Reviewerly/exHarmony huggingface dataset:
The GraphBRAIN System for Knowledge Graph Management and Advanced Fruition

The possibility of inter-relating different information items is crucial in the perspective of enhanced storage, handling and fruition of knowledge. GraphBRAIN is a general-purpose tool that allows to design and collaboratively populate knowledge graphs, and provides advanced solutions for their fruition, consultation and analysis. Its functionalities are also provided as Web services to other applications. A peculiarity of GraphBRAIN is its fusion of methods and tools coming from different research areas: ontologies to describe such variated knowledge, collaborative tools to collect the knowledge scattered across many people, graph databases to store the knowledge base, data mining and social network analysis tools for personalized fruition of the collected knowledge. It is currently used as the knowledge management platform in a tourism-related project.
Single Image Dehazing Via Artificial Multiple Shots And Multidimensional Context

The main challenge for single image dehazing is the lack of effective prior information for restoration. To address this issue, in this paper, we propose to generate artificial multiple shots for simulating the images captured under different haze degrees, and two context reasoning modules are developed to describe the relationship across different spatial regions and artificial shots. It brings two benefits in the inhomogeneous haze distribution. First, within one shot, the regions occluded in one location could be recovered with the help of other clear regions, which share the similar structures. Second, for the same spatial location, the regions distorted in one shot could be restored by means of other shots with clear content. We evaluate our method on different benchmark datasets. The results demonstrate that our method achieves superior performance over many state-of-the-art dehazing algorithms.
A Synthetic Supplemental Public Use File of Low-Income Information Return Data: Methodology, Utility, and Privacy Implications

US government agencies possess data that could be invaluable for evaluating public policy, but often may not be released publicly due to disclosure concerns. For instance, the Statistics of Income division (SOI) of the Internal Revenue Service releases an annual public use file of individual income tax returns that is invaluable to tax analysts in government agencies, nonprofit research organizations, and the private sector. However, SOI has taken increasingly aggressive measures to protect the data in the face of growing disclosure risks, such as a data intruder matching the anonymized public data with other public information available in nontax databases. In this paper, we describe our approach to generating a fully synthetic representation of the income tax data by using sequential Classification and Regression Trees and kernel density smoothing. This synthetic data file represents previously unreleased information useful for tax policy modeling. We also tested and evaluated the tradeoffs between data utility and disclosure risks of different parameterizations using a variety of validation metrics. The resulting synthetic data set has high utility, particularly for summary statistics and microsimulation, and low disclosure risk.
Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples

The last decade has witnessed the proliferation of Deep Learning models in many applications, achieving unrivaled levels of predictive performance. Unfortunately, the black-box nature of Deep Learning models has posed unanswered questions about what they learn from data. Certain application scenarios have highlighted the importance of assessing the bounds under which Deep Learning models operate, a problem addressed by using assorted approaches aimed at audiences from different domains. However, as the focus of the application is placed more on non-expert users, it results mandatory to provide the means for him/her to trust the model, just like a human gets familiar with a system or process: by understanding the hypothetical circumstances under which it fails. This is indeed the angular stone for this research work: to undertake an adversarial analysis of a Deep Learning model. The proposed framework constructs counterfactual examples by ensuring their plausibility, e.g. there is a reasonable probability that a human could generate them without resorting to a computer program. Therefore, this work must be regarded as valuable auditing exercise of the usable bounds a certain model is constrained within, thereby allowing for a much greater understanding of the capabilities and pitfalls of a model used in a real application. To this end, a Generative Adversarial Network (GAN) and multi-objective heuristics are used to furnish a plausible attack to the audited model, efficiently trading between the confusion of this model, the intensity and plausibility of the generated counterfactual. Its utility is showcased within a human face classification task, unveiling the enormous potential of the proposed framework.
T18: an ambiguous keyboard layout for smartwatches

We present a new ambiguous keyboard layout for smartwatches named T18. As the name implies, it is composed of 18 keys, and for this reason a key may correspond to more than one character. In those cases, the system chooses one of the characters based on a dictionary. The T18 keyboard layout is inspired by T9, with the main differences being that T18 has a greater number of keys and they are arranged in a QWERTY layout. The method was compared to OptimalT9, an improvement of the original T9 for smartwatches. The experiment showed a 39% advantage in text entry speed in favor of T18. In particular, participants typed at 15.7 wpm with T18 and at 11.3 wpm with Optimal-T9.
A Study of Radicalism Contents Detection in Twitter: Insights From Support Vector Machine Technique

Social media has been widely used to target, coordinate, disseminate and embed radicalism doctrine to the society. Radicalism gives destructive impacts to an attacked country and the global society. It is not only makes a serious threat to a nation unity, yet it also impacts the cultural, socio-political, and economic among the others. This study is aim to study radicalism intention using content detection. The model was developed using social media content like posting, comment and conversation to indicate the level of radicalism. Data was collected from a Twitter. It has chosen as a social media platform because Twitter can engage the community with powerful and impactful microblogging function. The data was analyzed using machine learning. To be specifically, a Support Vector Machine (SVM) in text mining employed for classification of Twitter's content in national language of republic Indonesia. There were two focus keywords used in this study. The first one is ISIS and follow by Syria. The result shows that 83.3% accuracy of test set tuples, 90% part of the contents indicates positive link to radicalism with no-radicalism class as a precision value. Besides recall value was calculated for accuracy, 95% and 82% part of actual text positively link as radicalism class with a proxy of no-radicalism class. This model is hoped to detect and overcome the terrorism issue in Indonesia.
Classification of Microcalcifications in Mammograms using 2D Discrete Wavelet Transform and Random Forest

Breast cancer is the most common form of cancer among women and the leading cause of female deaths from cancer worldwide. Microcalcifications, small crystals of calcium apatites, are considered the first sign of breast cancer. Since microcalcifications are small and have different shapes and low contrast, they can be easily missed or misinterpreted by radiologists. For these reasons, automatic image processing systems are being developed to make the diagnostic process easier for radiologists. In this work, we present a computer-based automated approach for segmentation and classification of breast microcalcifications in mammograms using discrete wavelet transform and random forest (DWT-RF). The proposed approach was tested on 966 images (322 benign, 322, malignant, and 322 normal) from the Digital Database for Screening Mammography. The results indicate that DWT-RF achieves a sensitivity of 93%, a specificity of 97 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , a false positive rate of 3%, an accuracy of 95%, and an area under the ROC curve of 0.92, which are comparable in terms of accuracy to state-of-the-art methods and other existing classifiers.
Quantum Collaborative K-means

Recently, more researchers are interested in the domain of quantum machine learning as it can manipulate and classify large numbers of vectors in high dimensional space in reasonable time. In this paper, we propose a new approach called Quantum Collaborative K-means which is based on combining several clustering models based on quantum K-means. This collaboration consists of exchanging the information of each algorithm locally in order to find a common underlying structure for clustering. Comparing the classical version of collaborative clustering to our approach, we notice that we have an exponential speed up: while the classical version takes O(K×L×M×N), the quantum version takes only O(K×L×log(M×N)). And comparing to the quantum version of K-means, we get a better solution in terms of the criteria of validation which means in terms of clustering. The empirical evaluations validate the benefits of the proposed approach.
Omnidirectional Tractable Three Module Robot

This paper introduces the Omnidirectional Tractable Three Module Robot for traversing inside complex pipe networks. The robot consists of three omnidirectional modules fixed 120° apart circumferentially which can rotate about their own axis allowing holonomic motion of the robot. The holonomic motion enables the robot to overcome motion singularity when negotiating T-junctions and further allows the robot to arrive in a preferred orientation while taking turns inside a pipe. We have developed a closed-form kinematic model for the robot in the paper and propose the `Motion Singularity Region' that the robot needs to avoid while negotiating T-junction. The design and motion capabilities of the robot are demonstrated both by conducting simulations in MSC ADAMS on a simplified lumped-model of the robot and with experiments on its physical embodiment.
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution

Single image super-resolution (SISR) is an ill-posed problem with an indeterminate number of valid solutions. Solving this problem with neural networks would require access to extensive experience, either presented as a large training set over natural images or a condensed representation from another pre-trained network. Perceptual loss functions, which belong to the latter category, have achieved breakthrough success in SISR and several other computer vision tasks. While perceptual loss plays a central role in the generation of photo-realistic images, it also produces undesired pattern artifacts in the super-resolved outputs. In this paper, we show that the root cause of these pattern artifacts can be traced back to a mismatch between the pre-training objective of perceptual loss and the super-resolution objective. To address this issue, we propose to augment the existing perceptual loss formulation with a novel content loss function that uses the latent features of a discriminator network to filter the unwanted artifacts across several levels of adversarial similarity. Further, our modification has a stabilizing effect on non-convex optimization in adversarial training. The proposed approach offers notable gains in perceptual quality based on an extensive human evaluation study and a competent reconstruction fidelity when tested on objective evaluation metrics.
An Efficient Underwater Image Enhancement Model With Extensive Beer-Lambert Law

We develop a simple yet effective model for enhancing single underwater image by mathematically extending the Beer-Lambert law. In the proposed model, we take advantage of the mean and variance of natural images to be the reference to correct color casts of underwater images. We propose an efficient strategy to recover better details of underwater images, which involves two steps: in the first step we establish a linear model associated with the mean and variance of underwater images to locate images regions containing more details, and in the second step we present a nonlinear adaptive weight scheme using this locating information to recover better details and prevent partial over-enhancement. Ultimate experiments are performed to demonstrate the effectiveness of the proposed method, and these experimental results show that our method yields better structural restoration, more naturalness color correction, and less time consumption.s.
From Possibilistic Rule-Based Systems to Machine Learning - A Discussion Paper

This paper is a plea for developing possibilistic learning methods that would be consistent with if-then rule-based reasoning. The paper first recall the possibility theory-based handling of cascading sets of parallel if-then rules. This is illustrated by an example describing a classification problem. It is shown that the approach is both close to a possibilistic logic handling of the problem and can also be put under the form of a max-min-based matrix calculus describing a function underlying a structure somewhat similar to a max-min neural network. The second part of the paper discusses how possibility distributions can be obtained from precise or imprecise statistical data, and then surveys the few existing works on learning in a possibilistic setting. A final discussion emphasizes the interest of handling learning and reasoning in a consistent way.
SimilarAPI

Establishing API mappings between libraries is a prerequisite step for library migration tasks. Manually establishing API mappings is tedious due to the large number of APIs to be examined, and existing methods based on supervised learning requires unavailable already-ported or functionality similar applications. Therefore, we propose an unsupervised deep learning based approach to embed both API usage semantics and API description (name and document) semantics into vector space for inferring likely analogical API mappings between libraries. We implement a proof-of-concept website SimilarAPI (https://similarapi.appspot.com) which can recommend analogical APIs for 583,501 APIs of 111 pairs of analogical Java libraries with diverse functionalities. Video: https://youtu.be/EAwD6l24vLQ
An efficient Bayesian network structure learning algorithm using the strategy of two-stage searches

It is important for Bayesian network (BN) structure learning, a NP-problem, to improve the accuracy and hybrid algorithms are a kind of effective structure learning algorithms at present. Most hybrid algorithms adopt the strategy of one heuristic search and can be divided into two groups: one heuristic search based on initial BN skeleton and one heuristic search based on initial solutions. The former often fails to guarantee globality of the optimal structure and the latter fails to get the optimal solution because of large search space. In this paper, an efficient hybrid algorithm is proposed with the strategy of two-stage searches. For first-stage search, it firstly determines the local search space based on Maximal Information Coefficient by introducing penalty factors p1, p2, then searches the local space by Binary Particle Swarm Optimization. For second-stage search, an efficient ADR (the abbreviation of Add, Delete, Reverse) algorithm based on three basic operators is designed to extend the local space to the whole space. Experiment results show that the proposed algorithm can obtain better performance of BN structure learning.
Temporal Segment Connection Network for Action Recognition

Two-stream Convolutional Neural Networks have shown excellent performance in video action recognition. Most existing works train each sampling group independently, or just fuse at the last level, which obviously ignore the continuity of action in temporal and the complementary information between action fragments. In this paper, a temporal segment connection network is proposed to overcome these limitations. On the one hand, the forget gate module of the long short-term memory (LSTM) network is used to establish feature-level connections between each sampling group. This not only strengthens the information transmission between the sampling groups to enhance the temporal connectivity, but also extracts the complementary information between the sampling groups to enhance the overall representation of the action. On the other hand, a bi-directional long short-term memory (Bi-LSTM) network is used to automatically evaluate the importance weights of each sampling group based on the deep feature sequence. The experimental results on UCF101 and HMDB51 datasets show that the proposed model can effectively improve the utilization rate of temporal information and the ability of overall action representation, thus significantly improves the accuracy of human action recognition.
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data

In machine learning based single image super-resolution, the degradation model is embedded in training data generation. However, most existing satellite image super-resolution methods use a simple down-sampling model with a fixed kernel to create training images. These methods work fine on synthetic data, but do not perform well on real satellite images. We propose a realistic training data generation model for commercial satellite imagery products, which includes not only the imaging process on satellites but also the post-process on the ground. We also propose a convolutional neural network optimized for satellite images. Experiments show that the proposed training data generation model is able to improve super-resolution performance on real satellite images.
Single Image Glare Removal Using Deep Convolutional Networks

Deep convolutional neural networks have been investigated for atmospheric particle removal and accomplished the state-of-the-art performance. Most of the previous studies however focus on removing the effects of atmospheric particles but not on glares caused by direct or reflected sunlight on images. In this paper, we propose a decompose-refine network for single image glare removal. Specifically, our network is composed of a glare detection subnetwork and a glare removal subnetwork, which are respectively in charge of glare detection and removal. Experimental results show that our network outperforms the state-of-the-art network baselines on testing dataset.
Leveraging Multilingual News Websites for Building a Kurdish Parallel Corpus

Machine translation has been a major motivation of development in natural language processing. Despite the burgeoning achievements in creating more efficient machine translation systems, thanks to deep learning methods, parallel corpora have remained indispensable for progress in the field. In an attempt to create parallel corpora for the Kurdish language, in this article, we describe our approach in retrieving potentially alignable news articles from multi-language websites and manually align them across dialects and languages based on lexical similarity and transliteration of scripts. We present a corpus containing 12,327 translation pairs in the two major dialects of Kurdish, Sorani and Kurmanji. We also provide 1,797 and 650 translation pairs in English-Kurmanji and English-Sorani. The corpus is publicly available under the CC BY-NC-SA 4.0 license. 1
Distributed and Parallel Ensemble Classification for Big Data Based on Kullback-Leibler Random Sample Partition

In this article, we use a Kullback-Leibler random sample partition data model to generate a set of disjoint data blocks, where each block is a good representation of the entire data set. Every random sample partition (RSP) block has a sample distribution function similar to the entire data set. To obtain the statistical measure between them, Kernel Density Estimation (KDE) with a dual-tree recursion data structure is firstly applied to fast estimate the probability density of each block. Then, based on the Kullback-Leibler (KL) divergence measure, we can obtain the statistical similarity between a randomly selected RSP data block and other RSP data blocks. We rank the RSP data blocks according to their divergence values in descending order and choose the first ten for an ensemble classification learning. The classification models are established in parallel for the selected RSP data blocks and the final ensemble classification model is obtained by the weighted voting ensemble strategy. The experiments were conducted by building XGboost model based on those ten blocks in parallel, and we incrementally ensemble them according to their KL values. The testing classification results show that our method can increase the generalization capability of the ensemble classification model. It could reduce the model building time in parallel computation environment by using less than \(15\%\) of the entire data, which could also solve the memory constraints of big data analysis.
Underwater Image Dehazing Using The Color Space Dimensionality Reduction Prior

Underwater images suffer from low visibility caused by absorption and scattering, which leads to haze and some further limitations. To overcome these issues, we propose a novel dehaze method based on a universal observation that all pixels of most underwater images tend to distribute nearby a specific plane in RGB space. This observation is named the color space dimensionality reduction prior. By projecting all pixels to this plane, namely the UV color space, the color distribution of these pixels can be reduced from the three-dimensional space (RGB space) to a new two-dimensional space (UV space) without causing any excessive color shift. By carefully setting the haze-free boundary in UV space, we can obtain the image transmission and finally produce an excellent dehazed image. Experimental results show that our method has competitive results compared with mainstream underwater single image dehazing methods.
3D Scene Geometry-Aware Constraint for Camera Localization with Deep Learning

Camera localization is a fundamental and key component of autonomous driving vehicles and mobile robots to localize themselves globally for further environment perception, path planning and motion control. Recently end-to-end approaches based on convolutional neural network have been much studied to achieve or even exceed 3D-geometry based traditional methods. In this work, we propose a compact network for absolute camera pose regression. Inspired from those traditional methods, a 3D scene geometry-aware constraint is also introduced by exploiting all available information including motion, depth and image contents. We add this constraint as a regularization term to our proposed network by defining a pixel-level photometric loss and an image-level structural similarity loss. To benchmark our method, different challenging scenes including indoor and outdoor environment are tested with our proposed approach and state-of-the-arts. And the experimental results demonstrate significant performance improvement of our method on both prediction accuracy and convergence efficiency.