Reviewerly/exHarmony huggingface dataset:
Evaluating Conjunction Disambiguation on English-to-German and French-to-German WMT 2019 Translation Hypotheses

We present a test set for evaluating an MT system’s capability to translate ambiguous conjunctions depending on the sentence structure. We concentrate on the English conjunction “but” and its French equivalent “mais” which can be translated into two different German conjunctions. We evaluate all English-to-German and French-to-German submissions to the WMT 2019 shared translation task. The evaluation is done mainly automatically, with additional fast manual inspection of unclear cases. All systems almost perfectly recognise the target conjunction “aber”, whereas accuracies for the other target conjunction “sondern” range from 78% to 97%, and the errors are mostly caused by replacing it with the alternative conjunction “aber”. The best performing system for both language pairs is a multilingual Transformer “TartuNLP” system trained on all WMT 2019 language pairs which use the Latin script, indicating that the multilingual approach is beneficial for conjunction disambiguation. As for other system features, such as using synthetic back-translated data, context-aware, hybrid, etc., no particular (dis)advantages can be observed. Qualitative manual inspection of translation hypotheses shown that highly ranked systems generally produce translations with high adequacy and fluency, meaning that these systems are not only capable of capturing the right conjunction whereas the rest of the translation hypothesis is poor. On the other hand, the low ranked systems generally exhibit lower fluency and poor adequacy.
Is it feasible to teach query programming in three different languages in a single session?

Undergraduates and postgraduates in science subjects are increasingly expected to conduct their data analyses using R, SQL and Python. This requires of instructors to develop resources that get students up and running quickly. This study presents and evaluates a learning design that (1) uses a pattern-oriented tutorial to teach language-independent key operations for implementing data analytic queries, and (2) uses cheat sheets to show how these operations map onto language-specific syntax. The evaluation study (N=21) concludes that using this approach, two thirds of the data science novices sampled could implement simple to moderately complex queries in all the aforementioned languages within two hours. A permutation test moreover produced a significant main effect of language, with SQL ranking the highest in accuracy. The results form part of a general discussion on the merits and language-dependent feasibility of pattern-oriented aids for accelerated data science instruction.
Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components

Recent work on overfitting Bayesian mixtures of distributions offers a powerful framework for clustering multivariate data using a latent Gaussian model which resembles the factor analysis model. The flexibility provided by overfitting mixture models yields a simple and efficient way in order to estimate the unknown number of clusters and model parameters by Markov chain Monte Carlo (MCMC) sampling. The present study extends this approach by considering a set of eight parameterizations, giving rise to parsimonious representations of the covariance matrix per cluster. A Gibbs sampler combined with a prior parallel tempering scheme is implemented in order to approximately sample from the posterior distribution of the overfitting mixture. The parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm. The contributed method and software are demonstrated and compared to similar models estimated using the Expectation-Maximization algorithm on simulated and real datasets. The software is available online at https://CRAN.R-project.org/package=fabMix.
The Hitchhiker’s Guide to Data in the History of Science

Every project in digital and computational history of science starts with the collection of data. Depending on the research project, the subject of study, and other factors, data can comprise a variety of different types, including full texts, images, audio, video, and bibliographic metadata. Publications and project reports generally describe their results and the methods and algorithms employed, but few discuss the challenges of the initial data collection process or how it fits into the overall research data life cycle. This essay discusses a concrete research data life cycle and takes a look at the difficulties it involves. It also explores the strategies and challenges of data collection and the question of the comparability of datasets.
The relational processing limits of classic and contemporary neural network models of language processing

Whether neural networks can capture relational knowledge is a matter of long-standing controversy. Recently, some researchers have argued that (1) classic connectionist models can handle relational structure and (2) the success of deep learning approaches to natural language processing suggests that structured representations are unnecessary to model human language. We tested the Story Gestalt model, a classic connectionist model of text comprehension, and a Sequence-to-Sequence with Attention model, a modern deep learning architecture for natural language processing. Both models were trained to answer questions about stories based on abstract thematic roles. Two simulations varied the statistical structure of new stories while keeping their relational structure intact. The performance of each model fell below chance at least under one manipulation. We argue that both models fail our tests because they can't perform dynamic binding. These results cast doubts on the suitability of traditional neural networks for explaining relational reasoning and language processing phenomena.
A Lightweight Neural Network For Crowd Analysis Of Images With Congested Scenes

For images with congested scenes, the task of crowd analysis, including crowd counting and crowd distribution prediction, becomes very difficult. To address these issues, various CNN-based approaches have been proposed. However, those methods usually have a large number of parameters and require huge computing resources. In this paper, we focus on low-complexity approaches and propose a lightweight endto-end network for crowd analysis. Our method utilizes an effective scale-aware module to extract multi-scale features and then regresses these features to density maps. The proposed network is consisted by three parts: multi-scale feature extraction, density map estimation and density map correction, and the network which only contains 0.86 M parameters (Lightweight). According to our experiments, our proposal obtain a better result than other existing methods on several testing sequences.
Tweet sentiment analysis using deep learning with nearby locations as features

Twitter classification using deep learning have shown a great deal of promise in recent times. Many works had been performed on twitter sentiment analysis but there has not been much work done investigating the effects of location on twitter sentiment analysis. In this study, we concatenated text and location features as a feature vector for twitter sentiment analysis using a deep learning classification approach specifically Convolutional Neural Network (CNN). The achieved results show that using location as a feature alongside text has increased the sentiment analysis accuracy.
Hierarchical Features Fusion for Image Aesthetics Assessment

Image aesthetics assessment is an interesting yet challenging topic which can be applied on numerous scenarios such as high quality image retrieval or recommendation systems. We propose a hierarchical features fusion aesthetic assessment (HFFAA) model for this task. HFFAA is a two-stream convolutional neural network (CNN) which is composed of two branches with heterogeneous and complementary aesthetic perceptual abilities. HFFAA learns the mapping from deep image representation into their ground-truth aesthetic labels (good or bad) in an end-to-end fashion. Extensive experiments demonstrate that the proposed model achieves superior performance on two widely evaluated public benchmark databases, i.e., CUHKPQ and AVA. We also validate the rationality of the designs of HFFAA through a series of ablation experiments.
Optimizing Multi Objective Based Dynamic Workflow Using ACO and Black Hole Algorithm in Cloud Computing

In cloud computing environment many scheduling algorithm are available to provide task scheduling easy, scalable and make task execution fast. In this paper we deal with many scheduling algorithm and comparative study of this task scheduling algorithm. These task scheduling algorithms are used by many cloud service providers and various researchers for static and dynamic task scheduling of many applications. Task scheduling primarily focuses on efficient utilization of resource and reduce the task Compilation time. Many different techniques are used to solve this problem of task scheduling. Here we provide propose flow for optimizing multi objective based dynamic workflow using two scheduling algorithms Ant Colony Optimization and Black Hole Algorithm to reduce the makespan and total execution cost to the user. And try to evaluate the best scheduling result.
Artificial Intelligence in Cybersecurity: The Use of AI Along the Cyber Kill Chain

The current challenge with defense against cyberattacks is that the speed and quantity of threats often outpace human-centered cyber defense capabilities. That is why a new Artificial Intelligence driven approach may enhance the effectiveness of security controls. However, it can also be used by adversaries to create more sophisticated and adaptable attack mechanisms. Distinguishing three key AI capabilities (knowledge acquisition, human-like perception and decision making), the goal of this paper is to assert where within the cyber kill chain have AI capabilities already been applied, and which phase holds the greatest near-term potential given recent developments and publications. Based on literature review, authors see the strongest potential for deploying AI capabilities during the reconnaissance, intrusion, privilege escalation and data exfiltration steps of the cyber kill chain with other uses being deployed in the remaining steps.
Deep CNN-Based Recognition of JSL Finger Spelling

In this paper, we present a framework for recognition of static finger spelling in Japanese Sign Language on RGB images. The finger spelled signs were recognized by an ensemble consisting of a ResNet-based convolutional neural network and two ResNet quaternion convolutional neural networks. A 3D articulated hand model has been used to generate synthetic finger spellings and to extend a dataset consisting of real hand gestures. Twelve different gesture realizations were prepared for each of 41 signs. Ten images have been rendered for each realization through interpolations between the starting and end poses. Experimental results demonstrate that owing to sufficient amount of training data a high recognition rate can be attained on images from a single RGB camera. Results achieved by the ResNet quaternion convolutional neural network are better than results obtained by the ResNet CNN. The best recognition results were achieved by the ensemble. The JSL-rend dataset is available for download.
Secret key rates of free‐space optical continuous‐variable quantum key distribution

Summary In this letter, we derive the maximal achievable secret key rates for continuous‐variable quantum key distribution (CVQKD) over free‐space optical (FSO) quantum channels. We provide a channel decomposition for FSO‐CVQKD quantum channels and study the SNR (signal‐to‐noise ratio) characteristics. The analytical derivations focus particularly on the low‐SNR scenarios. The results are convenient for wireless quantum key distribution and for the quantum Internet.
Optimality of SVM: Novel proofs and tighter bounds

We provide a new proof that the expected error rate of consistent support vector machines matches the minimax rate (up to a constant factor) in its dependence on the sample size and margin. The upper bound was originally established by [1], while the lower bound follows from an argument of [2] together with reasoning about the VC dimension of large-margin classifiers. Our proof differs from the original in that many of our steps concern reasoning about the primal space, while the original carried out these steps by reasoning about the dual space. Our approach provides a unified framework for analyzing both the homogeneous and non-homogeneous cases, with slightly better results for the former. The fact that our analysis explicitly handles the non-homogeneous case offers significant improvements in the bounds compared to the usual textbook approach of reducing to the homogeneous case. We also extend our proof to provide a new upper bound on the error rate of transductive SVM, which yields an improved constant factor compared to inductive SVM. In addition to these bounds on the expected error rate, we also provide a simple proof of a margin-based PAC-style bound for support vector machines, and an extension of the agnostic PAC analysis that explicitly handles the non-homogeneous case.
Seneca: Fast and Low Cost Hyperparameter Search for Machine Learning Models

The goal of our work is to simplify and expedite the construction and evaluation of machine learning models using autoscaled cloud computing resources. To enable this, we develop an open source system called Seneca, which leverages the serverless programming model and its implementation in Amazon Web Services (AWS) Lambda. Seneca takes a machine learning application, dataset, and a list of possible hyperparameter options as input and automatically constructs an AWS Lambda function. The function ingresses and splits the input dataset into training and testing subsets and constructs, tests, and evaluates (i.e. scores) a machine learning model for a given set of hyperparameter values. Seneca concurrently invokes functions for all combinations of the hyperparameters specified. It then returns the configuration (or model) that results in the best score to the user. In this paper, we overview the design and implementation of Seneca, and empirically evaluate its performance for a popular classification application.
A Malicious Web Site Identification Technique Using Web Structure Clustering

Epidemic cyber incidents are caused by malicious websites using exploit kits. The exploit kit facilitate attackers to perform the drive-by download (DBD) attack. However, it is reported that malicious websites using an exploit kit have similarity in their website structure (WS)-trees. Hence, malicious website identification techniques leveraging WS-trees have been studied, where the WS-trees can be estimated from HTTP traffic data. Nevertheless, the defensive component of the exploit kit prevents us from capturing the WS-tree perfectly. This paper shows, hence, a new WS-tree construction procedure by using the fact that a DBD attack happens in a certain duration. This paper proposes, moreover, a new malicious website identification technique by clustering the WS-tree of the exploit kits. Experiment results assuming the D3M dataset verify that the proposed technique identifies exploit kits with a reasonable accuracy even when HTTP traffic from the malicious sites are partially lost.
Context-Aware Automatic Occlusion Removal

Occlusion removal is an interesting application of image enhancement, for which, existing work suggests manually-annotated or domain-specific occlusion removal. No work tries to address automatic occlusion detection and removal as a context-aware generic problem. In this paper, we present a novel methodology to identify objects that do not relate to the image context as occlusions and remove them, reconstructing the space occupied coherently. The proposed system detects occlusions by considering the relation between foreground and background object classes represented as vector embeddings, and removes them through inpainting. We test our system on COCO-Stuff dataset and conduct a user study to establish a baseline in context-aware automatic occlusion removal.
Face merged generative adversarial network with tripartite adversaries

With the development of deep learning, the accuracy of face recognition of learning based approaches has even exceeded that of humans in some circumstances. However, identifying faces that are heavily deflected is still a challenging issue. To synthesize the frontal view from large-pose faces, a face merged generative adversarial network (FM-GAN) equipped with two generators and one discriminator is proposed in this paper. Generally, generative adversarial network (GAN) has only one generator and one discriminator to compete with each other to make the network convergence. By introducing an additional generator, the confrontation power is greatly enhanced and thus the performance of the designed model is improved. In the proposed framework, the first generator learns the upper and lower parts of a face to capture essential facial features. These high-dimensional information of the merged face together with the multi-scale encoded profile are transmitted into the second generator. Based on the competition with the discriminator, the final frontal face is synthesized by the second generator. To reduce the model complexity, FM-GAN only encodes the original image once through a pre-trained network, and the extracted features are shared by the two decoders. In addition, the first generator simultaneously produces the upper and lower parts of a face by the same encoder. Therefore, only the parameters of two decoders and one discriminator are required to be updated during the training process. The experimental results show that the frontal faces synthesized by our model can better preserve the facial identity and photorealistic than by some existing GANs.
A Malfunction Detection Method For PV Systems

Worldwide solar energy production increased in the recent years with the rapid population of the rooftop PV systems. This growth caused operational and planning problems due to the uncertainty of the solar power-based generation. Therefore, proper monitoring of solar generations is critical for distribution system operators, as the operators use solar generation data collected from the field, to make accurate forecasts. In case of a malfunction, the system operator should be aware, and make the required modifications in the forecast process. On the other hand, PV system owners also require monitoring of their systems to take action in case of a malfunction. Considering that number of operational PV systems is very high, and occurrence of a malfunction is rare, use of automatic malfunction detection methods necessitates. This paper develops a method that detects any malfunction of a PV system. The malfunction may refer to failure of communication network, sensor malfunctions or data logging unit errors as well as problems of inverters and PV panels. The proposed method employs Deep Feed Forward Neural Network to predict solar generation data based on current weather conditions and solar radiation measurements, and compares the prediction with the measurements using the normalized residuals test. The proposed method is tested using real field data recorded at the Ayasli Research Center of the Middle East Technical University.
Improved Naive Bayes with optimal correlation factor for text classification

Naive Bayes (NB) estimator is widely-used in text classification problems. However, it does not perform well with small-size training datasets. Most previous literature focuses on either creating and modifying features or combing clustering to improve the performance of NB. We directly tackle the problem by constructing a new estimator, called Naive Bayes with correlation factor. We introduce a correlation factor to NB estimator that incorporates overall correlation among the different classes. This effectively exploits the idea of bootstrapping, which reuses data for all classes even if they only belong to one class. Moreover, we obtain a formula for the optimal correlation factor by balancing bias and variance of the estimator. Experimental results on real-world data show that our estimator achieves better accuracy compared with traditional Naive Bayes, yet at the same time maintaining the simplicity of NB.
Visual tracking via dynamic weighting with pyramid-redetection based Siamese networks

Siamese network based similarity-learning algorithm is currently a significant branch of visual tracking. However, most of existing deep Siamese networks depend much on the offline-trained knowledge and always assume the same importance for different prediction views. In this paper, we first introduce a dynamic weighting module in Siamese framework, which could make the offline-trained network adapt to the current circumstance well and weight predictive response maps discriminatively. The thought stems from the basis that different maps have different predictive preference, which should not be treated equally. Secondly, in order to focus more on the accurate preference, we then introduce the residual structure to form the residual dynamic weighting module. Thirdly, we construct a simple online pyramid-redetection module to avoid local search and also consider the global viewpoint. Extensive experiments on both short-term and long-term tracking demonstrate that the proposed tracker possesses the competitive tracking performance over many mainstream state-of-the-art trackers.
Reconstruction of Privacy-Sensitive Data from Protected Templates

In this paper, we address the problem of data reconstruction from privacy-protected templates, based on recent concept of sparse ternary coding with ambiguization (STCA). The STCA is a generalization of randomization techniques which includes random projections, lossy quantization, and addition of ambiguization noise to satisfy the privacy-utility trade-off requirements. The theoretical privacy-preserving properties of STCA have been validated on synthetic data. However, the applicability of STCA to real data and potential threats linked to reconstruction based on recent deep reconstruction algorithms are still open problems. Our results demonstrate that STCA still achieves the claimed theoretical performance when facing deep reconstruction attacks for the synthetic i.i.d. data, while for real images special measures are required to guarantee proper protection of the templates.
Research on 3D Reconstruction Method Based on Laser Rotation Scanning

A three-dimensional reconstruction algorithm based on line laser rotation scanning is proposed. The rotating platform vision measurement system is designed. The system calibration method consisting of laser, camera and turntable is studied. The axis-eye calibration algorithm is introduced to register the point cloud in the same coordinate. In the system, the camera driven by the turntable is rotated to scan the target object for three-dimensional measurement. The method can obtain the point cloud information of the object by linear laser rotation scanning, and further recover the geometric shape of the three-dimensional visible surface of the object, which not only meets the requirements of the three-dimensional reconstruction target, but also verifies the feasibility and effectiveness of the system.