

Cydex is a platform that provides neural search infrastructure for domain-specific scholarly literature.The platform represents an abstraction of Covidex, our recently developed full-stack open-source search engine for the COVID-19 Open Research Dataset (CORD-19) from AI2.While Covidex takes advantage of the latest best practices for keyword search using the popular Lucene search library as well as stateof-the-art neural ranking models using T5, parts of the system were hard coded to only work with CORD-19.This paper describes our efforts to generalize Covidex into Cydex, which can be applied to scholarly literature in different domains.By decoupling corpusspecific configurations from the frontend implementation, we are able to demonstrate the generality of Cydex on two very different corpora: the ACL Anthology and a collection of hydrology abstracts.Our platform is entirely open source and available at cydex.ai.
A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining

Classifying and resolving coreferences of objects (e.g., product names) and attributes (e.g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance.However, the task is challenging as one often needs to consider domain-specific knowledge (e.g., iPad is a tablet and has aspect resolution) to identify coreferences in opinionated reviews.Also, compiling a handcrafted and curated domain-specific knowledge base for each domain is very time consuming and arduous.This paper proposes an approach to automatically mine and leverage domain-specific knowledge for classifying objects and attribute coreferences.The approach extracts domain-specific knowledge from unlabeled review data and trains a knowledgeaware neural coreference classification model to leverage (useful) domain knowledge together with general commonsense knowledge for the task.Experimental evaluation on realworld datasets involving five domains (product types) shows the effectiveness of the approach.
Analisis Optimasi Algoritma Klasifikasi Support Vector Machine, Decision Trees, dan Neural Network Menggunakan Adaboost dan Bagging

The accuracy value of a classification algorithm shows whether the algorithm is good or not in classifying data which can affect the results of the classification method in data mining processing. In this study, the author will analyze the effect of optimization using the adaboost and bagging methods on the results of the classification algorithm accuracy value on support vector machines, decision trees, and neural networks. This study uses a software in data mining processing that is using the Weka application version 3.8.1. The test method used was a percentage split of 70%. In this study, the results show that adaboost optimization can increase the accuracy value of the support vector machine algorithm from 88.93% to 89.10%, decision trees from 90.24% to 90.36%, and neural network from 88.53% to 88.61%, while bagging optimization can only increase Algortima decision trees become 90.55%, and the neural network becomes 90.38%, because the accuracy value of the support vector machine algorithm is the same as the accuracy value of bagging, which is 88.93%.
ClusterDataSplit: Exploring Challenging Clustering-Based Data Splits for Model Performance Evaluation

This paper adds to the ongoing discussion in the natural language processing community on how to choose a good development set.Motivated by the real-life necessity of applying machine learning models to different data distributions, we propose a clustering-based data splitting algorithm.It creates development (or test) sets which are lexically different from the training data while ensuring similar label distributions.Hence, we are able to create challenging cross-validation evaluation setups while abstracting away from performance differences resulting from label distribution shifts between training and test data.In addition, we present a Python-based tool for analyzing and visualizing data split characteristics and model performance.We illustrate the workings and results of our approach using a sentiment analysis and a patent classification task.
Exploration in NetHack With Secret Discovery

Roguelike games generally feature exploration problems as a critical yet often repetitive element of gameplay. Automated approaches, however, face challenges in terms of optimality, as well as due to incomplete information, such as from the presence of secret doors. This paper presents an algorithmic approach to exploration of roguelike dungeon environments. Our design aims to minimize exploration time, balancing coverage and discovery of secret areas with resource cost. Our algorithm is based on the concept of occupancy maps popular in robotics, adapted to encourage efficient discovery of secret access points. Through extensive experimentation on NetHack maps, we show that this technique is significantly more efficient than simpler greedy approaches and an existing automated player. We further investigate optimized parameterization for the algorithm through a comprehensive data analysis. These results point toward better automation for players, as well as heuristics applicable to fully automated gameplay.