Reviewerly/exHarmony huggingface dataset:
T–S Fuzzy Modeling for Aircraft Engines: The Clustering and Identification Approach

This paper presents a data-based Takagi-Sugeno (T–S) fuzzy modeling approach for aircraft engines in the flight envelope. We propose a series of T–S fuzzy models for engines with flight conditions as premises and engine linear dynamic models as consequences. By engine dynamic clustering, we determine rough T–S fuzzy models to approximate the nonlinear dynamics of engines in the flight envelope. After that, the maximum–minimum distance-based fuzzy c-means (MMD-FCM) algorithm comes to refine the fuzzy rules and the least square method (LSM) comes to identify premise parameters for each single rough model. The proposed MMD-FCM algorithm guarantees the refined results are stable and reasonable, and the identification improves the accuracy of the steady and transient phases. The model verification showed that the T–S fuzzy models for engines had a high accuracy with a steady error less than 5%, and that the root mean squared error (RMSE) of transient errors was less than 8 × 10−4 with good generalization ability in the flight envelope.
Group Signatures with Message-Dependent Opening: Formal Definitions and Constructions

This paper introduces a new capability for group signatures called message-dependent opening . It is intended to weaken the high trust placed on the opener; i.e., no anonymity against the opener is provided by an ordinary group signature scheme. In a group signature scheme with message-dependent opening (GS-MDO), in addition to the opener, we set up an admitter that is not able to extract any user’s identity but admits the opener to open signatures by specifying messages where signatures on the specified messages will be opened by the opener. The opener cannot extract the signer’s identity from any signature whose corresponding message is not specified by the admitter. This paper presents formal definitions of GS-MDO and proposes a generic construction of it from identity-based encryption and adaptive non-interactive zero-knowledge proofs. Moreover, we propose two specific constructions, one in the standard model and one in the random oracle model. Our scheme in the standard model is an instantiation of our generic construction but the message-dependent opening property is bounded. In contrast, our scheme in the random oracle model is not a direct instantiation of our generic construction but is optimized to increase efficiency and achieves the unbounded message-dependent opening property. Furthermore, we also demonstrate that GS-MDO implies identity-based encryption, thus implying that identity-based encryption is essential for designing GS-MDO schemes.
A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation

Neural sequence-to-sequence models for dialog systems suffer from the problem of favoring uninformative and non replier-specific responses due to lacking of the global and relevant information guidance.The existing methods model the generation process by leveraging the neural variational network with simple Gaussian.However, the sampled information from latent space usually becomes useless due to the KL divergence vanishing issue, and the highly abstractive global variables easily dilute the personal features of replier, leading to a non replier-specific response.Therefore, a novel Semi-Supervised Stable Variational Network (SSVN) is proposed to address these issues.We use a unit hypersperical distribution, namely the von Mises-Fisher (vMF), as the latent space of a semi-supervised model, which can obtain the stable KL performance by setting a fixed variance and hence enhance the global information representation.Meanwhile, an unsupervised extractor is introduced to automatically distill the replier-tailored feature which is then injected into a supervised generator to encourage the replier-consistency.Experimental results on two large conversation datasets show that our model outperforms the competitive baseline models significantly, and can generate diverse and replier-specific responses.
A Tale of Three Signatures: Practical Attack of ECDSA with wNAF

Attacking ECDSA with wNAF implementation for the scalar multiplication first requires some side channel analysis to collect information, then lattice based methods to recover the secret key. In this paper, we reinvestigate the construction of the lattice used in one of these methods, the Extended Hidden Number Problem (EHNP). We find the secret key with only 3 signatures, thus reaching a known theoretical bound, whereas best previous methods required at least 4 signatures in practice. Given a specific leakage model, our attack is more efficient than previous attacks, and for most cases, has better probability of success. To obtain such results, we perform a detailed analysis of the parameters used in the attack and introduce a preprocessing method which reduces by a factor up to 7 the total time to recover the secret key for some parameters. We perform an error resilience analysis which has never been done before in the setup of EHNP. Our construction find the secret key with a small amount of erroneous traces, up to $$2\%$$ of false digits, and $$4\%$$ with a specific type of error.
Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling

Dialogue systems benefit greatly from optimizing on detailed annotations, such as transcribed utterances, internal dialogue state representations and dialogue act labels.However, collecting these annotations is expensive and time-consuming, holding back development in the area of dialogue modelling.In this paper, we investigate semi-supervised learning methods that are able to reduce the amount of required intermediate labelling.We find that by leveraging un-annotated data instead, the amount of turn-level annotations of dialogue state can be significantly reduced when building a neural dialogue system.Our analysis on the MultiWOZ corpus, covering a range of domains and topics, finds that annotations can be reduced by up to 30% while maintaining equivalent system performance.We also describe and evaluate the first end-to-end dialogue model created for the MultiWOZ corpus.
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks

Goal-oriented dialogue systems are now being widely adopted in industry where it is of key importance to maintain a rapid prototyping cycle for new products and domains.Datadriven dialogue system development has to be adapted to meet this requirement -therefore, reducing the amount of data and annotations necessary for training such systems is a central research problem.In this paper, we present the Dialogue Knowledge Transfer Network (DiKTNet), a stateof-the-art approach to goal-oriented dialogue generation which only uses a few example dialogues (i.e.few-shot learning), none of which has to be annotated.We achieve this by performing a 2-stage training.Firstly, we perform unsupervised dialogue representation pre-training on a large source of goal-oriented dialogues in multiple domains, the MetaLWOz corpus.Secondly, at the transfer stage, we train DiKTNet using this representation together with 2 other textual knowledge sources with different levels of generality: ELMo encoder and the main dataset's source domains.Our main dataset is the Stanford Multi-Domain dialogue corpus.We evaluate our model on it in terms of BLEU and Entity F1 scores, and show that our approach significantly and consistently improves upon a series of baseline models as well as over the previous state-of-the-art dialogue generation model, ZSDG.The improvement upon the latter -up to 10% in Entity F1 and the average of 3% in BLEU score -is achieved using only 10% equivalent of ZSDG's in-domain training data.
A Debiased MDI Feature Importance Measure for Random Forests

Tree ensembles such as Random Forests have achieved impressive empirical success across a wide variety of applications. To understand how these models make predictions, people routinely turn to feature importance measures calculated from tree ensembles. It has long been known that Mean Decrease Impurity (MDI), one of the most widely used measures of feature importance, incorrectly assigns high importance to noisy features, leading to systematic bias in feature selection. In this paper, we address the feature selection bias of MDI from both theoretical and methodological perspectives. Based on the original definition of MDI by Breiman et al. \cite{Breiman1984} for a single tree, we derive a tight non-asymptotic bound on the expected bias of MDI importance of noisy features, showing that deep trees have higher (expected) feature selection bias than shallow ones. However, it is not clear how to reduce the bias of MDI using its existing analytical expression. We derive a new analytical expression for MDI, and based on this new expression, we are able to propose a debiased MDI feature importance measure using out-of-bag samples, called MDI-oob. For both the simulated data and a genomic ChIP dataset, MDI-oob achieves state-of-the-art performance in feature selection from Random Forests for both deep and shallow trees.
Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings

The recent success of neural machine translation models relies on the availability of high quality, in-domain data.Domain adaptation is required when domain-specific data is scarce or nonexistent.Previous unsupervised domain adaptation strategies include training the model with in-domain copied monolingual or back-translated data.However, these methods use generic representations for text regardless of domain shift, which makes it infeasible for translation models to control outputs conditional on a specific domain.In this work, we propose an approach that adapts models with domain-aware feature embeddings, which are learned via an auxiliary language modeling task.Our approach allows the model to assign domain-specific representations to words and output sentences in the desired domain.Our empirical results demonstrate the effectiveness of the proposed strategy, achieving consistent improvements in multiple experimental settings.In addition, we show that combining our method with back translation can further improve the performance of the model. 1
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components

Abstract Neural networks are state-of-the-art classification approaches but are generally difficult to interpret. This issue can be partly alleviated by constructing a precise decision process within the neural network. In this work, a network architecture, denoted as Classification-By-Components network (CBC), is proposed. It is restricted to follow an intuitive reasoning based decision process inspired by Biederman's recognition-by-components theory from cognitive psychology. The network is trained to learn and detect generic components that characterize objects. In parallel, a class-wise reasoning strategy based on these components is learned to solve the classification problem. In contrast to other work on reasoning, we propose three different types of reasoning: positive, negative, and indefinite. These three types together form a probability space to provide a probabilistic classifier. The decomposition of objects into generic components combined with the probabilistic reasoning provides by design a clear interpretation of the classification decision process. The evaluation of the approach on MNIST shows that CBCs are viable classifiers. Additionally, we demonstrate that the inherent interpretability offers a profound understanding of the classification behavior such that we can explain the success of an adversarial attack. The method's scalability is successfully tested using the ImageNet dataset.
Multi-objective Optimisation of Online Distributed Software Update for DevOps in Clouds

This article studies synchronous online distributed software update, also known as rolling upgrade in DevOps, which in clouds upgrades software versions in virtual machine instances even when various failures may occur. The goal is to minimise completion time, availability degradation, and monetary cost for entire rolling upgrade by selecting proper parameters. For this goal, we propose a stochastic model and a novel optimisation method. We validate our approach to minimise the objectives through both experiments in Amazon Web Service (AWS) and simulations.
Generating Modern Poetry Automatically in Finnish

We present a novel approach for generating poetry automatically for the morphologically rich Finnish language by using a genetic algorithm.The approach improves the state of the art of the previous Finnish poem generators by introducing a higher degree of freedom in terms of structural creativity.Our approach is evaluated and described within the paradigm of computational creativity, where the fitness functions of the genetic algorithm are assimilated with the notion of aesthetics.The output is considered to be a poem 81.5% of the time by human evaluators.
Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects

Arabic sentiment analysis models have employed compositional embedding features to represent the Arabic dialectal content. These embeddings are usually composed via ordered, syntax-aware composition functions and learned within deep neural frameworks. With the free word order and the varying syntax nature across the different Arabic dialects, a sentiment analysis system developed for one dialect might not be efficient for the others. Here we present syntax-ignorant n-gram embeddings to be used in sentiment analysis of several Arabic dialects. The proposed embeddings were composed and learned using an unordered composition function and a shallow neural model. Five datasets of different dialects were used to evaluate the produced embeddings in the sentiment analysis task. The obtained results revealed that, our syntax-ignorant embeddings could outperform word2vec model and doc2vec both variant models in addition to hand-crafted system baselines, while a competent performance was noticed towards baseline systems that adopted more complicated neural architectures.
Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name.
Multi-Granular Text Encoding for Self-Explaining Categorization

Self-explaining text categorization requires a classifier to make a prediction along with supporting evidence.A popular type of evidence is sub-sequences extracted from the input text which are sufficient for the classifier to make the prediction.In this work, we define multigranular ngrams as basic units for explanation, and organize all ngrams into a hierarchical structure, so that shorter ngrams can be reused while computing longer ngrams.We leverage a tree-structured LSTM to learn a contextindependent representation for each unit via parameter sharing.Experiments on medical disease classification show that our model is more accurate, efficient and compact than BiL-STM and CNN baselines.More importantly, our model can extract intuitive multi-granular evidence to support its predictions.
An Automated Approach for Identification of Non-Functional Requirements using Word2Vec Model

Non-Functional Requirements (NFR) are embedded in functional requirements in requirements specification document.Identification of NFR from the requirement document is a challenging task.Ignorance of NFR identification in early stages of development increase cost and ultimately cause the failure of the system.The aim of this approach is to help the analyst and designers in architect and design of the system by identifying NFR from the requirements document.Several supervised learningbased solutions were reported in the literature.However, for accurate identification of NFR, a significant number of pre-categorized requirements are needed to train supervised text classifiers and system analysts perform the categorization process manually.This study proposed an automated semantic similarity based approach which does not needs pre-categorized requirements for identification of NFR from requirements documents.The approach uses an application of Word2Vec model and popular keywords for identification of NFR.Performance of approach is measured in term of precision-recall and F-measure by applying the approach to PROMISE-NFR dataset.The empirical evidence shows that the automated semi-supervised approach reduces manual human effort in the identification of NFR.
An approach for fault prediction in SOA-based systems using machine learning techniques

Purpose Software fault prediction is an important concept that can be applied at an early stage of the software life cycle. Effective prediction of faults may improve the reliability and testability of software systems. As service-oriented architecture (SOA)-based systems become more and more complex, the interaction between participating services increases frequently. The component services may generate enormous reports and fault information. Although considerable research has stressed on developing fault-proneness prediction models in service-oriented systems (SOS) using machine learning (ML) techniques, there has been little work on assessing how effective the source code metrics are for fault prediction. The paper aims to discuss this issue. Design/methodology/approach In this paper, the authors have proposed a fault prediction framework to investigate fault prediction in SOS using metrics of web services. The effectiveness of the model has been explored by applying six ML techniques, namely, Naïve Bayes, Artificial Networks (ANN), Adaptive Boosting (AdaBoost), decision tree, Random Forests and Support Vector Machine (SVM), along with five feature selection techniques to extract the essential metrics. The authors have explored accuracy, precision, recall, f -measure and receiver operating characteristic curves of the area under curve values as performance measures. Findings The experimental results show that the proposed system can classify the fault-proneness of web services, whether the service is faulty or non-faulty, as a binary-valued output automatically and effectively. Research limitations/implications One possible threat to internal validity in the study is the unknown effects of undiscovered faults. Specifically, the authors have injected possible faults into the classes using Java C3.0 tool and only fixed faults are injected into the classes. However, considering the Java C3.0 community of development, testing and use, the authors can generalize that the undiscovered faults should be few and have less impact on the results presented in this study, and that the results may be limited to the investigated complexity metrics and the used ML techniques. Originality/value In the literature, only few studies have been observed to directly concentrate on metrics-based fault-proneness prediction of SOS using ML techniques. However, most of the contributions are regarding the fault prediction of the general systems rather than SOS. A majority of them have considered reliability, changeability, maintainability using a logging/history-based approach and mathematical modeling rather than fault prediction in SOS using metrics. Thus, the authors have extended the above contributions further by applying supervised ML techniques over web services metrics and measured their capability by employing fault injection methods.
LSTM with Uniqueness Attention for Human Activity Recognition

Deep neural network has promoted the development of human activity recognition research and becomes an indispensable tool for it. Deep neural networks, such as LSTM, can automatically learn important features from the data of human activities. But parts of these data are irrelevant and correspond to the Null activity [3], which can affect the recognition performance. Therefore, we propose a uniqueness attention mechanism to solve this problem. Every human activity consists of many atom motions. Some of these atom motions only occur in one single human activity. This kind of motion is more effective to discriminate human activities, and should therefore receive more attention. We design a model, named LSTM with Uniqueness Attention. When we identify the category of an unknown activity, our model first discover unknown activity’s atom motions which are unique to a known activity, and then use these motions to discriminate this unknown activity. In this way, irrelevant information can be filtered out. Moreover, by discovering an activity’s unique atom motion, we can get more insights to understand this human activity. We evaluate our approach on two public datasets and obtain state-of-the-art results. We also visualize this uniqueness attention, which has an excellent interpretability and goes pretty well with common sense.
Esports Arms Race: Latency and Refresh Rate for Competitive Gaming Tasks

In the world of esports (competitive video games), hardware and software configurations are optimized for winning. In many games this means minimizing latency (‘I see you before you see me’) and maximizing refresh rate (‘I see your move more accurately’). Most esports athletes and competitive players ensure this by using high-end hardware (computers, monitors, and GPUs) while turning off superfluous in-game graphics features. By doing so, one can achieve a latency of 15 ms and a refresh rate of 240 Hz. These figures are remarkable, but do they benefit competitive game players? Gamers have made anecdotal claims favoring 240 Hz, but no scientific studies have yet demonstrated the competitive benefit of modern display technologies. We conducted two studies that compare esports performance using common display settings. The task was to click on a moving target (visual size: 1 deg, speed: 5 deg/sec, direction changing at 1.5 times per sec). Two task versions were used: hitscan (eliminating the target with a single click) and tracking (requiring one sec of accumulated aiming to destroy the target). Experiment 1 compared the effect of refresh rates (60, 120, 240 Hz) using an LCD monitor. The result showed that performance improved monotonically with refresh rate. Experiment 2 further examined whether the effect in Experiment 1 originated from latency only. With the refresh rate fixed to 240 Hz, we added artificial latencies of 0, 4, 8 ms; this created average latencies similar to 240, 120, 60 Hz conditions in Experiment 1. Although performance was worse with longer latency, the effect was less pronounced than seen in Experiment 1. We conclude that 240 hz, today’s highest-speed display technology, provides a competitive advantage. We also observed that latency, often considered the more important factor for competitive gaming, can matter less than refresh rate for some tasks.