Reviewerly/exHarmony huggingface dataset:
Acoustic model-based subword tokenization and prosodic-context extraction without language knowledge for text-to-speech synthesis

This paper presents text tokenization and context extraction without using language knowledge for text-to-speech (TTS) synthesis. To generate prosody, statistical parametric TTS synthesis typically requires the professional knowledge of the target language. Therefore, languages suitable for TTS synthesis are limited to only rich-resource languages. To achieve TTS synthesis without using language knowledge, we propose acoustic model-based subword tokenization and unsupervised extraction of prosodic contexts. The subword tokenization can determine language units suitable for prosody generation. The context extraction can retrieve contexts from pairs of subwords and prosody. The proposed methods function without language knowledge and can improve F0 prediction accuracy. Experimental evaluation demonstrates that 1) the training of proposed subword tokenization, which uses the expectation-maximization algorithm and deep neural networks, is empirically stable, 2) the proposed subword tokenization tokenizes text into subwords that are close to language-specific units, and 3) the proposed methods outperform the conventional methods using language model-based tokenization in terms of synthetic speech quality.
Small Object Detection Based on Deep Learning

With the improving of the intelligent driving awareness, object detection as an important part of intelligent driving, has now become a research hotspot in the world. In recent years, convolutional neural network (CNN) has attracted more and more attention in the field of computer vision. CNN has made a series of important breakthroughs in the field of object detection. This paper introduces the object detection method based on deep learning. This paper mainly introduces the detection algorithm based on regional suggestion and regression, and analyzes the advantages and disadvantages of the detection algorithm and detection performance from two aspects of accuracy and speed. Then, the disadvantages of these detection methods in detecting small objects and the difficulties in detecting small objects are analyzed. On this basis, the public data sets and evaluation criteria related to small object detection are introduced.
Attention-Guided Deep Domain Adaptation for Brain Dementia Identification with Multi-site Neuroimaging Data

Deep learning has demonstrated its superiority in automated identification of brain dementia based on neuroimaging data, such as structural MRIs. Previous methods typically assume that multi-site data are sampled from the same distribution. Such an assumption may not hold in practice due to the data heterogeneity caused by different scanning parameters and subject populations in multiple imaging sites. Even though several deep domain adaptation methods have been proposed to mitigate data heterogeneity between sites, they usually require a portion of labeled target data for model training, and rarely consider the potentially different contributions of different brain regions to disease prognosis. To address these limitations, we propose an attention-guided deep domain adaptation (AD\(^2\)A) framework for brain dementia prognosis, which does not need label information of the target domain and can automatically identify discriminative locations in whole-brain MR images. The proposed AD\(^2\)A framework consists of three key components: 1) a feature encoding module for representation learning of input MR images, 2) an attention discovery module for automatically locating dementia-related discriminative regions in brain MRIs, and 3) a domain transfer module with adversarial learning for knowledge transfer between the source and target domains. Extensive experiments have been conducted on three benchmark neuroimaging datasets, with results suggesting the effectiveness of our method in both tasks of brain dementia identification and disease progression prediction.
Comparison of synchronous and asynchronous parallelization of extreme surrogate-assisted multi-objective evolutionary algorithm

Abstract This paper investigates the integration of a surrogate-assisted multi-objective evolutionary algorithm (MOEA) and a parallel computation scheme to reduce the computing time until obtaining the optimal solutions in evolutionary algorithms (EAs). A surrogate-assisted MOEA solves multi-objective optimization problems while estimating the evaluation of solutions with a surrogate function. A surrogate function is produced by a machine learning model. This paper uses an extreme learning surrogate-assisted MOEA/D (ELMOEA/D), which utilizes one of the well-known MOEA algorithms, MOEA/D, and a machine learning technique, extreme learning machine (ELM). A parallelization of MOEA, on the other hand, evaluates solutions in parallel on multiple computing nodes to accelerate the optimization process. We consider a synchronous and an asynchronous parallel MOEA as a master-slave parallelization scheme for ELMOEA/D. We carry out an experiment with multi-objective optimization problems to compare the synchronous parallel ELMOEA/D with the asynchronous parallel ELMOEA/D. In the experiment, we simulate two settings of the evaluation time of solutions. One determines the evaluation time of solutions by the normal distribution with different variances. On the other hand, another evaluation time correlates to the objective function value. We compare the quality of solutions obtained by the parallel ELMOEA/D variants within a particular computing time. The experimental results show that the parallelization of ELMOEA/D significantly reduces the computational time. In addition, the integration of ELMOEA/D with the asynchronous parallelization scheme obtains higher quality of solutions quicker than the synchronous parallel ELMOEA/D.
A Malicious URL Detection Model Based on Convolutional Neural Network

With the development of Internet technology, network security is under diverse threats. In particular, attackers can spread malicious uniform resource locators (URL) to carry out attacks such as phishing and spam. The research on malicious URL detection is significant for defending against these attacks. However, there are still some problems in the current research. For instance, malicious features cannot be extracted efficiently. Some existing detection methods are easy to evade by attackers. We design a malicious URL detection model based on a dynamic convolutional neural network (DCNN) to solve these problems. A new folding layer is added to the original multilayer convolution network. It replaces the pooling layer with the k-max-pooling layer. In the dynamic convolution algorithm, the width of feature mapping in the middle layer depends on the vector input dimension. Moreover, the pooling layer parameters are dynamically adjusted according to the length of the URL input and the depth of the current convolution layer, which is beneficial to extracting more in-depth features in a wider range. In this paper, we propose a new embedding method in which word embedding based on character embedding is leveraged to learn the vector representation of a URL. Meanwhile, we conduct two groups of comparative experiments. First, we conduct three contrast experiments, which adopt the same network structure and different embedding methods. The results prove that word embedding based on character embedding can achieve higher accuracy. We then conduct the other three experiences, which use the same embedding method proposed in this paper and use different network structures to determine which network is most suitable for our model. We verify that the model designed in this paper has the highest accuracy (98%) in detecting malicious URL through these experiences.
A novel method of generating phase-shifting sinusoidal fringes for 3D shape measurement

In this paper, a new generating technique of sinusoidal fringes for 3D shape measurement is presented. Specifically, a sinusoidal fringe is generated by expanding the inverted image of a filled binary sinusoidal pattern in a specified direction, and further, the phase shift is realized by switching the backlight sources of a set of sinusoidal patterns. In theory, sinusoidal fringes can be formed whether the projection is focused or defocused. Moreover, the technique has the potential for high frame rates due to the way the phase shift is performed. In particular, since the high shape precision of the binary sinusoidal pattern and the high accuracy of the phase shift can be easily realized by the photolithography process, the technique is very easy to implement.
Coordinating multiple mobile robots for obstacle avoidance using cloud computing

Abstract This study presents a motion planning method for coordinating multiple mobile robots for collision‐free navigation. A control architecture that uses cloud computing is proposed for the acquisition of real‐time robotic data and to coordinate multiple robots in an unstructured environment. The system consists of collision‐free path planning for multiple robots, obstacle avoidance, and navigation control. The path planning method was developed based on the optimal reciprocal collision avoidance (ORCA) algorithm to generate a velocity set of each robot in the system. A laser scanner is used to detect obstacles for each robot. Appropriate obstacle avoidance behaviors are generated using sensory information. A behavior‐fusion control scheme combines obstacle avoidance and collision‐free path planning to coordinate multiple robots. The proposed anti‐collision motion planning method for multiple robots prevents collisions with unexpected obstacles and with other robots in the environment. The experimental results show that multiple mobile robots can navigate to targets that are assigned by the cloud server without colliding with other robots, regardless of whether there are unexpected static or dynamic obstacles in the environment.
Measuring Quality of Service in a Robotized Comprehensive Geriatric Assessment Scenario

Comprehensive Geriatric Assessment (CGA) is an integrated clinical process to evaluate frail elderly people in order to create therapy plans that improve their quality and quantity of life. The whole process includes the completion of standardized questionnaires or specific movements, which are performed by the patient and do not necessarily require the presence of a medical expert. With the aim of automatizing these parts of the CGA, we have designed and developed CLARC (smart CLinic Assistant Robot for CGA), a mobile robot able to help the physician to capture and manage data during the CGA procedures, mainly by autonomously conducting a set of predefined evaluation tests. Using CLARC to conduct geriatric tests will reduce the time medical professionals have to spend on purely mechanical tasks, giving them more time to develop individualised care plans for their patients. In fact, ideally, CLARC will perform these tests on its own. In parallel with the effort to correctly address the functional aspects, i.e., the development of the robot tasks, the design of CLARC must also deal with non-functional properties such as the degree of interaction or the performance. We argue that satisfying user preferences can be a good way to improve the acceptance of the robot by the patients. This paper describes the integration into the software architecture of the CLARC robot of the modules that allow these properties to be monitored at run-time, providing information on the quality of its service. Experimental evaluation illustrates that the defined quality of service metrics correctly capture the evolution of the aspects of the robot’s activity and its interaction with the patient covered by the non-functional properties that have been considered.
Interaction with Smartwatches Using Gesture Recognition: A Systematic Literature Review

Smartwatches are wearable devices of emerging mobile technology and are increasingly in people's daily lives. They allow you to perform various tasks; however, the interaction with these devices still proves to be a significant challenge. Several studies carried out interaction studies with smartwatches using gesture recognition. Thus, this work presents a comprehensive Systematic Review of Literature (SRL) on interaction with smartwatches using gesture recognition, showing what has already been developed and what is state of the art. We searched in four databases with relevant scientific scope: ACM Digital Library, IEEE Xplore Digital Library, Science Direct, and Scopus. Work on this theme is diverse; gesture recognition is used to perform operations on smartwatches itself or even control other devices or virtual environments. We hope that this work will provide a rich base on the methods developed and that it will become a source of research for future researchers and that can support the development of other methods and applications.
CasQNet: Intrinsic Image Decomposition Based on Cascaded Quotient Network

Intrinsic image analysis plays an important role for image understanding, since it can provide accurate reflectance, shape and illumination information of the scene. However, intrinsic image analysis is an ill-posed problem which need to apply extra constrains for the decomposition of reflectance image and shading image from a single image. Recently deep neural networks are introduced for intrinsic image analysis, which can produce two intrinsic components simultaneously. In fact, the mutually exclusive relationship between reflectance image and shading image is not only a constraint for decomposition but also can improve the decomposition results. However, this relationship is always omitted in the current networks. In order to address this problem, we propose a novel deep network called as Cascaded Quotient Network (CasQNet) for intrinsic image decomposition. The CasQNet consists of two sub-networks: a Pyramid Mini-U-Net (PyNet) that specifically extracts the reflectance image in multi-scale and a Shading Optimization Network (SoNet) that optimizes the resulting shading. These two sub-networks are cascaded by a quotient operation, which directly enforces the mutually exclusive relationship between reflectance image and shading image in the network architecture. In PyNet, the task of reconstructing reflectance image is achieved by a series of nested multi-scale U-Nets, which simplified the learning task for each U-Net. SoNet is designed to address the unsmooth and blur problems of extreme points caused by the quotient operation. PyNet and SoNet are trained alternately and finally jointed in cascaded structure. Furthermore, we combine multiple loss functions, which consist of data loss, correlation loss and reconstruction loss, for improving the learning effectiveness. To evaluate our proposed algorithm, extensive experiments are performed on three datasets, i.e., ShapeNet, BOLD Surface and MIT Intrinsic Image datasets. Qualitative and quantitative results show that our model achieves the best performance compared to the state-of-the-art methods.
Robust Network Enhancement from Flawed Networks

Network data in real-world tends to be error-prone. In this paper, we aim to reconstruct a reliable network from a fiawed, undirected, unweighted network, a process referred to network enhancement. More specifically, network enhancement aims to detect the noisy links that are observed in the network but should not exist in the real world, as well as to predict the missing links that do indeed exist in the real world yet remain unobserved. While some attempts have been made to detect either noisy links or missing links, few of these works have considered unifying these two tasks, even though they are inter-dependent and capable of mutually boosting each others’ performance. In this paper, we therefore propose E-Net, an end-toend graph neural network model, to leverage the mutual influence of these two tasks in order to achieve both goals more effectively. On one hand, detecting noisy links can benefit the performance of missing link prediction, while on the other hand, predicting missing links can provide indirect supervision for detecting noisy link detection when the labels of these noisy links are unavailable. The experimental results demonstrate the significance of our proposed model in missing link prediction and noisy link detection task.
TrueRMA: Learning Fast and Smooth Robot Trajectories with Recursive Midpoint Adaptations in Cartesian Space

We present TrueRMA, a data-efficient, model-free method to learn cost-optimized robot trajectories over a wide range of starting points and endpoints. The key idea is to calculate trajectory waypoints in Cartesian space by recursively predicting orthogonal adaptations relative to the midpoints of straight lines. We generate a differentiable path by adding circular blends around the waypoints, calculate the corresponding joint positions with an inverse kinematics solver and calculate a time-optimal parameterization considering velocity and acceleration limits. During training, the trajectory is executed in a physics simulator and costs are assigned according to a user-specified cost function which is not required to be differentiable. Given a starting point and an endpoint as input, a neural network is trained to predict midpoint adaptations that minimize the cost of the resulting trajectory via reinforcement learning. We successfully train a KUKA iiwa robot to keep a ball on a plate while moving between specified points and compare the performance of TrueRMA against two baselines. The results show that our method requires less training data to learn the task while generating shorter and faster trajectories.
Use of Classification Algorithms in Health Care

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a comprehensive overview pertaining to the background and history of the classification algorithms. This will be followed by an extensive discussion regarding various techniques of classification algorithm in machine learning (ML) hence concluding with their relevant applications in data analysis in medical science and health care. To begin with, the initials of this chapter will deal with the basic fundamentals required for a profound understanding of the classification techniques in ML which will comprise of the underlying differences between Unsupervised and Supervised Learning followed by the basic terminologies of classification and its history. Further, it will include the types of classification algorithms ranging from linear classifiers like Logistic Regression, Naïve Bayes to Nearest Neighbour, Support Vector Machine, Tree-based Classifiers, and Neural Networks, and their respective mathematics. Ensemble algorithms such as Majority Voting, Boosting, Bagging, Stacking will also be discussed at great length along with their relevant applications. Furthermore, this chapter will also incorporate comprehensive elucidation regarding the areas of application of such classification algorithms in the field of biomedicine and health care and their contribution to decision-making systems and predictive analysis. To conclude, this chapter will devote highly in the field of research and development as it will provide a thorough insight to the classification algorithms and their relevant applications used in the cases of the healthcare development sector.
Clustering driving styles via image processing

Abstract It has become of key interest in the insurance industry to understand and extract information from telematics car driving data. Telematics car driving data of individual car drivers can be summarised in so-called speed–acceleration heatmaps. The aim of this study is to cluster such speed–acceleration heatmaps to different categories by analysing similarities and differences in these heatmaps. Making use of local smoothness properties, we propose to process these heatmaps as RGB images. Clustering can then be achieved by involving supervised information via a transfer learning approach using the pre-trained AlexNet to extract discriminative features. The K -means algorithm is then applied on these extracted discriminative features for clustering. The experiment results in an improvement of heatmap clustering compared to classical approaches.
Thread-level resource consumption control of tenant custom code in a shared JVM for multi-tenant SaaS

Software-as-a-Service (SaaS) providers commonly support customization of their services to allow them to attract larger tenant bases. The nature of these customizations in practice ranges from anticipated configuration options to sophisticated code extensions. From a SaaS provider viewpoint, the latter category is particularly challenging as it involves executing untrusted tenant custom code in the SaaS production environment. Proper isolation of custom code in turn requires the ability to control resource consumption of each tenant. In current practice, OS-level virtualization tools such as hypervisors or containers are predominantly used for this purpose. These techniques, however, constrain the number of tenants that a single node can cost-effectively accommodate. Furthermore, additional overhead will be incurred for (un)marshaling objects and possibly a network round-trip of data when these techniques are applied. In this paper, we present a practical solution for thread-level resource consumption control of tenant-provided custom code. This solution provides control mechanism for four types of resources, namely CPU, memory, network and storage. Usage data are gathered using the Java Resource Consumption Management API (JSR-284). In case of CPU and memory, where this API is not capable of imposing limits, both the Java Runtime Environment (JRE) bytecode and tenant code are instrumented with usage control checkpoints which ensures that CPU and memory usage of tenants remain within their Service-level Agreements (SLA) limits. Our experiments show that the proposed solution outperforms containers in terms of tenant accommodation capacity and performance overhead. It is shown that 84 times more tenants can be accommodated on a single node when our solution is adopted while, at the same time, the performance overhead of the control mechanism is less than hosting the tenant code as a remote process.
Efficient Graph Hierarchical Decomposition with User Engagement and Tie Strength

Graph decomposition methods using k-core and k-truss hierarchically group vertices and edges from external to internal by degrees of vertices or tie strength of edges. As both the user engagement of nodes and the strength of relationships are important, the (k,s)-core model is proposed in the literature to discover strong communities. Nevertheless, the decomposition algorithm regarding (k,s)-core is not yet investigated. In this paper, we propose (k,s)-core algorithms to decompose a graph into its hierarchical structures considering both user engagement and tie strength. We first present the basic (k,s)-core decomposition methods. Then, we propose the advanced algorithms DES and DEK which index the support of edges to enable higher-level cost-sharing in the peeling process. In addition, effective pruning strategies are applied to DES/DEK to further enhance performance. Moreover, we build a novel index based on the decomposition result and investigate an efficient (k,s)-core query algorithm based on our index. Extensive experimental evaluations on 12 real-world datasets verify the efficiency of our proposed decomposition algorithms and show that our index-based query algorithm can speed up the state-of-the-art query algorithms by up to three orders of magnitude.