Reviewerly/exHarmony huggingface dataset:
Voice conversion with SI-DNN and KL divergence based mapping without parallel training data

We propose a Speaker Independent Deep Neural Net (SI-DNN) and Kullback- Leibler Divergence (KLD) based mapping approach to voice conversion without using parallel training data. The acoustic difference between source and target speakers is equalized with SI-DNN via its estimated output posteriors, which serve as a probabilistic mapping from acoustic input frames to the corresponding symbols in the phonetic space. KLD is chosen as an ideal distortion measure to find an appropriate mapping from each input source speaker’s frame to that of the target speaker. The mapped acoustic segments of the target speaker form the construction bases for voice conversion. With or without word transcriptions of the target speaker’s training data, the approach can be either supervised or unsupervised. In a supervised mode where adequate training data can be utilized to train a conventional, statistical parametric TTS of the target speaker, each input frame of the source speaker is converted to its nearest sub-phonemic “senone”. In an unsupervised mode, the frame is converted to the nearest clustered phonetic centroid or a raw speech frame, in the minimum KLD sense. The acoustic trajectory of the converted voice is rendered with the maximum probability trajectory generation algorithm. Both objective and subjective measures used for evaluating voice conversion performance show that the new algorithm performs better than the sequential error minimization based DNN baseline trained with parallel training data.
Foreground Detection for Infrared Videos With Multiscale 3-D Fully Convolutional Network

Foreground detection for infrared (IR) videos is an important and fundamental problem in many applications, e.g., IR surveillance, IR object tracking, and so on. Conventional foreground detection algorithms developed for visible videos do not focus on the problems for IR videos, e.g., low contrast, coarse texture, lack of color information, and so on. Recent foreground detection methods based on deep neural network (DNN) demonstrated significant improvement, but most of them still use only spatial features, which is less obvious in IR images. In this letter, we add deeply learned multiscale temporal features to improve the performance of background subtraction for IR videos. We propose a novel multiscale 3-D fully convolutional network (MFC3-D) to establish a mapping from image sequences to pixelwise classification results and to learn deep and hierarchical multiscale spatial-temporal features of the input images sequence. The experimental results show that the MFC3-D can learn spatial-temporal features effectively and achieved state-of-the-art results on the test data set, comparing to other DNN-based methods and traditional background subtraction methods.
Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations

This paper highlights the role of ground manifold modeling for stixel calculations; stixels are medium-level data representations used for the development of computer vision modules for self-driving cars. By using single-disparity maps and simplifying ground manifold models, calculated stixels may suffer from noise, inconsistency, and false-detection rates for obstacles, especially in challenging datasets. Stixel calculations can be improved with respect to accuracy and robustness by using more adaptive ground manifold approximations. A comparative study of stixel results, obtained for different ground-manifold models (e.g., plane-fitting, line-fitting in v-disparities or polynomial approximation, and graph cut), defines the main part of this paper. This paper also considers the use of trinocular stereo vision and shows that this provides options to enhance stixel results, compared with the binocular recording. Comprehensive experiments are performed on two publicly available challenging datasets. We also use a novel way for comparing calculated stixels with ground truth. We compare depth information, as given by extracted stixels, with ground-truth depth, provided by depth measurements using a highly accurate LiDAR range sensor (as available in one of the public datasets). We evaluate the accuracy of four different ground-manifold methods. The experimental results also include quantitative evaluations of the tradeoff between accuracy and run time. As a result, the proposed trinocular recording together with graph-cut estimation of ground manifolds appears to be a recommended way, also considering challenging weather and lighting conditions.
A Robust and Efficient Video Anti-Shaking Algorithm for Low-End Smartphone Platforms

Feature phones or low-end smart phones are an important part of our daily life. While lacking the powerful embedded camera and processing capability of the high-end smart phone, the feature phones with the low-end camera and processing capability are still widely used owing to its lower cost. Consequently, image and video processing algorithms are needed for many intelligent applications on the low-end smart phones. In this paper, we propose a novel robust and efficient video anti-shaking algorithm for the low-end smartphone platform. It provides an integrated application for the low-end smartphone platform, which includes a novel global motion estimation method, and a rapid and efficient local motion detection method. The overall framework is proposed considering the limited camera perception quality and limited mobile processing capability of the feature phone. The proposed algorithm can not only obtain a good visual effect but also be implemented in real-time for the low-end mobile platforms with very limited computational resource. Experimental results demonstrate that the proposed method can achieve a similar video stabilization effect to the Optical Image Stabilization hardware in high-end smartphones.
ADSaS: Comprehensive Real-Time Anomaly Detection System

Since with massive data growth, the need for autonomous and generic anomaly detection system is increased. However, developing one stand-alone generic anomaly detection system that is accurate and fast is still a challenge. In this paper, we propose conventional time-series analysis approaches, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Seasonal Trend decomposition using Loess (STL), to detect complex and various anomalies. Usually, SARIMA and STL are used only for stationary and periodic time-series, but by combining, we show they can detect anomalies with high accuracy for data that is even noisy and non-periodic. We compared the algorithm to Long Short Term Memory (LSTM), a deep-learning-based algorithm used for anomaly detection system. We used a total of seven real-world datasets and four artificial datasets with different time-series properties to verify the performance of the proposed algorithm.
Multi-Task Hierarchical Feature Learning for Real-Time Visual Tracking

Recently, the tracking community leads a fashion of end-to-end feature learning using convolutional neural networks (CNNs) for visual object tracking. Traditional trackers extract feature maps from the last convolutional layer of CNNs for feature representation. This single-layer representation ignores target information captured in the earlier convolutional layers. In this paper, we propose a novel hierarchical feature learning framework, which captures both high-level semantics and low-level spatial details using multi-task learning. Particularly, feature maps extracted from both the shallow layer and the deep layer are input into a correlation filter layer to encode fine-grained geometric cues and coarse-grained semantic cues, respectively. Our network performs these two feature learning tasks with a multi-task learning strategy. We conduct extensive experiments on three popular tracking datasets, including OTB, UAV123, and VOT2016. Experimental results show that our method achieves remarkable performance improvement while running in real time.
Integrating Multisourced Texts in Online Business Intelligence Systems

Online business intelligence systems often collect the texts from different sources, such as social media and news websites that can be heterogeneous in practice. These collections bring the difficulties of managing and organizing the comprehensive information hidden in different texts of the system. To more effectively organize the multisourced texts and help online users acquire wider knowledge, we propose a business intelligence system which integrates the multisourced texts from multisources. Regarding in many occasions, multisourced texts share some common contents with respect to the same topics. For example, a tweet and a news report may talk about the same event. Therefore, our goal is to correlate such texts of different sources with respect to the similar topics and get integrated more comprehensive information to facilitate other data mining tasks as well as online applications. To handle the problem, we propose a heterogeneous information network-based text aligning (HINTA) framework in this paper. HINTA applies meta-paths to calculate the text similarities, and constructs correlated pairs between the two types of texts. Next, HINTA first applies anchored pairs as bridges to combine the different types of texts. Finally, three different inference methods are employed to align the multisourced texts. Experimental results on real-world dataset show the effectiveness and efficiency of the framework in addressing the texts alignment problem.
Evaluating Architectural Choices for Deep Learning Approaches for Question Answering Over Knowledge Bases

The task of answering natural language questions over knowledge bases has received wide attention in recent years. Various deep learning architectures have been proposed for this task. However, architectural design choices are typically not systematically compared nor evaluated under the same conditions. In this paper, we contribute to a better understanding of the impact of architectural design choices by evaluating four different architectures under the same conditions. We address the task of answering simple questions, consisting in predicting the subject and predicate of a triple given a question. In order to provide a fair comparison of different architectures, we evaluate them under the same strategy for inferring the subject, and compare different architectures for inferring the predicate. The architecture for inferring the subject is based on a standard LSTM model trained to recognize the span of the subject in the question and on a linking component that links the subject span to an entity in the knowledge base. The architectures for predicate inference are based on i) a standard softmax classifier ranging over all predicates as output, ii) a model that predicts a low-dimensional encoding of the property and subject entity, iii) a model that learns to score a pair of subject and predicate given the question as well as iv) a model based on the well-known FastText model. The comparison of architectures shows that FastText provides better results than other architectures.
Open-access cloud resources contribute to mainstream REDD+: The case of Mozambique

The objective of this work is to investigate how Earth Observation data and processing platforms accessible on the cloud can facilitate the implementation of REDD+ in developing countries. For that, we explore newly available open-access satellite data, cloud processing, and a ready-made land cover map to assess the extent to which such resources can directly respond to monitoring and measuring, reporting, and verification requisites. Mozambique is one of the 47 countries selected to benefit from the Forest Carbon Partnership Facility to implement REDD+ strategies. However, to meet funding agreements, the country needs to periodically produce national land cover and land cover change maps at given resolution and accuracy levels. The work presented here shows that the land cover mapping requisites of REDD+ may be quickly and cost-effectively met through the development and use of newly available cloud resources. The study relies on an experimental design that tests the results of image processing approaches with algorithms available or developed in Google Earth Engine against country-wide reference data collected by a team of national experts. The results show that, in addition to pre-processing advantages, which facilitate multi-temporal compositing and mosaicking of very large and heavy data sets, developments in cloud processing and image classification swiftly produce large extent and high-resolution land cover maps, tailored to a specific objective. The comparison of results between the in-house map obtained using Google Earth Engine, and the pan-African map produced by the European Space Agency (2016) at the same spatial resolution, reveals that both maps meet REDD+ requirements for a binary Forest/Non-forest legend. However, the in-house map is more accurate and reaches considerably better results if a more complex, six class IPCC legend is required. Nevertheless, this study shows that, given adequate reference data, the need to periodically produce high resolution land cover maps for national forest monitoring purposes is no longer an obstacle for mainstreaming the implementation of REDD+.
Parameter optimization of cascade in quantum key distribution

Quantum key distribution (QKD) protocol mainly includes two stages: signal transmission stage based on quantum channel and post-processing stage based on classical channel, and the post-processing stage has gradually become a bottleneck restricting the high performance of QKD. Information reconciliation (IR) is a key step in post-processing. Cascade has been widely used in IR due to its excellent error correction performance. In this study, we analyze original Cascade and adjust parameters to improve the partition strategy. As a result, more errors are corrected under near-optimal conditions. We consider both reconciliation efficiency and frame error rate to measure protocol performance. Simulation shows that the improved Cascade can effectively improve the reconciliation efficiency while ensuring a small frame error rate. Overall, our improved Cascade has better performance on the final secret key rate.
Multi-task Deep Learning for Fine-Grained Classification/Grading in Breast Cancer Histopathological Images

The fine-grained classification or grading of breast cancer pathological images is of great value in clinical application. However, the manual feature extraction methods not only require professional knowledge, but also the cost of feature extraction is high, especially the high quality features. In this paper, we devise an improved deep convolution neural network model to achieve accurate fine-grained classification or grading of breast cancer pathological images. Meanwhile, we use online data augmentation and transfer learning strategy to avoid model overfitting. According to the issue that small inter-class variance and large intra-class variance exist in breast cancer pathological images, multi-class recognition task and verification task of image pair are combined in the representation learning process; in addition, the prior knowledge (different subclasses with relatively large distance and small distance between the same subclass) are embedded in the process of feature extraction. At the same time, the prior information that pathological images with different magnification belong to the same subclass will be embedded in the feature extraction process, which will lead to less sensitive with image magnification. Experimental results on two different pathological image datasets show that the performance of our method is better than that of state-of-the-arts, with good robustness and generalization ability.
Fuzzy theoretic model based analysis of image features

Recently, membership functions based image descriptors were introduced as competing alternative to the classical histograms based image descriptors. The design of a suitable mathematical criterion for matching image descriptors to detect the correspondences between the images remains as one of the basic problems of image matching and computer vision. This study derives analytically a fuzzy theoretic model of local image features to facilitate a mathematical analysis of the correspondences between descriptors of multiple images. The analytical model of the local image features defines a membership function on the descriptors as a finite mixture of the descriptor’s memberships to different descriptor-prototypes. The so-defined membership function involves parameter vectors with a special structure such that all elements of the vector are non-negatives and sum to unity. These parameter vectors are considered as uncertain and are modeled by Dirichlet type membership functions. The membership functions are determined analytically by solving a deterministic constrained optimization problem using variational optimization. The membership functions based analysis leads to significantly more accurate and reliable multi-image matching algorithm that can be applied under different scenarios including that of Collage creation and fully automated image clustering.
Multifrequency Polarimetric SAR Image Despeckling by Iterative Nonlocal Means Based on a Space-Frequency Information Joint Covariance Matrix

This paper presents an iterative nonlocal means (NLM) filtering method under the Bayesian framework to deal with the issue of multifrequency fully polarimetric synthetic aperture radar (PolSAR) image despeckling. Differing from most of the PolSAR filters designed for single-frequency data, the proposed NLM method is developed based on a space-frequency information joint covariance matrix, which can not only utilize multifrequency polarimetric information but also exploit the correlation between any two pixels in an image patch. Furthermore, with the aim of accelerating the filtering procedure and better retaining image details, an effective preselection step is employed. The filtering results obtained with both a simulated dataset and real multifrequency PolSAR datasets acquired by the AIRSAR system confirm the good performance of the proposed method in both reducing speckle and retaining details, when compared with some of the state-of-the-art despeckling algorithms.
An Improved Generic Bet-and-Run Strategy with Performance Prediction for Stochastic Local Search

A commonly used strategy for improving optimization algorithms is to restart the algorithm when it is believed to be trapped in an inferior part of the search space. Building on the recent success of BET-AND-RUN approaches for restarted local search solvers, we introduce a more generic version that makes use of performance prediction. It is our goal to obtain the best possible results within a given time budget t using a given black-box optimization algorithm. If no prior knowledge about problem features and algorithm behavior is available, the question about how to use the time budget most efficiently arises. We first start k ≥ 1 independent runs of the algorithm during an initialization budget t1 &lt; t, pause these runs, then apply a decision maker D to choose 1 ≤ m &lt; k runs from them (consuming t2 ≥ 0 time units in doing so), and then continue these runs for the remaining t3 = t−t1−t2 time units. In previous BET-AND-RUN strategies, the decision maker D = currentBest would simply select the run with the best-so-far results at negligible time. We propose using more advanced methods to discriminate between “good” and “bad” sample runs with the goal of increasing the correlation of the chosen run with the a-posteriori best one. In over 157 million experiments, we test different approaches to predict which run may yield the best results if granted the remaining budget. We show (1) that the currentBest method is indeed a very reliable and robust baseline approach, and (2) that our approach can yield better results than the previous methods.
Smart Information Retrieval: Domain Knowledge Centric Optimization Approach

In the age of the Internet of Things, online data have witnessed a significant growth in terms of volume and diversity, and research into information retrieval has become one of the important research themes in the Internet-oriented data science research. This paper introduces a novel domain knowledge centric methodology aimed at improving the accuracy of using machine learning methods for relation extraction from text data, which is critical to the accuracy and efficiency of information retrieval-based applications, including recommender systems and sentiment analysis. The proposed methodology makes a significant contribution to the processes of domain knowledge-based relation extraction including interrogating Linked Open Datasets to generate the relation classification training data, addressing the imbalanced classification in the training datasets, determining the probability threshold of the best learning algorithm, and establishing the optimum parameters for genetic algorithms, which were utilized to optimize the feature selection for the learning algorithms. The experimental evaluation of the proposed methodology reveals that the adopted machine-learning algorithms exhibit higher precision and recall in relation extraction in the reduced feature space optimized by our implementation. The considered machine learning includes support vector machine, perceptron algorithm uneven margin, and K-nearest neighbors. The outcome is verified by comparing against the random mutation hill-climbing optimization algorithm using Wilcoxon signed-rank statistical analysis.
Identification of variables affecting production outcome in prawn ponds: A machine learning approach

A number of variables can affect the harvest yield in prawn ponds including dissolved oxygen, ammonia, pH, nitrite, and so on. A set of industry standards are there to maintain these variables within specific ranges for maintaining ideal growing environments for the prawns. However recent harvest results in a prominent prawn farm in South East Asia have shown different performance across ponds even after maintaining these variables within the industry standard ranges. An experiment was conducted recently to collect data on different influence variables (mentioned above) by measuring them at different times over the whole prawn growing season. We have conducted a set of analytical experiments on this data set using machine learning methods to answer three questions: (1) What level of predictive power do the influence variables have i.e. how well they can differentiate between good and bad performing ponds, (2) What is the relative importance of influence variables in predicting pond performance, and (3) How the perceived variables influence the harvest metrics. The paper presents a set of machine learning based analytical approaches undertaken to answer these questions.
Person Re-Identification Based on Heterogeneous Part-Based Deep Network in Camera Networks

In this paper, we propose a new deep learning model named heterogeneous part-based deep network for person re-identification in camera networks, which simultaneously learns the alignment and discrimination for parts of pedestrian images. Concretely, several parts are obtained through the uniform partition on the convolutional layer for each pedestrian image. Then, we present part-aligned distances to perform alignment by searching the shortest local distances between image parts in a certain range. Meanwhile, we utilize the batch hard triplet loss and cross-entropy loss to learn more discriminative part-based features in different aspects. Experiments are conducted on three challenging datasets, Market-1501, CUHK03, and DukeMTMC-reID, and we achieve 94.0%, 64.3%, and 83.6% rank-1 accuracy and 81.2%, 58.2%, and 68.0% mAP, outperforming the state-of-the-art methods by a large margin.