Reviewerly/exHarmony huggingface dataset:
Generating Adversarial Examples in One Shot With Image-to-Image Translation GAN

Deep Neural Networks (DNNs) provide state-of-the-art results for most machine learning and computer vision tasks.However, they have been found susceptible to adversarial examples.In the recent literature, many ways of generating adversarial examples have been discovered.In this work, we propose a novel method to generate adversarial examples with generative adversarial networks (GANs).Compared to traditional optimisation-based methods, our method provides a fast yet powerful alternative for adversary generation.Unlike other GAN-based approaches in the literature which learn to generate an intermediate perturbation vector, our method generates adversarial examples from benign input images in a straightforward manner.By directly generating adversarial examples from given input images, our method produces perturbations that better align with the underlying edge and shape contained in the inputs, hence more natural-looking and imperceptible to human eyes.We evaluate our method on the MNIST and the CIFAR-10 dataset and demonstrate that it outperforms the state-of-the-art GAN-based attack AdvGAN with similar attack capability in terms of distortion.We show that our method produces competitive results to notable optimisation-based attacks in the literature including the strongest Carlini & Wagner (CW) attack.
Testing Chatbots Using Metamorphic Relations

Modern-day demands for services often require an availability on a 24/7 basis as well as online accessibility around the globe. For this sake, personalized software systems, called chatbots, are applied. Chatbots offer services, goods or information in natural language. These programs respond to the user in real-time and offer an intuitive and simple interface to interact with. Advantages like these makes them increasingly popular. Chatbots can even act as substitutes for humans for specific purposes. Since the chatbot market is growing, chatbots might outperform and replace classical web applications in the future. For this reason, ensuring correct functionality of chatbots is of high and increasing importance. However, since different implementations and user behavior result in unpredictable results, the chatbot's output is difficult to predict and classify as well. In fact, testing of chatbots represents a challenge because of the unavailability of a test oracle. In this paper, we introduce a metamorphic testing approach for chatbots. In general, metamorphic testing can be applied to situations where no expected values are available. In addition, we discuss how to obtain test cases for chatbots, i.e. sequences of interactions with a chatbot, in an according manner. We demonstrate our approach using a hotel booking system and discuss first experimental results.
SVRPF: An Improved Particle Filter for a Nonlinear/Non-Gaussian Environment

The performance of a particle filter (PF) in nonlinear and non-Gaussian environments is often affected by particle degeneracy and impoverishment problems.In this paper, these two problems are reassessed using the concepts of importance region (IR) selection and particle density (PD), where IR describes the distribution region of particles, and PD describes the density of particles in IR.Based on these two factors, a support vector regression PF (SVRPF) is proposed to overcome the problems from nonlinear and non-Gaussian environments, especially in regard to narrow observation noise.Furthermore, the consistency of the SVRPF and Bayes' filtering is demonstrated.A numerical simulation shows that the performance of the SVRPF is more stable than other filter algorithms.Provided that other conditions are the same, when the observation noise variance is 0.1 and 5, the root-mean-square errors of the SVRPF decrease by 0.5 and 0.03, respectively, compared with that of a general PF.
Frame-Based Detection of Figurative Language in Tweets [Application Notes]

T his paper analyzes the problem of figurative language detection on social media, with a focus on the use of semantic features for identifying irony and sarcasm.Framester, a novel resource that acts as a hub between Frame Net, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero and others, has been used to extract semantic features from text.These semantic features are used to enrich the representations of tweets with event information using frames and word senses in addition to lexical units.The data set used for experimentation purposes contains tweets taken from different corpora including both figurative (containing irony and sarcasm) and non-figurative language.Two major tasks were performed: (i) detecting figurative language in tweets in a dataset containing both figurative and non-figurative tweets, (ii) classifying tweets containing irony and sarcasm.A 10-fold cross-validation experiment shows that the obtained accuracy for both tasks increases significantly when the semantic features such as linguistic frames and word senses are used in addition to lexical units, indicating that they may be important clues for figurative language.The approach was devel-oped on top of Apache Spark so that it is easily scalable to much higher volumes of data, allowing for real-time analysis.
Liver Segmentation in CT based on ResUNet with 3D Probabilistic and Geometric Post Process

This paper proposes a novel liver segmentation framework using ResUNet with 3D probabilistic and geometric post process. Our semantic segmentation model ResUNet adds residual unit and batch normalization layer to up sampling and down sampling part of U-Net to construct a deeper network. To quick converge, we propose a new loss function DCE, which is linearly combined by Dice loss and cross entropy loss. We use continuous several CT images as input for training and testing to explore more context information. Based on initial segmentation of ResUNet, fully connected 3D conditional random field is used to refine segmentation results by exploring 2D neighbor regions and 3D volume information. Finally, 3D connected components analyzing is used to remain some large components and reduce segmentation noise. The experimental results on public dataset LiTS show our proposed framework achieve the state of the art performance for liver segmentation.
AENN: A GENERATIVE ADVERSARIAL NEURAL NETWORK FOR WEATHER RADAR ECHO EXTRAPOLATION

Abstract. Weather radar echo is one of the fundamental data for meteorological workers to weather systems identification and classification. Through the technique of weather radar echo extrapolation, the future short-term weather conditions can be predicted and severe convection storms can be warned. However, traditional extrapolation methods cannot offer accurate enough extrapolation results since their modeling capacity is limited, the recent deep learning based methods make some progress but still remains a problem of blurry prediction when making deeper extrapolation, which may due to they choose the mean square error as their loss function and that will lead to losing echo details. To address this problem and make a more realistic and accurate extrapolation, we propose a deep learning model called Adversarial Extrapolation Neural Network (AENN), which is a Generative Adversarial Network (GAN) structure and consist of a conditional generator and two discriminators, echo-frame discriminator and echo-sequence discriminator. The generator and discriminators are trained alternately in an adversarial way to make the final extrapolation results be realistic and accurate. To evaluate the model, we conduct experiments on extrapolating 0.5h, 1h, and 1.5h imminent future echoes, the results show that our proposed AENN can achieve the expected effect and outperforms other models significantly, which has a powerful potential application value for short-term weather forecasting.
Supporting Technology 4.0: Ethoconstructivist Multimedia for Elementary Schools

Education is a processor effort carried out by someone to gain knowledge, skills, and habits in life. Today education in Indonesia is much influenced by globalization and the development of technology, information, and communication (ICT). ICT has become one of the aspects that is very much considered in the world of education from now on. The use of ICT in planning, process, and evaluation of learning is a very lively discussion discussed by Indonesian education practitioners. The teacher must be able to process the development of ICT in a positive direction so that students do not forget their original culture as the Indonesian nation. many things teachers can do by using Tats in the learning process. Teachers can find more educational references, teachers can use ICT as a learning medium. By using e-learning, students will become more motivated in learning. One form of e-learning based learning is to use a 3D page flip book application. The e-book is an innovative book that is designed to produce printed books because it produces products in the form of soft and economical distribution files. The ethnic-constructivistic module developed here will use a professional 3D page flip. In this research, we will see how the implementation of e-learning-based learning, ethnoconstuctivist multimedia with module uses a 3D page flip book. In addition, in this study, it will also be seen how students' responses and motivation after learning using the 3D page flip book application.
Enhancing Multi-Layer Perceptron for Breast Cancer Prediction

Breast cancer (BC) is a standout disease of the most well-known cancers among women around the world.The analysis and prediction of BC leads to early manage the disease and protect the patients from further medical complications.In the light of its noticeable focal points in basic highlights identification from complex BC datasets, Machine Learning (ML) is generally perceived as the technique of decision in BC design order and gauge displaying.Because of the high performance of the Multi-layer Perceptron (MLP) algorithm as one of the ML techniques, we conducted experiments in order to enhance the accuracy rate of MLP by tuning its hyper-parameters along with studying the effect of feature selection methods and feature reduction of MLP.As feature selection results indicated that an increase in the number of input parameters tends to reduce the error associated with the estimator model.The tuned MLP proposed in this paper, based MLP best fit hyper-parameters along with feature selection is applied for breast cancer classification using Wisconsin Diagnostic Breast Cancer (WDBC) dataset.As the tuned MLP experimental result shows an accuracy reached 97.70% as it outperforms the basic MLP.
Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Many restaurant review analysis have been done, however only few analysis have been done for specific aspects of a restaurant.In this context this paper proposes aspect based restaurant analysis which includes Physical environment, Food quality, Service quality and Price fairness.The analysis steps include Aspect Term Extraction (ATE), Aspect Keyword Extraction (AKE), Aspect Categorization (AC) and Sentiment Analysis (SA).ATE employs the modification of Double Propagation method and several Topic Modelling methods, AKE utilizes Term Frequency-Inverse Cluster Frequency (TF-ICF), in AC we propose Hybrid ELMo-Wikipedia (HEW), and in SA we propose Hybrid Expanded Opinion Lexicon-SentiCircle (HEOLS).The results show that our modification of the methods used in ATE could increase the f1measure of the AC by average 2%, then the HEW that we proposed had better f1measure compared to other similar methods by average 6%.Other than that, our proposed HEOLS can expand and redetermine the Opinion Lexicon polarity and can increase f1measure of SA by 6%.
Improving Siamese Networks for One-Shot Learning Using Kernel-Based Activation Functions

The lack of a large amount of training data has always been the constraining factor in solving many problems in machine learning, making One-Shot Learning one of the most intriguing ideas in machine learning. It aims to learn necessary objective information from one or only a few training examples. This process of learning in neural networks is generally accomplished by using a proper objective function (loss function) and embedding extraction (architecture). In this paper, we discussed metric-based deep learning architectures for one-shot learning such as siamese neural networks [10] and present a method to improve on their accuracy using Kafnets (kernel-based non-parametric activation functions for neural networks) [17] by learning finer embeddings with relatively less number of epochs. Using kernel activation functions, we are able to achieve strong results that exceed ReLU-based deep learning models in terms of embedding structure, loss convergence, and accuracy. The project code with results can be found at https://github.com/shruti-jadon/Siamese-Network-for-One-shot-Learning .
Non-Hermitian approach for quantum plasmonics

We examine the limits of applicability of a simple non-Hermitian model for exciton/plasmon interactions in the presence of dissipation and dephasing. The model can be used as an alternative to the more complete Lindblad density matrix approach and is computationally and conceptually simpler. We find that optical spectra in the linear regime can be adequately described by this approach. The model can fail, however, under continuous optical driving in some circumstances. In the case of two quantum dots or excitons interacting with a plasmon, the model can also describe coherences and entanglement qualitatively when both dissipation and dephasing are present, and quantitatively in the limit with no dephasing. The model can also be extended to the case of many quantum dots interacting with a plasmon and results, within the single-excitation manifold, are presented for the fifty quantum dot case.
Falcon: Towards Computation-Parallel Deep Learning in Heterogeneous Parameter Server

Parameter server paradigm has shown great performance superiority for handling deep learning (DL) applications. One crucial issue in this regard is the presence of stragglers, which significantly retards DL training progress. Previous solutions for solving straggler may not fully exploit the computation capacity of a cluster as evidenced by our experiments. This motivates us to make an attempt at building a new parameter server architecture that mitigates and addresses stragglers in heterogeneous DL from the perspective of computation parallelism. We introduce a novel methodology named straggler projection to give a comprehensive inspection of stragglers and reveal practical guidelines for resolving this problem: (1) reducing straggler emergence frequency via elastic parallelism control and (2) transferring blocked tasks to pioneer workers for fully exploiting cluster computation capacity. Following the guidelines, we propose the abstraction of parallelism as an infrastructure and elaborate the Elastic-Parallelism Synchronous Parallel (EPSP) that supports both enforced-and slack-synchronization schemes. The whole idea has been implemented in a prototype called Falcon which efficiently accelerates the DL training progress with the presence of stragglers. Evaluation under various benchmarks with baseline comparison evidences the superiority of our system. Specifically, Falcon yields shorter convergence time, by up to 61.83%, 55.19%, 38.92% and 23.68% reduction over FlexRR, Sync-opt, ConSGD and DynSGD, respectively.
Neural Speech Translation using Lattice Transformations and Graph Networks

Speech translation systems usually follow a pipeline approach, using word lattices as an intermediate representation.However, previous work assume access to the original transcriptions used to train the ASR system, which can limit applicability in real scenarios.In this work we propose an approach for speech translation through lattice transformations and neural models based on graph networks.Experimental results show that our approach reaches competitive performance without relying on transcriptions, while also being orders of magnitude faster than previous work.
Neural Machine Translation for Low-resource English-Bangla

Neural machine translation has recently been able to gain stateof-the-art translation quality for many language pairs.However, neural machine translation has been less tested for English-Bangla language pair, two linguistically distant and widely spoken languages.In this paper, we apply neural machine translation to the task of English-Bangla translation in both directions and compare it against a standard phrase-based statistical machine translation system.We obtain up to +0.30 and +4.95 BLEU improvement over phrase-based statistical machine translation for Englishto-Bangla and Bangla-to-English respectively.Due to low-resource and morphological richness of Bangla, English-Bangla translation task produces a large number of rare words.We apply subword segmentation with byte pair encoding to handle this rare words issue.We obtain up to +0.69 and +0.30BLEU improvement over baseline neural machine translation for English-to-Bangla and Bangla-to-English respectively.We further investigate our system output for several challenging linguistic properties like subject-verb agreement, noun inflection, long distance reordering and rare words translation.We observe that neural machine translation with and without subword segmentation significantly outperform the phrase-based statistical machine translation system, thus establishing itself as the stateof-the-art technology for low-resource English-Bangla machine translation.
QR Code-based Learning Development: Accessing Math Game for Children Learning Enhancement

Mathematics in elementary school is difficult to understand, boring, formal, theoretical, and book-based learning, which makes students tired of listening and paying attention. The purpose of this study is to create a learning prototype based on QR codes, especially mathematics learning in elementary schools. Using the QR code, students can access math games related to the course. This learning model could encourage students to learn mathematics. The research included seven steps in research and development named after Borg &amp;amp; Gall, such as need assessment, plan, early product development, first test, revise early product, field test and revise product. The object of the research is QR code-based learning, and the respondents are elementary school students and teachers. After analyzing data in the first and field test, researchers found the result. QR code-based learning could support children for learning mathematics.
DT-CP: A Double-TTPs-Based Contract-Signing Protocol With Lower Computational Cost

This paper characterizes a contract signing protocol with high efficiency in Internet of Things.Recent studies show that existing contract-signing protocols can achieve abuse-freeness and resist inference attack, but cannot meet the high-efficiency and convenience requirement of the future Internet of things applications.To solve this problem, we propose a novel contract-signing protocol.Our proposed protocol includes two main parts: 1) we use the partial public key of the sender, instead of the zero-knowledge protocol, to verify the intermediate result; 2) we employ two independent Trusted Third Parties (TTPs) to prevent the honest-but-curious TTP.Our analysis shows that our double TTP protocol can not only result in lower computational cost, but also can achieve abuse-freeness with trapdoor commitment scheme.In a word, our proposed scheme performs better than the state of the art in terms of four metrics: encryption time, number of exponentiations, data to be exchanged and exchange steps in one round contract-signing.
On Efficient Retrieval of Top Similarity Vectors

Retrieval of relevant vectors produced by representation learning critically influences the efficiency in natural language processing (NLP) tasks.In this paper we demonstrate an efficient method for searching vectors via a typical nonmetric matching function: inner product.Our method, which constructs an approximate Inner Product Delaunay Graph (IPDG) for top-1 Maximum Inner Product Search (MIPS), transforms retrieving the most suitable latent vectors into a graph search problem with great benefits of efficiency.Experiments on data representations learned for different machine learning tasks verify the outperforming effectiveness and efficiency of the proposed IPDG.
Taking FAIR on the ChIN: The Chemistry Implementation Network

The Chemistry Implementation Network (ChIN) is focused on supporting the FAIR Data needs of the research community regarding chemical related data. An Implementation Network is a consortium drawn from a community, in this case the chemistry discipline, committed to defining and constructing standards, materials and software in the spirit of the FAIR data principles and under the structure of the GO FAIR project. Furthermore, as a core science the ChIN has to reach beyond the chemistry community and support the use of chemical information in other disciplines. This will be facilitated through connections in the GO FAIR ecosystem of Implementation Networks. Examples of the FAIR chemical concepts that need to be supported include molecular and materials structures, chemical reactions, nomenclature and other chemical terminology and conventions. The ChIN aims to drive forward the application of the FAIR Data Principles relating to the full range of chemistry concepts that are key to the transparent and efficient communication of chemical information. Realizing the goal of FAIR chemistry data will require a culture change across the discipline. However this is best addressed once a critical mass of tools and approaches has been developed.
Transfer in Deep Reinforcement Learning Using Knowledge Graphs

Text adventure games, in which players must make sense of the world through text descriptions and declare actions through text descriptions, provide a stepping stone toward grounding action in language.Prior work has demonstrated that using a knowledge graph as a state representation and question-answering to pre-train a deep Q-network facilitates faster control policy learning.In this paper, we explore the use of knowledge graphs as a representation for domain knowledge transfer for training text-adventure playing reinforcement learning agents.Our methods are tested across multiple computer generated and human authored games, varying in domain and complexity, and demonstrate that our transfer learning methods let us learn a higher-quality control policy faster.
Felt: A Simple Story Sifter

Story sifting, also known as story recognition, has been identified as one of the major design challenges currently facing interactive emergent narrative. However, despite continued interest in emergent narrative approaches, there has been relatively little work in the area of story sifting to date, leaving it unclear how a story sifting system might best be implemented and what challenges are likely to be encountered in the course of implementing such a system. In this paper, we present Felt, a simple query language-based story sifter and rules-based simulation engine that aims to serve as a first step toward answering these questions. We describe Felt's architecture, discuss several design case studies of interactive emergent narrative experiences that make use of Felt, reflect on what we have learned from working with Felt so far, and suggest directions for future work in the story sifting domain.