Reviewerly/exHarmony huggingface dataset:
Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization

Alternating direction method of multipliers (ADMM) is a popular optimization tool for the composite and constrained problems in machine learning. However, in many machine learning problems such as black-box learning and bandit feedback, ADMM could fail because the explicit gradients of these problems are difficult or even infeasible to obtain. Zeroth-order (gradient-free) methods can effectively solve these problems due to that the objective function values are only required in the optimization. Recently, though there exist a few zeroth-order ADMM methods, they build on the convexity of objective function. Clearly, these existing zeroth-order methods are limited in many applications. In the paper, thus, we propose a class of fast zeroth-order stochastic ADMM methods (\emph{i.e.}, ZO-SVRG-ADMM and ZO-SAGA-ADMM) for solving nonconvex problems with multiple nonsmooth penalties, based on the coordinate smoothing gradient estimator. Moreover, we prove that both the ZO-SVRG-ADMM and ZO-SAGA-ADMM have convergence rate of $O(1/T)$, where $T$ denotes the number of iterations. In particular, our methods not only reach the best convergence rate of $O(1/T)$ for the nonconvex optimization, but also are able to effectively solve many complex machine learning problems with multiple regularized penalties and constraints. Finally, we conduct the experiments of black-box binary classification and structured adversarial attack on black-box deep neural network to validate the efficiency of our algorithms.
Alteration mapping with short wavelength infrared (SWIR) spectroscopy on Xiaokelehe porphyry Cu-Mo deposit in the Great Xing’an Range, NE China: Metallogenic and exploration implications

The newly discovered Xiaokelehe porphyry Cu-Mo deposit (PCD) is situated in northern part of the Great Xing'an metallogenic belt, northeastern China. The syn-mineralization granodiorite porphyry was intruded by multiple phases of post-ore barren monzonite, diorite porphyry and granite porphyry, leading to complex and overprinting hydrothermal alteration patterns. This paper present new data on the alteration mineralogy, vein paragenesis, sulfide assemblages and SWIR (short wavelength infrared) spectroscopy at Xiaokelehe, and discuss how SWIR spectral data can help to understand hydrothermal fluid evolution and assist PCD exploration. At Xiaokelehe, oligoclase-epidote alteration is distributed at the deeper levels of the granodiorite porphyry but played little role in mineralization. The following potassic alteration (hydrothermal biotite and K-feldspar) was developed in the granodiorite porphyry with the main Cu-Mo mineralization. Drill core logging suggests that the subsequent chlorite-illite alteration affected almost the entire granodiorite porphyry, and is spatially and genetically associated with minor Cu mineralization. The youngest quartz-muscovite-pyrite alteration dominated in the upper level and outer parts of the PCD, and overprints the earlier alteration phases with only very minor mineralization. The short wavelength infrared (SWIR) analysis results indicate that chlorite and white micas (illite and muscovite) are the most abundant alteration minerals at Xiaokelehe. The wavelengths of Fe-OH band (Pos2250) of chlorite show a decreasing trend away from the granodiorite porphyry into the surrounding sedimentary wall rocks, probably reflecting a drop in temperature and Fe contents of the hydrothermal fluids. The Illite crystallinity (IC) values of white micas show a reverse trend, different from many PCDs, which is likely caused by the distribution pattern of illite and muscovite formed in the ore-proximal chlorite-illite and ore-distal quartz-muscovite-pyrite alteration, respectively. At Xiaokelehe, Cu and Mo mineralization is mainly distributed in quartz veins or disseminated within the potassic alteration zones, resembling typical PCDs. Meanwhile, chlorite-illite alteration is also associated with minor Cu mineralization. Thus, areas with intensive chlorite-illite alteration overlapping on potassic alteration likely represent the best exploration targets for high grade Cu-Mo mineralization. These target areas coincide spatially with areas of lower IC (<0.8) and higher Pos2250 (>2245 nm) values, suggesting the latter two are effective vectoring tools for PCD exploration at Xiaokelehe.
Market attention and Bitcoin price modeling: theory, estimation and option pricing

The goal of this paper is to provide a novel quantitative framework to describe the Bitcoin price behavior, estimate model parameters and study the pricing problem for Bitcoin derivatives. To this end, we propose a continuous time model for Bitcoin price motivated by the findings in recent literature on Bitcoin, showing that price changes are affected by sentiment and attention of investors, see e.g., (Kristoufek in Sci Rep 3:3415, 2013, PLoS ONE 10(4):e0123923, 2015; Bukovina and Marticek in Sentiment and bitcoin volatility. Technical report, Mendel University in Brno, Faculty of Business and Economics 2016). Economic studies, such as Yermack (Handbook of Digital Currency, chapter second. Elsevier, Amsterdam, pp 31–43, 2015), have also classified Bitcoin as a speculative asset rather than a currency due to its high volatility. Building on these outcomes, the price dynamics in our suggestion is indeed affected by an exogenous factor which represents market attention in the Bitcoin system. We prove the model to be arbitrage-free under a mild condition and we fit the model to historical data for the Bitcoin price; after obtaining a approximate formula for the likelihood, parameter values are estimated by means of the profile likelihood method. In addition, we derive a closed pricing formula for European-style derivatives on Bitcoin, the performance of which is assessed on a panel of market prices for Plain Vanilla options quoted on www.deribit.com .
Review of different techniques for object detection using deep learning

Human brain takes less than a minute to identify the location of object inside the image as well as recognize it as soon as it sees to it; but machine needs time and large amount of data to do the same task. Deep neural network based on convolution neural network gives high accuracy and great results in object detection and classification. To train deep neural networks, large amount of data such as (images and videos) and time is required. As computational cost of computer vision is very high, transfer-learning technique, where a model trained on one task is reused on another related task, gives better results. Authors have proposed various deep learning based algorithms for object detection and classification like Region based Convolutional neural network, Fast Region based Convolutional neural network, Faster Region based Convolutional neural network, Mask Region based Convolutional neural network and You Only Look Once. In this paper, a comparative study of different algorithms is given.
Coordinated multi-robot planning while preserving individual privacy

We consider the problem of multiple robots that must cooperate within a shared environment, but which wish to limit the information they disclose during their coordination efforts. Specifically, we examine the problems of privacy-preserving rendezvous and persistent monitoring. In the former, the robots construct a joint plan to have them meet, without either knowing beforehand where or when the meeting will occur. In the latter, multiple robots dynamically cover a region of space-they plan collective motions which are collision-free but with the assurance that agents remain ignorant of the paths of others. Accordingly, the tasks are sort of inverses in that the robots must collectively determine whether their joint paths collide or not, then, using this, achieve their collective task. Other than what is learned by the outcome of the joint-collision determination, the robots possess no details of the other paths. Our approach builds on garbled circuits and homomorphic encryption to realize basic secure path intersection primitives. We present algorithms, a software implementation, and a physical experiment on mobile robots to test the practical feasibility of our approach. We believe that these ideas provide a valuable direction for adoption in small Unmanned Systems belonging to different stakeholders.
Development of Public Key Cryptographic Algorithm Using Matrix Pattern for Tele-Ultrasound Applications

A novel public key cryptographic algorithm using a matrix pattern is developed to improve encrypting strength. Compared to the Rivest–Sharmir–Adleman (RSA) and Elliptic Curve Cryptography (ECC) algorithms, our proposed algorithm has superior encrypting strength due to several unknown quantities and one additional sub-equation during the encrypting process. Our proposed algorithm also provides a faster encoding/decoding speed when the patient’s images for tele-ultrasound applications are transmitted/received, compared to the RSA and ECC encrypting algorithms, because it encodes/decodes the plain memory block by simple addition and multiplication operations of n terms. However, the RSA and ECC algorithms encode/decode each memory block using complex mathematical exponentiation and congruence. To implement encrypting algorithms for tele-ultrasound applications, a streaming server was constructed to transmit the images to the systems using ultrasound machines. Using the obtained ultrasound images from a breast phantom, we compared our developed algorithm, utilizing a matrix pattern, with the RSA and ECC algorithms. The elapsed average time for our proposed algorithm is much faster than that for the RSA and ECC algorithms.
Converting quantum coherence to genuine multipartite entanglement and nonlocality

We study the relations between quantum coherence and quantum nonlocality, genuine quantum entanglement, and genuine quantum nonlocality. We show that the coherence of a qubit state can be converted to the nonlocality of two-qubit states via incoherent operations. The results are also generalized to the qudit case. Furthermore, rigorous relations between the quantum coherence of a single-partite state and the genuine multipartite quantum entanglement, as well as the genuine three-qubit quantum nonlocality, are established.
Feature Selection Using an Improved Gravitational Search Algorithm

Feature selection is an important issue in the field of machine learning, which can reduce misleading computations and improve classification performance. Generally, feature selection can be considered as a binary optimization problem. Gravitational Search Algorithm (GSA) is a population-based heuristic algorithm inspired by Newton’s laws of gravity and motion. Although GSA shows good performance in solving optimization problems, it has a shortcoming of premature convergence. In this paper, the concept of global memory is introduced and the definition of exponential <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Kbest$ </tex-math></inline-formula> is used in an improved version of GSA called IGSA. In this algorithm, the position of the optimal solution obtained so far is memorized, which can effectively prevent particles from gathering together and moving slowly. In this way, the exploitation ability of the algorithm gets improved, and a proper balance between exploration and exploitation gets established. Besides, the exponential <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Kbest$ </tex-math></inline-formula> can significantly decrease the running time. In order to solve feature selection problem, a binary IGSA (BIGSA) is further introduced. The proposed algorithm is tested on a set of standard datasets and compared with other algorithms. The experimental results confirm the high efficiency of BIGSA for feature selection.
Gradient Boosting with Piece-Wise Linear Regression Trees

Gradient Boosted Decision Trees (GBDT) is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, several variants of GBDT training algorithms and implementations have been designed and heavily optimized in some very popular open sourced toolkits including XGBoost, LightGBM and CatBoost. In this paper, we show that both the accuracy and efficiency of GBDT can be further enhanced by using more complex base learners. Specifically, we extend gradient boosting to use piecewise linear regression trees (PL Trees), instead of piecewise constant regression trees, as base learners. We show that PL Trees can accelerate convergence of GBDT and improve the accuracy. We also propose some optimization tricks to substantially reduce the training time of PL Trees, with little sacrifice of accuracy. Moreover, we propose several implementation techniques to speedup our algorithm on modern computer architectures with powerful Single Instruction Multiple Data (SIMD) parallelism. The experimental results show that GBDT with PL Trees can provide very competitive testing accuracy with comparable or less training time.
A telehealth system framework for assessing knee-joint conditions using vibroarthrographic signals

Hectic lifestyle coupled sometimes with deficient choices in food and exercise, has led to an increase in the number of individuals being diagnosed with joint generation disorders. In addition, a substantial number of individuals in specialized occupations (such as army) or sports suffer from joint related injuries, thereby leading to surgical replacements and in some severe cases as disability. Standard methods of detecting joint disorders or disabilities include arthroscopy and arthrogram. Vibroarthrography (VAG) is a non-invasive technique wherein the specialist listens to sounds generated from joint movements recorded using actigraphs. In this study, we propose a system which encodes, analyses and segments actigraphy-based VAG data for assessing the severity of cartilage degeneration, and highlighting instances of activity which causes crackling sounds during limb movements. The proposed system encapsulates IoMT (Internet of Medical Things) requirements by providing efficient data compression and analysis at the source. Using an actigraphy dataset from 89 participants, our experiments yielded that the system is able to compress the actigraphy data into 3-bits per sample, thereby reducing the signal size by about 88%, without losing any vital limb movement information. This has been further validated using a simple pattern classification of 3-bit quantized VAG data from participants with healthy and unhealthy knee joints. The method, which yielded a recognition accuracy of 84.6%, as compared to raw VAG data which yielded 80% accurate results. In addition, the proposed adaptive segmentation scheme in the system, leverages the 3-bit encoding by correctly identifying 90% of the segments of interest from each VAG signal.
Positive and negative association rule mining in Hadoop’s MapReduce environment

In this paper, we present a Hadoop implementation of the Apriori algorithm. Using Hadoop’s distributed and parallel MapReduce environment, we present an architecture to mine positive as well as negative association rules in big data using frequent itemset mining and the Apriori algorithm. We also analyze and present the results of a few optimization parameters in Hadoop’s MapReduce environment as it relates to this algorithm. The results are presented based on the number of rules generated as well as the run-time efficiency. We find that, a higher amount of parallelization, which means larger block sizes, will increase the run-time efficiency of the Hadoop implementation of the Apriori algorithm.
Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes

Radio-over-fiber (RoF) orthogonal frequency division multiplexing (OFDM) systems have been revealed as the solution to support secure, cost-effective, and high-capacity wireless access for the future telecommunication systems. Unfortunately, the bandwidth-distance product in these schemes is mainly limited by phase noise that comes from the laser linewidth, as well as the chromatic fiber dispersion. On the other hand, the single-hidden layer feedforward neural network subject to the extreme learning machine (ELM) algorithm has been widely studied in regression and classification problems for different research fields, because of its good generalization performance and extremely fast learning speed. In this work, ELMs in the real and complex domains for direct-detection OFDM-based RoF schemes are proposed for the first time. These artificial neural networks are based on the use of pilot subcarriers as training samples and data subcarriers as testing samples, and consequently, their learning stages occur in real-time without decreasing the effective transmission rate. Regarding the feasible pilot-assisted equalization method, the effectiveness and simplicity of the ELM algorithm in the complex domain are highlighted by evaluation of a QPSK-OFDM signal over an additive white Gaussian noise channel at diverse laser linewidths and chromatic fiber dispersion effects and taking into account several OFDM symbol periods. Considering diverse relationships between the fiber transmission distance and the radio frequency (for practical design purposes) and the duration of a single OFDM symbol equal to 64 ns, the fully-complex ELM followed by the real ELM outperform the pilot-based correction channel in terms of the system performance tolerance against the signal-to-noise ratio and the laser linewidth.
Improving the Robustness of Scagnostics

In this paper, we examine the robustness of scagnostics through a series of theoretical and empirical studies. First, we investigate the sensitivity of scagnostics by employing perturbing operations on more than 60M synthetic and real-world scatterplots. We found that two scagnostic measures, Outlying and Clumpy, are overly sensitive to data binning. To understand how these measures align with human judgments of visual features, we conducted a study with 24 participants, which reveals that i) humans are not sensitive to small perturbations of the data that cause large changes in both measures, and ii) the perception of clumpiness heavily depends on per-cluster topologies and structures. Motivated by these results, we propose Robust Scagnostics (RScag) by combining adaptive binning with a hierarchy-based form of scagnostics. An analysis shows that RScag improves on the robustness of original scagnostics, aligns better with human judgments, and is equally fast as the traditional scagnostic measures.
Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog

We propose a semantic parser for parsing compositional utterances into Task Oriented Parse (TOP), a tree representation that has intents and slots as labels of nesting tree nodes.Our parser is span-based: it scores labels of the tree nodes covering each token span independently, but then decodes a valid tree globally.In contrast to previous sequence decoding approaches and other span-based parsers, we (1) improve the training speed by removing the need to run the decoder at training time; and(2) introduce edge scores, which model relations between parent and child labels, to mitigate the independence assumption between node labels and improve accuracy.Our best parser outperforms previous methods on the TOP dataset of mixed-domain task-oriented utterances in both accuracy and training speed.
ner and pos when nothing is capitalized

For those languages which use it, capitalization is an important signal for the fundamental NLP tasks of Named Entity Recognition (NER) and Part of Speech (POS) tagging.In fact, it is such a strong signal that model performance on these tasks drops sharply in common lowercased scenarios, such as noisy web text or machine translation outputs.In this work, we perform a systematic analysis of solutions to this problem in English, modifying only the casing of the train or test data using lowercasing and truecasing methods.While prior work and first impressions might suggest training a caseless model, or using a truecaser at test time, we show that the most effective strategy is a concatenation of cased and lowercased training data, producing a single model with high performance on both cased and uncased text.As shown in our experiments, this result holds across tasks and input representations.Finally, we show that our proposed solution gives an 8% F1 improvement in mention detection on noisy out-of-domain Twitter data.
Semantic Role Labeling with Iterative Structure Refinement

Modern state-of-the-art Semantic Role Labeling (SRL) methods rely on expressive sentence encoders (e.g., multi-layer LSTMs) but tend to model only local (if any) interactions between individual argument labeling decisions.This contrasts with earlier work and also with the intuition that the labels of individual arguments are strongly interdependent.We model interactions between argument labeling decisions through iterative refinement.Starting with an output produced by a factorized model, we iteratively refine it using a refinement network.Instead of modeling arbitrary interactions among roles and words, we encode prior knowledge about the SRL problem by designing a restricted network architecture capturing non-local interactions.This modeling choice prevents overfitting and results in an effective model, outperforming strong factorized baseline models on all 7 CoNLL-2009 languages, and achieving state-of-the-art results on 5 of them, including English.
Knowledge Graph Application in Education: a Literature Review

In modern and developing economic systems, Knowledge Management (KM) is considered to be one of the most important activities of almost any organization. KM process at universities includes didactic processes, among which we distinguish the process of individualization of education. Such process requires a large amount of information to be processed both by university workers and students. This paper suggests that Knowledge Graphs are a technology that facilitates and enhances KM processes at universities and gives an extended review of a Knowledge Graph phenomenon.
EED: Extended Edit Distance Measure for Machine Translation

Over the years a number of machine translation metrics have been developed in order to evaluate the accuracy and quality of machinegenerated translations.Metrics such as BLEU and TER have been used for decades.However, with the rapid progress of machine translation systems, the need for better metrics is growing.This paper proposes an extension of the edit distance, which achieves better human correlation, whilst remaining fast, flexible and easy to understand.
Relative Importance of Binocular Disparity and Motion Parallax for Depth Estimation: A Computer Vision Approach

Binocular disparity and motion parallax are the most important cues for depth estimation in human and computer vision. Here, we present an experimental study to evaluate the accuracy of these two cues in depth estimation to stationary objects in a static environment. Depth estimation via binocular disparity is most commonly implemented using stereo vision, which uses images from two or more cameras to triangulate and estimate distances. We use a commercial stereo camera mounted on a wheeled robot to create a depth map of the environment. The sequence of images obtained by one of these two cameras as well as the camera motion parameters serve as the input to our motion parallax-based depth estimation algorithm. The measured camera motion parameters include translational and angular velocities. Reference distance to the tracked features is provided by a LiDAR. Overall, our results show that at short distances stereo vision is more accurate, but at large distances the combination of parallax and camera motion provide better depth estimation. Therefore, by combining the two cues, one obtains depth estimation with greater range than is possible using either cue individually.