Reviewerly/exHarmony huggingface dataset:
Local Covering: Adaptive Rule Generation Method Using Existing Rules for XCS

This paper focuses on the covering mechanism in Learning Classifier System (LCS) which generates a new classifier (i.e., an if-then rule) when no classifier matches the input. We propose Local Covering, a niche-based rule generation method for XCS, to reduce the sensitivity of a hyperparameter that determines the generalization probability of initial rules. In Local Covering, the system looks up classifiers in the population and finds the closest match from the input. After that, the selected classifier is copied as the covering classifier and its condition is generalized to cover the input. To integrate the Local Covering method with XCS, XCS-LCPCI (XCS with Local Covering for Previously Covered Inputs) is proposed, which executes Local Covering only if the input has been previously covered by the covering processes. The experimental results with three different problems show that XCS-LCPCI successfully acquires the general classifiers with any of six different parameter settings, while the performance of the conventional XCS varies depending on the parameter settings.
Path Planning of Mobile Robot Group Based on Neural Networks

This article is devoted to development of a neural control system for group of drones. The control system estimates the environmental complexity, selects the best path planning algorithm, and plans the path of the copters. Algorithm of the complexity estimation is a logical classification of the environmental state. The complexity depends of obstacle’s location in the environment. Selection of the best path planning algorithm is a neural classification of the environment. The neural network selects the best path planning algorithm from two algorithms. The first one is the shortest path planning algorithm, and the second one is the safest path planning algorithm. A hybrid learning algorithm is proposed for the neural classifier of the environment. This learning algorithm consists of a supervised and an unsupervised unit. Evaluation of the neural classifier is performed by the multi criterion including the motion time, the path length, and the minimal distance from the path to the obstacles. The path planning algorithms are implemented by neural networks also. These networks are trained by supervised algorithms. The neural planner output is the next desired location of the drone, but not whole path to the target point. This approach allows the group of drones to operate in a dynamical environment without recalculating the whole path. Also this research proposes algorithms of drone’s formation in an uncertain unmapped 3-D environment.
Decision Space Coverage of Random Walks

Fitness landscape analysis is an approach used to mathematically characterize optimization problems. Random walk algorithms are used to sample fitness landscapes in order to perform fitness landscape analysis. Random walk algorithms have an advantage over random samples, in that random walk algorithms keep note of successive points in the walk, along with the relationships between them. It is important that the sample generated by a random walk algorithm is representative of the entire fitness landscape. A representative sample can be said to have good coverage of the decision space of the optimization problem. A new measure of the coverage of random walk algorithms, i.e. the Hausdorff distance, is proposed. The coverage of random walk algorithms found in the literature is investigated using the Hausdorff distance. This study shows that it is not sufficient to consider only the robustness of a random walk algorithm when performing fitness landscape analysis, but that the coverage of decision space should also be considered. This study shows that there is no significant difference in the coverage provided by the random walk algorithms investigated. However, the differences between the coverage of the random walk algorithms is more prominent when the length of the random walks is short, or the dimensionality of the optimization problem is increased.
Classification of Cyberbullying Sinhala Language Comments on Social Media

Due to technological revolution over the years, bullying which was confined to physical boundaries has now moved online. Denigration or insult is one form of cyberbullying. According to Sri Lanka Computer Emergency Readiness Team, social media cyberbullying incidents are escalating. Insulting words are dynamic, and same word can have several meanings according to the context. Simply because a comment contains such a word, it cannot be classified as bullying. Hence, when labeling comments, simple keyword spotting techniques are inadequate. Other languages have addressed this issue using lexical databases such as WordNet which provides synonyms and homonyms of words. Since there is no proper lexical database developed for Sinhala language, detecting a word as bullying is a challenge. Therefore, we used rules to overcome this issue. Twitter comments with profane words were collected, outliers were removed, and remaining tweets were pre-processed. To determine insult in the text, five rules were used for feature extraction. Afterward, we applied Support Vector Machine (SVM), K-nearest neighbor (KNN) and Naïve Bayes algorithms. The results show that SVM with an RBF kernel performs better with an F1-score of 91%. Novelty of this research is the focus on Sinhala language cyberbully detection which has not been addressed before.
Attention-Based Deep Reinforcement Learning for Virtual Cinematography of 360$^{\circ}$ Videos

Virtual cinematography refers to automatically selecting a natural-looking normal field-of-view (NFOV) from an entire 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ}$</tex-math></inline-formula> video. In fact, virtual cinematography can be modeled as a deep reinforcement learning (DRL) problem, in which an agent makes actions related to NFOV selection according to the environment of 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ}$</tex-math></inline-formula> video frames. More importantly, we find from our data analysis that the selected NFOVs attract significantly more attention than other regions, i.e., the NFOVs have high saliency. Therefore, in this paper, we propose an attention-based DRL (A-DRL) approach for virtual cinematography in 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ}$</tex-math></inline-formula> video. Specifically, we develop a new DRL framework for automatic NFOV selection with the input of both the content, and saliency map of each 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ}$</tex-math></inline-formula> frame. Then, we propose a new reward function for the DRL framework in our approach, which considers the saliency values, ground-truth, and smooth transition for NFOV selection. Subsequently, a simplified DenseNet (called Mini-DenseNet) is designed to learn the optimal policy via maximizing the reward. Based on the learned policy, the actions of NFOV can be made in our A-DRL approach for virtual cinematography of 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ}$</tex-math></inline-formula> video. Extensive experiments show that our A-DRL approach outperforms other state-of-the-art virtual cinematography methods, over the datasets of Sports-360 video, and Pano2Vid.
A Novel Approach for Generating Synthetic Datasets for Digital Forensics

Increases in the quantity and complexity of digital evidence necessitate the development and application of advanced, accurate and efficient digital forensic tools. Digital forensic tool testing helps assure the veracity of digital evidence, but it requires appropriate validation datasets. The datasets are crucial to evaluating reproducibility and improving the state of the art. Datasets can be real-world or synthetic. While real-world datasets have the advantage of relevance, the interpretation of results can be difficult because reliable ground truth may not exist. In contrast, ground truth is easily established for synthetic datasets. This chapter presents the hystck framework for generating synthetic datasets with ground truth. The framework supports the automated generation of synthetic network traffic and operating system and application artifacts by simulating human-computer interactions. The generated data can be indistinguishable from data generated by normal human-computer interactions. The modular structure of the framework enhances the ability to incorporate extensions that simulate new applications and generate new types of network traffic.
A Unified Multi-Task Learning Architecture for Fast and Accurate Pedestrian Detection

We present a unified multi-task learning architecture for fast and accurate pedestrian detection. Different from existing methods which often focus on either a new loss function or architecture, we propose an improved multi-task convolutional neural network learning architecture to effectively and efficiently interfuse the task of pedestrian detection and semantic segmentation. To achieve this, we integrate a lightweight semantic segmentation branch to Faster R-CNN detection framework that enables end-to-end hard parameter sharing in order to boost the detection performance and maintain computational efficiency as follows. Firstly, a Semantic Segmentation to Feature Module (SS2FM) refines the convolutional features in RPN stage by integrating the features generated from the semantic segmentation branch. Secondly, a Semantic Segmentation to Confidence Module (SS2CM) refines the classification confidence in RPN stage by fusing it with the semantic segmentation confidence. We also introduce an effective anchor matching point transform to alleviate the problem of feature misalignment for heavily occluded pedestrians. The proposed unified multi-task learning architecture lends itself well to more robust pedestrian detection in diverse scenarios with negligible computation overhead. In addition, the proposed architecture can achieve high detection performance with low resolution input images, which significantly reduces the computational complexity. Experiment results on CityPersons and Caltech datasets show that our method is the fastest among all state-of-the-art pedestrian detection methods while exhibiting competitive detection performance.
Analytic Continued Fractions for Regression: Results on 352 datasets from the physical sciences

We report on the results of a new memetic algorithm that employs analytic continued fractions as the basic representation of mathematical functions used for regression problems. We study the performance of our method in comparison with other ten machine learning approaches provided by the scikit-learn software collection. We used 352 datasets collected by Schaffer, which originated from real experiments in the physical sciences at the turn of the 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century for which measurements were tabulated, and a governing functional relationship was postulated. Using leave-one-out cross-validation, in training our method ranks first in 350 out of the 352 datasets. Only six machine learning algorithms ranked first in at least one of the 352 datasets on testing; our approach ranked first 192 times, i.e. more all of the other algorithms combined. The results favourably speak about the robustness of our methodology. We conclude that the use of analytic continued fractions in regression deserves further study and we also advocate that Schaffer's data collection should also be included in the repertoire of datasets to test the performance of machine learning and regression algorithms.
Identity-Aware Deep Face Hallucination via Adversarial Face Verification

In this paper, we address the problem of face hallucination by proposing a novel multi-scale generative adversarial network (GAN) architecture optimized for face verification. First, we propose a multi-scale generator architecture for face hallucination with a high up-scaling ratio factor, which has multiple intermediate outputs at different resolutions. The intermediate outputs have the growing goal of synthesizing small to large images. Second, we incorporate a face verifier with the original GAN discriminator and propose a novel discriminator which learns to discriminate different identities while distinguishing fake generated HR face images from their ground truth images. In particular, the learned generator cares for not only the visual quality of hallucinated face images but also preserving the discriminative features in the hallucination process. In addition, to capture perceptually relevant differences we employ a perceptual similarity loss, instead of similarity in pixel space. We perform a quantitative and qualitative evaluation of our framework on the LFW and CelebA datasets. The experimental results show the advantages of our proposed method against the state-of-the-art methods on the 8× downsampled testing dataset.
Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices

High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators. To improve the overall solution quality as well as to boost the design productivity, efficient algorithm and accelerator co-design methodologies are indispensable. In this paper, we first discuss the motivations and challenges for the Algorithm/Accelerator co-design problem, and then provide several effective solutions. Especially, we highlight three leading works of effective co-design methodologies: 1) the first simultaneous DNN/FPGA co-design method; 2) a bi-directional light weight DNN and accelerator co-design method; 3) a differentiable and efficient DNN and accelerator co-search method. We demonstrate the effectiveness of the proposed co-design approaches using extensive experiments on both FPGAs and GPUs, with comparisons to existing works. This paper emphasizes the importance and efficacy of algorithm-accelerator co-design, and calls for more research breakthroughs in this interesting and demanding area.
Hierarchical Grammar-Guided Genetic Programming Techniques for Scheduling in Heterogeneous Networks

Grammar-Guided Genetic Programming is already outperforming humans at creating efficient transmission schedulers for large heterogeneous communications networks. We have previously proposed a multi-level grammar approach which achieved significantly better results than the canonical Grammar-Guided Genetic Programming approach. Initially, a restricted `small' grammar is utilised in order to discover suitable structures. A full grammar is then adopted after this initial phase. Hence, evolution can focus on maximising performance, by fine-tuning the well-structured models. In this work, we propose to use a hierarchical approach by employing multiple small grammars instead of a unique small grammar at the lower level, in conjunction with the full grammar at the upper level. To use multiple small grammars while maintaining the same computational budget, we have to use either (i) reduce the number of generations, or (ii) reduce the size of the population for the evolution with each of the small grammars. In this work, we confirm that the hierarchical grammar approach using the division of number of generations strategy achieves significantly better results than the multi-level approach, but requires defining an ideal number of small grammars to achieve the best performance. We also show that the hierarchical grammar approach using the division of population size strategy achieves significantly better results than the multi-level approach. However the division of population size strategy is less sensitive to the number of small grammars.
CNN-based steganalysis and parametric adversarial embedding:A game-theoretic framework

CNN-based steganalysis has recently achieved very good performance in detecting content-adaptive steganography. At the same time, recent works have shown that, by adopting an approach similar to that used to build adversarial examples, a steganographer can adopt an adversarial embedding strategy to effectively counter a target CNN steganalyzer. In turn, the good performance of the steganalyzer can be restored by retraining the CNN with adversarial stego images. A problem with this model is that, arguably, at training time the steganalyzer is not aware of the exact parameters used by the steganographer for adversarial embedding and, vice versa, the steganographer does not know how the images that will be used to train the steganalyzer are generated. In order to exit this apparent deadlock, we introduce a game theoretic framework wherein the problem of setting the parameters of the steganalyst and the steganographer is solved in a strategic way. More specifically, we propose two slightly different game-theoretic formulations of the above problem, the difference between the two games corresponding to the way the output of the steganalyzer network is thresholded to make the final decision. In both cases, the goal of the steganographer is to increase the missed detection probability, while the steganalyst aims at reducing the overall error probability in the first case, and the missed detection probability for a given false alarm rate, in the second one. We instantiated the two games by considering a specific adversarial embedding scheme (namely a modified version of the adversarial embedding scheme proposed by Tang et al. (2019), and we run several experiments to derive the equilibrium points and the corresponding payoff for the two versions of the game. By comparing the error probabilities at the equilibrium, with those obtained by using other strategies, like the adoption of a worst case assumption or the use of the adversarial embedding scheme by Tang et al. (2019), the benefits of addressing the interplay between the steganographer and the steganalyst in a game-theoretic fashion come out.
Visual privacy-preserving level evaluation for multilayer compressed sensing model using contrast and salient structural features

Recognition and classification tasks in images or videos are ubiquitous, but they can lead to privacy issues. People increasingly hope that camera systems can record and recognize important events and objects, such as real-time recording of traffic conditions and accident scenes, elderly fall detection, and in-home monitoring. However, people also want to ensure these activities do not violate the privacy of users or others. The sparse representation classification and recognition algorithms based on compressed sensing (CS) are robust at recognizing human faces from frontal views with varying expressions and illuminations, as well as occlusions and disguises. This is a potential way to perform recognition tasks while preserving visual privacy. In this paper, an improved Gaussian random measurement matrix is adopted in the proposed multilayer CS (MCS) model to realize multiple image CS and achieve a balance between visual privacy-preserving and recognition tasks. The visual privacy-preserving level evaluation for MCS images has important guiding significance for image processing and recognition. Therefore, we propose an image visual privacy-preserving level evaluation method for the MCS model (MCS-VPLE) based on contrast and salient structural features. The basic concept is to use the contrast measurement model based on the statistical mean of the asymmetric alpha-trimmed filter and the salient generalized center-symmetric local binary pattern operator to extract contrast and salient structural features, respectively. The features are fed into a support vector regression to obtain the image quality score, and the fuzzy c-means algorithm is used for clustering to obtain the final evaluated image visual privacy-preserving score. Experiments on three constructed databases show that the proposed method has better prediction effectiveness and performance than conventional methods.
Evaluating the Security and Economic Effects of Moving Target Defense Techniques on the Cloud

Moving Target Defense (MTD) is a proactive security mechanism that changes the attack surface with the aim of confusing attackers. Cloud computing leverages MTD techniques to enhance the cloud security posture against cyber threats. While many MTD techniques have been applied to cloud computing, there has so far been no joint evaluation of the effectiveness of MTD techniques with respect to security and economic metrics. In this paper, we first introduce mathematical definitions for the combination of three MTD techniques: Shuffle, Diversity, and Redundancy. Then, we utilize four security metrics – namely, system risk, attack cost, return on attack, and reliability – to assess the effectiveness of the combined MTD techniques applied to large-scale cloud models. Second, we focus on a specific context based on a cloud model for e-health applications to evaluate the effectiveness of the MTD techniques using security and economic metrics. We introduce (1) a strategy to effectively deploy the Shuffle MTD technique using a virtual machine placement technique, and (2) two strategies to deploy the Diversity MTD technique through operating system diversification. As deploying the Diversity technique incurs costs, we formulate the optimal diversity assignment problem (O-DAP), and solve it as a binary linear programming model to obtain the assignment that maximizes the expected net benefit.
Cost efficiency under mixed serverless and serverful deployments

Function as a Service (FaaS) is an integral part of the serverless computing paradigm.It delivers to its adopters a true pay-per-use billing model, and releases developers from the burden of managing the application stack.Existing works have already started the discussion on whether this model is more appropriate for cloud computing users in terms of accruing costs when compared to more "traditional" solutions using the Infrastructure and Platform as a Service delivery models.However, by treating this subject as a regular service selection problem, these approaches fail to exploit the space created by distributing the load between simultaneous FaaS and non-FaaS deployments of an application in a hybrid deployment model.This work aims to provide the means for application owners to not only decide which deployment scenario is cost optimal for their needs, but in case that is a hybrid one, also what is the optimal number of virtual machines that will need to be provisioned.An extensible and configurable FAASSIMULATOR open source tool is presented for these purposes.