Reviewerly/exHarmony huggingface dataset:
ManyModalQA: Modality Disambiguation and QA over Diverse Inputs

We present a new multimodal question answering challenge, ManyModalQA, in which an agent must answer a question by considering three distinct modalities: text, images, and tables. We collect our data by scraping Wikipedia and then utilize crowdsourcing to collect question-answer pairs. Our questions are ambiguous, in that the modality that contains the answer is not easily determined based solely upon the question. To demonstrate this ambiguity, we construct a modality selector (or disambiguator) network, and this model gets substantially lower accuracy on our challenge set, compared to existing datasets, indicating that our questions are more ambiguous. By analyzing this model, we investigate which words in the question are indicative of the modality. Next, we construct a simple baseline ManyModalQA model, which, based on the prediction from the modality selector, fires a corresponding pre-trained state-of-the-art unimodal QA model. We focus on providing the community with a new manymodal evaluation set and only provide a fine-tuning set, with the expectation that existing datasets and approaches will be transferred for most of the training, to encourage low-resource generalization without large, monolithic training sets for each new task. There is a significant gap between our baseline models and human performance; therefore, we hope that this challenge encourages research in end-to-end modality disambiguation and multimodal QA models, as well as transfer learning.
An Adaptive Hierarchical Detection Method for Ship Targets in High-Resolution SAR Images

With the improvement of image resolution in synthetic aperture radars (SARs), sea clutter characteristics become more complex, which poses new challenges to traditional ship target detection missions. In this paper, to detect ship targets quickly and efficiently in a complex background, we propose an adaptive hierarchical detection method based on a coarse-to-fine mechanism. This method constructs a new visual attention mechanism to strengthen ship targets and obtain the candidate targets adaptively by the means dichotomy method. On this basis, the precise detection results of the targets are obtained using the speed block kernel density estimation method, which maintains constant false alarm characteristics. Compared with existing methods, the adaptive hierarchical detection method has simple, fast, and accurate characteristics. Experiments based on GF-III satellite and airborne SAR datasets are presented to demonstrate the effectiveness of the proposed method.
Continuous Tracking of Targets for Stereoscopic HFSWR Based on IMM Filtering Combined with ELM

High frequency surface wave radar (HFSWR) plays an important role in marine surveillance on account of its ability to provide wide-range early warning detection. However, vessel target track breakages are common in large-scale marine monitoring, which limits the continuous tracking ability of HFSWR. The following are the possible reasons for track fracture: highly maneuverable vessels, dense channels, target occlusion, strong clutter/interference, long sampling intervals, and low detection probabilities. To solve this problem, we propose a long-term continuous tracking method for multiple targets with stereoscopic HFSWR based on an interacting multiple model extended Kalman filter (IMMEKF) combined with an extreme learning machine (ELM). When the trajectory obtained by IMMEKF breaks, a new section of the track will start on the basis of the observation data. For multiple-target tracking, a number of broken tracks can be obtained by IMMEKF tracking. Then the ELM classifies the segments from the same vessel by extracting different features including average velocity, average curvature, ratio of the arc length to the chord length, and wavelet coefficient. Both the simulation and the field experiment results validate the method presented here, showing that this method can achieve long-term continuous tracking for multiple vessels, with an average correct track segment association rate of over 91.2%, which is better than the tracking performance of conventional algorithms, especially when the vessels are in dense channels and strong clutter/interference area.
Novel Machine Learning Approach for Analyzing Anonymous Credit Card Fraud Patterns

Fraudulent credit card transactions are on the rise and have become a significantly problematic issue for financial intuitions and individuals. Various methods have already been implemented to handle the issue, but the embezzlers have always managed to employ innovative tactics to circumvent a number of security measures and execute the fraudulent transactions. Thus, instead of a rule-based system, an intelligent and adaptable machine learning based algorithm should be an answer to tackle such sophisticated digital theft. The presented framework uses k-NN for classification and utilises Principal Component Analysis (PCA) for raw data transformation. Neighbours (anomalies in data) were created using Synthetic Minority Oversampling Technique (SMOTE) and a distance-based feature selection method was employed. The proposed process performed well by having a precision and F-Score of 98.32% and 97.44% respectively for k-NN and 100% and 98.24% respectively for Time subset when using the misclassified instances. This work also demonstrates a larger and clearer classification breakdown, which aids in achieving higher precision rate and improved recall rate. In a view to accomplish such high accuracy, the original datum was transformed using Principal Component Analysis (PCA), neighbours (anomalies in data) were created using Synthetic Minority Oversampling Technique (SMOTE) and a distance based feature selection method was employed. The proposed process performed well when using the misclassified instances in the test dataset used in the previous work, while demonstrating a larger and clearer classification breakdown. To cite this document: Sylvester Manlangit, Sami Azam, Bharanidharan Shanmugam, and Asif karim, "Novel Machine Learning Approach for Analyzing Anonymous Credit Card Fraud Patterns", International Journal of Electronic Commerce Studies, Vol.10, No.2, pp.175-202, 2019. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.1732
Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support

In sports and rehabilitation processes where isotonic movements such as bodybuilding are performed, it is vital for individuals to be able to correct the wrong movements instantly by monitoring the trainings simultaneously, and to be able to train healthily and away from the risks of injury. For this purpose, we designed a new real-time athlete support system using Kinect V2 and Expert System. Lateral raise (LR) and dumbbell shoulder press (DSP) movements were selected as examples to be modeled in the system. Kinect V2 was used to obtain angle and distance changes in the shoulder, elbow, wrist, hip, knee, and ankle during movements in these movement models designed. For the rule base of Expert System developed according to these models, a 28-state rule table was designed, and 12 main rules were determined that could be used for both actions. In the sample trainings, it was observed that the decisions made by the system had 89% accuracy in DSP training and 82% accuracy in LR training. In addition, the developed system has been tested by 10 participants (25.8 ± 5.47 years; 74.69 ± 14.81 kg; 173.5 ± 9.52 cm) in DSP and LR training for four weeks. At the end of this period and according to the results of paired t-test analysis (p &lt; 0.05) starting from the first week, it was observed that the participants trained more accurately and that they enhanced their motions by 58.08 ± 11.32% in LR training and 54.84 ± 12.72% in DSP training.
Dual-stream generative adversarial networks for distributionally robust zero-shot learning

Zero-shot learning (ZSL) in visual classification aims to recognize novel categories for which few or even no training samples are available. Through recent advances using generative adversarial networks (GANs) for cross-modal generation, several generative methods have been investigated for ZSL to classify unseen categories with synthetic samples. However, these GAN-based ZSL approaches still struggle to generate samples with semantic consistency and significant between-class discrepancy while preserving within-class diversity, which are vital to building classifiers for unseen classes. Accordingly, in this paper, we propose a robust dual-stream GAN to synthesize satisfactory samples for zero-shot visual classification. In more detail, the inter-class discrepancy is maximized by a backbone compatibility loss, which drives the center of the synthesized samples to move towards the center of real samples of the same class while moving further away from samples of different classes. Secondly, in order to preserve the intra-class diversity ignored by most extant paradigms, we propose a stochastic dispersion regularization to encourage the synthesized samples to be distributed at arbitrary points in the visual space of their categories. Finally, unlike previous methods that project visual samples back into semantic space and consequently cause an information degradation problem, we design a dual-stream generator to synthesize visual samples and reconstruct semantic embedding simultaneously, thereby ensuring semantic consistency. Our model outperforms the state-of-the-arts by 4.7% and 3.0% on average in two metrics over four real-world datasets, demonstrating its effectiveness and superiority.
Prediction Intervals for Granular Data Streams Based on Evolving Type-2 Fuzzy Granular Neural Network Dynamic Ensemble

Granular data streams (GDSs) are a class of high-level abstract multitime scale description of data streams. Prediction intervals (PIs) for GDSs that provide estimated values as well as their corresponding reliability play an important role for assisting on-site workers to perceive the nonstationary environment in real time. However, constructing reliable PIs for GDSs constitutes a significant challenge. To provide a solution to the problem, an interval type-2 (IT2) fuzzy granular neural network (FGNN) dynamic ensemble approach (IT2FGNNDEnsemble) is proposed in this article. To fully reflect the uncertainty of GDSs, an interval value learning algorithm based IT2FGNN is developed, which can automatically generate, prune, merge, and realize recall in a single-pass learning mode. In addition, an evolving dynamic ensemble method is presented by providing an adaptive structure that considers a tradeoff between coverage and width of PIs, which can dynamically generate and prune the element of an ensemble according to current data tendency. A number of synthetic and industrial data streams experimentally validate the performance of the proposed IT2FGNNDEnsemble by using the state-of-the-art comparative methods. It is demonstrated that the proposed approach exhibits a good performance on PIs for practical applications.
A Two-Component Deep Learning Network for SAR Image Denoising

Speckle noise reduction and detail preservation, between which a balance is hard to achieve, are two main purposes of synthetic aperture radar (SAR) image denoising. For different regional characteristics of SAR images, satisfactory denoising results are obtained by complex parameter fine-tuning in most methods, and a solution with strong robustness seems difficult to find. In this paper, a novel two-component deep learning (DL) network is proposed to solve the above problem. First, the texture estimation subnetwork is constructed to produce the texture level map (TLM), which evaluates the randomness and scale of the texture distribution. Then, the noise removal subnetwork learns a spatially variable mapping between the noise and clean images with the help of TLM. Once the network has been trained, it can automatically quantify the texture feature and decide whether to smooth the local noise or maintain the detail. Comprehensive experiments on simulated and real SAR images demonstrate the superior performance of the proposed method over the state-of-the-art methods with respect to both the visual effect and quantitative analysis.
$CAG$ : Stylometric Authorship Attribution of Multi-Author Documents Using a Co-Authorship Graph

Unlike AISD, where each document is written by one single author, AIMD focuses on handling multi-author documents.Due to the combinatoric nature of documents, AIMD lacks the ground truth information-that is, information on writing and non-writing authors in a multi-author document-which makes this problem more challenging to solve.Previous AIMD solutions have a number of limitations: (i) the best stylometry-based AIMD solution has a low accuracy, less than 30%; (ii) increasing the number of co-authors of papers adversely affects the performance of AIMD solutions; and (iii) AIMD solutions were not designed to handle the non-writing authors (NWAs).However, NWAs exist in real-world cases-that is, there are papers for which not every co-author listed has contributed as a writer.This paper proposes an AIMD framework called the Co-Authorship Graph that can be used to (i) capture the stylistic information of each author in a corpus of multi-author documents and (ii) make a multi-label prediction for a multi-author query document.We conducted extensive experimental studies on one synthetic and three real-world corpora.Experimental results show that our proposed framework (i) significantly outperformed competitive techniques; (ii) can effectively handle a larger number of co-authors in comparison with competitive techniques; and (iii) can effectively handle NWAs in multi-author documents.
Survey on Blockchain-Based Electronic Voting

With the progress of society and the improvement of people’s democratic consciousness, voting, as a channel to fully develop democracy, is playing an increasingly important part in many application scenarios. In the era of Internet, electronic voting has replaced the traditional paper voting with the advantages of low cost, high efficiency and few mistakes. The fact that the data of electronic voting system is stored in the central database gives rise to the following problems: the voting data is not open and transparent enough, and it is easy to be tampered with and forged; the users’ private information faces the risk of being leaked; voters can not verify the voting results. Fortunately blockchain technology can make up for the shortcomings of the current voting system, making the voting process open and transparent, preventing fraudulent votes, enhancing the security of voting data and verifying the voting results. The application of blockchain electronic voting system has very important significance and prospects. In this paper, we first introduce the development process of electronic voting system at home and abroad, then introduce the development status of blockchain-based electronic voting system, finally we compare and summarize several typical blockchain-based electronic voting schemes.
gl2vec

Learning network representation has a variety of applications, such as network classification. Most existing work in this area focuses on static undirected networks and does not account for presence of directed edges or temporal changes. Furthermore, most work focuses on node representations that do poorly on tasks like network classification. In this paper, we propose a novel network embedding methodology, gl2vec, for network classification in both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs. We demonstrate the efficacy and usability of gl2vec over existing state-of-the-art methods on network classification tasks such as network type classification and subgraph identification in several real-world static and temporal directed networks. We argue that gl2vec provides additional network features that are not captured by state-of-the-art methods, which can significantly improve their classification accuracy by up to 10% in real-world applications.
Protocol for Implementing Quantum Nonparametric Learning with Trapped Ions

Nonparametric learning is able to make reliable predictions by extracting information from similarities between a new set of input data and all samples. Here we point out a quantum paradigm of nonparametric learning which offers an exponential speedup over the sample size. By encoding data into quantum feature space, similarity between the data is defined as an inner product of quantum states. A quantum training state is introduced to superpose all data of samples, encoding relevant information for learning in its bipartite entanglement spectrum. We demonstrate that a trained state for prediction can be obtained by entanglement spectrum transformation, using quantum matrix toolbox. We further work out a feasible protocol to implement the quantum nonparametric learning with trapped ions, and demonstrate the power of quantum superposition for machine learning.
Towards interpretable and robust hand detection via pixel-wise prediction

The lack of interpretability of existing CNN-based hand detection methods makes it difficult to understand the rationale behind their predictions. In this paper, we propose a novel neural network model, which introduces interpretability into hand detection for the first time. The main improvements include: (1) Detect hands at pixel level to explain what pixels are the basis for its decision and improve transparency of the model. (2) The explainable Highlight Feature Fusion block highlights distinctive features among multiple layers and learns discriminative ones to gain robust performance. (3) We introduce a transparent representation, the rotation map, to learn rotation features instead of complex and non-transparent rotation and derotation layers. (4) Auxiliary supervision accelerates the training process, which saves more than 10 hours in our experiments. Experimental results on the VIVA and Oxford hand detection and tracking datasets show competitive accuracy of our method compared with state-of-the-art methods with higher speed.
ASSESSMENT OF HEAVY METALS CONTAMINATION IN SURFACE SOILS IN MEIDUK COPPER MINE AREA, SE IRAN

Mining activities are potentially a significant source of metals contamination in the environment.The purpose of this research is to investigate the environmental impacts of mining activities in the vicinity of Meiduk copper mine.In this study, we use the pollution indices such as contamination factor, enrichment factor and geo-accumulation index for evaluating the metal contaminants, including Pb, Ni, Se, Mo and Zn.The fieldwork consisted of 60 surface soil samples drawn at the depth of 0-30 cm.The samples were, then, analyzed for metals using ICP-MS method.Principal component analysis of compositional data (PCA) was also applied as a multivariate method to find relationships among metals.In the first component (PC1), with 51% of the total variance, strong and positive loadings related to Pb, Se, Mo and Zn were evident.The second component (PC2), with 34 % of the total variance, reflected a positive weighting factor of Ni metal.The obtained results showed that heavy metals levels and distribution were found higher at sites which were in the vicinity of mine pits and waste dumps and were probable sources of metal pollution.As an application of the method proposed in this research, project management will be capable of programming timely and suitable response to these risks by identifying risk factors and their contamination level.
Deep Learning from Small Dataset for BI-RADS Density Classification of Mammography Images

Mammography is a breast imaging technique that has been widely used in breast cancer diagnosis and screening. The Breast Imaging Reporting and Data System (BI-RADS) defines a six-point overall cancer risk scale from negative to highly suggestive of malignancy based on mammography, and also a four-point breast density based cancer risk scale. Automatic BI-RADS density classification of mammogram images is still a challenge. The current state of the art is about 80% on the MIAS (Mammogram Image Analysis Society) database. In this paper we present a deep learning study of BI-RADS density classification using MIAS, based on a lightweight Convolutional Neural Networks (CNNs) architecture. This is a small data problem as MIAS has only 322 images with ground truth, so we use image pre-processing and augmentation to solve the problem. Five-fold cross validation is used to evaluate the proposed approach, and has achieved a test accuracy of 83.6% on average. This suggests that deep learning has the potential to address the small data problem in mammography, which is prevalent in many medical image analysis tasks. The experience we have, especially in how to optimize the deep learning architecture, will benefit other researchers and medical practitioners.
Weakly supervised semantic segmentation by iterative superpixel-CRF refinement with initial clues guiding

In the recent years, there have been remarkable improvements in the semantic segmentation based on deep convolutional neural networks. However, DCNN-based weakly supervised segmentation approaches are still inferior to the fully supervised manner. We observe that the performance gap mainly comes from the limitation of producing high-quality dense object localization clues from image-level labels. To mitigate this gap, in this paper, we are committed to finding more precise and complete pixel-level annotations from image-level tags. So this paper proposes a new iterative training framework for progressively refining pixel-wise labels and training the segmentation network. We first propose a new attention map generating method to locate more discriminative object regions. To find out less-discriminative regions and rectify wrong object location clues, the method fuses saliency map into the attention map to generate the initial pseudo pixel-level annotations. In the iteration training process, we train the segmentation network by treating pseudo pixel-level annotations as supervision. In order to correct the inaccurate labels of segmentation masks produced by current segmentation network, a superpixel-CRF refinement model is exploited to produce more accurate pixel-level annotations and use these annotations as supervision to train the segmentation network again. Our framework iterates between refining pixel-level annotations and optimizing the segmentation network. Experimental results demonstrate that our method significantly outperforms other previous weakly supervised semantic segmentation methods, and obtains the state-of-the-art performance, which are 64.7% mIoU score on PASCAL VOC 2012 test set and 26.3% mIoU score on MS COCO validation set.