Reviewerly/exHarmony huggingface dataset:
A radix sorting parallel algorithm suitable for graphic processing unit computing

Summary Radix sorting is an essential basic data processing operation in many computer fields. It has important practical significance to accelerate its performance through Graphic Processing Unit (GPU). The heterogeneous parallel computing technology attracts much attention and is widely applied for its effective computation efficiency and parallel real‐time data processing capability. Taking advantage of the parallelism of GPU in numerical computation processing, a parallelization design method of the Binary_Least Significant Digit (LSD) first Radix Sorting (B_LSD_RS) algorithm based on Open Computing Language (OpenCL) is proposed. The radix sorting algorithm is divided into multiple kernel tasks, and the kernels are sequentially controlled by the event information transfer. The parallel algorithm is implemented and verified on the GPU + CPU heterogeneous platform. The experimental results show that compared with the performance of the B_LSD_RS sequential algorithm based on AMD Ryzen5 1600X CPU, B_LSD_RS parallel algorithm based on Open Multi‐Processing (OpenMP) and B_LSD_RS parallel algorithm based on Compute Unified Device Architecture (CUDA), the B_LSD_RS parallel algorithm based on OpenCL obtained 28.86 times, 11.01 times and 2.14 times speedup in the NVIDIA GTX 1070 computing platform respectively, not only achieves high performance but also achieves performance portability among different GPU computing platforms.
Security Enhancement for IoT Systems Based on SoC FPGA Platforms

Security is an important issue in the era of IoT, where a large number of devices are connected. In this paper, we propose a design to enhance the security of the IoT connection using SoC FPGA platforms. The design is a successful combination of the powerful processing and system management capabilities of ARM processors and the flexibility and customizability of FPGA technology. Particularly, we have developed a Digital Signature cryptosystem on a DE10-Standard SoC FPGA board using the built-in ARM processor core and two self-developed IP cores acting as 1024-bit RSA and 256-bit SHA co-processors. Furthermore, we also applied the DMA technique to achieve high-speed data transfer. As a result, the proposed cryptosystem is compact but achieves high performance even with low frequencies. In more detail, the DMA operating at the frequency of 150 MHz can achieve a speed of 1200 Mbps. Also, the 1024-bit RSA core and the 256-bit SHA operating at the frequencies of 50 MHz and 100 MHz have throughputs of 25 Kbps and above 700 Mbps, respectively.
Actor–critic-based decision-making method for the artificial intelligence commander in tactical wargames

In a tactical wargame, the decisions of the artificial intelligence (AI) commander are critical to the final combat result. Due to the existence of fog-of-war, AI commanders are faced with unknown and invisible information on the battlefield and lack of understanding of the situation, and it is difficult to make appropriate tactical strategies. The traditional knowledge rule-based decision-making method lacks flexibility and autonomy. How to make flexible and autonomous decision-making when facing complex battlefield situations is a difficult problem. This paper aims to solve the decision-making problem of the AI commander by using the deep reinforcement learning (DRL) method. We develop a tactical wargame as the research environment, which contains built-in script AI and supports the machine–machine combat mode. On this basis, an end-to-end actor–critic framework for commander decision making based on the convolutional neural network is designed to represent the battlefield situation and the reinforcement learning method is used to try different tactical strategies. Finally, we carry out a combat experiment between a DRL-based agent and a rule-based agent in a jungle terrain scenario. The result shows that the AI commander who adopts the actor–critic method successfully learns how to get a higher score in the tactical wargame, and the DRL-based agent has a higher winning ratio than the rule-based agent.
Three-dimensional reconstruction algorithm based on inverse perspective transformation

In order to solve the problems of difficulty and low accuracy in three-dimensional reconstruction based on 2D images, this paper proposes a three-dimensional reconstruction method based on inverse perspective transformation for 2D images. This method through the detection and segmentation in the images of the water wall tube welding area, a two-dimensional image by inverse perspective transformation welding area of the reduction in the three-dimensional world, is obtained by cubic spline interpolation on flow surface area boundary point, using three-dimensional kriging interpolation method involves cloud model, the point cloud model for grid operation, reconstruction of three-dimensional welding zone model. The experimental results show that this method is simple and efficient, and can effectively carry out three-dimensional reconstruction based on 2d images, which provides a new way of thinking for three-dimensional reconstruction of 2D images.
How are you? How a Robot can Learn to Express its own Roboceptions

This work is framed on investigating how a robot can learn associations between linguistic elements, such as words or sentences, and its bodily perceptions, that we named “roboceptions”. We discuss the possibility of defining such a process of an association through the interaction with human beings. By interacting with a user, the robot can learn to ascribe a meaning to its roboceptions to express them in natural language. Such a process could then be used by the robot in a verbal interaction to detect some words recalling the previously experimented roboceptions. In this paper, we discuss a Dual-NMT approach to realize such an association. However, it requires adequate training corpus. For this reason, we consider two different phases towards the realization of the system, and we show the results of the first phase, comparing two approaches: one based on the Latent Semantic Analysis paradigm and one based on the Random Indexing methodology.
Component Analysis for Visual Question Answering Architectures

Recent research advances in Computer Vision and Natural Language Processing have introduced novel tasks that are paving the way for solving AI-complete problems. One of those tasks is called Visual Question Answering (VQA). This system takes an image and a free-form, open-ended natural-language question about the image, and produce a natural language answer as the output. Such a task has drawn great attention from the scientific community, which generated a plethora of approaches that aim to improve the VQA predictive accuracy. Most of them comprise three major components: (i) independent representation learning of images and questions; (ii) feature fusion so the model can use information from both sources to answer visual questions; and (iii) the generation of the correct answer in natural language. With so many approaches being recently introduced, it became unclear the real contribution of each component for the ultimate performance of the model. The main goal of this paper is to provide a comprehensive analysis regarding the impact of each component in VQA models. Our extensive set of experiments cover both visual and textual elements, as well as the combination of these representations in form of fusion and attention mechanisms. Our major contribution is to identify core components for training VQA models so as to maximize their predictive performance.
QoE Estimation of DASH-Based Mobile Video Application Using Deep Reinforcement Learning

An increasing number of video content providers have adopted adaptive bitrate (ABR) streaming via the HTTP protocol. The client players usually run an ABR algorithm to determine the optimal quality of video playback in the next few seconds. Faced with unpredictable bandwidth variability, the latest ABR algorithm attempts to achieve the best balance between competing goals of high bitrate, less rebuffering and high smoothness. However, there is no guarantee that optimal resource utilization ensures a high quality of experience (QoE). QoE is also affected by users' preferences for video content. Even for the same movie clip, different users have varied preferences for characters, scenes, plots and other content. In this paper, we propose a Deep-Q Learning Network (DQN) based ABR algorithm to optimize the use of network and client resources in video playback and also improve QoE of users.
Hypocrates+: A Virtual Reality Medical Education Platform with Intelligent Real-Time Help System

Virtual reality, an immersing and interactive information communication technology, is changing the fundamental of medical education by creating more astonishing vivid realistic clinical environment and cases. Here, we proposed a virtual reality medical education platform with intelligent real-time emotion evaluation and help system, named Hypocrates+. By this platform, medical students can gain their medical knowledge and experience through well designed virtual clinical cases and environments with less frustration. Experiments shows that the overall mean frustration before getting the help was 0.53 and the mean frustration after was 0.50; better performance is related to lower frustration. We concluded that Hypocrates+ is a good start for developing a popular virtual medical education platform.
Effective Deep Reinforcement Learning Setups for Multiple Goals on Visual Navigation

Deep Reinforcement Learning (DRL) represents an interesting class of algorithms, since its objective is to learn a behavioral policy through interaction with the environment, leveraging the function approximation properties of neural net-works. Nonetheless, for episodic problems, it is usually modeled to deal with a unique goal. In this sense, some works showed that it is possible to learn multiple goals when using a Universal Value Function Approximator (UVFA), i.e. a method to learn a universal policy by taking information about the current state of the agent and the goal. Their results are promising but show that there is still space for new contributions regarding the integration of the goal information into the model. For this reason, we propose using the Hadamard product or the Gated-Attention module in the UVFA architecture for visual-based problems. Also, we propose a hybrid exploration strategy based on the ε-greedy and the categorical probability distribution, namely ε-categorical. By systematically comparing different architectures of UVFA for different exploration strategies, and applying or not the Trust Region Policy Optimization (TRPO), we demonstrate through experiments that, for visual topologic navigation, combining visual information of the current and goal states through Hadamard product or Gated-Attention module allows the network learning near-optimal navigation policies. Also, we empirically show that the ε-categorical policy helps to avoid local minimums during the training, which facilitates the convergence to better results.
Application of improved wavelet denoising method in low-frequency oscillation analysis of power system

Abstract The Prony algorithm has been widely used in low-frequency oscillation analysis of power system due to its good mathematical characteristics. However, the Prony algorithm is very sensitive to noise and requires a high level of input signal. This paper overcomes the shortcomings of traditional soft and hard threshold denoising methods by improving the threshold and threshold function of wavelet denoising. Using the improved wavelet threshold denoising method to preprocess the sampled signal can effectively improve the anti-interference ability of the Prony algorithm. The simulation experiment is performed with MATLAB and the experimental results show the effectiveness of the improved method.
Improving Abstractive Summarization with Iterative Representation

In the neural abstractive summarization field, comprehensive document representation and summary embellishment are two major challenges. To tackle the above problems, we propose an Iterative Abstractive Summarization (IAS) model through iterating the document and summary representation. Specifically, (1) we design a selective gated strategy to constantly update the input representation in the encoder, which is consistent with the repeated updating of human memory information in human writing. (2) We design an iterative unit to revise the comprehensive representation iteratively for polishing the summary. Moreover, we utilize reinforcement learning to optimize our model for the non-differentiable metric ROUGE, which can alleviate the exposure bias during predicting words effectively. Experiments on the CNN/Daily Mail, Gigaword and DUC-2004 datasets show that the IAS model can generate high-quality summaries with varied length, and outperforms baseline methods significantly in terms of ROUGE and Human metrics.
VITALSE

Recent research in empirical software engineering is applying techniques from neurocognitive science and breaking new grounds in the ways that researchers can model and analyze the cognitive processes of developers as they interact with software artifacts. However, given the novelty of this line of research, only one tool exists to help researchers represent and analyze this kind of multi-modal biometric data. While this tool does help with visualizing temporal eyetracking and physiological data, it does not allow for the mapping of physiological data to source code elements, instead projecting information over images of code. One drawback of this is that researchers are still unable to meaningfully combine and map physiological and eye tracking data to source code artifacts. The use of images also bars the support of long or multiple code files, which prevents researchers from analyzing data from experiments conducted in realistic settings. To address these drawbacks, we propose VITALSE, a tool for the interactive visualization of combined multi-modal biometric data for software engineering tasks. VITALSE provides interactive and customizable temporal heatmaps created with synchronized eyetracking and biometric data. The tool supports analysis on multiple files, user defined annotations for points of interest over source code elements, and high level customizable metric summaries for the provided dataset. VITALSE, a video demonstration, and sample data to demonstrate its capabilities can be found at http://www.vitalse.app.
Intelligent Cooperative Method for Medical Image Segmentation

This paper presents a robust segmentation scheme for noisy medical images exploiting hybrid cooperative segmentation. Two competing process use duality between edge detection and region extraction methods to improve their mutual results. Parallel execution and information exchange are combined to reduce the error rates generated by each of the methods executed independently. Proposed adaptive scheme is evaluated using Mean Squared Error (MSE) and Shannon entropy methods. The obtained results show the effectiveness of the method applied on real clinical data.
Modified AttrakDiff in UX Evaluation of a Mobile Prototype

We are developing a novel mobile application with a visual user interface for the visitors of museums and art galleries. In this paper we focus on the AttrakDiff method and the modifications we have made to it for our User Experience (UX) evaluation work. The modifications we have made help in getting a deeper user insight from the AttrakDiff questionnaire than with the original method.
Knapsack encoding for secured quantum key distribution protocol

Quantum cryptography has of late opened up the possibilities of exploiting the characteristics of quantum mechanics in the realm of network security. An interesting problem in cryptography is the distribution of the encryption key between the two parties involved in communication. This paper proposes a secure quantum key distribution protocol using the properties of super increasing knapsack sequences. The mapping from the knapsack sequences to the quantum states is achieved by rotating a three-bit quantum tuple.
Energy Consumption Analysis and Proposed Power-Aware Scheduling Algorithm in Cloud Computing

Cloud computing is a large-scale paradigm using remote servers hosted on the internet. Servers over the data centers provide services in the form of SaaS, PaaS, and IaaS. The concept of cloud computing is the virtualization of resources that provides services. Cloud is an infinite resource pool along with the availability of these resources to multiple users. The energy consumption in a cloud environment is due to the number of parameters that have fixed values or it is variable. In the paper, we had rectified a model for energy consumption and an algorithm is proposed for the power awareness scheduling algorithm. Also, the paper shows a relationship between workload, utility, power, and energy consumption is presented as a model.
A Bayesian Approach to Reinforcement Learning of Vision-Based Vehicular Control

In this paper, we present a state-of-the-art reinforcement learning method for autonomous driving. Our approach employs temporal difference learning in a Bayesian framework to learn vehicle control signals from sensor data. The agent has access to images from a forward facing camera, which are preprocessed to generate semantic segmentation maps. We trained our system using both ground truth and estimated semantic segmentation input. Based on our observations from a large set of experiments, we conclude that training the system on ground truth input data leads to better performance than training the system on estimated input even if estimated input is used for evaluation. The system is trained and evaluated in a realistic simulated urban environment using the CARLA simulator. The simulator also contains a benchmark that allows for comparing to other systems and methods. The required training time of the system is shown to be lower and the performance on the benchmark superior to competing approaches.
Towards Precise End-to-end Semi-Supervised Human Head Detection Network

Head detection, as a fundamental task in practice for many head-related problems, requires an enormous number of annotated boxes to maintain the performance. To alleviate the time and cost of labeling each image in the dataset, we propose an end-to-end semi-supervised head detection frame-work, which shows competitive results with only a small set of data. Specifically, under the setting of semi-supervised, we introduce a weak boxes generate branch and a weak boxes refine branch to produce pseudo ground truth label for unlabeled images with the guidance of annotated images. The weak boxes generate branch is embedded in the detection framework taking the proposals as input and outputting the initial weak boxes that coarsely locate the place of the head. Then, the weak boxes refine branch adjusts the weak boxes more accurate gradually by training a transferred sub-network with the established relation between proposals, weak boxes and labeled boxes. In the training process, we jointly train the two branches in an end-to-end manner, which can generate better pseudo bounding boxes with a small dataset online to avoid over-fitting and obtain a more precise head detector. The results on the public head detection benchmark Brainwash and SCUT-HEAD show the effectiveness of our method.
An Analytical Survey on Image Compression

There has been a significant increase in the number of smartphones and other devices that have been highly useful as a means of communication and entertainment. Therefore, there is a need to secure such devices as the majority of the smartphones that are being sold have been equipped with inbuilt biometric authentication system. Facial features are very diverse and have some kind of similarity with features in other people's faces, especially relatives and siblings. Therefore, there is a need to offload the facial authentication system and the recognition images to the cloud platform for computation. To this end, this research article has analyzed the past works based on the facial image compression paradigm for their improvements and limitations to assist in the implementation of a fast and effective facial image compression technique that will be discussed in detail in the upcoming publications.
Hessian-based Bounds on Learning Rate for Gradient Descent Algorithms

Learning rate is a crucial parameter governing the convergence rate of any learning algorithm. Most of the learning algorithms based on stochastic gradient descent (SGD) method depend on heuristic choice of learning rate. In this paper, we derive bounds on the learning rate of SGD based adaptive learning algorithms by analyzing the largest eigenvalue of the Hessian matrix from first principles. The proposed approach is analytical. To illustrate the efficacy of the analytical approach, we considered several high-dimensional data sets and compared the rate of convergence of error for the neural gas algorithm and showed that the proposed bounds on learning rate result in a faster rate of convergence than AdaDec, Adam, and AdaDelta approaches which require hyper-parameter tuning.
<i>FLAME‐D</i> Database: An Integrated System for the Study of Archaeometallurgy

This paper discusses the reorganisation of archaeometallurgical legacy data for future research. When archaeometallurgical research aims to answer questions that involve significant movements of raw material or metal objects, it needs to rely on large sets of data. These data are available but scattered across hundreds publications, where they are differently organised, based on the focus of the original papers. The FLAME‐D database aims to collect this corpus of data and include it in a versatile structure that also maintains the information about the original data organization. The production of such a database requires transparent data transformations. The database is complemented by a series of online tools that make data available to answer new questions.
The Portability and Other Required Transfers Impact Assessment: Assessing Competition, Privacy, Cybersecurity, and Other Considerations

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
CrossWidgets

Filtering is one of the basic interaction techniques in Information Visualization, with the main objective of limiting the amount of displayed information using constraints on attribute values. Research focused on direct manipulation selection means or on simple interactors like sliders or check-boxes: while the interaction with a single attribute is, in principle, straightforward, getting an understanding of the relationship between multiple attribute constraints and the actual selection might be a complex task. To cope with this problem, usually referred as cross-filtering, the paper provides a general definition of the structure of a filter, based on domain values and data distribution, the identification of visual feedbacks on the relationship between filters status and the current selection, and guidance means to help in fulfilling the requested selection. Then, leveraging on the definition of these design elements, the paper proposes CrossWidgets, modular attribute selectors that provide the user with feedback and guidance during complex interaction with multiple attributes. An initial controlled experiment demonstrates the benefits that CrossWidgets provide to cross-filtering activities.