Reviewerly/exHarmony huggingface dataset:
Training Hybrid Language Models by Marginalizing over Segmentations

In this paper, we study the problem of hybrid language modeling, that is using models which can predict both characters and larger units such as character ngrams or words.Using such models, multiple potential segmentations usually exist for a given string, for example one using words and one using characters only.Thus, the probability of a string is the sum of the probabilities of all the possible segmentations.Here, we show how it is possible to marginalize over the segmentations efficiently, in order to compute the true probability of a sequence.We apply our technique on three datasets, comprising seven languages, showing improvements over a strong character level language model.
Comparison of Methods for Determining the Thresholds of Geochemical Anomalies and the Prospecting Direction—A Case of Gold Deposits in the Gouli Exploration Area, Qinghai Province

Geochemical anomalies play an important role in mineral exploration, because they are closely related to the formation of ore deposits. In this paper, the Au in the geochemical samples of 1:50,000 stream sediments in the Gouli exploration area, Qinghai Province, is selected as an example. The cumulative frequency method, local singularity analysis, and exploration data analysis method are used to process the Au data and determine the abnormal thresholds. Robust principal component analysis is used to explore the elemental associations. Among them, the effect of local singularity analysis is the best and it delineates the weak geochemical anomalies not delineated by cumulative frequency method and exploration data analysis method, which shows its superiority. Robust principal component analysis shows that Sb, W, and As are closely related to Au mineralization. Three anomalous areas are delineated in the Gouli exploration area. Gold ore-body is found to occur within anomaly area I. The other anomalous areas are highly consistent with abnormal elemental composition of Sb, W, and As. Moreover, these two areas are extremely conformable with the high-value areas determined by the binary state method. The optimality of the three methods are compared comprehensively, it is pointed out that it is more reasonable to use the local singularity analysis to determine the abnormal threshold. The anomaly areas II and III of Au determined by local singularity analysis have shown great potential for prospecting.
Improving spaCy dependency annotation and PoS tagging web service using independent NER services

Dependency parsing is often used as a component in many text analysis pipelines. However, performance, especially in specialized domains, suffers from the presence of complex terminology. Our hypothesis is that including named entity annotations can improve the speed and quality of dependency parses. As part of BLAH5, we built a web service delivering improved dependency parses by taking into account named entity annotations obtained by third party services. Our evaluation shows improved results and better speed. Keywords: dependency parsing, named entity recognition, natural language processing
Security Analysis of a Certificateless Provable Data Possession Scheme in Cloud

He et al. proposed a certificateless provable data possession protocol for big data storage on cloud. They claimed that the scheme is not only secure, but also can achieve data integrity checking without downloading the stored data from the cloud server. However, in this paper, we show that He et al.'s protocol has some security flaw and cannot get the property of data integrity checking at all. Specifically, by observing certificateless signature used in their provable data possession protocol, we find that the cloud server (or any user who gets signature-message pairs) can generate a valid signature of any message. Then, the cloud server can tamper data stored by the data owner and successfully passes the data integrity checking via two different conditions according to the verifier knows or does not know the identity of blocks of data.
Fine-grained complexity meets IP = PSPACE

In this paper we study the fine-grained complexity of finding exact and approximate solutions to problems in P. Our main contribution is showing reductions from an exact to an approximate solution for a host of such problems.As one (notable) example, we show that the Closest-LCS-Pair problem (Given two sets of strings A and B, compute exactly the maximum LCS(a, b) with (a, b) eA x B) is equivalent to its approximation version (under near-linear time reductions, and with a constant approximation factor). More generally, we identify a class of problems, which we call BP-Pair-Class, comprising both exact and approximate solutions, and show that they are all equivalent under near-linear time reductions.Exploring this class and its properties, we also show:• Under the NC-SETH assumption (a significantly more relaxed assumption than SETH), solving any of the problems in this class requires essentially quadratic time.• Modest improvements on the running time of known algorithms (shaving log factors) would imply that NEXP is not in non-uniform NC1.• Finally, we leverage our techniques to show new barriers for deterministic approximation algorithms for LCS.A very important consequence of our results is that they continue to hold in the data structure setting. In particular, it shows that a data structure for approximate Nearest Neighbor Search for LCS (NNSLCS) implies a data structure for exact NNSLCS and a data structure for answering regular expression queries with essentially the same complexity.At the heart of these new results is a deep connection between interactive proof systems for bounded-space computations and the fine-grained complexity of exact and approximate solutions to problems in P. In particular, our results build on the proof techniques from the classical IP = PSPACE result.
Data mining ancient scripts to investigate their relationships and origins

This paper describes a data mining study of a set of ancient scripts in order to discover their relationships, including their possible common origin from a single root script. The data mining uses convolutional neural networks and support vector machines to find the degree of visual similarity between pairs of symbols in eight different ancient scripts. Among the surprising results of the data mining are the following: (1) the Indus Valley Script is visually closest to Sumerian pictographs, and (2) the Linear B script is visually closest to the Cretan Hieroglyphic script.
Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes

Inference on unknown quantities in dynamical systems via observational data is essential for providing meaningful insight, furnishing accurate predictions, enabling robust control, and establishing appropriate designs for future experiments. Merging mathematical theory with empirical measurements in a statistically coherent way is critical and challenges abound, e.g., ill-posedness of the parameter estimation problem, proper regularization and incorporation of prior knowledge, and computational limitations. To address these issues, we propose a new method for learning parameterized dynamical systems from data. We first customize and fit a surrogate stochastic process directly to observational data, front-loading with statistical learning to respect prior knowledge (e.g., smoothness), cope with challenging data features like heteroskedasticity, heavy tails, and censoring. Then, samples of the stochastic process are used as “surrogate data" and point estimates are computed via ordinary point estimation methods in a modular fashion. Attractive features of this two-step approach include modularity and trivial parallelizability. We demonstrate its advantages on a predator-prey simulation study and on a real-world application involving within-host influenza virus infection data paired with a viral kinetic model, with comparisons to a more conventional Markov chain Monte Carlo (MCMC) based Bayesian approach.
Statistical evaluation of the geochemical content of beach sand along the Sarisu - Kemer coastline of Antalya, Turkey

In this study, statistical techniques were applied to evaluate the geochemistry of 47 beach sand samples collected from the Sarisu-Kemer coastline in Antalya, Turkey. The samples were prepared any analyzed for their geochemical content using XRF at Akdeniz University. Element identified in order of average abundance includes: CaO > SiO2>MgO>Al2O3 > Fe2O3>K2O and Ti > Cl > Mn > S > Sr > Cr > P > Co > Ni > V > Zn > Sn for the major and trace elements respectively. Most of the elements indicated a right skewed distribution except CaO, Fe2O3, Mn and P with left skewed distribution. There are wide disparities in the distribution of some elements, particularly heavy metals within the samples such as: V was detected in 3 samples only, Sn in 5 samples, Zn in 6 samples, Ni in 10 samples etc. Cr, Sr, V, Cl, P, Ti and S all showed high anomalies in some samples. In similar of studies, multivariate statistical technique has been successful in geochemical evaluation and can be applied to other researchers.
Hierarchical Nested Named Entity Recognition

In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in mentions, such as those related to specific symptoms or diseases associated with different anatomical regions. Unlike previous approaches, we build a transition-based parser that explicitly models an arbitrary number of hierarchical and nested mentions, and propose a loss that encourages correct predictions of higher-level mentions. We further introduce a set of modifier classes which introduces certain concepts that change the meaning of an entity, such as absence, or uncertainty about a given disease. Our proposed model achieves state-of-the-art results in medical entity recognition datasets, using both nested and hierarchical mentions.
Research and implementation of parallel artificial bee colony algorithm based on ternary optical computer

The artificial bee colony (ABC) algorithm is a widely used algorithm in the field of function optimization problems. The traditional ABC algorithm has long search time, slow convergence speed and easy to fall into local optimum at the end of the search. In this paper, the design scheme and method of implementing parallel ABC algorithm are studied, which makes use of the characteristics of many data bits and easy expansion of data bits of the ternary optical computer (TOC). First, by analysing the traditional ABC algorithm, we can find the parallel parts and parallel design. Then the detailed algorithm implementation flow is given and the clock cycle of the algorithm is analysed. Finally, the correctness of the parallel scheme is verified by experiments. Compared with the ABC algorithm and parallel ABC algorithms based on computer (PABC), the ABC algorithm based on TOC (TOC-PABC) effectively shortens the search time, improves the optimization performance of complex multimodal function optimization problems and obtains a higher speedup.
Trajectory Tracking Control for Robotic Vehicles Using Counterexample Guided Training of Neural Networks

We investigate approaches to train neural networks for controlling vehicles to follow a fixed reference trajectory robustly, while respecting limits on their velocities and accelerations. Here robustness means that if a vehicle starts inside a fixed region around the reference trajectory, it remains within this region while moving along the reference from an initial set to a target set. We consider the combination of two ideas in this paper: (a) demonstrations of the correct control obtained from a model-predictive controller (MPC) and (b) falsification approaches that actively search for violations of the property, given a current candidate. Thus, our approach creates an initial training set using the MPC loop and builds a first candidate neural network controller. This controller is repeatedly analyzed using falsification that searches for counterexample trajectories, and the resulting counterexamples are used to create new training examples. This process proceeds iteratively until the falsifier no longer succeeds within a given computational budget. We propose falsification approaches using a combination of random sampling and gradient descent to systematically search for violations. We evaluate our combined approach on a variety of benchmarks that involve controlling dynamical models of cars and quadrotor aircraft.
Successor Options: An Option Discovery Framework for Reinforcement Learning

The options framework in reinforcement learning models the notion of a skill or a temporally extended sequence of actions. The discovery of a reusable set of skills has typically entailed building options, that navigate to bottleneck states. In this work, we instead adopt a complementary approach, where we attempt to discover options that navigate to landmark states. These states are prototypical representatives of well-connected regions and can hence access the associated region with relative ease. In this work, we propose Successor Options, which leverages Successor representations to build a model of the state space. The intra-option policies are learnt using a novel pseudo-reward and the model scales to high-dimensional spaces since it does not construct an explicit graph of the entire state space. Additionally, we also propose an Incremental Successor Options model that iterates between constructing Successor representations and building options, which is useful when robust Successor representations cannot be built solely from primitive actions. We demonstrate the efficacy of our approach on a collection of grid-worlds, and on the high-dimensional robotic control environment of Fetch.
AugBoost: Gradient Boosting Enhanced with Step-Wise Feature Augmentation

Gradient Boosted Decision Trees (GBDT) is a widely used machine learning algorithm, which obtains state-of-the-art results on many machine learning tasks. In this paper we introduce a method for obtaining better results, by augmenting the features in the dataset between the iterations of GBDT. We explore a number of augmentation methods: training an Artificial Neural Network (ANN) and extracting features from it's last hidden layer (supervised), and rotating the feature-space using unsupervised methods such as PCA or Random Projection (RP). These variations on GBDT were tested on 20 classification tasks, on which all of them outperformed GBDT and previous related work.
From Words to Sentences: A Progressive Learning Approach for Zero-resource Machine Translation with Visual Pivots

The neural machine translation model has suffered from the lack of large-scale parallel corpora. In contrast, we humans can learn multi-lingual translations even without parallel texts by referring our languages to the external world. To mimic such human learning behavior, we employ images as pivots to enable zero-resource translation learning. However, a picture tells a thousand words, which makes multi-lingual sentences pivoted by the same image noisy as mutual translations and thus hinders the translation model learning. In this work, we propose a progressive learning approach for image-pivoted zero-resource machine translation. Since words are less diverse when grounded in the image, we first learn word-level translation with image pivots, and then progress to learn the sentence-level translation by utilizing the learned word translation to suppress noises in image-pivoted multi-lingual sentences. Experimental results on two widely used image-pivot translation datasets, IAPR-TC12 and Multi30k, show that the proposed approach significantly outperforms other state-of-the-art methods.
Deep Learning for Vietnamese Sign Language Recognition in Video Sequence

With most of Vietnamese hearing impaired individuals, Vietnamese Sign Language (VSL) is the only choice for communication.Thus, there are more and more study about the automatic translation of VSL to make a bridge between hearing impaired people and normal ones.However, automatic VSL recognition in video brings many challenges due to the orientation of camera, hand position and movement, inter hand relation, etc.In this paper, we present some feature extraction approaches for VSL recognition including spatial and scene-based features.Instead of relying on a static image, we specifically capture motion information between frames in a video sequence.For the recognition task, beside the traditional method of sign language recognition such as SVM, we additionally propose to use deep learning technique for VSL recognition for finding the dependence of each frame in video sequences.We collected two VSL datasets of the relative family topic (VSL-WRF) like father, mother, uncle, aunt....The first one includes 12 words in Vietnamese language which only have a little change between frames.While the second one contains 15 with gestures involving the relative position of the body parts and orientation of the motion.Moreover, the data augmentation technique is proposed to gain more information of hand movement and hand position.The experiments achieved the satisfactory results with accuracy of 88.5% (traditional SVM) and 95.83% (deep learning).It indicates that deep learning combining with data augmentation technique provides more information about the orientation or movement of hand, and it would be able to improve the performance of VSL recognition system.
Linear Registration of Brain MRI Using Knowledge-Based Multiple Intermediator Libraries

Linear registration is often the crucial first step for various types of image analysis. Although this is mathematically simple, failure is not uncommon. When investigating the brain by magnetic resonance imaging (MRI), the brain is the target organ for registration but the existence of other tissues, in addition to a variety of fields of view, different brain locations, orientations and anatomical features, poses some serious fundamental challenges. Consequently, a number of different algorithms have been put forward to minimize potential errors. In the present study, we tested a knowledge-based approach that can be combined with any form of registration algorithm. This approach consisted of a library of intermediate images (mediators) with known transformation to the target image. Test images were first registered to all mediators and the best mediator was selected to ensure optimum registration to the target. In order to select the best mediator, we evaluated two similarity criteria: the sum of squared differences and mutual information. This approach was applied to 48 mediators and 96 test images. In order to reduce one of the main drawbacks of the approach, increased computation time, we reduced the size of the library by clustering. Our results indicated clear improvement in registration accuracy.
Mastcam Image Resolution Enhancement with Application to Disparity Map Generation for Stereo Images with Different Resolutions

In this paper, we introduce an in-depth application of high-resolution disparity map estimation using stereo images from Mars Curiosity rover’s Mastcams, which have two imagers with different resolutions. The left Mastcam has three times lower resolution as that of the right. The left Mastcam image’s resolution is first enhanced with three methods: Bicubic interpolation, pansharpening-based method, and a deep learning super resolution method. The enhanced left camera image and the right camera image are then used to estimate the disparity map. The impact of the left camera image enhancement is examined. The comparative performance analyses showed that the left camera enhancement results in getting more accurate disparity maps in comparison to using the original left Mastcam images for disparity map estimation. The deep learning-based method provided the best performance among the three for both image enhancement and disparity map estimation accuracy. A high-resolution disparity map, which is the result of the left camera image enhancement, is anticipated to improve the conducted science products in the Mastcam imagery such as 3D scene reconstructions, depth maps, and anaglyph images.
Approximate pattern matching on elastic-degenerate text

An elastic-degenerate string is a sequence of n sets of strings of total length N. It has been introduced to represent a multiple alignment of several closely-related sequences (e.g., pan-genome) compactly. In this representation, substrings of these sequences that match exactly are collapsed, while in positions where the sequences differ, all possible variants observed at that location are listed. The natural problem that arises is finding all matches of a deterministic pattern of length m in an elastic-degenerate text. There exists a non-combinatorial O(nm1.381+N)-time algorithm to solve this problem on-line [1]. In this paper, we study the same problem under the edit distance model and present an O(k2mG+kN)-time and O(m)-space algorithm, where G is the total number of strings in the elastic-degenerate text and k is the maximum edit distance allowed. We also present a simple O(kmG+kN)-time and O(m)-space algorithm for solving the problem under Hamming distance.
Image Forgery Detection: A Low Computational-Cost and Effective Data-Driven Model

Nowadays, Image Forgery Detection contributes an indispensable role in digital forensics, while there are increasingly more sophisticated forgery methods.In overall, almost conventional methods just focus on identifying specific features in tampered images, therefore, such methods cannot cover whole possible cases in reality.Recently, some data-driven proposals have been exploited to handle these barriers and attained prominent results.However, almost these ones are hungry to data because of the complication in deep architectures, which requires a large amount of data and an energetic implementation hardware.In this paper, we propose a low computational-cost and effective data-driven model as a modified deep learning-based model to solve the existing problems above.The process of approach is overviewed as follows: Firstly, the Daubechies Wavelet transform is utilized to extract features of size 450, representing YCrCb patches inside the image.Then, a neural network is used to classify forged patches.However, when conducting a discrimination analysis, we found that the luminance channel (Y) does not play an essential role in the forgery detection, whereas, it is better by using two chrominance channels (Cr and Cb).The idea is stated by removing these luminance features, then the feature vector dimension changes to as two-thirds as its origin, which reduces efficiently the computational cost in both of training and testing processes.The experimental results reveal that our proposed method reaches a high detection accuracy of 97.11%, even the model suffers in some difficult circumstances (e.g., narrowness, and lack of positive training samples).As a result, the proposed model is effective to address the mentioned challenges.