Reviewerly/exHarmony huggingface dataset:
Dynamic Motion Planning Algorithm in Human-Robot Collision Avoidance

The collision-free robotic motion planning algorithm in a dynamic environment is an effective method for the prevention of collision in a human-robot interaction scenario. In this paper, an improvement of the Rapidly-exploring Random Tree (RRT) algorithm is proposed to avoid the potential collision between robot and human by taking the costmap of robot workspace into consideration. The depth camera and Kalman filter are applied to estimate the positions and velocities of dynamic obstacles. The main contribution of this paper is that the spatiotemporal information of moving obstacles is integrated into the robotic motion planning algorithm with the costmap of workspace. Besides, a new costmap generation method is designed based on the velocities of moving obstacles. Finally, a local replanning method is used to accelerate the robotic motion planning algorithm. Experimental results show that the proposed method can efficiently protect human workers from colliding with the robot and the robot can dynamically generate a collision-free path in the human-robot coexisting environment.
AGI Brain: A Learning and Decision Making Framework for Artificial General Intelligence Systems Based on Modern Control Theory

In this paper a unified learning and decision making framework for artificial general intelligence (AGI) based on modern control theory is presented. The framework, called AGI Brain, considers intelligence as a form of optimality and tries to duplicate intelligence using a unified strategy. AGI Brain benefits from powerful modelling capability of state-space representation, as well as ultimate learning ability of the neural networks. The model emulates three learning stages of human being for learning its surrounding world. The model was tested on three different continuous and hybrid (continuous and discrete) Action/State/Output/Reward (ASOR) space scenarios in deterministic single-agent/multi-agent worlds. Successful simulation results demonstrate the multi-purpose applicability of AGI Brain in deterministic worlds.
A Novel Database for Arabic Handwritten Recognition (NDAHR) System

Any proposed system for Arabic handwriting recognition systems requires a real and substantial database. In this paper, A Novel Database for Arabic Handwritten Recognition (NDAHR) is designed as it contains all sets of Arabic language. The original form of the proposed database consists of 6 pages, 24, 23, and 14 shapes in each page and 123 in overall of pages written by 100 writers divided into two groups. The first group consisted of faculty members and students in two universities in Iraq. The second group included employees in some government departments and some primary and secondary school students. After scanning all the forms by a high-quality scanner, we applied the cropping process using the Adobe Photoshop program to cut all images and classify them into different sets and then apply some methods of preprocessing to enhance the images. The collected database contains 61500 images divided into two groups: 80% training and 20% testing.
Efficient Architecture for Integral Image Computation on Heterogeneous FPGAs

Integral image (IIM) is an intermediate image representation, employed in several computer vision algorithms. Although only simple arithmetic operations are required to compute an IIM, the total number of additions increases quadratically with the input image size. For this reason, the design of hardware architectures able to accelerate the IIM computation receives a great deal of attention. Unfortunately, existing solutions are not appropriate for the integration within high-performance embedded systems, which are currently realized within modern heterogeneous CPU-FPGA System on Chips (SoCs). In this paper, we present a novel hardware architecture for accelerating the IIM computation. The proposed design outperforms existing competitors by parallelizing operations along both rows and columns of the input image. Experiments, conducted on a Zynq-7000 XC7Z020 SoC, demonstrate that the novel accelerator achieves a speed per computation unit up to 124 times higher than prior works, saving more than 70Mbits of on-chip memory resources for 1920×1080 frame resolutions.
A Flexible and High-Performance Self-Organizing Feature Map Training Acceleration Circuit and Its Applications

Self-organizing feature map (SOFM) is a type of artificial neural network based on an unsupervised learning algorithm. In this work, we present a circuit for accelerating SOFM training, which forms the foundation for an effective, efficient, and flexible SOFM training platform for different network geometries, including array, rectangular, and binary tree. FPGA validation was also conducted to examine the speedup ratio of this circuit when compared with training using software. In addition, we applied our design to three applications: chromaticity diagram learning, MNIST handwritten numeral auto-labeling, and image vector quantization. All three experiments show that the proposed circuit architecture indeed provides a high-performance and cost-effective solution to SOFM training.
A Computational Model for Promoting Targeted Communication and Supplying Social Explainable Recommendations

An emerging paradigm of Explainable Recommender Systems (ERS) leverages social friend information to supply users with his/her friends' public interests as explained recommendation. In this work, we consider this issue from a theoretical pont of view. We define a computational problem aimed to support the formation of communities of patients (users) and health services (items), thus stimulating targeted communication in social spaces. Using this formulation, dedicated ERS can benefit from social information to supply optimized recommended clarification. In particular, we report the conceptual framework with numerical results applying random graph models.
Weighted I-Vector Based Text-Independent Speaker Verification System

Speaker recognition is one of the most common and user-friendly methods for biological signals based people identification. Nowadays, Speaker verification based on factor analysis and i-vector space has a great impact on the performance improvement of these systems. In this paper, a method is proposed for weighting the model and test vectors, which utilizes the statistical characteristics of target training vectors. The effect of the use of weighted vectors on the accuracy of scoring and the performance of the entire speaker verification system was evaluated for Mel-frequency cepstral coefficients (MFCC) and power-normalized cepstral coefficients (PNCC) feature vectors, and two scoring methods, i.e., the cosine distance and probabilistic linear discriminant analysis (PLDA). TIMIT database has been used in the evaluation of the system. The test results indicate that the use of proposed weighted vectors reduces the error rate of the speaker verification system significantly.
Perceptually Transparent &amp; Robust Audio Watermarking Algorithm Using Multi Resolution Decomposition &amp; Cordic QR Decomposition

This paper proposes an audio watermarking algorithm having good balance between perceptual transparency, robustness, and payload. The proposed algorithm is based on Cordic QR decomposition and multi-resolution decomposition meeting all the necessary audio watermarking design requirements. The use of Cordic QR decomposition provides good robustness and use of detailed coefficients of multi-resolution decomposition help to obtain good transparency at high payload. Also, the proposed algorithm does not require original signal or the embedded watermark for extraction. The binary data embedding capacity of the proposed algorithm is 960.4 bps and the highest SNR obtained is 35.1380 dB. The results obtained in this paper show that the proposed method has good perceptual transparency, high payload and robustness under various audio signal processing attacks.
QoE-Aware Dual Control System to Guarantee Battery Lifetime for Mobile Video Applications

The use of consumer-electronics mobile terminals to decode and view digital video is constantly growing. Consumers expect from them a good quality of experience (QoE) during the complete viewing of the video content, which is not always possible due to the battery-operated nature of this type of devices. In this paper, a dual closed-loop control system is proposed to act on the CPU frequency such that, by default, it controls a QoE-related variable to be at its lowest valid level in order to save as much energy as possible. This energy savings are computed as an energy bonus (EB) that can be further used in moments of higher power consumption. Anyway, when the EB decreases below a dynamic threshold, the system switches to an exception mode in which it controls the EB to be enough to guarantee the expected playback lifetime at the expense of lowering QoE. Besides, the system can be configured by users to be more or less aggressive in risking lifetime vs QoE by adjusting the mode-switching threshold. Upon implementation in the operating system of a commercial development board, results show how the system is able to meet the desired lifetime with a reduction of up to 70% of the time with QoE restrictions with respect to a previous lifetime-guaranteeing approach. Furthermore, the worst-case computation-time overhead of the system is only a 4.2%.
Multi-GANs and its application for Pseudo-Coloring

Generative Adversarial Networks (GANs) has shown its dramatical success, especially in computer vision applications. In this paper, inspired by traditional GANs, we propose Multi-GANs which is an architecture of multiple generative adversarial networks that works together. Whilst, the GANs are successful to generate images which looks realistic but the real-world problems are much more complicated than a GANs can perform a desirable outcome to the whole of the problem space. Therefore, our approach divides each problem space into the several smaller and of course much more homogeneous subspaces. We propose then a GANs for each sub-space that can learn to mimic any distribution of data with lower lost. The results of each GANs for all sub-spaces then merge together to perform the original preliminary space. We evaluated our approach on Pseudo-Coloring which is a very difficult and ill-posed problem among the computer vision community. The experimental results show much more realistic characteristics for the generated images also its superiority in comparison to the traditional approaches.
Residual Dilated Network with Attention for Image Blind Denoising

Image denoising has recently witnessed substantial progress. However, many existing methods remain suboptimal for texture restoration due to treating different image regions and channels indiscriminately. Also they need to specify the noise level in advance, which largely hinders their use in blind denoising. Therefore, we introduce both attention mechanism and automatic noise level estimation into image denoising. Specifically, we propose a new, effective end-toend attention-embedded neural network for image denoising, named as Residual Dilated Attention Network (RDAN). Our RDAN is composed of a series of tailored Residual Dilated Attention Blocks (RDAB) and Residual Conv Attention Blocks (RCAB). The RDAB and RCAB incorporates both non-local and local operations, which enable a comprehensive capture of structural information. In addition, we incorporate a Gaussian-based noise level estimation into RDAN to accomplish blind denoising. Experimental results have demonstrated that our RDAN can substantially outperforms the state-of-the-art denoising methods as well as promisingly preserve image texture.
Content-Aware Perspective Projection Optimization for Viewport Rendering of 360° Images

To visualize the spherical (360°) content on 2D planar displays, a selected part of the sphere must be projected into a 2D plane. The resulting 2D area is called viewport and is created according some fixed and limited field of view, usually much less than 360°. To generate the viewport, two geometric projections are usually employed: rectilinear and stereographic. For both cases, this process leads to geometric distortions in the viewport image which, depending on the content, may be visually unpleasant to users. Rectilinear projection stretches the image borders while the stereographic projection bends straight lines. In this paper, the viewport rendering problem is tackled with the formulation of a generalized perspective projection (which includes many other popular projection, besides perspective and stereographic) and a perceptual optimization of its key parameter: the projection center. The optimization process is performed according to the viewport characteristics, namely with perceptually important features that characterize the amount of line distortion and stretching of salient regions. The aim is to find an optimal, content dependent, projection center that minimizes the content distortions and thus provides better quality of experience (QoE) in the visualization of spherical images. Experimental results on several 360° images show that the proposed optimized projection achieves better results than the benchmark projections both quantitatively and qualitatively.
Bees algorithm enhanced with Nelder and Mead method for numerical function optimisation

The Bees Algorithm is a population-based optimisation algorithm inspired by the food foraging behaviour of honey bees. Over the years, this algorithm has been successfully applied to many optimisation problems. In order to enhance its accuracy and convergence rate, it is proposed to employ the Nelder and Mead (NM) method to implement the local search phase of the algorithm. The enhanced algorithm uses directional information to direct recruited bees towards better fitness positions within the local search area. The performance of the proposed algorithm was tested on a set of seventeen well-known benchmark functions. Numerical results show that the proposed algorithm generally performs better than the standard Bees Algorithm.
Fast Convolution Algorithm for Convolutional Neural Networks

Recent advances in computing power made possible by developments of faster general-purpose graphics processing units (GPGPUs) have increased the complexity of convolutional neural network (CNN) models. However, because of the limited applications of the existing GPGPUs, CNN accelerators are becoming more important. The current accelerators focus on improvement in memory scheduling and architectures. Thus, the number of multiplier-accumulator (MAC) operations is not reduced. In this study, a new convolution layer operation algorithm is proposed using the coarse-to-fine method instead of hardware or architecture approaches. This algorithm is shown to reduce the MAC operations by 33%. However, the accuracy of the Top 1 is decreased only by 3% and the Top 5 only by 1%.
Subject Classification of Learning Resources Using Word Embeddings and Semantic Thesauri

Open Educational Resources (OERs) are often scattered among various sources and may follow different metadata schemata. In addition, they may not include exhaustive annotations; even worse, their subject characterization, if any, may be represented by arbitrary, ad-hoc keywords instead of standard, controlled vocabularies, a fact that stretches up the search space and hampers interoperability. To address this issue, in this paper we propose a twofold method based on two seemingly disjoint technology stacks: machine learning and the semantic web. First, OERs harvested from various repositories are assigned subject terms from a formal, standard thesaurus for a domain of interest, by discovering the semantic matches of the harvesting keyword within the thesaurus ontology. Then, we use word embeddings to represent an item's metadata and compute its similarity with the thesaurus keywords. These word embeddings are learned by a doc2vec model that has been trained with already annotated corpora from the biomedical domain. By combining both worlds, we show that it is possible to produce a reasonable set of thematic suggestions which exceed a certain similarity threshold.
A Framework for Testing Android Apps by Reusing Test Cases

Android apps are generally developed by an individual developer or a small team of developers, and the developers may not have experience of testing Android apps or they may not have experience of testing any software systems. Furthermore, even an individual that does not have a basic knowledge of Android can build Android apps using various app generator tools available in the market. In this environment, apps may not get tested at all or developers may not prioritize the testing activities, which may result in low-quality or error-prone apps. Eventually, users may give negative reviews to the apps or they may abandon the apps due to bugs. Therefore, instead of designing and writing new test cases, developers need tools and techniques that can automatically test their apps by utilizing the test cases of existing apps. It will not only help novice app developers in testing their apps but also help experience app developers in reducing time and effort to test their apps. In this paper, we propose a framework for testing Android apps by reusing test cases. The framework leverages test cases and domain knowledge of existing open-source Android apps to test new Android apps.
Investigating the Suitability of Blockchain for Smart Grid

The electrical grid evolved markedly over the past years and new Smart Grid phenomenon was born. Many new technological trends and concepts were introduced including also the blockchain. In the recent years, many theoretical ideas of using blockchain in Smart Grid were discussed, which resulted in new start-ups and test pilots deployed. This paper focuses on the investigation of peer-to-peer blockchain based energy trading suitability. We offer more broad discussion on the challenges and issues related with the selected application, which should help to understand the relations between application levels.
Low-Cost Navigation based on Reinforcement Learning for Autonomous Vehicles

Existing navigation systems of autonomous vehicles depend significantly on high-precision positioning devices, e.g., GPS and IMU, and detailed environment maps that are known a prior. However, these high-precision positioning devices are generally expensive, and the maps are difficult to be known exactly all the time for large open driving scenarios. In this paper, we propose a "low-cost" navigation framework based on the reinforcement learning. Specifically, we propose a motion planner that computes the continuous front wheel angle with given obstacle information and reference waypoints. Moreover, only imprecise reference waypoints, low-precision positioning devices and low-dimensional obstacle information are necessary. Therefore, this method reduces the cost and thus enhances the popularity of autonomous vehicles. Simulation results show that the vehicle can be successfully navigated to the destination with obstacle avoidance in the adversary localization environment.
Impact of Denoising on Watermarking: A Perspective for Information Retrieval

This article presents a watermark technique for embedding a watermark in the frequency domain by applying a Fast Fourier Transform (FFT) algorithm. The frequency domain analysis takes advantages of FFT shift to embed the watermark, bringing low frequency components to the center of the cover image in order to avoid user's attention. With heterogeneous radial calculation, watermark is embedded in magnitude components, after separation of phase and magnitude components of the image. Moreover, protection of watermark is achieved by error correction coding and Advanced Encryption Standard (AES-128) as a double level of security. Extraction of the watermark is done by inverting the steps taken during embedding process. The impact of multiple noise elimination techniques is analyzed for the watermark technique proposed under additive white Gaussian noise (AWGN). The performance of the impact of multiple noise elimination techniques for the proposed watermark technique is evaluated in terms of image quality evaluation metrics (IQA) and the number of bits that are retrieved.
Database thinking and deep description: designing a digital archive of the National Synchrotron Light Source

Abstract Over the past few years, research carried out at large-scale materials science facilities in the USA and elsewhere has undergone a phase transition that affected its character and culture. Research cultures at these facilities now resemble ecosystems, comprising of complex and evolving interactions between individuals, institutions, and the overall research environment. The outcome of this phase transition, which has been gradual and building since the 1980s, is known as the New (or Ecologic) Big Science [Crease, R. and Westfall, C. (2016). The new big science. Physics Today, 69: 30–6]. In this article, we describe this phase transition, review the practical challenges that it poses for historians, review some potential digital tools that might respond to these challenges, and then assess the theoretical implications posed by “database history’.
Time to collision estimation for vehicles coming from behind using in-vehicle camera

In recent years the number of Advanced Driver-Assistance Systems (ADAS) implemented in vehicles has been rising significantly. In this paper an algorithm for ADAS that estimates the time to potential collision (TTC) with the vehicle coming from behind, by processing only the video frames captured by the camera located in the outside rear-view mirror, is proposed. After preprocessing of the input video frame, vehicle detection is performed, followed by vehicle tracking. When the vehicle is successfully detected and tracked, it is possible to estimate the distance between the oncoming vehicle and the camera, as well as the oncoming vehicle speed. Using the calculated distance and the speed of the oncoming vehicle, the proposed algorithm outputs the final information about the TTC, i.e. time remaining for safe change of the driving lane. The algorithm has been tested on different real video sequences and it achieves high performance in terms of estimated TTC accuracy, while processing 25 Full HD frames per second.
Web of Slides

Lecture slides covering many topics are becoming increasingly available online, but they are scattered, making it a challenge for anyone to instantly access all slides relevant to a learning context. To address this challenge, we propose to create links between those scattered slides to form a Web of Slides (WOS). Using the sequential nature of slides, we present preliminary results of studying how to automatically create a basic link based on similarity of slides as an initial step toward the vision of WOS. We also explore interesting future research directions using different link types and the unique features of slides.