Reviewerly/exHarmony huggingface dataset:
Image Hashing based on Shape Context and Speeded Up Robust Features (SURF)

Image Authentication is one of the key issue in today's age of multimedia technology. With the availability of many hacking techniques and picture editing tools like Photoshop, image security is a major research field. Local feature points are elaborately used in resolving many problems, i.e., object detection, robust matching etc., but its use in the field of image hashing is not explored. In this work, we propose image hashing based on shape-context via Speeded Up Robust Features (SURF). The work is motivated by SIFT-based approach. Our contributions are: 1) SURF based algorithm is several times faster than SIFT-based algorithm. 2) It is more robust against different type of image modifications than SIFT. 3) We have incorporated rotation invariance property. Rotation is a difficult content preserving operation to model. The experiments are carried out on 1000 images and have shown that the proposed image hashing technique is robust against content-preserving operations. The region of convergence shows the efficacy of the proposed method compared to many state-of-the-art methods.
Arabic Text Diacritization Using Deep Neural Networks

Diacritization of Arabic text is both an interesting and a challenging problem at the same time with various applications ranging from speech synthesis to helping students learning the Arabic language. Like many other tasks or problems in Arabic language processing, the weak efforts invested into this problem and the lack of available (open-source) resources hinder the progress towards solving this problem. This work provides a critical review for the currently existing systems, measures and resources for Arabic text diacritization. Moreover, it introduces a much-needed free-for-all cleaned dataset that can be easily used to benchmark any work on Arabic diacritization. Extracted from the Tashkeela Corpus, the dataset consists of 55K lines containing about 2.3M words. After constructing the dataset, existing tools and systems are tested on it. The results of the experiments show that the neural Shakkala system significantly outperforms traditional rule-based approaches and other closed-source tools with a Diacritic Error Rate (DER) of 2.88% compared with 13.78%, which the best DER for the non-neural approach (obtained by the Mishkal tool).
Advancing Acoustic-to-Word CTC Model With Attention and Mixed-Units

The acoustic-to-word model based on the Connectionist Temporal Classification (CTC) criterion is a natural end-to-end (E2E) system directly targeting word as output unit. Two issues exist in the system: first, the current output of the CTC model relies on the current input and does not account for context weighted inputs. This is the hard alignment issue. Second, the word-based CTC model suffers from the out-of-vocabulary (OOV) issue. This means it can model only frequently occurring words while tagging the remaining words as OOV. Hence, such a model is limited in its capacity in recognizing only a fixed set of frequent words. In this study, we propose addressing these problems using a combination of attention mechanism and mixed-units. In particular, we introduce Attention CTC, Self-Attention CTC, Hybrid CTC, and Mixed-unit CTC. First, we blend attention modeling capabilities directly into the CTC network using Attention CTC and Self-Attention CTC. Second, to alleviate the OOV issue, we present Hybrid CTC which uses a word and letter CTC with shared hidden layers. The Hybrid CTC consults the letter CTC when the word CTC emits an OOV. Then, we propose a much better solution by training a Mixed-unit CTC which decomposes all the OOV words into sequences of frequent words and multi-letter units. Evaluated on a 3400 hours Microsoft Cortana voice assistant task, our final acoustic-to-word solution using attention and mixed-units achieves a relative reduction in word error rate (WER) over the vanilla word CTC by 12.09%. Such an E2E model without using any language model (LM) or complex decoder also outperforms a traditional context-dependent (CD) phoneme CTC with strong LM and decoder by 6.79% relative.
UAV-Net: A Fast Aerial Vehicle Detector for Mobile Platforms

Vehicle detection in aerial imagery is a challenging task due to small object sizes, high object density and partial occlusions. While past research mostly focused on improving detection accuracy, inference speed is another important factor when using CNN object detectors in a real life scenario - especially when targeting mobile platforms like unmanned aerial vehicles (UAVs). In this work, we compare several established detection frameworks in terms of their accuracy-speed trade-off and show that the Single Shot MultiBox Detector (SSD) offers the best compromise. We subsequently undertake a thorough evaluation of several design choices to further increase detection speed while sacrificing little to no accuracy. This includes the choice of base network architecture, improved prediction layers and an automatic model pruning approach. Given our evaluation results, we finally construct UAV-Net - a novel aerial vehicle detector that has a model size of less than 0.4 MiB and is more than 16 times faster than current top performing approaches. UAV-Net is well suited for on-board processing and operates in real time on a Jetson TX2 platform. Nevertheless, its accuracy is on par with state-of-the-art approaches on the DLR 3K, VEDAI and UAVDT datasets. Code and models are available on the project website.
A Full HD 60 fps CNN Super Resolution Processor with Selective Caching based Layer Fusion for Mobile Devices

A high-throughput CNN super resolution (SR) processor is proposed for memory efficient SR processing. It has three key features: 1) selective caching based layer fusion to minimize external memory access (EMA), 2) memory compaction scheme for smaller on-chip memory footprint, and 3) cyclic ring core architecture to increase the throughput with improved core utilization. As a result, the implemented processor achieves 60 frames-per-second throughput in generating full HD images.
Vulnerability-based risk assessment and mitigation strategies for edge devices in the Internet of Things

Edge computing envisages a distributed decentralized processing of data through a set of edge devices in the proximity of the data origins to improve the network latency and bandwidth usage. However, as the edge devices process important parameters that can often control the performance of the system, they become attractive targets of attackers. In this work, we address the security issues pertaining to the edge devices in the IoT ecosystem because of the existence of vulnerabilities in the network devices. We propose a multi-attacker multi-target graphical model for risk assessment, representing the attackers, targets, and the vulnerability relations in the network. Further, we propose risk mitigation strategies for securing the edge devices.
Efficient Training Techniques for Multi-Agent Reinforcement Learning in Combat Tasks

Multi-agent combat scenarios often appear in many real-time strategy games. Efficient learning for such scenarios is an indispensable step towards general artificial intelligence. Multi-agent reinforcement learning (MARL) algorithms have attracted much interests, but few of them have been shown effective for such scenarios. Most of previous research is focused on revising the learning mechanism of MARL algorithms, for example, trying different types of neural networks. The study of training techniques for improving the performance of MARL algorithms has not been paid much attention. In this paper we propose three efficient training techniques for a multi-agent combat problem which is originated from an unmanned aerial vehicle (UAV) combat scenario. The first one is the scenario-transfer training, which utilizes the experience obtained in simpler combat tasks to assist the training for complex tasks. The next one is the self-play training, which can continuously improve the performance by iteratively training agents and their counterparts. Finally, we consider using combat rules to assist the training, which is named as the rule-coupled training. We combine the three training techniques with two popular multi-agent reinforcement learning methods, multi-agent deep q-learning and multi-agent deep deterministic policy gradient (proposed by Open AI in 2017), respectively. The results show that both the converging speed and the performance of the two methods are significantly improved through the three training techniques.
Robust nonparallel support vector machines via second-order cone programming

A novel binary classification approach is proposed in this paper, extending the ideas behind nonparallel support vector machine (NPSVM) to robust machine learning. NPSVM constructs two twin hyperplanes by solving two independent quadratic programming problems and generalizes the well-known twin support vector machine (TWSVM) method. Robustness is conferred on the NPSVM approach by using a probabilistic framework for maximizing model fit, which is cast into two second-order cone programming (SOCP) problems by assuming a worst-case setting for the data distribution of the training patterns. Experiments on benchmark datasets confirmed the theoretical virtues of our approach, showing superior average performance compared with various SVM formulations.
Semantic Segmentation Guided Pixel Fusion for Image Retargeting

Image retargeting aims to obtain high visual quality of target images for human vision. Through semantic segmentation and understanding of input images, we can better preserve the important semantic regions, so as to effectively improve the performance of image retargeting. Benefit from the successful application of deep neural network in the field of semantic segmentation, in this paper, we propose a novel image retargeting approach using semantic segmentation and pixel fusion. Compared with existing image retargeting methods, our approach can effectively reduce geometric distortion during image retargeting by finely reallocating scaling factors for each region based on the semantic segmentation results. Experimental results demonstrate that the proposed approach can well preserve important semantic regions while leaving less unnatural geometric distortion. Our approach also shows the important role of semantic segmentation and understanding of scenes in image retargeting in detail.
A Novel Self-Organizing Fuzzy Neural Network to Learn and Mimic Habitual Sequential Tasks

In this paper, a new self-organizing fuzzy neural network model is presented which is able to learn and reproduce different sequences accurately. Sequence learning is important in performing skillful tasks, such as writing and playing piano. The structure of the proposed network is composed of two parts: 1-sequence identifier which computes a novel sequence identity value based on initial samples of a sequence, and detects the sequence identity based on proper fuzzy rules, and 2-sequence locator, which locates the input sample in the sequence. Therefore, by integrating outputs of these two parts in fuzzy rules, the network is able to produce the proper output based on current state of the sequence. To learn the proposed structure, a gradual learning procedure is proposed. First, learning is performed by adding new fuzzy rules, based on coverage measure, using available correct data. Next, the initialized parameters are fine-tuned, by gradient descent algorithm, based on fed back approximated network output as the next input. The proposed method has a dynamic structure which is able to learn new sequences online. The proposed method is used to learn and reproduce different sequences simultaneously which is the novelty of this method.
Human Activity Recognition in Smart Home With Deep Learning Approach

Vision-based human activity recognition in smart homes has become a significant issue in terms of developing the next generation technologies which can improve healthcare and security of smart homes. Recently, deep learning models that aim to automatic extraction of low-level to high-level features of input data instead of using complicated conventional feature extraction methods have achieved significant improvements in the classification of a large amount of data especially vision-based datasets. Therefore, in this study in order to recognize human action of a smart home video dataset (DMLSmartActions) Convolutional Neural Networks (CNNs) architecture as a deep learning model has been proposed. Moreover, the performance of the proposed method has been compared with the previous methods which have used traditional machine learning methods on the same dataset. Experimental results demonstrated that the proposed deep learning model has achieved 82.41% accuracy rate in the classification of human activity.
Quaternion weighted spherical Bessel-Fourier moment and its invariant for color image reconstruction and object recognition

Moments with capable of representing global features have been widely used in image analysis, pattern recognition and computer vision applications. However, existing image moment theories are still mainly paid close attention to gray-scale images. In this paper, a new quaternion moment termed as Quaternion weighted Spherical Bessel–Fourier Moment (QSBFM) is proposed to address the issue of color image analysis. Moreover, the relationship between QSBFM and the conventional weighted spherical Bessel–Fourier moment has been established so that the computational cost of QSBFM can be efficiently reduced. Furthermore, the natively geometric invariance of QSBFM is presented and discussed in detail. Experimental results show that the proposed QSBFM outperforms frequently-used quaternion moments including quaternion Bessel–Fourier moment, quaternion Radial-Harmonic-Fourier moment, quaternion Chebyshev-Fourier moment and quaternion orthogonal Fourier–Mellin moment in terms of the color image reconstruction capability and invariant recognition accuracy in noise-free, Salt & Pepper noise and the Gaussian noise conditions.
Gender Politics and Discourses of #mansplaining, #manspreading, and #manterruption on Twitter

This article presents the findings of a corpus linguistic analysis of the hashtags #mansplaining, #manspreading, and #manterruption, three lexical blends which have recently found widespread use across a variety of online media platforms. Focusing on the social media and microblogging site Twitter, we analyze a corpus of over 20,000 tweets containing these hashtags to examine how discourses of gender politics and gender relations are represented on the site. More specifically, our analysis suggests that users include these hashtags in tweets to index their individual evaluations of, and assumptions about, “proper” gendered behavior. Consequently, their metadiscursive references to the respective phenomena reflect their beliefs of what constitutes appropriate (verbal) behavior and the extent to which gender is appropriated as a variable dictating this behavior. As such, this article adds to our knowledge of the ways in which gendered social practices become sites of contestation and how contemporary gender politics play out in social media sites.
A Computationally Efficient Skull Scraping Approach for Brain MR Image

Background: In the process of volumetric evaluation of the damaged region in the human brain from a MR image it is very crucial to remove the non-brain tissue from the acquainted image. At times there is a chance during the process of assessing the damaged region through automated approaches might misinterpret the non-brain tissues like skull as damaged region due to their similar intensity features. So in order to address such issues all such artefacts. Objective: In order to mechanize an efficient approach that can effectively address the issue of removing the non-brain tissues with minimal computation effort and precise accuracy. It is very essential to keep the computational time to be as minimal as possible because the processes of skull removal is used in conjunction with segmentation algorithm, and if the skull scrapping approach has consumed a considerable amount of time, they it would impact the over segmentation and volume assessment time which is not advisable. Methods: In this paper a completely novel approach named Structural Augmentation has been proposed, that could efficiently remove the skull region from the MR image. The proposed approach has several phases that include applying of Hybridized Contra harmonic and Otsu AWBF filtering for noise removal and threshold approximation through Otsu based approach and constructing the bit map based on the approximated threshold. Morphological close operation followed by morphological open operation with reference to a structural element through the generated bitmap image. Results: The experiment are carry forwarded on a real time MR images of the patient at KGH hospital, Visakhapatnam and the images from open sources repositories like fmri. The experiment is conducted on the images of varied noise variance that are tabulated in the results and implementation section of the article. The accuracy of the proposed method has been evaluated through metrics like Accuracy, Sensitivity, Specificity through true positive, true negative, False Positive and False negative evaluations. And it is observed that the performance of the proposed algorithm seems to be reasonable good. Conclusion: The skull scrapping through structural Augmentation is computationally efficient when compared with other conventional approaches concerning both computational complexity and the accuracy that could be observed on experimentation. The Adaptive Weighted Bilateral Filter that acquire the weight value from the approximated contra harmonic mean will assist in efficient removal of poison noised by preserving the edge information and Otsu algorithm is used to determine the appropriate threshold value for constructing the bitmap image of the original MRI image which is efficient over the earlier mean based approach for estimating the threshold. Moreover, the efficiency of the proposed approach could be further improved by using customized structural elements and incorporating the fuzzy based assignments among the pixels that belong to brain tissue and skull effectively.
Using Neural Networks for Classifying Human-Robot Contact Situations

State-of-the-art robotic manipulators come with functions that detect unforeseen contact situations with the environment. However, for collaborative manipulators that are specifically designed to allow physical Human-Robot Interaction (pHRI), contact situations can be either intended (the operator may intentionally interact with the robot for instance to modify its trajectory) or undesired (a human accidentally runs into the robot). An appropriate reaction strategy for the robot manipulator depends on a correct classification and localization of the contact situation. To this end, this work proposes an approach using supervised learning techniques to distinguish between unintended contact situations (labeled collisions) and foreseen ones (labeled interactions), and to infer whether a contact occured on the upper or lower arm of the robot. A neural network is trained on measurement data gathered from different contact situations between a human subject and an ABB YuMi robot. The proposed method is then evaluated online on the robot using simple reaction strategies on both the person who trained the network and on other human individuals.
Stochastic Dual Simplex Algorithm: A Novel Heuristic Optimization Algorithm

A new heuristic optimization algorithm is presented to solve the nonlinear optimization problems. The proposed algorithm utilizes a stochastic method to achieve the optimal point based on simplex techniques. A dual simplex is distributed stochastically in the search space to find the best optimal point. Simplexes share the best and worst vertices of one another to move better through search space. The proposed algorithm is applied to 25 well-known benchmarks, and its performance is compared with grey wolf optimizer (GWO), particle swarm optimization (PSO), Nelder-Mead simplex algorithm, hybrid GWO combined with pattern search (hGWO-PS), and hybrid GWO algorithm combined with random exploratory search algorithm (hGWO-RES). The numerical results show that the proposed algorithm, called stochastic dual simplex algorithm (SDSA), has a competitive performance in terms of accuracy and complexity.
Safely Probabilistically Complete Real-Time Planning and Exploration in Unknown Environments

We present a new framework for motion planning that wraps around existing kinodynamic planners and guarantees recursive feasibility when operating in a priori unknown, static environments. Our approach makes strong guarantees about overall safety and collision avoidance by utilizing a robust controller derived from reachability analysis. We ensure that motion plans never exit the safe backward reachable set of the initial state, while safely exploring the space. This preserves the safety of the initial state, and guarantees that that we will eventually find the goal if it is possible to do so while exploring safely. We implement our framework in the Robot Operating System (ROS) software environment and demonstrate it in a real-time simulation.
Improved l-diversity: Scalable anonymization approach for Privacy Preserving Big Data Publishing

In the era of big data analytics, data owner is more concern about the data privacy. Data anonymization approaches such as k-anonymity, l-diversity, and t-closeness are used for a long time to preserve privacy in published data. However, these approaches cannot be directly applicable to a large amount of data. Distributed programming framework such as MapReduce and Spark are used for big data analytics which add more challenges to privacy preserving data publishing. Recently, we identified few scalable approaches for Privacy Preserving Big Data Publishing in literature and majority of them are based on k-anonymity and l-diversity. However, these approaches require a significant improvement to reach the level of existing privacy preserving data publishing approaches, therefore, we propose Improved Scalable l-Diversity (ImSLD) approach which is the extension of Improved Scalable k-Anonymity (ImSKA) for scalable anonymization in this paper. Our approaches are based on scalable k-anonymization that uses MapReduce as a programming paradigm. We use poker dataset and synthesize big data versions of poker dataset to test our approaches. The result analysis shows significant improvement in terms of running time due to the lesser number of MapReduce iterations and also exhibits lower information loss as compared to existing approaches while providing the same level of privacy due to tight arrangement of the records in the initial equivalence class.