Reviewerly/exHarmony huggingface dataset:
Diving Deep onto Discriminative Ensemble of Histological Hashing &amp; Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy

Histopathological images (HI) encrypt resolution dependent heterogeneous textures & diverse color distribution variability, manifesting in micro-structural surface tissue convolutions & inherently high coherency of cancerous cells posing significant challenges to breast cancer (BC) multi-classification. As such, multi-class stratification is sparsely explored & prior work mainly focus on benign & malignant tissue characterization only, which forestalls further quantitative analysis of subordinate classes like adenosis, mucinous carcinoma & fibroadenoma etc, for diagnostic competence. In this work, a fully-automated, near-real-time & computationally inexpensive robust multi-classification deep framework from HI is presented.The proposed scheme employs deep neural network (DNN) aided discriminative ensemble of holistic class-specific manifold learning (CSML) for underlying HI sub-space embedding & HI hashing based local shallow signatures. The model achieves 95.8% accuracy pertinent to multi-classification, an 2.8% overall performance improvement & 38.2% enhancement for Lobular carcinoma (LC) sub-class recognition rate as compared to the existing state-of-the-art on well known BreakHis dataset is achieved. Also, 99.3% recognition rate at 200 & a sensitivity of 100% for binary grading at all magnification validates its suitability for clinical deployment in hand-held smart devices.
Attended Relation Feature Representation of Facial Dynamics for Facial Authentication

In psychology, it is known that facial dynamics benefit the perception of identity. This paper proposes a novel deep network framework to capture identity information from facial dynamics and their relations. In the proposed method, facial dynamics occurred from a smile expression are analyzed and utilized for facial authentication. Detailed changes in the local regions of a face such as wrinkles and dimples are encoded in the facial dynamic feature representation. The latent relationships of the facial dynamic features are learned by the facial dynamic relational network. In the facial dynamic relational network, the relation features of the facial dynamic are encoded and the relational importance is encoded based on the relation features. As a result, the proposed method has more attention on the important relation features in facial authentication. Through comprehensive and comparative experiments, the effectiveness of the proposed method has been verified in facial authentication.
Maximum entropy searching

This study presents a new perspective for autonomous mobile robots path searching by proposing a biasing direction towards causal entropy maximisation during random tree generation. Maximum entropy-biased rapidly-exploring random tree (ME-RRT) is proposed where the searching direction is computed from random path sampling and path integral approximation, and the direction is incorporated into the existing rapidly-exploring random tree (RRT) planner. Properties of ME-RRT including degenerating conditions and additional time complexity are also discussed. The performance of the proposed approach is studied, and the results are compared with conventional RRT/RRT* and goal-biased approach in 2D/3D scenarios. Simulations show that trees are generated efficiently with fewer iteration numbers, and the success rate within limited iterations has been greatly improved in complex environments.
Efficient Segmentation-Based PatchMatch for Large Displacement Optical Flow Estimation

Efficient optical flow estimation with high accuracy is a challenging problem in computer vision. In this paper, we present a simple but efficient segmentation-based PatchMatch framework to address this issue. Specifically, it firstly generates sparse seeds without losing important motion information by over-segmentation, and then yields sparse matches by adopting a coarse-to-fine PatchMatch with sparse seeds. Such a scheme enhances the robustness of global regularization and yields better matching results compared with the existing NNF techniques while leading to a significant speed-up due to the sparsity of these seeds. Simultaneously, we introduce an extended nonlocal propagation and adaptive random search to address the basic limitation of the traditional coarse-to-fine framework in handing motion details that often vanish at coarser levels. Finally, we obtain dense matches at the finest level through an efficient sparse-to-dense matching according to the cues of over-segmentation. While performing an efficient approximation for over-segmentation, the proposed algorithm runs significantly fast and is robust to large displacements while preserving important motion details. It also achieves good performance on the challenging MPI-Sintel and Kitti flow 2015 datasets.
Feature extraction based on the fractional Fourier transform for vibration signals with application to measuring the load of a tumbling mill

Feature extraction plays a major role in data preprocessing and soft sensing. In this paper, a feature extraction method based on the fractional Fourier transform (FrFT) is proposed to estimate the load of a tubular ball mill. The FrFT, a generalised form of FFT, can draw intermediate time–frequency representations for a signal. Vibration signals are first transformed into a frequency spectrum by using the FrFT with multiple-orders, yielding multi-order frequency spectrums (MFS). Then, according to the sensitivity thresholds under different wear conditions, multi-order characteristic frequencies (MCF) are selected from the MFS, and some statistical characteristics are extracted from MCF as initial features. Only informative features can significantly benefit the forecasting accuracy. Therefore, some candidate features used to build the soft-sensing model are chosen from statistical characteristics. Finally, the load of a tumbling ball mill can be obtained by fusing the results of multi-order regression models based on an adaptive-weighting coefficient. Compared with some feature extraction methods and the performance of the proposed method in the Fourier domain, experimental results show that the feature extraction method proposed is effective in modelling the load parameters of a tubular ball mill.
Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

Measuring toxicity is an important step in drug development. However, the current experimental methods which are used to estimate the drug toxicity are expensive and need high computational efforts. Therefore, these methods are not suitable for large-scale evaluation of drug toxicity. As a consequence, there is a high demand to implement computational models that can predict drug toxicity risks. In this paper, we used a dataset that consists of 553 drugs that biotransformed in the liver. In this data, there are four toxic effects, namely, mutagenic, tumorigenic, irritant and reproductive effects. Each drug is represented by 31 chemical descriptors. This paper proposes two models for predicting drug toxicity risks. The proposed models consist of three phases. In the first phase, the most discriminative features are selected using rough set-based methods to reduce the classification time and improve the classification performance. In the second phase, three different sampling algorithms, namely, Random Under-Sampling, Random Over-Sampling, and Synthetic Minority Oversampling Technique (SMOTE) are used to obtain balanced data. In the third phase, the first proposed model employs the Neutrosophic Rule-based Classification System (NRCS), and the second model uses Genetic NRCS (GNRCS) to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed models obtained high sensitivity (89–93%), specificity (91–97%), and GM (90–94%) for all toxic effects. Overall, the results of the proposed models indicate that it could be utilized for the prediction of drug toxicity in the early stages of drug development.
Automatic evaluation of end-to-end dialog systems with adequacy-fluency metrics

End-to-end dialog systems are gaining interest due to the recent advances of deep neural networks and the availability of large human–human dialog corpora. However, in spite of being of fundamental importance to systematically improve the performance of this kind of systems, automatic evaluation of the generated dialog utterances is still an unsolved problem. Indeed, most of the proposed objective metrics shown low correlation with human evaluations. In this paper, we evaluate a two-dimensional evaluation metric that is designed to operate at sentence level, which considers the syntactic and semantic information carried along the answers generated by an end-to-end dialog system with respect to a set of references. The proposed metric, when applied to outputs generated by the systems participating in track 2 of the DSTC-6 challenge, shows a higher correlation with human evaluations (up to 12.8% relative improvement at the system level) than the best of the alternative state-of-the-art automatic metrics currently available.
Bath engineering of a fluorescing artificial atom with a photonic crystal

We demonstrate how the dissipative interaction between a superconducting qubit and a microwave photonic crystal can be used for quantum bath engineering. The photonic crystal is created with a step-impedance transmission line which suppresses and enhances the quantum spectral density of states, influencing decay transitions of a transmon circuit. The qubit interacts with the transmission line indirectly via dispersive coupling to a cavity. We characterize the photonic crystal density of states from both the unitary and dissipative dynamics of the qubit. When the qubit is driven, it dissipates into the frequency dependent density of states of the photonic crystal. Our result is the deterministic preparation of qubit superposition states as the steady state of coherent driving and dissipation near the photonic crystal band edge, which we characterize with quantum state tomography. Our results highlight how the multimode environment from the photonic crystal forms a resource for quantum control.
Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions

In this paper, we examine the performance of several classifiers in the process of searching for very negative opinions. More precisely, we do an empirical study that analyzes the influence of three types of linguistic features (n-grams, word embeddings, and polarity lexicons) and their combinations when they are used to feed different supervised machine learning classifiers: Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM). The experiments we have carried out show that SVM clearly outperforms NB and DT in all datasets by taking into account all features individually as well as their combinations.
Automatic classification of speech overlaps: Feature representation and algorithms

Overlapping speech is a natural and frequently occurring phenomenon in human–human conversations with an underlying purpose. Speech overlap events may be categorized as competitive and non-competitive. While the former is an attempt to grab the floor, the latter is an attempt to assist the speaker to continue the turn. The presence and distribution of these categories are indicative of the speakers’ states during the conversation. Therefore, understanding these manifestations is crucial for conversational analysis and for modeling human–machine dialogs. The goal of this study is to design computational models to classify overlapping speech segments of dyadic conversations into competitive vs. non-competitive acts using lexical and acoustic cues, as well as their surrounding context. The designed overlap representations are evaluated in both linear – Support Vector Machines (SVM) – and non-linear – feed-forward (FFNN), convolutional (CNN) and long short-term memory (LSTM) neural network – models. We experiment with lexical and acoustic representations and their combinations from both speaker channels in feature and hidden space. We observe that lexical word-embedding features significantly increase the overall F1-measure compared to both acoustic and bag-of-ngrams lexical representations, suggesting that lexical information can be utilized as a powerful cue for overlap classification. Our comparative study shows that the best computational architecture is an FFNN along with a combination of word embeddings and acoustic features.
A nonstationary accelerating alternating direction method for frame-based Poissonian image deblurring

Poissonian image deblurring problem has been an attractive research subject in many areas of imaging science and engineering. In order to obtain accurate restorations, one has to run several times to manually select a good value of a regularization parameter in many models.In this paper, we first propose an optimization model by combining a new data-fitting term with a frame-based analysis regularizer and a nonnegative constraint. A discrepancy principle based nonstationary accelerating alternating direction method, called DP-NAADM, is then proposed to solve the optimization model, without estimating the regularization parameter. Numerical results show that DP-NAADM provides competitive restoration results compared to the well-known Poisson image deconvolution by augmented Lagrangian method called PIDAL. Also, DP-NAADM provides better restorations in terms of both accuracy and visual quality than the discrepancy principle based PIDAL method.
Clinical prediction rules: A systematic review of healthcare provider opinions and preferences

The act of predicting clinical endpoints and patient trajectories based on past and current states is on the precipice of a technological revolution. This systematic review summarises the available evidence describing healthcare provider opinions and preferences with respect to the use of clinical prediction rules. The primary goal of this work is to inform the design and implementation of future systems, and secondarily to identify gaps for the development of clinician education programs.Five databases were systematically searched in May 2016 for studies collecting empirical opinions of healthcare providers regarding clinical prediction rule usage. Reference lists were scanned for additional eligible materials and an update search was made in August 2017. Data was extracted on high-level study features, before in-depth thematic analysis was performed.45 articles were identified from 9 countries. Most studies utilised surveys (28) or interviews (14). Fewer employed focus groups (9) or formal usability testing (4). Three high-level themes were identified, which form the basis of healthcare provider opinions of clinical prediction rules and their implementation - utility, credibility and usability.Some of the objections and preferences stated by healthcare providers are inherent to the nature of the clinical problem addressed, which may or may not be within the designer's capacity to change; however, others (in particular - actionability, validation, integration and provision of high quality education materials) should be considered by prediction rule designers and implementation teams, in order to increase user acceptance and improve uptake of these tools. We summarise these findings across the clinical prediction rule lifecycle and pose questions for the rule developers, in order to produce tools that are more likely to successfully translate into clinical practice.
CMF-iteMS: An automatic threshold selection for detection of copy-move forgery

Taking into consideration that the prior CMF detection methods rely on several fixed threshold values in the filtering process, we propose an efficient CMF detection method with an automatic threshold selection, named as CMF-iteMS. The CMF-iteMS recommends a PatchMatch-based CMF detection method that adapts Fourier-Mellin Transform (FMT) as the feature extraction technique while a new automatic threshold selection based on iterative means of regions size (iteMS) procedure is introduced to have flexibility in changing the threshold value for various characteristics (quality, sizes, and attacks) in each input image. To ensure the reliability of the proposed CMF-iteMS, the method is compared with four state-of-the-art CMF detection methods based on Scale Invariant Feature Transform (SIFT), patch matching, multi-scale analysis and symmetry techniques using three available datasets that cover the variety of characteristics in CMF images. The results show that the F-score of the CMF-iteMS outperformed existing CMF detection methods by exceeding an average of 90% F-score values for image-level evaluation and 82% of F-score value for pixel-level evaluation for all datasets in original size. As special attention is given to the image resizing attack, the method is able to maintain the highest performance even if the images in the datasets are resized to 0.25 parameter.
RGB-D Image Multi-Target Detection Method Based on 3D DSF R-CNN

At present, the application of deep learning algorithms in two-dimensional color image detection is being continuously innovated and broken. With the popularity of depth cameras, color image detection methods with depth information need to be upgraded. To solve this problem, a multi-target detection algorithm based on 3D DSF R-CNN (Double Stream Faster R-CNN, Convolution Neural Network based on Candidate Region) is proposed in this paper. The RGB information and the depth information of the image are given to two input elements of the convolution network with the same structure and weight sharing, and an optimal fusion weight algorithm is used to determine the weight of the fusion target in accordance with the recognition accuracy of the recognition targets under the single modal information, so as to ensure the most efficient fusion result. After several convolution operations, the independent features are extracted and the two networks are fused according to the optimal weights in the convolution layer. With the conducting of convolution and extract the fused features, and finally get the output through the full link layer. Compared with the previous two-dimensional convolution network algorithm, this algorithm improves the detection rate and success rate while ensuring the detection time. The experimental result shows that this method has strong robustness for complex illumination and partial occlusion, and has excellent detection results under non-restrictive conditions.
A CNN Model for Semantic Person Part Segmentation With Capacity Optimization

In this paper, a deep learning model with an optimal capacity is proposed to improve the performance of person part segmentation. Previous efforts in optimizing the capacity of a convolutional neural network (CNN) model suffer from a lack of large datasets as well as the over-dependence on a single-modality CNN, which is not effective in learning. We make several efforts in addressing these problems. First, other datasets are utilized to train a CNN module for pre-processing image data and a segmentation performance improvement is achieved without a time-consuming annotation process. Second, we propose a novel way of integrating two complementary modules to enrich the feature representations for more reliable inferences. Third, the factors to determine the capacity of a CNN model are studied and two novel methods are proposed to adjust (optimize) the capacity of a CNN to match it to the complexity of a task. The over-fitting and under-fitting problems are eased by using our methods. Experimental results show that our model outperforms the state-of-the-art deep learning models with a better generalization ability and a lower computational complexity.
SPADE: Activity Prediction in Smart Homes Using Prefix Tree Based Context Generation

An automated smart home system is a key contributor to user assistance technology in modern civilization. Crucial merit of such a system is its ability to train itself through recorded data and recognize patterns in resident behaviors. Lack of sufficient prediction accuracy, exponential memory consumption, and extensive runtime prevent many of the current activity prediction approaches from being seamlessly integrated into consumer residences. This research introduces a sequence prediction algorithm which uses a prefix tree-based data model in order to learn and predict user actions. The algorithm applies episode discovery to detect correlated sensor events and learns the activities using a lossless data compression technique. This process assigns a probability of occurrence to sensor events and uses these probabilities to detect patterns in resident behavior. A complexity analysis of the algorithm is done to prove its efficiency in terms of memory usage and runtime. Using the presented technique, predictions are performed on popular datasets and contrasted with existing algorithms. The proposed algorithm achieves an 8.22% improvement in prediction accuracy over its predecessors, along with 66.69% better memory efficiency and 37% faster runtime.
Tracking-by-Fusion via Gaussian Process Regression Extended to Transfer Learning

This paper presents a new Gaussian Processes (GPs)-based particle filter tracking framework. The framework non-trivially extends Gaussian process regression (GPR) to transfer learning, and, following the tracking-by-fusion strategy, integrates closely two tracking components, namely a GPs component and a CFs one. First, the GPs component analyzes and models the probability distribution of the object appearance by exploiting GPs. It categorizes the labeled samples into auxiliary and target ones, and explores unlabeled samples in transfer learning. The GPs component thus captures rich appearance information over object samples across time. On the other hand, to sample an initial particle set in regions of high likelihood through the direct simulation method in particle filtering, the powerful yet efficient correlation filters (CFs) are integrated, leading to the CFs component. In fact, the CFs component not only boosts the sampling quality, but also benefits from the GPs component, which provides re-weighted knowledge as latent variables for determining the impact of each correlation filter template from the auxiliary samples. In this way, the transfer learning based fusion enables effective interactions between the two components. Superior performance on four object tracking benchmarks (OTB-2015, Temple-Color, and VOT2015/2016), and in comparison with baselines and recent state-of-the-art trackers, has demonstrated clearly the effectiveness of the proposed framework.
Sparse ICP With Resampling and Denoising for 3D Face Verification

Three-dimensional face recognition has shown its potential to obtain higher recognition accuracy than 2D methods. Among numerous face recognition methods, registration of two faces is comparatively intuitive. We propose a rigid registration method using surface resampling and denoising, which lowers the impact on registration residuals caused by sampling difference and noise, significantly improving the accuracy. While sparsity-inducing norms reduce sensitivity to outliers and missing data, with preprocessing and region segmentation methods, our registration method is applied to face verification. Without data-driven learning or training, only residuals of rigid registration are utilized, and verification rates at 0.1% FAR are as follows: 100% for n versus n, 96.9% for n versus all, and 98.6% for ROC III experiment on FRGC v2.0 database, and 100% for n versus n and 95.7% for n versus all on Bosphorus database. Experiments show that the proposed algorithm outperforms the state-of-the-art algorithms and is preferable in a verification scenario.