Reviewerly/exHarmony huggingface dataset:
Separation method of superimposed gratings in double-projector structured-light vision 3D measurement system

In structured-light vision (SLV) 3D measurement technology, double-projector SLV is often used to solve the defects in single projector SLV, such as the shadow caused by the self-occlusion of the measured object’s surface profile and the limitation of the projector’s visual angle. Usually, to avoid gratings’ superimposition, multiple projectors cannot work at the same time. In this paper, we design a new separation method to separate phase-shifted superimposed gratings by controlling the sequence in double-projector SLV. Without changing the original measurement system, the four-step phase-shifted method and dual-frequency method are combined to solve the unwrapped phase. Based on the proposed separation method, the wrapped phases of left and right projections are obtained only by six phase-shifted gratings, whose efficiency is improved by 25%. The separation method includes the process of averaging phase patterns, which improves the phase accuracy of the measured object, comparing with the single projector measurement. Experiments demonstrate that this method can separate phase-shifted superimposed gratings simply, quickly, effectively and accurately without additional hardware devices. This method is practical and general.
Agile Domain Adaptation

Domain adaptation investigates the problem of leveraging knowledge from a well-labeled source domain to an unlabeled target domain, where the two domains are drawn from different data distributions. Because of the distribution shifts, different target samples have distinct degrees of difficulty in adaptation. However, existing domain adaptation approaches overwhelmingly neglect the degrees of difficulty and deploy exactly the same framework for all of the target samples. Generally, a simple or shadow framework is fast but rough. A sophisticated or deep framework, on the contrary, is accurate but slow. In this paper, we aim to challenge the fundamental contradiction between the accuracy and speed in domain adaptation tasks. We propose a novel approach, named agile domain adaptation, which agilely applies optimal frameworks to different target samples and classifies the target samples according to their adaptation difficulties. Specifically, we propose a paradigm which performs several early detections before the final classification. If a sample can be classified at one of the early stage with enough confidence, the sample would exit without the subsequent processes. Notably, the proposed method can significantly reduce the running cost of domain adaptation approaches, which can extend the application scenarios of domain adaptation to even mobile devices and real-time systems. Extensive experiments on two open benchmarks verify the effectiveness and efficiency of the proposed method.
Enhanced Matching Network for Multi-turn Response Selection in Retrieval-Based Chatbots

Semantic representation and dependency information are of great importance for matching a response with its multi-turn context. In this paper, we propose an enhanced matching network (EMN) to enhance the matching ability for the multi-turn response selection system in terms of both constructing semantic representation and extracting dependency information. First, the commonly used recurrent neural network (RNN) is replaced with gated convolutional neural network (GCNN) in the matching network to construct more expressive semantic representations of sentences. Second, local inference modeling and inference composition in the enhanced sequential inference model (ESIM) are utilized here to capture enhanced interactive information between the response and each utterance in the context. Finally, EMN is based on a similar multi-turn structure to sequential matching network (SMN) for extracting turns' dependency information in the chronological order. We furthermore propose a combined model (EMN-SMN) to integrate SMN into EMN for distilling more important dependency information from sentence pairs. Experiments are carried out on Ubuntu Corpus and Douban Conversation Corpus. The results show that EMN can outperform the state-of-the-art methods and the combined model can further improve overall performance.
Fast cubature of high dimensional biharmonic potential based on approximate approximations

We derive new formulas for the high dimensional biharmonic potential acting on Gaussians or Gaussians times special polynomials. These formulas can be used to construct accurate cubature formulas of an arbitrary high order which are fast and effective also in very high dimensions. Numerical tests show that the formulas are accurate and provide the predicted approximation rate $$O(h^8)$$ up to the dimension $$10^7$$ .
Tackling online fraud

Online fraud continues to be one of the most challenging forms of cybercrime. It is an extremely varied species of crime, with multiple examples and definitions. The prevalence of online fraud is so vast that law enforcement and financial bodies can quickly become swamped. The EU recognises the threat that online fraud poses to the Union and has passed a new Directive that seeks to tackle online fraud. However, the Directive misses many forms of online fraud, potentially leaving citizens of Europe vulnerable to online fraud.
A Position-aware Transformation Network for Aspect-level Sentiment Classification

This paper introduce a novel Position-aware Transformation Network (PTNet) for aspect-level sentiment classification. On the one hand, attention mechanisms have been employed to model the relationship between aspect and context. However, the position information of aspect words is rarely emphasized for sentiment prediction. The truth is that we should pay more attention to the word which is close to the aspect, since the words with closer distance may have a greater impact on the sentiment polarity of a sentence toward the aspect. On the other hand, existing approaches often adopt the average of aspect vectors or context vectors to calculate the attention weights, which may cause information loss if the aspect and context is not a single word. Therefore, this paper propose a position-aware layer and a context transformation layer in our model to solve the above two issues respectively. Moreover, several convolution kernels are also used to extract the n-gram information for prediction. We examine the performance of our model on three datasets: the first two are from SemEval2014 including the reviews of restaurants and laptops, and the third is a tweet collection. Experimental results show that our model consistently outperforms the state-of-the-art methods on all three datasets.
Segmentation-driven Hierarchical RetinaNet for Detecting Protozoa in Micrograph

Protozoa detection and identification play important roles in many practical domains such as parasitology, scientific research, biological treatment processes, and environmental quality evaluation. Traditional laboratory methods for protozoan identification are time-consuming and require expert knowledge and expensive equipment. Another approach is using micrographs to identify the species of protozoans that can save a lot of time and reduce the cost. However, the existing methods in this approach only identify the species when the protozoan are already segmented. These methods study features of shapes and sizes. In this work, we detect and identify the images of cysts and oocysts of various species such as: Giardia lamblia, Iodamoeba butschilii, Toxoplasma gondi, Cyclospora cayetanensis, Balantidium coli, Sarcocystis, Cystoisospora belli and Acanthamoeba, which have round shapes in common and affect human and animal health seriously. We propose Segmentation-driven Hierarchical RetinaNet to automatically detect, segment, and identify protozoans in their micrographs. By applying multiple techniques such as transfer learning, and data augmentation techniques, and dividing training samples into life-cycle stages of protozoans, we successfully overcome the lack of data issue in applying deep learning for this problem. Even though there are at most 5 samples per life-cycle category in the training data, our proposed method still achieves promising results and outperforms the original RetinaNet on our protozoa dataset.
Authentication System Based on Hand Writing Recognition

In this paper, a proposed method to authentication system based on a hand writing recognition without segmentation to sub letters based on feature extraction Speed Up Robust Features (SURF) and Support Vector Machine (SVM) to enhance the accuracy. After feature extraction, the result can be clustered by using the K-means algorithm to standardize the number of features. These two operations together (feature extraction and feature clustering) called Bag of Word (BOW). It converts the arbitrary number of image features to uniform length features vector. The proposed method experimented using two databases; the standard (IFN/ENIT) database and the proposed database consist of 30 different writers with 70 margins images. The proposed system achieves (96.67%) recognition rate for IFN/IENT database.
Automatic Identification of Critical Digital Assets

Within the nuclear reactor domain, many of the assets which were once analog are being phased out and replaced with digital assets. The ability to automatically identify which of these digital assets are also critical assets as defined by the Nuclear Regulatory Commission (NRC) is an important step in building an effective cybersecurity program for the nuclear power domain. This paper presents an approach to automatically identify these critical digital assets. Three variations of this approach are presented in this paper. The runtime of these three implementations is obtained to demonstrate how each scales as network model sizes for nuclear reactors increase.
Optimization of trajectory durations based on flow rate scaling for a 4-DoF semi-automated hydraulic rockreaker

In order to increase the overall efficiency of mines as well as improve worker safety, efforts continue to be made toward the automation of various mining operations. Rock breaking, whereby large pieces of ore are broken down into smaller pieces prior to being sent to a jaw crusher, is one such operation. In this paper, a trajectory planning algorithm destined for use in a semi-automated rockbreaker system is described. The algorithm’s development is driven by the objective of minimizing the duration of rockbreaker motions. Paths between initial and final configurations are fully prescribed in the rockbreaker’s task space to allow for the (future) seamless integration of collision detection algorithms. The time parameterization of these paths is then performed using motion profiles that ensure continuity at the acceleration level. Continuous flow rate scaling of the trajectory is applied in order to fully exploit the flow rate capacities of the rockbreaker’s hydraulic pump and proportional valves, resulting in near time-optimal trajectories. The proposed trajectory planning algorithm is shown to lead to significant decreases in trajectory durations compared to other proposed approaches.
Gated Neural Network with Regularized Loss for Multi-label Text Classification

Multi-label text classification is generally more difficult for its exponential output label space and more flexible input document considering its corresponding number of labels. We observed that some long documents have only one or two labels while some short documents are related to much more labels. In this paper, we propose a dynamic Gated Neural Network architecture to simultaneously process the document through two parts, one to extract the most informative semantics and filter redundant information, the other to capture context semantics with most of the information in the document kept. And semantics from these two parts are dynamically controlled by a gate to perform subsequent classification. And to better the training we incorporate label dependencies into traditional binary cross-entropy loss by exploiting label co-occurrences. Experimental results on AAPD and RCV1-V2 datasets show that our proposed methods achieve state-of-art performance. Further analysis of experimental results demonstrate that the proposed methods not only capture enough feature information from both long and short documents assigned with various labels, but also exploit label dependency to regularize the proposed model to further improve its performance.
Learning Personalized End-to-End Task-Oriented Dialogue Generation

Building personalized task-oriented dialogue system is an important but challenging task. Significant success has been achieved by selecting the responses from the pre-defined template. However, preparing massive response template is time-consuming and human-labor intensive. In this paper, we propose an end-to-end framework based on the memory networks for responses generation in the personalized task-oriented dialog system. The static attention mechanism is used to encode the user-conversation relationship to form a global vector representation, and the dynamic attention mechanism is used to obtain import local information during the decoding phase. In addition, we propose a gating mechanism to incorporate user information into the network to enhance the personalized ability of the response. Experiments on the benchmark dataset show that our model achieves better performance than the strong baseline methods in personalized task-oriented dialogue generation.
Combination of Term Weighting and Integrated Color Intensity Co-occurrence Matrix for Two-Level Image Retrieval on Social Media Data

This paper proposes the two-level image retrieval that combines the text and image-based, to overcome the disadvantage of text or image as a query for image retrieval. In the text-based retrieval, three main steps are required. First, text and document pre-processing to retrieve words without affix, punctuation, and any stop words, to build the dictionary. Second, weighting the word from the dictionary, based on the frequency of words in text or document, using the Term Frequency-Inverse Document Frequency Model. Third, the similarity between a text query and the text document is calculated using Cosine Similarity. In the image-based approach for image retrieval, two main steps are required. First, feature extraction using Integrated Color Intensity Co-occurrence Matrix. This method will obtain two features at once, texture and color feature. Second, the similarity is calculated between an image query and database using Manhattan Distance. Social Media Data, Twitter, with Indonesian tweet and users, is used for the experiments. Image retrieval using a text, an image, and combination of both text and image, are compared in the experiments. The conducted experiments showed that the combination of text and image-based retrieval achieved the highest performance accuracy, compare with text or image-based retrieval.
SpreadOut: A Kernel Weight Initializer for Convolutional Neural Networks

Convolutional Neural Networks are based on the hierarchical extraction of features over many layers, by using convolutional filters, or kernels. Each kernel is coded by a set of weights, which represents a feature to be extracted from the input. On a given layer, it is undesirable to have redundant kernels and the training algorithm must learn to differentiate them. SpreadOut is a kernel weight initializer that differentiates kernels before training, so as to improve convergence rates. It does so by maximizing a distance metric calculated over all pairs of kernels on the same layer, after weights are initialized by a traditional technique. We show that SpreadOut improves convergence rates on several benchmark datasets when compared to traditional approaches.
Evaluating and Enhancing the Robustness of Retrieval-Based Dialogue Systems with Adversarial Examples

Retrieval-based dialogue systems have shown strong performances on both consistency and fluency according to several recent studies. However, their robustness towards malicious attacks remains largely untested. In this paper, we generate adversarial examples in black-box settings to evaluate the robustness of retrieval-based dialogue systems. On three representative retrieval-based dialogue models, our attacks reduce R $$_{10}@1$$ by 38.3 $$\%$$ , 45.0 $$\%$$ and 31.5 $$\%$$ respectively on the Ubuntu dataset. Moreover, with adversarial training using our generated adversarial examples, we significantly improve the robustness of retrieval-based dialogue systems. We conduct thorough analysis to understand the robustness of retrieval-based dialog systems. Our results provide new insights to facilitate future work on building more robust dialogue systems.
Multi-Scale Adaptive Level Set Segmentation Method Based on Saliency

Image segmentation is an important research of computer vision. Due to the effects of intensity inhomogeneity, target edge and background complex, it is still challenging to achieve effective segmentation of target adaptively. To solve these issues, an image segmentation method based on saliency and level set is proposed in this paper. First, adaptive initial contour of level set is got by wavelet-based feature probability evaluation (WFPE) model, the initial contour is closer to the target contour, which can reduce background interference and evolve faster. Second, in order to realize the best detection of intensity mutation and locate the target edge more accurately, an edge constraint energy term is introduced with multi-scale information obtained by wavelet transform. Finally, to improve segmentation adaptability and speed, the region information and edge constraint energy term are merged into the adaptive active contour model, the final evolution curve evolves in coarse scale, and then interpolates to get the final segmentation contour. Experimental results show that the proposed method achieves high efficiency in the following aspects: adaptability to images, speed of evolution, close to human visual perception.
Unsupervised Feature Learning for Visual Place Recognition in Changing Environments

Visual place recognition in changing environments is a challenging and critical task for autonomous robot navigation. Deep convolutional neural networks (ConvNets) have recently been used as efficient feature extractors and obtained excellent performance in place recognition. However the success of ConvNets' learning highly relies on the availability of large datasets with millions of labeled images, the collection of which is a tedious and costly burden. Thus we develop an unsupervised learning method (the siamese VisNet) to autonomously learn invariant features in changing environments from unlabeled images. The siamese VisNet has two identical branches of sub-networks. With a Hebbian-type of learning rule incorporating a trace of previous activity patterns, the siamese VisNet learns features with increasing invariance in changing environments from layer to layer. Experiments conducted on multiple datasets demonstrate the robustness of the siamese VisNet against viewpoint changes, appearance changes, and joint viewpoint-appearance changes. In addition, the siamese VisNet, with lower complexity in architecture, outperforms the state-of-the-art place recognition ConvNets such as the CaffeNet and the PlaceNet. The proposed siamese VisNet constitutes a biologically plausible yet efficient method for unsupervised place recognition.
Private Security for the Cloud Mobile via a Strong Authentication Method

In recent years, many consumers are storing a large amount of data in the cloud, so the security of this data is very important for users and companies. The companies need to provide secure data for consumers and to offer resource access only when it is needed. For this purpose, consumers require a much higher degree of authentication, such as strong authentication, comparing with the simple couple identifier and password based identification. In this work, we will study and propose a new strong authentication security method based on One Time Password (OTP) for mobile cloud. The proposed method will be based on images selection by the users, which guarantees a secure access and authentication for services usage, while taking into account the user’s preferences. We will also present and discuss experimental results, which illustrate a practical implementation of the proposed method and its benefits.
Creation of Optimal Materialized Views Using Bitmap Index and Firefly Algorithm in Data Warehouse

A sensitive issue in designing the data warehouse is using some mechanics like summary tables (materialized views) and indexes for speeding up the query processing time. Due to storage space constraints, it is impossible to materialize all views in a data warehouse; therefore, many researchers have proposed various methodologies aiming to solve this problem by selecting the optimal set of materialized views. In this paper, we define a new efficient framework to create optimal materialized views depending on three factors (MV processing time, MV storage space and MV access frequency) by applying aggregations over bitmap index and select optimal one using Firefly algorithm (FA) for optimizing complex queries performance and reducing queries response time. The results of the proposed system show that bitmap index performs best results for aggregation queries where the aggregations response time on one field in our DW selected directly to base tables was found to be 757 milliseconds whereas the response time of these aggregations on the same field over bitmap index was found to be 489 milliseconds. Also, results prove that the firefly algorithm was effective in term of creating optimal materialized view time.
Parallel External Memory Wavelet Tree and Wavelet Matrix Construction

We present the first parallel external memory wavelet tree and matrix construction algorithm. The algorithm’s throughput is nearly the same as the hard disk drives’ throughput, using six cores. We also present the fastest (parallel) semi-external construction algorithms for both wavelet trees and matrices.
Binary Optimisation with an Urban Pigeon-Inspired Swarm Algorithm

In this paper we introduce a metaheuristic for optimisation of discrete binary problems, derived from a recently proposed particle swarm algorithm inspired on the foraging behaviour of urban pigeons. The new variant of the algorithm is obtained by mapping the real–valued search space of the original version into a discrete binary–valued encoding. We illustrate the feasibility of the method on several binary benchmark problems and we study the impact of different running parameters such as problem dimension, population size and maximum number of evaluations. The potential of the method and possible extensions for improvement are also discussed.
Robust Semantic Video Segmentation through Confidence-based Feature Map Warping

One of the limiting factors when using deep learning methods in the field of highly automated driving is their lack of robustness. Objects that suddenly appear or disappear from one image to another due to inaccurate predictions as well as occurring perturbations in the input data can have devastating consequences. A possibility to increase model robustness is the use of temporal consistency in video data. Our approach aims for a confidence-based combination of feature maps that are warped from previous time stages into the current one. This enables us to stabilize the network prediction and increase its robustness against perturbations. In order to demonstrate the effectiveness of our approach, we have created a test data set with image perturbations such as image artifacts and adversarial examples in which we significantly outperform the baseline.
Data Collection and Preparation of Training Samples for Problem Diagnosis of Vision Pathologies

The paper presents an approach and a methodology that were used to collect and store training samples. Preliminary processing in the decision support system for diagnosing complex vision pathologies is described. A training sample consists of various records of biopotentials obtained from a special medical device.