Reviewerly/exHarmony huggingface dataset:
Moral Stance Recognition and Polarity Classification from Twitter and Elicited Text

We introduce a labelled corpus of stances about moral issues for the Brazilian Portuguese language, and present reference results for both the stance recognition and polarity classification tasks.The corpus is built from Twitter and further expanded with data elicited through crowd sourcing and labelled by their own authors.Put together, the corpus and reference results are expected to be taken as a baseline for further studies in the field of stance recognition and polarity classification from text.
Adaptive Normalized Attacks for Learning Adversarial Attacks and Defenses in Power Systems

Vulnerability of various machine learning methods to adversarial examples has been recently explored in the literature. Power systems which use these vulnerable methods face a huge threat against adversarial examples. To this end, we first propose a more accurate and computationally efficient method called Adaptive Normalized Attack (ANA) to attack power systems using generate adversarial examples. We then adopt adversarial training to defend against attacks of adversarial examples. Experimental analyses demonstrate that our attack method provides less perturbation compared to the state-of-the-art FGSM (Fast Gradient Sign Method) and DeepFool, while our proposed method increases misclassification rate of learning methods for attacking power systems. In addition, the results show that the proposed adversarial training improves robustness of power systems to adversarial examples compared to using state-of-the-art methods.
Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition

One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%.
Clouseau: Generating Communication Protocols from Commitments

Engineering a decentralized multiagent system (MAS) requires realizing interactions modeled as a communication protocol between autonomous agents. We contribute Clouseau, an approach that takes a commitment-based specification of an interaction and generates a communication protocol amenable to decentralized enactment. We show that the generated protocol is (1) correct—realizes all and only the computations that satisfy the input specification; (2) safe—ensures the agents' local views remain consistent; and (3) live—ensures the agents can proceed to completion.
Data processing over single-port homodyne detection to realize superresolution and supersensitivity

Performing homodyne detection at one port of squeezed-state light interferometer and then binarzing measurement data are important to achieve super-resolving and super-sensitive phase measurements. Here we propose a new data-processing technique by dividing the measurement quadrature into three bins (equivalent to a multi-outcome measurement), which leads to a higher improvement in the phase resolution and the phase sensitivity under realistic experimental condition. Furthermore, we develop a new phase-estimation protocol based on a combination of the inversion estimators of each outcome and show that the estimator can saturate the Cramer-Rao lower bound, similar to asymptotically unbiased maximum likelihood estimator.
Active quenching of superconducting nanowire single photon detectors

Superconducting nanowire single photon detectors are typically biased using a constant current source and shunted in a conductance which is over an order of magnitude larger than the peak normal domain conductance of the detector. While this design choice is required to ensure quenching of the normal domain, the use of a small load resistor limits the pulse amplitude, rising-edge slew rate, and recovery time of the detector. Here, we explore the possibility of actively quenching the normal domain, thereby removing the need to shunt the detector in a small resistance. We first consider the theoretical performance of an actively quenched superconducting nanowire single photon detector and, in comparison to a passively quenched device, we predict roughly an order of magnitude improvement in the slew rate and peak voltage achieved in this configuration. The experimental performance of actively and passively quenched superconducting nanowire single photon detectors are then compared. It is shown that, in comparison to a passively quenched device, the actively quenched detectors simultaneously exhibited improved count rates, dark count rates, and timing jitter.
Enhancing Statement Evaluation in Argumentation via Multi-labelling Systems

In computational models of argumentation, the justification of statements has drawn less attention than the construction and justification of arguments. Significant losses of sensitivity or expressibility on statement statuses can be incurred by otherwise appealing formalisms. In order to reappraise statement statuses and, more generally, to support a uniform modelling of different phases of the argumentation process we introduce multi-labelling systems, a generic formalism devoted to represent reasoning processes consisting of a sequence of labelling stages. In the argumentation context, two families of multi-labelling systens, called argument-focused and statement-focused approach are identified and compared. Then they are shown to be able to encompass several prominent literature proposals as special cases, thereby enabling a systematic comparison evidencing their merits and limits. Further, we show that the proposed model supports tunability of statement justification by specifying a few alternative statement justification labellings, and we illustrate how they can be seamlessly integrated into different formalisms.
Ordinal Multi-Task Part Segmentation With Recurrent Prior Generation

Semantic object part segmentation is a fundamental task in object understanding and geometric analysis. The clear understanding of part relationships can be of great use to the segmentation process. In this work, we propose a novel Ordinal Multi-task Part Segmentation (OMPS) approach which explicitly models the part ordinal relationship to guide the segmentation process in a recurrent manner. Quantitative and qualitative experiments are conducted first to explore the mutual impacts among object parts and then an ordinal part inference algorithm is formulated via experimental observations. Specifically, our framework is mainly composed of two modules, the forward module to segment multiple parts as individual subtasks with prior knowledge, and the recurrent module to generate appropriate part priors with the ordinal inference algorithm. These two modules work iteratively to optimize the segmentation performance and the network parameters. Experimental results show that our approach outperforms the state-of-the-art models on human and vehicle part parsing benchmarks. Comprehensive evaluations are conducted to demonstrate the effectiveness of our approach in object part segmentation.
A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory

The density peaks clustering (DPC) algorithm is not sensitive to the recognition of halo nodes.The halo nodes at the edge of the density peaks clustering algorithm has a lower local density.The outliers are distributed in halo nodes.The novel halo identification method based on density peaks clustering algorithm utilize the advantage of DBSCAN algorithm to quickly identify outliers, which improved the sensitivity to halo nodes.However, the identified halo nodes cannot be effectively assigned to adjacent clusters.Therefore, this paper will use K-nearest neighbor (KNN) algorithm to classify the identified halo nodes.K-nearest neighbor is the simplest and most efficient classification method.The KNN algorithm has the advantages of high accuracy, insensitivity to outliers and no input hypothesis data.Hence, we proposed a novel density peaks clustering halo node assignment algorithm based on K-nearest neighbor theory (KNN-HDPC).KNN-HDPC can grasp the internal relations between outliers and cluster nodes more deeply, so as to dig out the deeper relations between nodes.Experimental results demonstrate that the proposed algorithm can effectively cluster and reclassify a large number of complex data.We can quickly dig out the potential relationship between noise points and cluster points.The improved algorithm has higher clustering accuracy than the original DPC algorithm, and essentially has more robust clustering results. INDEX TERMS Density peaks clustering, halo
Soft Dropout And Its Variational Bayes Approximation

Soft dropout, a generalization of standard “hard” dropout, is introduced to regularize the parameters in neural networks and prevent overfitting. We replace the “hard” dropout mask following a Bernoulli distribution with the “soft” mask following a beta distribution to drop the hidden nodes in different levels. The soft dropout method can introduce continuous mask coefficients in the interval of [0, 1], rather than only zero and one. Meanwhile, in order to implement the adaptive dropout rate via adaptive distribution parameters, we respectively utilize the half-Gaussian distributed and the half-Laplace distributed variables to approximate the beta distributed masks and apply a variation of variational Bayes optimization called stochastic gradient variational Bayes (SGVB) algorithm to optimize the distribution parameters. In the experiments, compared with the standard soft dropout with fixed dropout rate, the adaptive soft dropout method generally improves the performance. In addition, the proposed soft dropout and its adaptive versions achieve performance improvement compared with the referred methods on both image classification and regression tasks.
Patch Selection Denoiser: An Effective Approach Defending Against One-Pixel Attacks

A one-pixel attack applies maliciously crafted and imperceptible perturbations on just one pixel or a few pixels in an image and can mislead a target deep learning classification model. Defending against this type of attack is a relatively unexplored development in adversarial defence. In this paper, we propose a Patch Selection Denoiser (PSD) approach that removes the few potential attacking pixels in local patches without changing many pixels in a whole image. Without clean training data, it can firstly add random impulse noises to a few images to produce huge amounts of noisy images as inputs and targets in a deep residual network. Next, we can obtain a denoising model based on the Noise2Noise framework. Finally, we design a patch selection algorithm to scan a denoised image in a patch window and compare it with the corresponding part on the test image. Only the patch whose number of pixels with significant absolute difference exceeds a threshold will be detected as the local part containing potential attacking pixels. Thus, this patch will be replaced by the part in the denoised image. Evaluating our approach on a public image dataset CIFAR-10 demonstrates that it can successfully defend against one-, three-, five-pixel and JSMA attacks 98.6%, 98.0%, 97.8% and 98.9% of the time, respectively. Meanwhile, it brings almost no side effects on clean images not subject to one-pixel attacks. The state-of-the-art high defence accuracy proves the effectiveness of our approach.
Applications of Artificial Intelligence in Marketing

Artificial intelligence is the simulation of human intelligence processes by machines, which covers a huge range of capabilities such as voice/image recognition, decision making, machine learning techniques and semantic search.Through secondary research, this paper aims at educating marketers on the present and future potential of artificial intelligence by providing some real-world examples of few early-adopting firms and connecting them with AI powered technologies that can improve marketing performance and transform their businesses.Through primary research, this paper aims at studying the impact of artificial intelligence on the overall marketing landscape.In the end the paper makes a modest attempt to identify those sectors which have shown good acceptability for AI in marketing and the ones which will benefit the most.
Experimental evaluation of the strain intensity factor at the rigid line inclusion tip embedded in an epoxy matrix using digital image correlation

The strain intensity factor for a rigid line inclusion tip is experimentally determined using the digital image correlation (DIC) technique using multi-parameter displacement field equations. The multi-parameter displacement field equations for a rigid line inclusion are derived based on the Airy’s stress function approach and are reported here in an elegant manner. The unknown coefficients in the multi-parameter equation, inclusion tip location, rigid body translations and rotation are determined using a linear least squares method in an over-deterministic manner. A rigid line inclusion specimen is prepared using 0.1 mm steel strip embedded in an epoxy matrix. Experiments are performed for two inclusion configurations when the embedding matrix is subjected to a remote tensile field. In the first configuration, the inclusion is placed along the tensile loading direction. In the second configuration, the inclusion is kept inclined to the loading direction. The experimentally obtained strain intensity factor agrees well with the analytical estimates with an error less than 6% for the seven parameter solution for both the configuration, thereby confirming the accuracy of the proposed methodology.
A Skeleton-based Action Recognition System for Medical Condition Detection

Progress in skeleton-based action recognition has enabled the contactless, ceaseless and portable surveillance on human daily behaviors which helps to reveal health hazards. Human action datasets like NTU RGB+D have provided medical-condition-related categories. Hence, in this paper, we propose an optimized view adaptive LSTM (VA-LSTM) with additional classification and regression subnetworks to such medical condition detection (MCD). We surpass previous works with a model accuracy of 79.6% (cross-subject) and 88.2% (cross-view). Besides, a dedicated hardware accelerator capable of multi-input processing is developed for this algorithm. Comparing to the algorithm implementations on general computation platforms such as Raspberry-Pi, Firefly-RK3399, and NVIDIA Titan X, the proposed accelerator achieves a less inference latency as well as a better energy-efficiency.
Unsupervised Text Style Transfer via Iterative Matching and Translation.

Text style transfer seeks to learn how to automatically rewrite sentencesfrom a source domain to the target domain in different styles, whilesimultaneously preserving their semantic contents. A major challenge in thistask stems from the lack of parallel data that connects the source and targetstyles. Existing approaches try to disentangle content and style, but this isquite difficult and often results in poor content-preservation andgrammaticality. In contrast, we propose a novel approach by first constructinga pseudo-parallel resource that aligns a subset of sentences with similarcontent between source and target corpus. And then a standardsequence-to-sequence model can be applied to learn the style transfer.Subsequently, we iteratively refine the learned style transfer function whileimproving upon the imperfections in our original alignment. Our method isapplied to the tasks of sentiment modification and formality transfer, where itoutperforms state-of-the-art systems by a large margin. As an auxiliarycontribution, we produced a publicly-available test set with human-generatedstyle transfers for future community use.
Ontology-Based Consistent Specification of Sensor Data Acquisition Plans in Cross-Domain IoT Platforms

Nowadays there is an high number of IoT applications that seldom can interact with each other because developed within different Vertical IoT Platforms that adopt different standards.Several efforts are devoted to the construction of cross-layered frameworks that facilitate the interoperability among cross-domain IoT platforms for the development of horizontal applications.Even if their realization poses different challenges across all layers of the network stack, in this paper we focus on the interoperability issues that arise at the data management layer.Specifically, starting from a flexible multi-granular Spatio-Temporal-Thematic data model according to which events generated by different kinds of sensors can be represented, we propose a Semantic Virtualization approach according to which the sensors belonging to different IoT platforms and the schema of the produced event streams are described in a Domain Ontology, obtained through the extension of the well-known Semantic Sensor Network ontology.Then, these sensors can be exploited for the creation of Data Acquisition Plans by means of which the streams of events can be filtered, merged, and aggregated in a meaningful way.A notion of consistency is introduced to bind the output streams of the services contained in the Data Acquisition Plan with the Domain Ontology in order to provide a semantic description of its final output.When these plans meet the consistency constraints, it means that the data they handle are well described at the Ontological level and thus the data acquisition process over passed the interoperability barriers occurring in the original sources.The facilities of the StreamLoader prototype are finally presented for supporting the user in the Semantic Virtualization process and for the construction of meaningful Data Acquisition Plans.
Italian ma ‘but’ in deverbal pragmatic markers: Forms, functions, and productivity of a pragma-dyad

This paper focuses on the development, paradigmaticization, and productivity of a set of complex pragmatic markers in Italian, which are constituted by two distinct elements, namely the adversative conjunction ma ‘but’ and a deverbal pragmatic marker (e.g., ma dai ‘come on! really!’, literally: ‘but give’, or ma piantala ‘just stop! give it a rest!’, literally ‘but dump it’). The main idea we will develop is that such a complex pattern can be better described in terms of a pragma-dyad, i.e., a dyadic construction with a pragmatic meaning, featuring a fixed element which systematically combines with a set of preferential fillers. In our case, the fixed element is ma, which generally signals a contrast with the interlocutors’ point of view, thus shaping the pragmatic meaning of the resulting complex in terms of interactional contrast. Such a meaning is then functionally enriched through a variety of fillers compatible with the schema, which actualize it in conveying mock politeness, disagreement, counter-expectation, and pragmatically neighbouring values. By providing a corpus-based study of the development and productivity of these complex markers, we illustrate the empirical and theoretical advantages which a pragma-dyadic approach can offer in exploring processes of functional enrichment involving complex markers.
Employer Expectations for the MS-Level Cytology Practitioner

Abstract Objectives The specific aims of the study are to determine skillsets most valuable to regional employers of cytotechnologists and assess the employers’ willingness to use graduating MS cytology practitioners in the expanded scope of practice. Methods Data from the greater New York region were collected via a web-based survey distributed to a broad sample of laboratory professionals involved with hiring in a variety of institutions. Results The three skillsets most important to New York employers and most employable are fine-needle aspiration adequacy assessment, regulatory acumen, and prescreening cell blocks. The skills that are of least important and least employable are prescreening high-volume tissue biopsy specimens and histology. Conclusions The results of this survey shed light on regional employers’ perspectives regarding the skillsets that are most important and marketable in the greater New York region and may be useful for subsequent curriculum development.
Dataset for file fragment classification of textual file formats

Classification of textual file formats is a topic of interest in network forensics. There are a few publicly available datasets of files with textual formats. Therewith, there is no public dataset for file fragments of textual file formats. So, a big research challenge in file fragment classification of textual file formats is to compare the performance of the developed methods over the same datasets.In this study, we present a dataset that contains file fragments of five textual file formats: Binary file format for Word 97-Word 2003, Microsoft Word open XML format, portable document format, rich text file, and standard text document. This dataset contains the file fragments in three different languages: English, Persian, and Chinese. For each pair of file format and language, 1500 file fragments are provided. So, the dataset of file fragments contains 22,500 file fragments.
Mirror-Generative Neural Machine Translation

Training neural machine translation models (NMT) requires a large amount of parallel corpus, which is scarce for many language pairs. However, raw non-parallel corpora are often easy to obtain. Existing approaches have not exploited the full potential of non-parallel bilingual data either in training or decoding. In this paper, we propose the mirror-generative NMT (MGNMT), a single unified architecture that simultaneously integrates the source to target translation model, the target to source translation model, and two language models. Both translation models and language models share the same latent semantic space, therefore both translation directions can learn from non-parallel data more effectively. Besides, the translation models and language models can collaborate together during decoding. Our experiments show that the proposed MGNMT consistently outperforms existing approaches in all a variety of scenarios and language pairs, including resource-rich and low-resource languages.