Reviewerly/exHarmony huggingface dataset:
A New Model for Simultaneous Detection of Phishing and Darknet Websites

In order to develop darknet users and expand the influence of darknet sites, darknet web addresses will be published and spread through ordinary networks, such as embedded in normal web pages, hidden in e-mail, etc. If we can distinguish these darknet websites from ordinary network sites and filter them out in time, we can effectively prevent the spread of darknet websites and curb the development of darknet. Therefore, this paper studies the darknet sites detection. In the study, another kind of malicious network address - phishing site is considered. Firstly, the density based clustering algorithm is used to cluster the darknet websites, phishing websites and normal websites. A novel attribute -- label category similarity is defined to measure the association between different kinds of sites. The clustering results show that darknet sites can be gathered together unsupervised. This shows that the darknet site has its own characteristics, which can be distinguished from phishing websites and normal websites. In view of the hidden darknet sites, this paper proposes a multi domain model algorithm to detect these darknet sites whose top domain is disguised to normal one. The experimental results show that the classification accuracy of darknet sites and phishing sites can reach more than 90%.
A Survey on data security challenges in multi cloud environment

Ensuring the security and privacy of data stored in cloud is one of critical requirement for enterprises. Recently multi cloud is proposed as a solution for ensuring securit., privacy and high availability of data for enterprises. Multi cloud is an integration of public, private and managed clouds with a single interface to the users. Data to be stored in the cloud is partitioned across different clouds in multi cloud based on the reliability of the cloud and degree of sensitivity of the data. This survey studies the existing multi cloud based security solutions to identify the open issues and scope for further improvement.
Constraint-based Diversification of JOP Gadgets

Modern software deployment process produces software that is uniform, and hence vulnerable to large-scale code-reuse attacks, such as Jump-Oriented Programming (JOP) attacks. Compiler-based diversification improves the resilience and security of software systems by automatically generating different assembly code versions of a given program. Existing techniques are efficient but do not have a precise control over the quality, such as the code size or speed, of the generated code variants. &#x0D; This paper introduces Diversity by Construction (DivCon), a constraint-based compiler approach to software diversification. Unlike previous approaches, DivCon allows users to control and adjust the conflicting goals of diversity and code quality. A key enabler is the use of Large Neighborhood Search (LNS) to generate highly diverse assembly code efficiently. For larger problems, we propose a combination of LNS with a structural decomposition of the problem. To further improve the diversification efficiency of DivCon against JOP attacks, we propose an application-specific distance measure tailored to the characteristics of JOP attacks. &#x0D; We evaluate DivCon with 20 functions from a popular benchmark suite for embedded systems. These experiments show that DivCon's combination of LNS and our application-specific distance measure generates binary programs that are highly resilient against JOP attacks (they share between 0.15% to 8% of JOP gadgets) with an optimality gap of 10%. Our results confirm that there is a trade-off between the quality of each assembly code version and the diversity of the entire pool of versions. In particular, the experiments show that DivCon is able to generate binary programs that share a very small number of gadgets, while delivering near-optimal code. &#x0D; For constraint programming researchers and practitioners, this paper demonstrates that LNS is a valuable technique for finding diverse solutions. For security researchers and software engineers, DivCon extends the scope of compiler-based diversification to performance-critical and resource-constrained applications.
Exposing Deepfake Videos with Spatial, Frequency and Multi-scale Temporal Artifacts

The deepfake technique replaces the face in a source video with a fake face which is generated using deep learning tools such as generative adversarial networks (GANs). Even the facial expression can be well synchronized, making it difficult to identify the fake videos. Using features from multiple domains has been proved effective in the literature. It is also known that the temporal information is particularly critical in detecting deepfake videos, since the face-swapping of a video is implemented frame by frame. In this paper, we argue that the temporal differences between authentic and fake videos are complex and can not be adequately depicted from a single time scale. To obtain a complete picture of the temporal deepfake traces, we design a detection model with a short-term feature extraction module and a long-term feature extraction module. The short-term module captures the gradient information of adjacent frames. which is incorporated with the frequency and spatial information to make a multi-domain feature set. The long-term module then reveals the artifacts from a longer period of context. The proposed algorithm is tested on several popular databases, namely FaceForensics++, DeepfakeDetection (DFD), TIMIT-DF and FFW. Experimental results have validated the effectiveness of our algorithm through improved detection performance compared with related works.
Learning to Guide a Saturation-Based Theorem Prover

Traditional automated theorem provers have relied on manually tuned heuristics to guide how they perform proof search. Recently, however, there has been a surge of interest in the design of learning mechanisms that can be integrated into theorem provers to improve their performance automatically. In this work, we describe TRAIL (Trial Reasoner for AI that Learns), a deep learning-based approach to theorem proving that characterizes core elements of saturation-based theorem proving within a neural framework. TRAIL leverages (a) an effective graph neural network for representing logical formulas, (b) a novel neural representation of the state of a saturation-based theorem prover in terms of processed clauses and available actions, and (c) a novel representation of the inference selection process as an attention-based action policy. We show through a systematic analysis that these components allow TRAIL to significantly outperform previous reinforcement learning-based theorem provers on two standard benchmark datasets (up to 36% more theorems proved). In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more theorems).
Towards assisting the decision‐making process for content creators in cinematic virtual reality through the analysis of movie cuts and their influence on viewers' behavior

Abstract Virtual Reality (VR) is gaining popularity in recent years due to the commercialization of personal devices. VR is a new and exciting medium to tell stories, however, the development of Cinematic VR (CVR) content is still in an exploratory phase. One of the main reasons is that in this medium the user has now total or partial control of the camera, therefore viewers create their own personal experiences by deciding what to see in every moment, which can potentially hinder the delivery of a pre‐established narrative. In the particular case of transitions from one shot to another (movie cuts), viewers may not be aligned with the main elements of the scene placed by the content creator to convey the story. This can result in viewers missing key elements of the narrative. In this work, we explore recent studies that analyze viewers' behavior during cinematic cuts in VR videos, and we discuss guidelines and methods that can help filmmakers with the decision‐making process when filming and editing their movies.
An Improved Secure Router Discovery Mechanism to Prevent Fake RA Attack in Link Local IPv6 Network

In Stateless Address Auto Configuration (SLAAC) in the IPv6 network, the host obtain the network prefix using Router Discovery (RD) protocol. The standard RD by design do not have trust mechanism to authenticate the legitimate host and router. This design flaw within RD protocol has led to Fake Router Advertisement (Fake RA) attack where the host is denied of the legitimate gateway. In order to address this issue, several prevention techniques such as Trust Neighbour Discovery (Trust-ND), CGA + Internet Protocol Security (IPSec) Authentication Header (AH) NDP mechanism and others have been proposed in the past. However, these techniques also face other vulnerabilities such as high computation cost, hash collision attacks and bootstrapping problem. Hence, this paper review shortcoming of these mechanisms and proposes an improved secure RD mechanism i.e. the SecMac-Secure Router Discovery (SecMac-SRD) mechanism to overcome the Fake RA attacks. SecMac-SRD mechanism provides 60.8% reduction of processing time compare to Trust-ND while preventing Fake RA attacks during the RD process in the link local communication of the IPv6 network.
HSMC: Hybrid Sentiment Method for Correlation to Analyze COVID-19 Tweets

Along with other catastrophes during covid-19, it is required to grasp the public opinions and reaction to detect how COVID-19 is affecting people emotions? This work proposes a Hybrid Sentiment Method for Correlational (HSMC) analysis to discover and distinguish the people’s opinions toward the recent outbreak by manipulating the English tweets of six countries from January to December 2020. The proposed method’s novelty is an assembling method of a modified Pearson Correlation Coefficient (mPCC) with NRC (National Research Council Canada) Emotion Lexicon dictionary. It engaged four different machine learning algorithms, to measure the HSMC method’s efficiency and compare the accuracy by confusion matrices. The experiments revealed that the NB’s accuracy with HSMC outperformed the LR and peak correlational fear level (27.5%) discovered in the USA tweets, and maximum sadness (20.96%) is detected in Brazilian tweets.
The Mixed Teaching Mode of Civil Aviation English in the Era of Big Data

With the continuous development of big data, the application of computer in all aspects of our life. Now, our life has entered the era of big data. The traditional teaching mode has been unable to meet the students’ application of Internet technology and the acquisition of resources. The teaching effect of Civil Aviation English has not been significantly improved, nor can it meet the needs of high-quality English talents in China's civil aviation industry. Therefore, combined with the current situation of China's education and with the help of network teaching platform, this paper introduces the learning network platform under big data into civil aviation English teaching to improve students’ interest in Civil Aviation English learning. In this paper, the traditional teaching methods are used in the experimental class. After the end of the semester, the comparative analysis shows that classroom learning based on network platform can improve students’ learning initiative, and the hybrid teaching mode has been recognized by students and teachers.
The Two-Stage PI<sup>2</sup> Control Strategy

PI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is a stochastic optimal control method generally regarded as a reinforcement learning algorithm. Recent work, however, suggests that the reinforcement learning aspect of PI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> actually appears when optimizing feedforward controls which will lead to optimal closed-loop performance once combined with feedback controls. These feedbacks are necessary to achieve the predicted performance, yet have been largely neglected in the literature and applications due to their complexity. In this letter, we show that the feedbacks actually take a simple-to-implement form for a wide range of system dynamics, paving way for future research and applications of PI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The correctness of the results is demonstrated through numerical simulations.
Configurable Environments in Reinforcement Learning: An Overview

Abstract Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex control tasks. In a typical RL problem, an agent interacts with the environment by perceiving observations and performing actions, with the ultimate goal of maximizing the cumulative reward. In the traditional formulation, the environment is assumed to be a fixed entity that cannot be externally controlled. However, there exist several real-world scenarios in which the environment offers the opportunity to configure some of its parameters, with diverse effects on the agent’s learning process. In this contribution, we provide an overview of the main aspects of environment configurability. We start by introducing the formalism of the Configurable Markov Decision Processes (Conf-MDPs) and we illustrate the solutions concepts. Then, we revise the algorithms for solving the learning problem in Conf-MDPs. Finally, we present two applications of Conf-MDPs: policy space identification and control frequency adaptation.
Noisy Optimization by Evolution Strategies With Online Population Size Learning

Optimization modeling of real-world application problems usually involves noise from various sources. Noisy optimization imposes challenges to optimization methods since the objective values can be different for multiple evaluations. In this article, we propose a novel online population size learning (OPL) technique of evolution strategies for handling noisy optimization problems. By re-evaluating a fraction of the candidates, we measure the strength of noise level of the re-evaluated candidate solutions and adapt the population size according to the noise level. The proposed OPL combines the advantages of both explicit averaging by re-evaluations and the implicit averaging by large population size and overcomes their limitations. We incorporate it with the covariance matrix adaptation evolution strategy (CMA-ES) and obtain OPL-CMA-ES. Compared with the existing noise handling technique, the proposed OPL is much simpler in both concepts and computation. We conduct comprehensive experiments to evaluate the algorithm’s performance on standard problems with Gaussian noise. We further evaluate the performance of OPL-CMA-ES on the black-box optimization benchmarks (BBOBs) noisy testbed, which is a standard platform for comparing black-box optimization algorithms, compared with the state-of-the-art noise-handling algorithms. The experimental results show that OPL-CMA-ES achieves remarkable performance and outperforms the compared variants.
An incremental ensemble learning system for Vietnamese e-commerce product classification

With the booming of e-commerce platforms, text classification models play an increasingly important role in businesses. Major challenges that businesses would face include dataset imbalance, continuously added data, language specificity and product specificity. In this paper, we propose a scalable incremental machine learning system for industrial-scale deployment in real-world business. The system also includes steps to optimize e-commerce product specifics. The proposal tactics including keyword dictionary mapping, sampling technique and ensemble learning delivered better performance when compared to models without them. Our experiments also showed that minibatch SVM produced good results and might be considerable in a lighter system.
CAT: Centerness-Aware Anchor-Free Tracker

Accurate and robust scale estimation in visual object tracking is a challenging task. To obtain a scale estimation of the target object, most methods rely either on a multi-scale searching scheme or on refining a set of predefined anchor boxes. These methods require heuristically selected parameters, such as scale factors of the multi-scale searching scheme, or sizes and aspect ratios of the predefined candidate anchor boxes. On the contrary, a centerness-aware anchor-free tracker (CAT) is designed in this work. First, the location and scale of the target object are predicted in an anchor-free fashion by decomposing tracking into parallel classification and regression problems. The proposed anchor-free design obviates the need for hyperparameters related to the anchor boxes, making CAT more generic and flexible. Second, the proposed centerness-aware classification branch can identify the foreground from the background while predicting the normalized distance from the location within the foreground to the target center, i.e., the centerness. The proposed centerness-aware classification branch improves the tracking accuracy and robustness significantly by suppressing low-quality state estimates. The experiments show that our centerness-aware anchor-free tracker, with its appealing features, achieves salient performance in a wide variety of tracking scenarios.
Using Automated Feature Selection for Building Case-Based Reasoning Systems: An Example from Patient-Reported Outcome Measurements

Feature selection for case representation is an essential phase of Case-Based Reasoning (CBR) system development. To (semi-)automate the feature selection process can ease the knowledge engineering process. This paper explores the feature importance provided for XGBoost models as basis for creating CBR systems. We use Patient-Reported Outcome Measurements (PROMs) on low back pain from the selfBACK project in our experiments. PROMs are a valuable source of information that capture physical, emotional as well as social aspects of well-being from the perspective of the patients. Leveraging the analytical capabilities of machine learning methods and data science techniques for exploiting PROMs have the potential of improving decision making. This paper presents a two-fold approach employed on our dataset for feature selection that combines statistical strength with data-driven knowledge modelling in CBR and compares it with permutation feature selection using XGBoost regressor. Furthermore, we compare the performance of the CBR models, built with the selected features, with two machine learning algorithms for predicting different PROMs.
Tractable Robust Supervised Learning Models

At the heart of supervised learning is a minimization problem with an objective function that evaluates a set of training data over a loss function that penalizes poor fitting and a regularization function that penalizes over-fitting to the training data. More recently, data-driven robust optimization based learning models provide an intuitive robustness perspective of regularization. However, when the loss function is not Lipschitz continuous, solving the robust learning models exactly can be computationally challenging. We focus on providing tractable approximations for robust regression and classification problems for loss functions derived from Lipschitz continuous functions raised to the power of p. We also show the equivalence of the type-p robust learning models to the pth-root regularization problems when the underlying support sets are unbounded. Inspired by Long et al. (2021), we also propose tractable type-p robust satisficing learning models that are specified by target loss parameters. We illustrate that the robust satisficing regression and classification models can be tractably solved for a large class of problems, and we also establish finite sample probabilistic guarantees for limiting losses beyond the specified target. While the family of solutions generated by regularization and robust satisficing can be the same, from empirical studies on popular datasets, the relative targets for reasonably good out-of-sample performance can be found within a narrow range. We also demonstrate in the numerical study that the target-based hyper-parameter is easier to determine via cross-validation and can improve out-of-sample performance compared to standard regularization approaches.
Supercomputer Supported Online Deep Learning Techniques for High Throughput EEG Prediction

Electroencephalogram (EEG) is a precise reflection of the brain activities and has been widely studied in clinical medicine, neuroscience, brain interface, etc. Intelligent prediction of the EEG’s evolution accurately plays important roles in several application areas, such as epilepsy seizure forecasting and neonatal brain monitoring. Nevertheless, when the prediction service is deployed as a business service on the Cloud and open for public usage, there are several problems that need to be resolved: (i) how to design a computation platform to process the high-throughput multi-source EEG data, which arrives sequentially and increases rapidly when the services are rapidly promoted, namely tackling the ‘high-throughput computing’ problem; (ii) how to develop a deep learning model to capture the complex EEG distribution as well as the anomaly patterns that could evolve dynamically, namely tackling the ‘concept drift’ problem for non-stationary EEG signals. To tackle these challenges, we propose an Evolutive Convolutional Neural Network (ECNN) and the corresponding supercomputer supported distributed computation system. The ECNN model can dynamically reweighting the sub-structure of the model from data streams in an online learning fashion, by which the capacity scalability and sustainability are introduced into the model. As far as we know, it is the first work that introduce supercomputer supported online deep learning techniques into EEG prediction research.
Optimality in noisy importance sampling

In this work, we analyze the noisy importance sampling (IS), i.e., IS working with noisy evaluations of the target density. We present the general framework and derive optimal proposal densities for noisy IS estimators. The optimal proposals incorporate the information of the variance of the noisy realizations, proposing points in regions where the noise power is higher. We also compare the use of the optimal proposals with previous optimality approaches considered in a noisy IS framework.
Masked Face Detection Using A Two-stage Classification Approach In the COVID-19 Era

Masked face detection is a challenging task in the surveillance applications due to complex backgrounds. In this paper, we propose a two-stage method for masked face detection: pre-detection and verification. Firstly, a masked face detector based on AdaBoost algorithm and histogram of orientation feature is exploited. It may provide sufficient candidate face regions. Secondly, a two-class classifier is trained by broad learning system, which is an incremental learning algorithm with high efficiency in training. It is used to distinguish realistic masked faces from background. Moreover, this paper proposes a masked face dataset that includes multiple masked faces captured from real-life scenes . It can be used for classifier training and evaluation. Experiments conducted on the dataset indicate the effectiveness of the proposed method with Recall 94.69% and Precision 97.72%.