Reviewerly/exHarmony huggingface dataset:
The Spike-and-Slab Lasso regression modeling with compositional covariates: An application on Brazilian children malnutrition data

There are considerable challenges in analyzing large-scale compositional data. In this paper, we introduce the Spike-and-Slab Lasso linear regression in the presence of compositional covariates for parameter estimation and variable selection. We consider the well-known isometric log-ratio (ilr) coordinates to avoid misleading statistical inference. The separable and non-separable (adaptative) Spike-and-Slab Lasso penalties are compared to verify the advantages of each approach. The proposed method is illustrated on simulated and on real Brazilian child malnutrition data.
Scenario-Based Digital Forensic Investigation of Compromised MySQL Database

Insider and outsider database threats have more often than not posed a greater challenge as far as integrity and investigation of databases is concerned. Database forensic investigation is a process through which scientifically proven methods can be used to create a hypothesis that can prove or disprove the occurrence of a potential security incident. This paper explores the techniques that can be used to conduct forensic investigations of a compromised MySQL database. The authors have simulated investigative scenarios that have aided to conduct forensic investigative processes and the results are promising.
What does AI’s success playing complex board games tell brain scientists?

In research, it sometimes happens that advances in one field unexpectedly inform another in a fundamental way. A case in point may be what computer-generated gameplay suggests about how brains operate. Spearheaded by Demis Hassabis, David Silver, and their colleagues at the artificial intelligence (AI) company Google DeepMind, a series of articles published over the past several years has reported ongoing improvement of a computational strategy (1⇓–3) that now beats all players in complex board games (4). Beginning in 2015 with a computer program that beat human opponents at all 49 games in the Atari 2600 suite (challenges that include Pong, Space Invaders, and Pac-Man) (1), the group progressed to a program called AlphaGo that beat the European and world champions at Go (2, 3), a territorial board game far more complicated than chess. The latest version in this effort, called AlphaZero (4), now beats the best players—human or machine—in chess and shogi (Japanese chess) as well as Go.Fig. 1. The search space of the territorial board game Go is intractable using logical algorithms. Image credit: Shutterstock/Saran Poroong.That machines can beat expert human players in board games is not news. The achievement of this long-standing goal in computer science first attracted widespread attention in 1997 when IBM's program “Deep Blue” beat Garry Kasparov, then the world champion chess player (5). Human players have since been shown to be weak opponents in such games compared with a variety of machine programs. What is different about Google DeepMind’s AlphaZero is that the system operates by learning on a wholly empirical (trial and error) basis.Previous game-playing machines, including earlier versions in the AlphaGo series, used a combination of brute force logic, tree search, successful moves made by experts in response to specific board positions, and other “hand-crafted” expedients. … [↵][1]1Email: purves{at}neuro.duke.edu. [1]: #xref-corresp-1-1
Fast motion estimation for field sequential imaging: Survey and benchmark

Field sequential (FS) imaging comprises image acquisition systems that capture image channels in temporal sequence in order to provide the final image. A classical application is multispectral imaging. In case of dynamic scenes, the sequential nature of the acquisition imposes motion artifacts, i.e., spatially misaligned images channels. Compensating motion artifacts for this kind of imagery is non-trivial, as common methods for motion estimation rely on the intensity consistency constraint that is violated in FS imaging. This paper surveys approaches to motion compensation in the context of FS imaging. We focus on accuracy in handling intensity inconsistent data and, secondarily, speed, as FS imaging is commonly done in real-time. We introduce a conceptual classification for algorithmic approaches for motion estimation for FS imagery and discuss known and modified approaches to tackle the intensity inconsistencies between adjacent image channels using image transformation and intensity correction methods. As result, we get a set of 379 variants of motion estimation methods applicable to FS data streams. We evaluate these methods using our benchmark database, which comprises data sets from the Middlebury and the MPI Sintel databases, modified to emulate FS imagery, as well as additionally captured multispectral short wave infrared (SWIR) and sRGB image sequences, as well as simulated Time-of-Flight (ToF) image sequences that consist of four channels (called phase images). In order to quantify the motion estimation techniques, we use a ranking scheme similar to Middlebury and combine it with a run-time evaluation.
3D anatomy learning system using Virtual Reality and VR Controller

Abstract Anatomy is the branch of biological science in medical education that focuses on structured parts of living things, especially the human body. This subject is quite difficult to learn especially if only done by reading a book. Technological developments, especially applications based on 3 dimensions, are expected to help the learning process of this science subject. In this study, we proposed the use of virtual reality (VR) with an assigned controller as a tool for studying anatomy. With VR along with the controller, users experienced immersive experience while studying anatomy that they cannot get through learning by reading books only.
Hybridization of Fuzzy Min-Max Neural Networks with kNN for Enhanced Pattern Classification

Fuzzy Min-Max Neural Networks (FMNN) is a single epoch learning Pattern Classification algorithm with several advantages for online learning. The information loss due to Contraction step of FMNN leads to several improvements in literature such as MLF, FMCN etc. These approaches do not use Contraction step and provide additional structures in FMNN for decision making in overlapped regions overcoming the problem of Contraction with the cost of an increase in training complexity of FMNN. This work proposes a hybridization of FMNN with kNN algorithm for achieving the ability to handle decision making in overlapped regions without altering the structure of FMNN. Comparative studies with existing approaches over benchmark decision systems have proved the utility of the proposed kNN-FMNN approach.
Online density estimation over high-dimensional stationary and non-stationary data streams

Efficient density estimation over an open-ended stream of high-dimensional data is of primary importance to machine learning. In general, parametric methods for density estimation are not suitable for high dimensions, and the widely used non-parametric methods like kernel density estimation (KDE) method fail for high-dimensional datasets. In this paper we present a framework for density estimation over stationary and non-stationary high-dimensional data streams. It is based on a blockized implementation of the Bayesian sequential partitioning (BSP) algorithm. The proposed framework satisfies the general design criteria for systems with the mission of online machine learning and data mining over data streams.
Reducing Wrong Labels for Distant Supervision Relation Extraction with Selective Capsule Network

Distant Supervision is a common technique for relation extraction from large amounts of free texts, but introduces wrong labeled sentences at the same time. Existing deep learning approaches mainly rely on CNN-based models. However, they fail to capture spatial patterns due to the inherent drawback of pooling operations and thus lead to suboptimal performance. In this paper, we propose a novel framework based on Selective Capsule Network for distant supervision relation extraction. Compared with traditional CNN-based models, the involvement of capsule layers in the sentence encoder makes it more powerful in encoding spatial patterns, which is very important in determining the relation expressed in a sentence. To address the wrong labeling problem, we introduce a high-dimensional selection mechanism over multiple instances. It is one generalization of traditional selective attention mechanism and can be seamlessly integrated with the capsule network based encoder. Experimental results on a widely used dataset (NYT) show that our model significantly outperform all the state-of-the-art methods.
A Face Detection Using Support Vector Machine: Challenging Issues, Recent Trend, Solutions and Proposed Framework

Face detection comes under the domain of object detection and tracking. Face detection is an integral part of the motion based object detection which combines digital image processing and computer vision for the detection of instances and faces as well. This paper provides a brief overview of the recent trends; current open challenging issues and their solutions available for efficient detection of faces form video stream or still images. This paper also discusses various approaches which are widely used to detect the faces in the dynamic background, illumination and other current challenges. In the last section, a framework for face detection is also proposed using SVM classifier.
An Improved Wavelet Packet Denoising Algorithm Based on Sample Entropy for IoT

In the wavelet packet threshold denoising algorithm, retaining as many of the original signal wavelet packet coefficients as possible is necessary while eliminating noise. Due to the lack of an adaptive parameter, the traditional soft and hard threshold functions cannot be adjusted adaptively, and the useful signal is always mixed with noise, which leads to insecurity of the Internet of Things (IoT) application. Therefore, this paper discusses the significance and feasibility of sample entropy as an adaptive parameter in the wavelet packet threshold function. Furthermore, a new threshold function is proposed to improve the traditional wavelet packet denoising algorithm. The algorithm can process the wavelet packet coefficients differently for different noise backgrounds to achieve better denoising effect, thereby improving the security of the IoT. The simulation results show that compared with the classical wavelet packet threshold function, the proposed improved algorithm achieves better denoising effect and is beneficial to enhance the security of IoT applications.
Trace class Markov chains for the Normal-Gamma Bayesian shrinkage model

High-dimensional data, where the number of variables exceeds or is comparable to the sample size, is now pervasive in many scientific applications. In recent years, Bayesian shrinkage models have been developed as effective and computationally feasible tools to analyze such data, especially in the context of linear regression. In this paper, we focus on the Normal-Gamma shrinkage model developed by Griffin and Brown [7]. This model subsumes the popular Bayesian lasso model, and a three-block Gibbs sampling algorithm to sample from the resulting intractable posterior distribution has been developed in [7]. We consider an alternative two-block Gibbs sampling algorithm, and rigorously demonstrate its advantage over the three-block sampler by comparing specific spectral properties. In particular, we show that the Markov operator corresponding to the two-block sampler is trace class (and hence Hilbert-Schmidt), whereas the operator corresponding to the three-block sampler is not even Hilbert-Schmidt. The trace class property for the two-block sampler implies geometric convergence for the associated Markov chain, which justifies the use of Markov chain CLT’s to obtain practical error bounds for MCMC based estimates. Additionally, it facilitates theoretical comparisons of the two-block sampler with sandwich algorithms which aim to improve performance by inserting inexpensive extra steps in between the two conditional draws of the two-block sampler.
The Ontology of Natural Language Processing

In this paper, we describe our proposed methodology for constructing an ontology of natural language processing (NLP). We use a semi-automatic method; a combination of rule-based and machine learning techniques; to construct and populate an ontology with bilingual (English-Persian) concept labels (lexicon) and evaluate it manually. This methodology results in a complete ontology in the natural language processing domain with 887 concepts, 88 relations, and 71 features. The built ontology is populated with near 36000 NLP related papers and 32000 authors, and about 201000 "is_Related_to", 83500 "is_Author_of", and 29000 "Presented_in" relations. The instantiation is done to enable applications find experts, publications and institutions related to various topics in NLP field.
An interoperable Open Specifications Framework for Smart City urban platforms

Global acceptance and demand for urban digitization to solve current and future ecological and socio-economic challenges is increasing. Technically, the Smart City concept integrates different vertical domains and uses an Open Standardized Urban Platform (OSUP) as a horizontal integration layer. In this sense, urban platforms play an important role in the digitisation process with the objective of creating more environmentally friendly cities. They face several questions like which ICT architecture to rely on, ensure interoperability, avoid vendor lock in... However, usage of open standards is still a gap regarding interoperability, which is a key concept in urban data platforms. Hence, the present paper describes an Open Specifications Framework, based on global open standards as enabler of urban platform developments under the interoperability and openness concepts. This framework is defined in the scope of the mySMARTLife project (GA #731297) and its deployment is carried out in three cities: Nantes, Hamburg and Helsinki.
A conceptual architecture for contractual data sharing in a decentralised environment

Machine Learning systems rely on data for training, input and ongoing feedback and validation. Data in the field can come from varied sources, often anonymous or unknown to the ultimate users of the data. Whenever data is sourced and used, its consumers need assurance that the data accuracy is as described, that the data has been obtained legitimately, and they need to understand the terms under which the data is made available so that they can honour them. Similarly, suppliers of data require assurances that their data is being used legitimately by authorised parties, in accordance with their terms, and that usage is appropriately recompensed. Furthermore, both parties may want to agree on a specific set of quality of service (QoS) metrics, which can be used to negotiate service quality based on cost, and then receive affirmation that data is being supplied within those agreed QoS levels. Here we present a conceptual architecture which enables data sharing agreements to be encoded and computationally enforced, remuneration to be made when required, and a trusted audit trail to be produced for later analysis or reproduction of the environment. Our architecture uses blockchainbased distributed ledger technology, which can facilitate transactions in situations where parties do not have an established trust relationship or centralised command and control structures. We explore techniques to promote faith in the accuracy of the supplied data, and to let data users determine trade-offs between data quality and cost. Our system is exemplified through consideration of a case study using multiple data sources from different parties to monitor traffic levels in urban locations.
Evolving boolean functions with conjunctions and disjunctions via genetic programming

Recently it has been proved that simple GP systems can efficiently evolve the conjunction of n variables if they are equipped with the minimal required components. In this paper, we make a considerable step forward by analysing the behaviour and performance of a GP system for evolving a Boolean function with unknown components, i.e. the target function may consist of both conjunctions and disjunctions. We rigorously prove that if the target function is the conjunction of n variables, then a GP system using the complete truth table to evaluate program quality evolves the exact target function in O(ℓ n log2 n) iterations in expectation, where ℓ ≥ n is a limit on the size of any accepted tree. Additionally, we show that when a polynomial sample of possible inputs is used to evaluate solution quality, conjunctions with any polynomially small generalisation error can be evolved with probability 1 - O(log2(n)/n). To produce our results we introduce a super-multiplicative drift theorem that gives significantly stronger runtime bounds when the expected progress is only slightly super-linear in the distance from the optimum.
OCCAM

We study the problem of inferring the structure of a communication network based only on network measurements made from a set of hosts situated at the network periphery. Our novel approach called "OCCAM" is based on the principle of occam's razor and finds the "simplest" network that explains the observed network measurements. OCCAM infers the internal topology of a communication network, including the internal nodes and links of the network that are not amenable to direct measurement. In addition to network topology, OCCAM infers the routing paths that packets take between the hosts. OCCAM uses path metrics measurable from the hosts and expresses the observed measurements as constraints of a mixed-integer bilinear optimization problem that can then be feasibly solved to yield the network topology and the routing paths. We empirically validate OCCAM on a wide variety of real-world ISP networks and show that its inferences agree closely with the ground truth. Specifically, OCCAM infers the topology with an average network similarity score of 93% and infers routing paths with a path edit distance of 0.20. Further, OCCAM is robust to error in its measured path metric inputs, producing high quality inferences even when 20-30% of its inputs are erroneous. Our work is a significant advance in network tomography as it proposes and empirically evaluates the first method that infers the complete network topology, rather than just logical routing trees from sources.
I-mRMR: Incremental Max-Relevance, and Min-Redundancy Feature Selection

An incremental method of feature selection based on mutual information, called incremental Max-Relevance, and Min-Redundancy (I-mRMR), is presented. I-mRMR is an incremental version of Max-Relevance, and Min-Redundancy feature selection (mRMR), which is used to handle streaming data or large-scale data. First, Incremental Key Instance Set is proposed which composes of the non-distinguished instances by the historical selected features. Second, an incremental feature selection algorithm is designed in which the incremental key instance set, replacing of all the seen instances so far, is used in the process of adding representative features. Since the Incremental Key Instance Set is far less than the whole instances, the incremental feature selection by using this key set avoids redundant computation and save computation time and space. Finally, the experimental results show that I-mRMR could significantly or even dramatically reduce the time of feature selection with an acceptable classification accuracy.
The computational complexity of calculating partition functions of optimal medians with Hamming distance

We study the complexity of computing the partition function of medians for binary strings with Hamming distance using various weight functions. When the weight function is the factorial function, this partition function has application in bioinformatics, counting the most parsimonious scenarios on a star tree under the Single Cut-or-Join model for genome rearrangement. Although this model is computationally simple, we show that it is #P-complete to compute the partition function. Our results are also extended to binary trees as we show that it is #P-complete to calculate the most parsimonious scenarios on an arbitrary binary tree under the Single Cut-or-Join model. These results also apply to substitution models for many biological sequences.
Weak Keys in the Faure–Loidreau Cryptosystem

Some types of weak keys in the Faure–Loidreau (FL) cryptosystem are presented. We show that from such weak keys the private key can be reconstructed with a computational effort that is substantially lower than the security level (≈225 operations for 80-bit security). The proposed key-recovery attack is based on ideas of generalized minimum distance (GMD) decoding for rank-metric codes.
An Efficient Design Flow for Accelerating Complicated-connected CNNs on a Multi-FPGA Platform

Convolutional Neural Networks (CNNs) have achieved impressive performance on various computer vision tasks. To facilitate better performance, some complicated-connected CNN models (e.g., GoogLeNet and DenseNet) have recently been proposed, and have achieved state-of-the-art performance in the fields of image classification and segmentation. However, CNNs are computation- and memory-intensive. Thus, it is significant to develop hardware accelerators in order to accelerate the inference and training processes of CNNs. Due to the high-performance, reconfigurable and energy-efficient nature of Field-Programmable Gate Arrays (FPGAs), many FPGA-based accelerators have been proposed to implement CNNs and have achieved higher throughput and energy efficiency. However, the large number of parameters involved in complicated-connected CNN models have exceeded the limited hardware resources of single FPGA board, which are unable to meet the memory and computation resource demands associated with mapping entire CNN models. Accordingly, in this paper, we propose a complete design flow to accelerate the inference of complicated-connected CNNs on a multi-FPGA platform, including DAG abstraction, mapping scheme generation and design space exploration. In addition, a multi-FPGA system with flexible inter-FPGA communications is proposed to efficiently support our design flow. Experimental results on representative models illustrate that the proposed multi-FPGA system design can achieve a throughput acceleration of up to 145.2× and 2.5× compared to CPU and GPU solutions, as well as an energy efficiency improvement of up to 139.1× and 4.8× compared to multi-core CPU and GPU solutions.
Counting Rational Points of an Algebraic Variety over Finite Fields

Let $${\mathbb {F}}_q$$ denote the finite field of odd characteristic p with q elements ( $$q=p^{n},n\in {\mathbb {N}} $$ ) and $${\mathbb {F}}_q^*$$ represent the nonzero elements of $${\mathbb {F}}_{q}$$ . In this paper, by using the Smith normal form we give a formula for the number of rational points of the algebraic varieties defined by the following system of equations over $${\mathbb {F}}_{q}$$ : $$\begin{aligned} \left\{ \begin{array}{ll} \sum \limits _{i=1}^{r_1}a_{1i}x_1^{e^{(1)}_{i1}} \ldots x_{n_1}^{e^{(1)}_{i,n_1}} +\sum \limits _{i=r_1+1}^{r_2}a_{1i}x_1^{e^{(1)}_{i1}} \ldots x_{n_2}^{e^{(1)}_{i,n_2}}-b_1=0,\\ \sum \limits _{j=1}^{r_3}a_{2j}x_1^{e^{(2)}_{j1}} \ldots x_{n_3}^{e^{(2)}_{j,n_3}} +\sum \limits _{j=r_3+1}^{r_4}a_{2j}x_1^{e^{(2)}_{j1}} \ldots x_{n_4}^{e^{(2)}_{j,n_4}}-b_2=0, \end{array}\right. \end{aligned}$$ where the integers $$1\le r_1<r_2$$ , $$1\le r_3<r_4$$ , $$1\le n_1<n_2$$ , $$1\le n_3<n_4$$ , $$n_1\le n_3$$ , $$b_1, b_2\in \mathbb {F}_{q}$$ , $$a_{1i}\in \mathbb {F}_{q}^{*}(1\le i\le r_2)$$ , $$a_{2j}\in \mathbb {F}_{q}^{*}(1\le j\le r_4)$$ and the exponent of each variable is positive integer. Our result provides a partial answer to an open problem raised in Hu et al. (J Number Theory 156:135–153, 2015).