Reviewerly/exHarmony huggingface dataset:
Quantifying the criteria for classification of mineral resources and reserves through the estimation of block model uncertainty using geostatistical methods: a case study of Khoshoumi Uranium deposit in Yazd, Iran

Investments and progress of mineral projects depend on the quantity (tonnage) and quality (grade) of mineral resources and reserves. This study examines the impact of various criteria used in the classification of mineral deposits or parameters defining these criteria. The data used in this study include the uranium assay analysis from 127 exploratory boreholes, which were then subjected to a three-directional variography after statistical studies to identify regional anisotropy. A grade block model was built using the optimal parameters of variograms and with the help of kriging estimator. Then, by using different methods of estimating the block model uncertainty including kriging estimation variance, block error estimation, kriging efficiency and slope of regression, classification of mineral reserves was carried out in accordance with the JORC standard code. Based on different cut-off grades, the tonnage and average grade were calculated and plotted. An innovative quantitative method based on the distribution function of the mentioned parameters and the fractal pattern of separation of populations was used for the classification of mineral reserves. The existence of the least difference between the use of standard and fractal patterns in the slope of regression method indicated less error and was a proof of more reliable results.
Robust active contours driven by order-statistic filtering energy for fast image segmentation

In this paper, a robust active contour model driven by order-statistic filtering (OSF) energy is proposed for fast image segmentation. The main idea of the order-statistic filtering energy is to construct the edge force function (EFF) to quickly and adaptively attract curves to evolve towards the boundaries of targets. Besides, because the EFF is computed before iterations, therefore, segmentation speed of the OSF model improves greatly. Furthermore, an optimized regularization term and an optimized length term are used in the OSF model to replace the traditional penalty and length term, respectively. Experiments on some synthetic and real images show that the OSF model can segment images with intensity inhomogeneity quickly and precisely. In addition, several experiments also show that the OSF model is robust to initial contour and parameters.
Bridging Mixture Density Networks with Meta-Learning for Automatic Speaker Identification

Speaker identification answers the fundamental question "Who is speaking" The identification technology enables various downstream applications to provide a personalized experience. Both the prevalent i-vector based solutions and the state-of-the-art deep learning solutions usually treat all users equally, with no distinctions between new users and existing users, during the training process. We notice that a good many new users start with limited labeled training data, which often results in inferior predicting performance of recognizing users' voices. To alleviate the disadvantage caused by training data deficiency, we propose a Mixture Density Network- based Meta-Learning method (MDNML) for speaker identification. MDNML emphasizes the expeditious process of learning to recognize new users where each has only a few seconds of labeled data. We conduct experiments on the LibriSpeech dataset and compare MDNML with four state-of-the-art baseline methods. The results conclude that MDNML achieves higher accuracy in recognizing new users with limited labeled utterances than all baseline methods. Our proposed solution significantly expedites the learning by transferring the knowledge learned from the existing user base through gradient-based meta-learning. We consider our work to be a steppingstone for more sophisticated meta-learning frameworks for accelerating voice recognition. Furthermore, we discuss a strategy for enhancing the accuracy by incorporating the notion of household-based acoustic profiles with MDNML.
The Open Brands Dataset: Unified Brand Detection and Recognition at Scale

Intellectual property protection(IPP) have received more and more attention recently due to the development of the global e-commerce platforms. brand recognition plays a significant role in IPP. Recent studies for brand recognition and detection are based on small-scale datasets that are not comprehensive enough when exploring emerging deep learning techniques. Moreover, it is challenging to evaluate the true performance of brand detection methods in realistic and open scenes. In order to tackle these problems, we first define the special issues of brand detection and recognition compared with generic object detection. Second, a novel brands benchmark called "Open Brands" is established. The dataset contains 1,437,812 images which have brands and 50,000 images without any brand. The part with brands in Open Brands contains 3,113,828 instances annotated in 3 dimensions: 4 types, 559 brands and 1216 logos. To the best of our knowledge, it is the largest dataset for brand detection and recognition with rich annotations. We provide in-depth comprehensive statistics about the dataset, validate the quality of the annotations and study how the performance of many modern models evolves with an increasing amount of training data. Third, we design a network called "Brand Net" to handle brand recognition. Brand Net gets state-of-art mAP on Open Brand compared with existing detection methods.
Robot magic show: human–robot interaction

Abstract The use of robots in performance arts is increasing. But, it is hard for robots to cope with unexpected circumstances during a performance, and it is almost impossible for robots to act fully autonomously in such situations. IROS-HAC is a new challenge in robotics research and a new opportunity for cross-disciplinary collaborative research. In this paper, we describe a practical method for generating different personalities of a robot entertainer. The personalities are created by selecting speech or gestures from a set of options. The selection uses roulette wheel selection to select answers that are more closely aligned with the desired personality. In particular, we focus on a robot magician, as a good magic show includes good interaction with the audience and it may also include other robots and performers. The magician with a variety of personalities increased the audience immersion and appreciation and maintained the audience’s interest. The magic show was awarded first prize in the competition for a comprehensive evaluation of technology, story, and performance. This paper contains both the research methodology and a critical evaluation of our research.
Design and Implementation of an Unmanned Aerial and Ground Vehicle Recharging System

The use of Unmanned Arial Vehicles (UAVs) is becoming increasingly popular due to a variety of applications such as security, delivery, search, and recovery. The limited battery capacity of the UAVs becomes one of the most crucial problems which calls for a solution of automatic recharging and battery optimization to extend the UAVs’ operation time. This paper provides the design and implementation of a UAGVR (Unmanned Aerial and Ground Vehicle Recharging) system which allows the UAV to receive on-demand wireless recharging from an unmanned ground vehicle (UGV). An object tracking camera on the UGV allows it to automatically maneuver towards the UAV to accept an autonomous landing and recharging. We present a cooperative mission planning algorithm for the UAGVR system. A proof of concept system is developed using a small ground vehicle and a miniature quadcopter. Experimental results confirm the validity of our system design and suggest future work.
Non-Local Nested Residual Attention Network for Stereo Image Super-Resolution

Nowadays CNN-based stereo image super-resolution(SR) methods have obtained remarkable performance. However, most of existing methods only superficially portrayed the low layer features without considering the uneven distribution of information, which is insufficient because stereo image warping and sub-pixel upsampling require discriminative features to identify corresponding pixels. To address this problem, in this paper, we propose a novel network named Non-local Nested Residual Attention Network (NNRANet). Specifically, a non-local dilated attention module (NDAM) is developed to exploit the rich hierarchical feature and capture the long-range dependencies between pixels. Moreover, we present a nested residual group (NRG) with dense connections and multiple nested residual sub-network, which not only continuously remembers and extracts the stereo fusion feature, but also enables training a deeper and more stable network. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on the Middlebury, KITTI 2012 and KITTI 2015 datasets.
An Empirical Exploration of Deep Recurrent Connections Using Neuro-Evolution

Neuro-evolution and neural architecture search algorithms have gained significant interest due to the challenges of designing optimal artificial neural networks (ANNs). While these algorithms possess the potential to outperform the best human crafted architectures, a less common use of them is as a tool for analysis of ANN topologies and structural components. By performing these techniques while varying the allowable components, the best performing architectures for those components can be found and compared to best performing architectures for other components, allowing for a best case comparison of component capabilities – a more rigorous examination than simply applying those components in some standard fixed topologies. In this work, we utilize the Evolutionary eXploration of Augmenting Memory Models (EXAMM) algorithm to perform a rigorous examination and comparison of recurrent neural networks (RNNs) applied to time series prediction. Specifically, EXAMM is used to investigate the capabilities of recurrent memory cells as well as various complex recurrent connectivity patterns that span one or more steps in time, i.e., deep recurrent connections. Over 10.56 million RNNs were evolved and trained in 5, 280 repeated experiments with varying components. Many modern hand-crafted RNNs rely on complex memory cells (which have internal recurrent connections that only span a single time step) operating under the assumption that these sufficiently latch information and handle long term dependencies. However, our results show that networks evolved with deep recurrent connections perform significantly better than those without. More importantly, in some cases, the best performing RNNs consisted of only simple neurons and deep time skip connections, without any memory cells. These results strongly suggest that utilizing deep time skip connections in RNNs for time series data prediction not only deserves further, dedicated study, but also demonstrate the potential of neuro-evolution as a means to better study, understand, and train effective RNNs.
Efficient Scene Text Detection with Textual Attention Tower

Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multi-oriented text in scene images. The proposed feature fusion mechanism allows us to use a shallower network to reduce the computational complexity. A self-attention mechanism is adopted to suppress false positive detections. Experiments on public benchmarks including ICDAR 2013, ICDAR 2015 and MSRA-TD500 show that our proposed approach can achieve better or comparable performances with fewer parameters and less computational cost.
Hyperparameter Tuning and Optimization in Machine Learning for Species Identification System

Hyper parameters are regulated parameters that are selected for training a model that controls the training process itself. Such a set of differently configured models can be achieved through the normal process of developing the network and tuning its hyper parameters. During this process, each model can be saved and a subset of the better models is selected for the set. A random forest creates many decision trees called forests and combines them together to obtain more accurate and stable forecasts. The proposed work presents hyper parameter tuning of random forest and its parameter when it achieves highest accuracy. The proposed work presents the tuning and selection of the best parameters specially for the bird species identification system. Birds play an important role in maintaining the ecological balance of the earth.
Investigation of Methods to Improve the Recognition Performance of Tamil-English Code-Switched Data in Transformer Framework

Code-switching (CS) refers to (inter/intra-word) switching between multiple languages in a single conversation. In multilingual countries like India, CS occurs very often in everyday speech, resulting in a new breed of languages in urban regions like Hinglish (Hindi-English), Tanglish (Tamil-English), etc. Research in Indic CS speech recognition is primarily affected by insufficient data. In this paper, we investigate methods to deal with such very low resource scenarios. Recently, Transformers have shown promising results on automatic speech recognition (ASR) tasks. In a Transformer based framework, we investigate two methods for Tamil-English CS speech recognition, namely, (i) well-trained encoders of Monolingual Transformers as feature extractors to provide language discrimination, (ii) language information as tokens at the targets. Our results show that CS is efficiently handled by the second method, while the first method was efficient in discriminating languages.
Steganography and its Detection in JPEG Images Obtained with the "TRUNC" Quantizer

Many portable imaging devices use the operation of "trunc" (rounding towards zero) instead of rounding as the final quantizer for computing DCT coefficients during JPEG compression. We show that this has rather profound consequences for steganography and its detection. In particular, side-informed steganography needs to be redesigned due to the different nature of the rounding error. The steganographic algorithm J-UNIWARD becomes vulnerable to steganalysis with the JPEG rich model and needs to be adjusted for this source. Steganalysis detectors need to be retrained since a steganalyst unaware of the existence of the trunc quantizer will experience 100% false alarm.
Mspec-Net : Multi-Domain Speech Conversion Network

In this paper, we present a multi-domain speech conversion technique by proposing a Multi-domain Speech Conversion Network (MSpeC-Net) architecture for solving the less-explored area of Non-Audible Murmur-to-SPeeCH (NAM2-SPCH) conversion. The murmur produced by the speaker and captured by the NAM microphone undergoes speech quality degradation. Hence, NAM2SPCH conversion becomes a necessary and challenging task for improving the intelligibility of NAM signal. MSpeC-Net contains three domain-specific autoencoders. The multiple encoder-decoders are aligned using latent consistency loss in such a way that the desired conversion is achieved by using the source encoder and target decoder only. We have performed zero-pair NAM2SPCH conversion using the interaction between source encoder and the target decoder. We evaluated our proposed method using both objective and subjective evaluations. With a Mean Opinion Score of 3.26 and 3.12 on an average in a direct NAM2SPCH, and an indirect NAM2SPCH (i.e., NAM-to-whisper-to-speech) conversion, respectively. MSpeC-Net achieves the perceptually significant improvement for NAM2SPCH conversion system.
Registration of UAV Images Using Improved Structural Shape Similarity Based on Mathematical Morphology and Phase Congruency

Image registration is a basic step for remote sensing image processing, such as classification, change detection, and so on. Registration of unmanned aerial vehicle (UAV) images is a challenging task because of illumination change, obvious rotation, viewpoint change, and similar textures. The change in viewing angle between adjacent images causes significant difficulties in image matching, especially for residential areas and high-rise buildings. Therefore, the feature points on the buildings are removed by morphological processing when detecting feature points in this study. Considering the similarity of terrain structural properties between adjacent UAV images, the scene shape similarity feature (SSSF) descriptor is utilized to capture the structural similarity. However, the SSSF descriptor is influenced by the illumination changes of UAV images. The phase congruency algorithm is adopted to replace the Canny operator to detect structural features when calculating the SSSF descriptors because of its robustness for illumination change. Then, the normalized correlation coefficient of SSSF descriptors is used as the similarity metric to detect tie points. Finally, the point pairs with large residuals are eliminated by random sample consensus algorithm and the tie points are detected by iteratively removing the mismatched points. The proposed method was compared to some recent related methods using correct matching ratio, root-mean-square error, and time. Five experiments including habitation, bare land, mixed terrain, farmland, and buildings are carried out to evaluate the effectiveness of the proposed method. Registration results demonstrate that the proposed method is robust for illumination and viewpoint change of UAV images and usually generates more accurate registration results than some popular registration methods.
Adversarial Multi-Task Learning for Speaker Normalization in Replay Detection

Spoofing detection algorithms in voice biometrics are adversely affected by differences in the speech characteristics of the various target users. In this paper, we propose a novel speaker normalisation technique that employs adversarial multi-task learning to compensate for this speaker variability. The proposed system is designed to learn a feature space that discriminates between genuine and replayed speech while simultaneously reduces the discrimination between different speakers. We initially characterise the impact of speaker variability and quantify the effect of the proposed speaker normalisation technique directly on the feature distributions. Following this, we validate the technique on spoofing detection experiments carried out on two different corpora, ASVSpoof 2017 v2.0 and BTAS 2016 replay, and demonstrate its effectiveness. We obtain EER of 7.11% and 0.83% on the two corpora respectively, lower than that of all relevant baselines.
Deep Matrix Completion on Graphs: Application in Drug Target Interaction Prediction

This work proposes matrix completion via deep matrix factorization on graphs. The work is motivated by the success of two very recent studies on (shallow) matrix completion on graphs and deep matrix factorization (without graphs). We show that the proposed deep matrix factorization on graphs improves over both - shallow techniques on graphs and deep matrix factorization. Experiments are carried out on the challenging real-life problem of modeling drug-target interactions.
Research on Load Prediction Based on Improve GWO and ELM in Cloud Computing

In order to promote the accuracy and stability of load short-term prediction, this paper proposes a model of combining improved grey wolf optimization (IGWO) and Extreme Learning Machine (ELM) for short-term cloud computing resource load prediction. The improved GWO algorithm is used to search for optimal ELM parameters, which are closely related to the prediction performance of ELM. The experimental results show that the IGWO-ELM model can precisely characterize the complicated trends of cloud computing resource short-term load, and can effectively improve the accuracy and stability of the prediction model compared with the reference models.
Neural Oracle Search on N-BEST Hypotheses

In this paper, we propose a neural search algorithm to select the most likely hypothesis using a sequence of acoustic representations and multiple hypotheses as input. The algorithm provides a sequence level score for each audio-hypothesis pair that is obtained by integrating information from multiple sources, such as the input acoustic representations, N-best hypotheses, additional 1st-pass statistics, and unpaired textual information through an external language model. These scores are then used to map the search problem of identifying the most likely hypothesis to a sequence classification problem. The definition of the proposed algorithm is broad enough to allow its use as an alternative to beam search in the 1st-pass or as a 2nd-pass, rescoring step. This algorithm achieves up to 12% relative reductions in Word Error Rate (WER) across several languages over state-of-the-art baselines with relatively few additional parameters. We also propose the use of a binary classifier gating function that can learn to trigger the 2nd-pass neural search model when the 1-best hypothesis is not the oracle hypothesis, thereby avoiding extra computation.
Regression Before Classification for Temporal Action Detection

Action classification combined with location regression is a widely-utilized mechanism in existing temporal action detection methods. However, there exists an inconsistency problem between locations and categories of action instances in this mechanism. More specifically, while the location of the proposal has been refined by the regressor, the action classifier still uses input and loss corresponding to the outdated unrefined proposal to predict category. In this paper, we propose to eliminate this inconsistency by making two modifi-cations to the action classifier: 1) redirecting the classification loss to the refined proposal, and 2) rearranging the location regressor before the action classifier so that the feature of the refined proposal is fed to the classifier. Extensive experiments show that eliminating the inconsistency problem can significantly promote the detection performance. Our method achieves state-of-the-art performance for temporal action detection on the challenging THUMOS'14 dataset.
Arnet: Attention-Based Refinement Network for Few-Shot Semantic Segmentation

Semantic segmentation is a challenging task for computer vision which aims to classify the objects from the pixel level. Previous methods based on deep learning have made some progress but the labeling work is very time-consuming. Few-shot semantic segmentation can alleviate this problem. In this paper, we propose an Attention-based Refinement Network (ARNet) for few-shot semantic segmentation, which consists of three branches: the guidance branch, the segmentation branch and the refinement branch. The Residual Attention Module (RAM) can highlight the features from segmentation branch, giving a better guidance to refinement branch. And the Parallel Dilated Convolution Module (PDCM) in the end of refinement branch can refine the segmentation results. Experiments on PASCAL VOC 2012 dataset show that our model achieves a mean Intersection-over-Union (mIoU) score of 48.1% for one-shot segmentation and 49.1% for five-shot segmentation, outperforming state-of-the-art methods by 1.8% and 2.0%, respectively.