Reviewerly/exHarmony huggingface dataset:
Detecting Breast Cancer in Women with Dense Breasts

Screening asymptomatic patients for cancer is based on a simple premise: cancers that are detected early before they produce symptoms should be easier to cure than those that progress to become clinically detectable. In practice, tools have been developed to realize the detection of clinically undetectable cancers, but the effect of their application on overall survival has been enormously challenging to demonstrate. Several factors contribute to the difficulty in proving the value of cancer screening. First, any test will have false negatives, in which case a patient’s cancer goes undetected. Second, tests for cancer are quite often falsely positive because . . .
A Novel Design of Adaptive and Hierarchical Convolutional Neural Networks using Partial Reconfiguration on FPGA

Nowadays most research in visual recognition using Convolutional Neural Networks (CNNs) follows the “deeper model with deeper confidence” belief to gain a higher recognition accuracy. At the same time, deeper model brings heavier computation. On the other hand, for a large chunk of recognition challenges, a system can classify images correctly using simple models or so-called shallow networks. Moreover, the implementation of CNNs faces with the size, weight, and energy constraints on the embedded devices. In this paper, we implement the adaptive switching between shallow and deep networks to reach the highest throughput on a resource-constrained MPSoC with CPU and FPGA. To this end, we develop and present a novel architecture for the CNNs where a gate makes the decision whether using the deeper model is beneficial or not. Due to resource limitation on FPGA, the idea of partial reconfiguration has been used to accommodate deep CNNs on the FPGA resources. We report experimental results on CIFAR-10, CIFAR-100, and SVHN datasets to validate our approach. Using confidence metric as the decision making factor, only 69.8%, 71.8%, and 43.8% of the computation in the deepest network is done for CIFAR10, CIFAR-100, and SVHN while it can maintain the desired accuracy with the throughput of around 400 images per second for SVHN dataset. https://github.com/mfarhadi/AHCNN.
Maximum-Margin Hamming Hashing

Deep hashing enables computation and memory efficient image search through end-to-end learning of feature representations and binary codes. While linear scan over binary hash codes is more efficient than over the high-dimensional representations, its linear-time complexity is still unacceptable for very large databases. Hamming space retrieval enables constant-time search through hash lookups, where for each query, there is a Hamming ball centered at the query and the data points within the ball are returned as relevant. Since inside the Hamming ball implies retrievable while outside irretrievable, it is crucial to explicitly characterize the Hamming ball. The main idea of this work is to directly embody the Hamming radius into the loss functions, leading to Maximum-Margin Hamming Hashing (MMHH), a new model specifically optimized for Hamming space retrieval. We introduce a max-margin t-distribution loss, where the t-distribution concentrates more similar data points to be within the Hamming ball, and the margin characterizes the Hamming radius such that less penalization is applied to similar data points within the Hamming ball. The loss function also introduces robustness to data noise, where the similarity supervision may be inaccurate in practical problems. The model is trained end-to-end using a new semi-batch optimization algorithm tailored to extremely imbalanced data. Our method yields state-of-the-art results on four datasets and shows superior performance on noisy data.
OpenVINO Deep Learning Workbench: Comprehensive Analysis and Tuning of Neural Networks Inference

A task of maximizing deep learning neural networks performance is a challenging and actual goal of modern hardware and software development. Regardless the huge variety of optimization techniques and emerging dedicated hardware platforms, the process of tuning the performance of the neural network is hard. It requires configuring dozens of hyper parameters of optimization algorithms, selecting appropriate metrics, benchmarking the intermediate solutions to choose the best method, platform etc. Moreover, it is required to setup the hardware for the specific inference target. This paper introduces a sophisticated software solution (Deep Learning Workbench) that provides interactive user interface, simplified process of 8-bit quantization, speeding up convolutional operations using the Winograds minimal filtering algorithms, measuring accuracy of the resulting model. The proposed software is built over the open source OpenVINO framework and supports huge range of modern deep learning models.
MFC: Initialization method for multi-label feature selection based on conditional mutual information

Feature selection is widely used in multi-label classification because of its simplicity, efficiency, and accuracy. Specifically, evolutionary algorithm (EA)-based multi-label feature selection methods have produced good potential. However, in conventional EA-based approaches, the initial population is usually generated at random, because knowledge about the input dataset is not usually available. In this study, we propose a method for generating the initial population of an EA-based multi-label feature selection method considering dependencies between features and labels. To the best of our knowledge, this is the first initialization method described for EA-based multi-label feature selection. The initial population generated by the proposed method can be easily applied to the EA-based feature selection method because it is independent of EA-based feature selection algorithms.
Hybrid Approach for Sentiment Analysis of Twitter Posts Using a Dictionary-based Approach and Fuzzy Logic Methods

Recently, sentiment analysis of social media has become a hot topic because of the huge amount of information that is provided in these networks. Twitter is a popular social media application offers businesses and government the opportunities to share and acquire information. This article proposes a technique that aims at measuring customers' satisfaction with cloud service providers, based on their tweets. Existing techniques focused on classifying sentimental text as either positive or negative, while the proposed technique classifies the tweets into five categories to provide better information. A hybrid approach of dictionary-based and Fuzzy Inference Process (FIP) is developed for this purpose. This direction was selected for its advantages and flexibility in addressing complex problems, using terms that reflect on human behaviors and experiences. The proposed hybrid-based technique used fuzzy systems in order to accurately identify the sentiment of the input text while addressing the challenges that are facing sentiment analysis using various fuzzy parameters.
Blockchain Based Car-Sharing Platform

The car-sharing market is constantly growing and recently it has become even more popular than car ownership. However, classic car-sharing system is based on a centralized database server which can often lead to hacker attacks or password leaks. Moreover, in a classic car-sharing system, the owners of the cars can misuse customers' data. As seen nowadays from a lot of use cases, the best solution to these problematic issues is to use blockchain technology. Blockchain as decentralized, immutable, public ledger provides the customers with security that is impossible to tamper. The aim of the proposed solution is to create and implement peer-to-peer short term car-sharing application based on blockchain technology and smart contracts. For the implementation of smart contracts, Solidity programming language is used. Solidity works with Ethereum blockchain. The key novelty of the article is introducing a peer-to-peer car sharing service without a central authority, what reflects a decrease of costs and increase of data transparency in that system. Also token based solution gives us ability to cover business-to-business (B2B) and business-to-customer (B2C) use cases.
Low-rank nonnegative matrix factorization on Stiefel manifold

Low rank is an important but ill-posed problem in the development of nonnegative matrix factorization (NMF) algorithms because the essential information is often encoded in a low-rank intrinsic data matrix, whereas noise and outliers are contained in a residue matrix. Most existing NMF approaches achieve low rank by directly specifying the dimensions of the factor matrices. A few others impose the low rank constraint on the factor matrix and use the alternating direction method of multipliers to solve the optimization problem. In contrast to previous approaches, this paper proposes a novel method for low-rank nonnegative matrix factorization on a Stiefel manifold (LNMFS), which utilizes the low rank structure of intrinsic data and transforms it into a Frobenius norm of the latent factors. To obtain orthogonal factors as distinct patterns, we further impose orthogonality constraints by assuming that the basis matrix lies on a Stiefel manifold. In addition, to improve the robustness of the data in a manifold structure, we incorporate the graph smoothness of the coefficient matrix. Finally, we develop an efficient alternative iterative algorithm to solve the optimization problem and provide proof of its convergence. Extensive experiments on real-world datasets demonstrate the superiority of the proposed method compared with other representative NMF-based algorithms.
Building of the Predicate Recognition System for the NLP Ontology Learning Module

The practical approach to recognizing a description logic predicate in a natural language sentence is presented. The approach is based on using the open source tool - Link Grammar Parser in combination with Protégé-OWL API. Machine learning method of recognition based on naïve Bayes allows distinguishing kind of previously learned binary predicate and roles of its attributes. The predicate recognition system had been included into ontology learning module of the CROCUS application.
Linkage Between Piecewise Constant Mumford--Shah Model and Rudin--Osher--Fatemi Model and Its Virtue in Image Segmentation

The piecewise constant Mumford--Shah (PCMS) model and the Rudin--Osher--Fatemi (ROF) model are two important variational models in image segmentation and image restoration, respectively. In this paper, we explore a linkage between these models. We prove that for the two-phase segmentation problem a partial minimizer of the PCMS model can be obtained by thresholding the minimizer of the ROF model. A similar linkage is still valid for multiphase segmentation under specific assumptions. Thus it opens a new segmentation paradigm: image segmentation can be done via image restoration plus thresholding. This new paradigm, which circumvents the innate nonconvex property of the PCMS model, therefore, improves the segmentation performance in both efficiency (much faster than state-of-the-art methods based on the PCMS model, particularly when the phase number is high)the and effectiveness (producing segmentation results with better quality) due to the flexibility of the ROF model in tackling degraded images, such as noisy images, blurry images, or images with information loss. As a by-product of the new paradigm, we derive a novel segmentation method, called thresholded-ROF (T-ROF) method, to illustrate the virtue of managing image segmentation through image restoration techniques. The convergence of the T-ROF method is proved, and elaborate experimental results and comparisons are presented.
Trajectory Optimization of a Redundant Serial Robot Using Cartesian via Points and Kinematic Decoupling

Moving from a given position to another with an industrial robot can be a challenging problem when the task is redundant around the tool axis. In this case, there are infinite ways of choosing both the starting and the ending configurations, so that the movement between the given points is not uniquely defined. In this paper, an algorithm that calculates the suboptimal movement between two positions is proposed, which automatically generates a cloud of safe via points around the workpiece and then by exploiting such points finds the suboptimal safe path between the two positions that minimizes movement time. The proposed method, in which the search of the suboptimal path is based on graph theory and the Dijkstra algorithm, can iteratively evaluate a high number of starting and ending configurations in low computational time, allowing performing a reasonably wide search of the suboptimal path within the infinite possible motions between the given points.
Task Scheduling based on Modified Grey Wolf Optimizer in Cloud Computing Environment

Task scheduling is considered as one of the most critical problems in cloud computing environment. The main target of task scheduling includes scheduling jobs on virtual machines as well as improves performance. This study employed Grey Wolf Optimization (GWO) algorithm with modifications on the fitness function by making it handles multi-objectives in single fitness; the makespan and cost are the objectives included in the fitness in order to solve task scheduling problem. The main target of this technique is to reduce both cost and makespan. CloudSim tool is used to evaluate the objectives of the proposed method. The simulation results showed that the proposed method (Modified Grey Wolf Optimizer - MGWO) has better performance than both the traditional Grey Wolf Optimization Algorithm (GWO) and Whale Optimization Algorithm (WOA) with makespan based fitness in terms of makespan, cost and degree of imbalance.
A novel approach for modeling positive vectors with inverted Dirichlet-based hidden Markov models

Hidden Markov Models (HMMs) are among the most remarkably powerful probabilistic models, that although been acknowledged for decades have recently made a huge resurgence in the machine learning field. Their ever-growing use to model diversified and heterogeneous data (image,video, audio, time series) in numerous important practical situations is the subject of all forms of perpetual extensions. This work presents what we believe to be the first integration of the Inverted Dirichlet (ID) Mixture Models into the framework of HMMs. The proposed method uses the inverted Dirichlet mixtures to model the emission probabilities also known as observation probabilities. This extension (IDHMM), is motivated by the proven capacity of these mixtures to deal with positive vectors and overcome mixture models’ capability to take into account any ordering or temporal constraints relative to information. The complete inference and parameter estimation are detailed in this work. Applications in the context of image categorization and indoor occupancy detection demonstrate higher performance compared to the extensively used Gaussian mixture-based Hidden Markov Model (GHMM) and the Dirichlet mixture-based hidden Markov Model (DHMM).
Multiobjective ResNet pruning by means of EMOAs for remote sensing scene classification

Convolutional neural networks have achieved remarkable success in the field of computer vision. However, due to their high storage and expensive computations, recently, there has been a lot of work focusing on reducing the complexity of convolutional neural networks. In this work, we propose a random filter pruning method by means of evolutionary multiobjective optimization algorithms to accelerate the Siamese ResNet-50 for remote sensing scene classification. We have conduct experiments on NWPU-RESISC45, UC Merced Land-Use and SIRI-WHU datasets for performance evaluation of the proposed method. The experimental results demonstrate that the classification performance of our pruned model has been improved while keeping a certain degree of sparsity of the model.
Lightweight Selective Encryption for Social Data Protection Based on EBCOT Coding

Online social media today has a large number of users and has become a huge platform to collect and share the social data generated by the end users. In addition, based on the development of social applications, the social sensing system has been greatly developed to generate, transmit, and store, which is helping the prosperous of the social computing systems. However, the violation of the security of end users' social data stored and shared through the social computing system becomes a serious and urgent issue. The data protection on social media platforms is very different compared with the scenario of the traditional encryption algorithms, and most of the existing schemes are not suitable for data protection in the current social sensing and data-sharing system. In this article, we present a novel design based on the agnostic selective encryption concept to efficiently protect the social data based on the embedded block coding with optimized truncation system. By selectively encrypting only a small portion of the bitstreams in the middle layer of this coding system, a high level of protection and efficiency can both be achieved. We also experiment with our method on four common social data formats, and the security analysis tests are performed to verify the high protection level of our method.
Visual Scanpath Prediction Using IOR-ROI Recurrent Mixture Density Network

A visual scanpath represents the human eye movements when scanning the visual field for acquiring and receiving visual information. Predicting visual scanpaths when a certain stimulus is presented plays an important role in modeling overt human visual attention and search behavior. In this paper, we presented an 'Inhibition of Return - Region of Interest' (IOR-ROI) recurrent mixture density network based framework learning to produce human-like visual scanpaths under task-free viewing conditions. The proposed model simultaneously predicts a sequence of ordered fixation positions and their corresponding fixation durations. Our model integrates bottom-up features and semantic features extracted by convolutional neural networks. Then the integrated feature maps are fed into the IOR-ROI Long Short-Term Memory (LSTM) which is the core component of the proposed model. The IOR-ROI LSTM is a dual LSTM unit, i.e., the IOR-LSTM and the ROI-LSTM, capturing IOR dynamics and gaze shift behavior simultaneously. IOR-LSTM simulates the visual working memory to adaptively maintain and update visual information regarding previously fixated regions. ROI-LSTM is responsible for predicting the next possible ROIs given the spatially inhibited image feature maps on the feature-wise basis. Fixation duration is predicted by a regression neural network given the viewing history and image feature maps corresponding to currently fixated ROI. Considering the eye movement pattern variations among subjects, a mixture density network is adopted to model the next fixation distribution as Gaussian mixtures and the fixation duration is also modeled using Gaussian distribution. Our model is evaluated on the OSIE and MIT low resolution eye-tracking datasets and experimental results indicate that the proposed method can achieve superior performance in predicting visual scanpaths. The code will be publicly available on URL: https://github.com/sunwj/scanpath.
NorthEuraLex: a wide-coverage lexical database of Northern Eurasia

Abstract This article describes the first release version of a new lexicostatistical database of Northern Eurasia, which includes Europe as the most well-researched linguistic area. Unlike in other areas of the world, where databases are restricted to covering a small number of concepts as far as possible based on often sparse documentation, good lexical resources providing wide coverage of the lexicon are available even for many smaller languages in our target area. This makes it possible to attain near-completeness for a substantial number of concepts. The resulting database provides a basis for rich benchmarks that can be used to test automated methods which aim to derive new knowledge about language history in underresearched areas.
A Multilayer Interval Type-2 Fuzzy Extreme Learning Machine for the recognition of walking activities and gait events using wearable sensors

In this paper, a novel Multilayer Interval Type-2 Fuzzy Extreme Learning Machine (ML-IT2-FELM) for the recognition of walking activities and Gait events is presented. The ML-IT2-FELM uses a hierarchical learning scheme that consists of multiple layers of IT2 Fuzzy Autoencoders (FAEs), followed by a final classification layer based on an IT2-FELM architecture. The core building block in the ML-IT2-FELM is an IT2-FELM, which is a generalised model of the Interval Type-2 Radial Basis Function Neural Network (IT2-RBFNN) and that is functionally equivalent to a class of simplified IT2 Fuzzy Logic Systems (FLSs). Each FAE in the ML-IT2-FELM employs an output layer with a direct-defuzzification process based on the Nie-Tan algorithm, while the IT2-FELM classifier includes a Karnik-Mendel type-reduction method (KM). Real data was collected using three inertial measurements units attached to the thigh, shank and foot of twelve healthy participants. The validation of the ML-IT2-FELM method is performed with two different experiments. The first experiment involves the recognition of three different walking activities: Level-Ground Walking (LGW), Ramp Ascent (RA) and Ramp Descent (RD). The second experiment consists of the recognition of stance and swing phases during the gait cycle. In addition, to compare the efficiency of the ML-IT2-FELM with other ML fuzzy methodologies, a kernel-based ML-IT2-FELM that is inspired by kernel learning and called KML-IT2-FELM is also implemented. The results from the recognition of walking activities and gait events achieved an average accuracy of 99.98% and 99.84% with a decision time of 290.4ms and 105ms, respectively, by the ML-IT2-FELM, while the KML-IT2-FELM achieved an average accuracy of 99.98% and 99.93% with a decision time of 191.9ms and 94ms. The experiments demonstrate that the ML-IT2-FELM is not only an effective Fuzzy Logic-based approach in the presence of sensor noise, but also a fast extreme learning machine for the recognition of different walking activities.
Analysis of Requirements-Related Arguments in User Forums

In the past, users were asked to express their needs and intentions by writing a structured requirements document in natural language. Due to the pervasive use of online forums and social media, user feedback is more accessible today. However, the information obtained is often fragmented, involving multipleperspectives from multiple parties on an on-going basis. In this paper, we propose a Crowd-based Requirements Engineering approach by Argumentation (CrowdRE-Arg), which analyses the conversations from user forum, identifies the arguments in favor or opposing of a given requirements related discussion topic. By generating the argumentation model of the involved user statements, we are able to recover the conflicting viewpoints, to reason about the winning arguments for informed requirements decisions. The proposed approach is illustrated with a data set of sample conversations about the design of a new Google-Map feature from Reddit. Also, we apply natural language processing techniques and machine learning algorithms to support the automated execution of the CrowdRE-Arg approach.