Reviewerly/exHarmony huggingface dataset:
Questions in English and Swedish fiction texts

Abstract The aim of this article is to shed new light on the use and translation of English and Swedish questions in fiction by using a combination of parallel and comparable corpus data extracted from the bidirectional English-Swedish Parallel Corpus. In particular, the study examines questions containing a question mark (QMquestions) categorised into wh -interrogatives, polar interrogatives, alternative questions, tag questions (including those with invariant tags), declarative questions, wh -fragments and non- wh -fragments. The parallel analysis shows that most QMquestion types are more often translated congruently into English than into Swedish. The focus is on types with low mutual correspondence scores: fragments, tag questions and declarative questions. The comparable analyses concern both bilingual contrasts between the original texts and monolingual contrasts between the translation and original subcorpora in both languages. The bilingual analysis aligns with several preliminary findings in the parallel analysis, e.g. the favouring of tag questions and some types of wh -fragments in English. The monolingual analysis reveals both over- and underuse in translations and points to a strong effect of source-language influence.
Efficient Privacy Preserving Decision Tree Inference Service

Machine learning inference services have emerged recently: service providers encapsulate trained machine learning models as an interface and provide it as a service. Anyone can submit their own data and get inferred results. The popularity of machine learning inference services greatly reduced the threshold for machine learning, but in the current system, clients need to submit data in clear text, sacrificing their own privacy. In machine learning ecosystem, decision tree models occupy half of the world. Therefore, how to design an efficient decision tree inference service system with privacy protection characteristics has become a research focus. In this paper, we exploit the process of decision tree evaluation and divides the entire system design into four basic modules: an attribute selection module, a comparison operation module, a decision index vector generation module, and a decision result evaluation module. We design customized and efficient secure two-party computation protocols based on secret sharing. Compared with the straightforward generic solution, the performance has been greatly improved. Our scheme does not need expensive public key cryptography primitives, therefore greatly reducing computation and communication overhead, and enabling the scheme to run on lightweight devices such as mobile phones. We perform the experiments by simulating the real-world network environment to prove the practicability of the scheme.
Underwater Image Restoration using Scene Depth Estimation Technique

Underwater image restoration is a difficult task due to the shortage of reference image and changing underwater environment. The images are distorted by absorption and scattering eff ect of light. In deep water, light undergoes wavelength-dependent attenuation. Thus, red color light that has a large wavelength attenuates more than blue light and any other color. As depth increases, light attenuates most of the red content and image appears in bluish-green color. These images also have low contrast, color cast and hazy appearance. Existing methods may need specialized hardware or it may be based on multiple images of the same scene. Thus they cannot be used in real-time or video acquisition task. So, it's better to form an effective method for color enhancement and image restoration of images. Here depth map estimation along with image blurriness is proposed. As light travels deeper into the water, the image gets blurred and this is used to obtain depth map. The backlight is also obtained. These factors are substituted in the IFM (Image Formation Model) to restore the image.
Unsupervised Cross-View Facial Expression Image Generation and Recognition

We propose an unsupervised cross-view facial expression adaptation network (UCFEAN) to simultaneously generate and recognize cross-view facial expressions in images in an unsupervised manner. The main idea of UCFEAN is to convert the unsupervised domain adaptation between two image spaces with different appearance into semi-supervised learning (SSL) in feature spaces with the same semantic content. The cyclic image generation of cross-view facial expressions based on the generative adversarial network (GAN) is carried out to project unlabelled target images and labelled source images to the corresponding feature spaces with the same semantic content. This helps realize the unsupervised feature learning of the target image. Labels of facial expressions represented in the projected target features can then be learned using the projected source features, because the distributions of the projected features in the two domains are close enough for knowledge transfer by using SSL. Three techniques are developed to train UCFEAN in an effective and stable manner. Extensive experiments are conducted to evaluate the UCFEAN on two multi-view facial expression image databases including RaFD and Multi-PIE. The results show that the proposed method can generate realistic target images of the facial expression and recognize cross-view facial expressions with high precision.
Tracking of Maneuvering Star-Convex Extended Target Using Modified Adaptive Extended Kalman Filter

Tracking extended targets aims to estimate the kinematic state and shape of the target of interest with a varying number of noisy detections received by a sensor. The key challenge in this problem stems from its nonlinearity and high dimensionality due to the target maneuver and model complexities. This paper presents a modified adaptive extended Kalman filter based on the random hypersurface model (RHM) to address this problem. First, the target maneuver is judged by using the input estimate (IE) chi-square detector. Then, the magnitude of the target maneuver is used to modify the prior of the shape parameters. Based on the prior information, we derive an extended Kalman filter for a closed-form recursive measurement update. The simulation and experimental results demonstrate the usefulness of the proposed method for tracking the maneuvering star-convex extended target.
Data inference from encrypted databases

Due to increasing concerns of data privacy, databases are being encrypted before they are stored on an untrusted server. To enable search operations on the encrypted data, searchable encryption techniques have been proposed. Representative schemes use order-preserving encryption (OPE) for supporting efficient Boolean queries on encrypted databases. Yet, recent works showed the possibility of inferring plaintext data from OPE-encrypted databases, merely using the order-preserving constraints, or combined with an auxiliary plaintext dataset with similar frequency distribution. So far, the effectiveness of such attacks is limited to single-dimensional dense data (most values from the domain are encrypted), but it remains challenging to achieve it on high-dimensional datasets (e.g., spatial data), which are often sparse in nature. In this paper, for the first time, we study data inference attacks on multi-dimensional encrypted databases (with 2-D as a special case). We formulate it as a 2-D order-preserving matching problem and explore both unweighted and weighted cases, where the former maximizes the number of points matched using only order information and the latter further considers points with similar frequencies. We prove that the problem is NP-hard, and then propose a greedy algorithm, along with a polynomial-time algorithm with approximation guarantees. Experimental results on synthetic and real-world datasets show that the data recovery rate is significantly enhanced compared with the previous 1-D matching algorithm.
Robust Vehicle Tracking with Monocular Vision using Convolutional Neuronal Networks

In this paper we present a robust tracking system that enables an autonomous vehicle to follow a specific convoy leader. Images from a single camera are used as input data, from which predefined keypoints on the lead vehicle are detected by a convolutional neural network. This approach was inspired by the idea of human pose estimation and is shown to be significantly more accurate compared to standard bounding box detection approaches like YOLO.The estimation of the dynamic state of the leading vehicle is realized by means of a moving horizon estimator. We show the practical capabilities and usefulness of the system in real-world experiments. The experiments show that the tracking system, although it only operates with images, is competitive with earlier approaches that also used other sensors such as LiDAR.
Decision Support System For Determining Exemplary Students Using SAW Method

In order to motivate students to continue to excel, MTs Al Falah undertakes activities to develop students' potential through determining exemplary students. However, the decision to determine exemplary students is not based on academic and non-academic abilities, but on the subjectivity of the principal and teachers. So that many complain about the decision of the selection of exemplary students who are not well targeted or deserve to be exemplary students. There is no information system that supports the determination of exemplary students on MTs Al Falah, It is less precise in determining the exemplary students on MTs Al Falah, decision support systems in Determination of the Exemplary Students using the Simple Additive Weighting (SAW) method is based on 5 criteria, namely the value of knowledge, the value of skills, class rank, extracurricular activity, extracurricular values. The results obtained will be in the form of exemplary student rankings. Then the student who gets the highest score in five categories with a percentage of 0.97 is Afifah Angelia Azhariyanti. The Simple Additive Weight method can help the school especially in determining a number of issues regarding education, one of which is to determine exemplary students. Because this method is a weighted method of rating the performance of each alternative.
On the Security of Randomized Defenses Against Adversarial Samples

Deep Learning has been shown to be particularly vulnerable to adversarial samples. To combat adversarial strategies, numerous defensive techniques have been proposed. Among these, a promising approach is to use randomness in order to make the classification process unpredictable and presumably harder for the adversary to control. In this paper, we study the effectiveness of randomized defenses against adversarial samples. To this end, we categorize existing state-of-the-art adversarial strategies into three attacker models of increasing strength, namely blackbox, graybox, and whitebox (a.k.a. adaptive) attackers. We also devise a lightweight randomization strategy for image classification based on feature squeezing, that consists of pre-processing the classifier input by embedding randomness within each feature, before applying feature squeezing. We evaluate the proposed defense and compare it to other randomized techniques in the literature via thorough experiments. Our results indeed show that careful integration of randomness can be effective against both graybox and blackbox attacks without significantly degrading the accuracy of the underlying classifier. However, our experimental results offer strong evidence that in the present form such randomization techniques cannot deter a whitebox adversary that has access to all classifier parameters and has full knowledge of the defense. Our work thoroughly and empirically analyzes the impact of randomization techniques against all classes of adversarial strategies.
Overview of the NLPCC 2020 Shared Task: AutoIE

This is an overview paper of the NLPCC 2020 shared task on AutoIE, which aims to evaluate the information extraction solutions under low data resource. Given an unlabeled corpus, entity lists covering \(30\%\) entities in the corpus and some labeled validation samples, participants are required to build a named entity recognition system. There are 44 registered teams and 16 of them submitted results, the top system achieve 0.041 and 0.133 F1 score improvement upon the baseline system with or without labeled validation data respectively. The evaluation result indicates that it is possible to use less human annotation for information extraction system. All information about this task may be found at https://github.com/ZhuiyiTechnology/AutoIE.
MixLab: An Informative Semi-supervised Method for Multi-label Classification

Multi-label classification is an intensively studied topic in data analysis. In spite of the considerable improvements, recent deep learning-based methods overlook the existence of unlabeled data, which consumes too much time on instance annotation. To circumvent this difficulty, semi-supervised multi-label classification aims to exploit the readily-available unlabeled data to help build multi-label classification model. To make full use of labeled and unlabeled data, this paper propose a novel approach named MixLab, encourages the model classifications to be accurate with label-correlated information and consistency regularization. It utilizes label correlations to enhance predicted labels for augmented unlabeled instances as targets and regularizes predictions to be consistent with this targets. We empirically validate the effectiveness of our framework by extensive experiments on four real datasets of textual content.
Deep Learning Architectures and Tools

Deep learning is one of the popular machine learning strategies that learns in a supervised or unsupervised manner by forming a cascade of multiple layers of non-linear processing units. It is inspired by the way of information processing and communication pattern of the typical biological nervous system. The deep learning algorithms learn through multiple levels of abstractions and hierarchy of concepts; as a result, it is found to be more efficient than the conventional non-deep machine learning algorithms. This chapter explains the basics of deep learning by highlighting the necessity of deep learning over non-deep learning. It also covers discussion on several recently developed deep learning architectures and popular tools available in market for deep learning, which includes Tensorflow, PyTorch, Keras, Caffe, Deeplearning4j, Pylearn2, Theano, CuDDN, CUDA-Convnet, and Matlab.
An experimental evaluation method for deformation measurement of large satellite antenna based on photogrammetry

Photogrammetry technology has been gradually applied in the development of large satellite antenna, and the experimental evaluation method for deformation measurement of large satellite antenna proposed in this paper is derived from the development task of large flexible antenna, which needs to conduct reflector measurement in a vacuum and cryogenic environment based on the principle of photogrammetry.Therefore, it is necessary to establish an evaluation method for large satellite antenna deformation measurement based on photogrammetry.The repeatability and accuracy of the measurement results can be judged by the method.In this paper, a method of measuring repeatability and measuring accuracy is proposed, and the measurement results are evaluated by defining the concepts of single point measuring evaluation factor and length measuring evaluation factor.This evaluation method has been successfully applied to the measurement of large antenna deformation in vacuum and cryogenic environment.
Evaluation of Similarity Measures in a Benchmark for Spanish Paraphrasing Detection

In this paper, we present a similarity-based approach towards paraphrase detection in Spanish. We evaluate various models for semantic similarity computation using a gold-standard paraphrase corpus. It contains one original document and paraphrased documents on different levels (low and high), and reference documents on the same topic or same vocabulary. It allows to assess the similarity between a pair of texts or individual sentences. We found that some of the similarity metrics have a larger difference when comparing paraphrased sentences than others. Finally, we obtained a threshold for each of the similarity metrics with the aim of determining a classification boundary to decide if two sentences are paraphrased.
Clinical Decision Support Systems in Practice: Current Status and Challenges

Decision support systems (DSS) are computer programs based on artificial intelligence methods that contribute to reaching a correct decision in an often-narrow domain of interest. Clinical decision support systems (CDSS) are such DSSs that may be used by medical professionals in clinics and hospitals. They are used for diagnosis, treatment protocol recommendations, treatment outcome predictions and other tasks. CDSS are constructed based on symbolic and machine learning (including deep learning) approaches to represent and infer medical knowledge. The aim of this work is to provide an overview of past and current methods in designing a successful CDSS. The study considers the systems that were claimed to be implemented in clinical practice. Currently, the development of a CDSS is mostly pursued in two directions: 1) a more traditional approach based on rules, ontologies, probabilistic models, and the use of standards; 2) machine learning based approach. Both approaches may be used complementary within a healthcare information system. This work seeks to provide an objective view on the advantages and limitations of the approaches as well to discuss future research avenues that could lead to more accurate and trustworthy CDSS and improved healthcare.
Video Captioning Using Attention Based Visual Fusion with Bi-temporal Context and Bi-modal Semantic Feature Learning

Video captioning is a recent emerging task that describes a video through generating a natural language sentence. Practically videos are untrimmed where both localizing and describing the event of interest is crucial for many vision based- real life applications. This paper proposes a deep neural network framework for effective video event localization through using a bidirectional Long Short Term Memory (LSTM) that encodes past, current and future context information. Our framework adopts an encoder decoder network that accepts the event proposal with highest temporal intersection with ground truth for captioning. Our encoder is fed with attentively fused visual features, extracted by a two stream 3D convolution neural network, along with the proposal’s context information for generating an effective representation. Our decoder accepts learnt semantic features that represent bi-modal (two modes) high-level semantic concepts. We conduct experiments to demonstrate that utilizing both semantic features and contextual information provides better captioning performance.
Evolving Connections in Group of Neurons for Robust Learning

Artificial neural networks inspired from the learning mechanism of the brain have achieved great successes in machine learning, especially those with deep layers. The commonly used neural networks follow the hierarchical multilayer architecture with no connections between nodes in the same layer. In this article, we propose a new group architectures for neural-network learning. In the new architecture, the neurons are assigned irregularly in a group and a neuron may connect to any neurons in the group. The connections are assigned automatically by optimizing a novel connecting structure learning probabilistic model which is established based on the principle that more relevant input and output nodes deserve a denser connection between them. In order to efficiently evolve the connections, we propose to directly model the architecture without involving weights and biases which significantly reduce the computational complexity of the objective function. The model is optimized via an improved particle swarm optimization algorithm. After the architecture is optimized, the connecting weights and biases are then determined and we find the architecture is robust to corruptions. From experiments, the proposed architecture significantly outperforms existing popular architectures on noise-corrupted images when trained only by pure images.
DPODA: Differential Privacy-based Online Double Auction for Pervasive Edge Computing Resource Allocation

In recent years, edge computing transforms the network edge into an intelligent platform to provide better services for users by making storage, computing, control and network function closer to end-users, things, and sensors. However, due to the limitation of edge resources and the diversity of edge resource providers, there is no good mechanism to solve the resource allocation problem in the edge computing resource allocation market. Aiming at the problem of resource allocation in edge computing, this paper proposes a double auction scheme-DPODA. This scheme establishes the auction market of edge computing resources, which can improve the social welfare of auction to the maximum extent. Moreover, as the private information of users in the auction market may affect market transactions, in order to solve this problem, this paper uses differential privacy technology to effectively protect the privacy of both sides of the auction, so as to avoid the unfairness in the auction market. A large number of simulation experiments show that the algorithm proposed in this paper not only achieves the research purpose but also ensures the privacy information security of the auctioneer.
Norm-Referenced Achievement Grading: Methods and Comparison

Grading informs learners and instructors of both current learning ability levels and necessary improvement. For norm referenced grading, the instructors conventionally use a statistical method. This paper proposes an algorithm for the norm referenced grading. Moreover, the rise of artificial intelligence nowadays makes us curious how a machine learning technique is efficient in the norm referenced grading. We therefore compare the statistical method and our algorithm with the machine learning method. The experiment relies on the data sets of both normal and skewed distributions. The comparative evaluation reveals that our algorithm and the machine learning method yield similar grading results in several cases. On the other hand, in overall, the algorithm, machine learning, and statistical methods produce the best, moderate, and lowest grading qualities, respectively.
Authenticity verification on social data outsourcing

In the social data transaction model, online social networks sell their collected social data to a third-party data service provider, which commonly resells such social data to data users for further mining potential information. However, the data service provider may not be trustworthy and collude with others to return fake data to users. To prevent such malicious activities, data users should verify the correctness and completeness of social data purchased from the data service provider to make sure that no data would tamper and no qualifying results would be omitted. Accordingly, we first propose an authenticity verification scheme, called FakeDetection, for one-dimensional data query. To make our scheme becoming efficient, we further devise an enhanced probabilistic scheme FakeDetection+, which takes partial vertices and neighbors with the identical profile value to generate auxiliary information. To evaluate the efficiency of our schemes, we utilize the real Twitter datasets with 1.6M twitters to perform the experiments. The Twitter with FakeDetection+, takes 69K edges (47M in FakeDetection) into account to detect fake activities with a probability of more than 99%. For the computation overhead, the FakeDetection+ scheme only consumes 27.9s (3.8% of that in FakeDetection) to generate auxiliary information for a social graph with 1.6M twitters and their social network.