Reviewerly/exHarmony huggingface dataset:
Assessing the Capacity of DRDoS-For-Hire Services in Cybercrime Markets

Over the last few years the market for distributed denial of service (DDoS) attacks has changed from a pay-per-attack model executed by botnets, to a subscription service of booters and stressers where “subscribers” launch their own attacks through a web-based front end. The DDoS attack strength of booters and stressers has significantly increased to rival that of the largest botnets, making them an ideal resource for attacks. The size of attacks offered depends on reflection and amplification (DRDoS) attacks where vulnerable servers are used to reflect attacks towards their victim. This increases the volume of attack traffic, while masking the source of the attack from the victim. To better understand this new form of cybercrime as a service, this study provides a comparative analysis of 155 unique reflective attacks performed by 21 DRDoS service providers against a real target. The underlying infrastructure of reflection servers was analyzed across the different providers, along with the type of attacks advertised relative to the actual type of attacks launched. The findings demonstrate there are distinct differences in the quality and capacity of service providers, and the language in posted advertisements does not necessarily conform to the realities of their real-time attacks. Implications for the disruption and mitigation of booter services are discussed in detail.
Scavenge: an intelligent multi-agent based voice-enabled virtual assistant for LMS

The existing Learning Management Systems (LMSs) are profoundly effective in empowering the organization of e-learning, however, lacking in usability and learnability. The complex navigation and an immature search system are catalysing the issues that needs vigorous improvement. This paper aims to enhance the usability of LMSs by introducing an intelligent multi-agent based voice-enabled virtual assistant, named Scavenge. The Scavenge can work with LMS to speed-up user tasks through natural voice-command (or text). It can perform many internal and external actions without a mouse or keyboard clicks, and capable to provide the results of user search queries. A prototype system was developed with Moodle LMS and conducted a usability test based on ISO/IEC 9126-4 usability metrics that are effectiveness, efficiency, and satisfaction. A group of University students were selected, who were involved in the usability testing of the proposed system. The results have shown that the proposed system has a positive impact on students’ performance and can enhance the usability of LMS. The approach and techniques used in Scavenge are novel and innovative that will be a valuable contribution to the overall field of knowledge.
Reweighted sparse representation with residual compensation for 3D human pose estimation from a single RGB image

3D human pose estimation from 2D joints of an image is a worthwhile and challenging research topic. Since a specific 2D pose could be projected from various 3D poses, the ambiguity becomes a difficult obstacle when recovering 3D pose from 2D. Many supervised learning solutions have been proposed in recent years, however, most of them require an abundant of well-annotated training samples to get satisfied estimation performance. In this paper, an unsupervised approach that built upon sparse representation (SR) is presented and its two enhancement schemes are provided. As the first scheme, the reweighting one improves the sparsity of the SR model which leads to a more accurate solution. Furthermore, based on the resulting minimization residual of the loss function in 2D, the discrepancy between the estimated 3D pose and the target 3D pose is used to adjust the estimated 3D pose to reach a better accuracy. Comprehensive experiments have been conducted on four well-recognized benchmarks for evaluation. Significant and consistent improvements over existing SR models are observed in our experiments. Furthermore, the proposed approach even outperforms many supervised learning works.
Designing digital image encryption using 2D and 3D reversible modular chaotic maps

This paper presents a gray level image encryption method based on permutation operation and diffusion operation on image pixels. In diffusion operation, the image is subdivided into equal size sub-images, and the pixels of each sub-image are changed with the help of logical operators and circular shifts. In the permutation operation, the image is first divided into four bit-plate images, and the pixels of each bit-plate image are permuted in parallel by chaotic maps. In this research, reversible two-dimensional and three-dimensional chaotic maps based on modular mathematics are proposed to increase the key space, improve speed and period. The key space exponentially is increased as compared to standard chaotic maps through defining the reversibility in residue matrices, chaotic map in 3D space and changing the key in each round. In the proposed modular 3D chaotic map, the 2D image is first converted into 3D space, and then mapping operations are carried out for permuting the pixels into 3D space. The proposed encryption method for images improves the standard parameters of evaluation such as entropy, adjacent pixel correlations, histogram, and expanded key space.
Survey: The Evolution of the Usage of Augmented Reality in Industry 4.0

Abstract The usage of Augmented Reality (AR) in industrial and modern manufacturing is more and more growing since the fourth industrial revolution. Using AR boost the digitization of the industrial production lines, gain time and money and improve maintenance tasks as well as the human-machine interaction. This paper is a literature review of the use of AR in industries including the use cases in different type of application such as design, simulation, maintenance, remote assistance, human-robot interaction and robot programming.
Air temperature forecasting using artificial neural networks with delayed exogenous input

Solar systems are influenced by their functioning temperature, which is mainly a result of the ambient air temperature as well as the level of sunlight. For this reason, we have proposed an intelligent autoregressive model based on the multilayer neural networks with delayed exogenous input sequence representing the Global Solar Radiation (GSR) data to forecast the Air Temperature (AT) values on a half hour scale in Marrakesh, Morocco, which is recorded during 2014. The developed model will helps us to monitor and to overcome such a problem related to the intermittence or the absence of AT measurements in this region. For building this model, the database is divided into two subsets containing the same size of data. The first one used to train the model using Levenberg-Marquardt algorithm, and the second is used to test the performance and the robustness of the proposed model. The obtained results shown that the developed model has two hidden layers with a few hidden neurons number can predict accurately the target that is demonstrated by the high value of the correlation coefficient, which reaches 99.80.
Robust and efficient multi-source image matching method based on best-buddies similarity measure

Robust and efficient feature matching in multi-source image is a difficult task duo to significant noise and intensity changes. Motivated by problems encountered in present methods, a local similarity descriptor, called local central-tendency similarity (LCTS), is proposed to eliminate the effects of noise and intensity changes by establishing the correlation between the central pixels and surrounding regions of interest. Then, an efficient template-matching framework–namely, the best-buddies direction pairs (BBDP)–is developed for multi-source images by calculating the number of nearest-neighbor points between template and target. When tested on 200 pairs of multi-source images acquired from unconstrained environments, the results demonstrate that BBDP achieves higher computational efficiency and better matching performance than many popular multi-source image matching algorithms.
A Complete and Optimized Key Mismatch Attack on NIST Candidate NewHope

In CT-RSA 2019, Bauer et al. have analyzed the case when the public key is reused for the NewHope key encapsulation mechanism (KEM), a second-round candidate in the NIST Post-quantum Standard process. They proposed an elegant method to recover coefficients ranging from $$-6$$ to 4 in the secret key. We repeat their experiments but there are two fundamental problems. First, even for coefficients in [−6, 4] we cannot recover at least 262 of them in each secret key with 1024 coefficients. Second, for the coefficient outside [−6, 4], they suggested an exhaustive search. But for each secret key on average there are 10 coefficients that need to be exhaustively searched, and each of them has 6 possibilities. This makes Bauer et al.'s method highly inefficient. We propose an improved method, which with $$99.22\%$$ probability recovers all the coefficients ranging from $$-6$$ to 4 in the secret key. Then, inspired by Ding et al.'s key mismatch attack, we propose an efficient strategy which with a probability of $$96.88\%$$ succeeds in recovering all the coefficients in the secret key. Experiments show that our proposed method is very efficient, which completes the attack in about 137.56 ms using the NewHope parameters.
Rapid and Robust Background Modeling Technique for Low-Cost Road Traffic Surveillance Systems

Fast and accurate detection of vehicles on road traffic scenes captured by traffic surveillance cameras, is essential for large-scale deployment of automated traffic surveillance systems. The state-of-the-art techniques typically employ background modeling for low-complexity foreground detection. However, this is a challenging problem as these methods need to be robust to varying road scene conditions (such as illumination changes, camera jitter, stationary vehicles, and heavy traffic) leading to huge computation cost. In this paper, we propose a highly accurate yet low-complexity foreground (i.e., vehicle) detection technique, which can effectively deal with the varying road scene conditions, and generate accurate pixel-level foreground masks in real-time. We propose a novel robust block-based feature suitable for modeling road background and detecting vehicles as foreground, and employ Bayesian probabilistic modeling on these features. The experimental evaluations on widely used traffic datasets demonstrate that the proposed method can achieve comparable accuracy to the existing state-of-the-art techniques but at a much higher processing frame rate (40x speedup over PAWCS). The real-time performance of the proposed system has also been demonstrated by implementing it on a low-cost embedded platform, Odroid XU-4, that still achieves a frame rate of over 80 frames/s, thereby enabling the real-time detection of foreground objects in road scenes.
The Effect of Interactive Learning Media on Students' Science Learning Outcomes

This study aims to determine the effect of interactive learning media on student science learning outcomes. The influence was obtained through processing data from several previous research results with similar problems through the meta-analysis method. Data analysis was carried out in a quantitative manner by percentage. The research unit uses written documents in the form of journals and research reports on interactive learning media. The results of the study showed an average value of effect size of 0.64 (SD = 1.06). This means that interactive learning media has an effect of 23.89% on science learning outcomes for students. Interactive learning media type which gives the biggest influence of 39.44% is interactive multimedia. The concentration of science lessons which has the biggest effect size was Biology of 41.77%, and the biggest effect size there was on the elementary schoolof 38.88%.
Tight Leakage-Resilient CCA-Security from Quasi-Adaptive Hash Proof System

We propose the concept of quasi-adaptive hash proof system (QAHPS), where the projection key is allowed to depend on the specific language for which hash values are computed. We formalize leakage-resilient(LR)-ardency for QAHPS by defining two statistical properties, including LR- $$\langle \mathscr {L}_0, \mathscr {L}_1 \rangle $$ -universal and LR- $$\langle \mathscr {L}_0, \mathscr {L}_1 \rangle $$ -key-switching. We provide a generic approach to tightly leakage-resilient CCA (LR-CCA) secure public-key encryption (PKE) from LR-ardent QAHPS. Our approach is reminiscent of the seminal work of Cramer and Shoup (Eurocrypt’02), and employ three QAHPS schemes, one for generating a uniform string to hide the plaintext, and the other two for proving the well-formedness of the ciphertext. The LR-ardency of QAHPS makes possible the tight LR-CCA security. We give instantiations based on the standard k-Linear (k-LIN) assumptions over asymmetric and symmetric pairing groups, respectively, and obtain fully compact PKE with tight LR-CCA security. The security loss is $${{O}}(\log {Q_{{e}}})$$ where $${Q_{{e}}}$$ denotes the number of encryption queries. Specifically, our tightly LR-CCA secure PKE instantiation from SXDH has only 4 group elements in the public key and 7 group elements in the ciphertext, thus is the most efficient one.
Improved Low-Bitrate HEVC Video Coding Using Deep Learning Based Super-Resolution and Adaptive Block Patching

Good-quality video coding for low-bitrate applications is essential for narrow bandwidth transmission and limited capacity storage. In this paper, we propose an adaptive downsampling-based coding model to improve the low-bitrate compression efficiency of high-efficiency video coding (HEVC). At the encoder, the video sequence is adaptively divided into key frames (KFs) and nonkey frames (NKFs), which are encoded at the original resolution and at a reduced resolution, respectively. At the decoder, a super-resolution method based on deep learning and gradient transformation is used to upscale the NKFs. To improve the quality of NKFs without additional information during decoding, we use motion estimation to find the most similar blocks between the upscaled NKFs and the associated high-resolution KFs. Then, an adaptive patching-based method is used to warp the low-quality NKF blocks with the high-quality KF blocks. Experimental results indicate that for standard high-definition test video sequences, the maximum improvement in the peak signal-to-noise ratio can reach 3.54 dB, and the critical bitrate can reach 9.89 Mb/s at a low bitrate when compared to HEVC. These results demonstrate significant improvements compared to existing methods.
A Novel Scaled dirichlet-based statistical framework for count data modeling: Unsupervised learning and exponential approximation

The multinomial distribution and the Dirichlet Compound Multinomial (DCM) are widely accepted to model count data. However, recent research showed that the Dirichlet is not the best choice as a prior to multinomial. We propose a novel model called the Multinomial Scaled Dirichlet (MSD) distribution that is the composition of the scaled Dirichlet distribution and the multinomial. Moreover, to improve the computation efficiency in high-dimensional spaces, we propose to approximate the MSD as a member of the exponential family. The performance evaluation of the proposed models is conducted through a set of extensive empirical experiments on challenging applications, namely, text classification, facial expression recognition, and texture images clustering. The results show that the proposed model, and its approximation, strive to achieve higher accuracy compared to the state-of-the-art generative models for count data clustering, while the approximation EMSD is many times faster than the corresponding MSD.
Speculative Distributed CSV Data Parsing for Big Data Analytics

There has been a recent flurry of interest in providing query capability on raw data in today's big data systems. These raw data must be parsed before processing or use in analytics. Thus, a fundamental challenge in distributed big data systems is that of efficient parallel parsing of raw data. The difficulties come from the inherent ambiguity while independently parsing chunks of raw data without knowing the context of these chunks. Specifically, it can be difficult to find the beginnings and ends of fields and records in these chunks of raw data. To parallelize parsing, this paper proposes a speculation-based approach for the CSV format, arguably the most commonly used raw data format. Due to the syntactic and statistical properties of the format, speculative parsing rarely fails and therefore parsing is efficiently parallelized in a distributed setting. Our speculative approach is also robust, meaning that it can reliably detect syntax errors in CSV data. We experimentally evaluate the speculative, distributed parsing approach in Apache Spark using more than 11,000 real-world datasets, and show that our parser produces significant performance benefits over existing methods.
REEDS: Relevance and enhanced entropy based Dempster Shafer approach for next word prediction using language model

Next word prediction (NWP) is an acute problem in the arena of natural language processing. The recent approaches are solely based on the probability distribution of the Language Model. Word prediction is the problem of guessing which word is likely to continue a given initial text fragment. A Language Model consists of a number of documents and each document consists of a set of words. Each document will generate a group of evidence based on the information contained in it. The main task is to fuse the evidence to get a reasonable result. In this paper, a novel relevance and enhanced Deng entropy based Dempster's combination rule is proposed where various documents act as evidence source namely relevance and enhanced entropy based Dempster Shafer approach (REEDS) to predict the next probable word from the Language Model. The rationale behind this approach is to handle the conflicting evidence, reduce computational burden and eradicate long learning time of the neural network. The proposed technique can efficiently manage high conflicting evidence and upgrade the performance of convergence. A numerical example is given to demonstrate the efficiency of the proposed method with other combination schemes. Moreover, the proposed method is implemented in Matlab and the outcomes are compared with feedforward neural network on the same Language Model for validation of predicted next word by the proposed method. The proposed method, REEDS is better than neural approach as it relieves from the computational burden and prolonged learning time.
Unconstrained convex minimization based implicit Lagrangian twin random vector Functional-link networks for binary classification (ULTRVFLC)

In the spirit of twin support vector machine (TWSVM) and based on random vector Functional-link (RVFL) network, we propose implicit Lagrangian twin random vector Functional-link network via unconstrained minimization problem for better generalization performance. Unlike RVFL, which is a least-squares method, inequality constraints are employed to the proposed optimization problem to allow tolerating certain amount of error for handling overfitting problem. In this paper, the proposed constrained optimization problem is first reformulated to an unconstrained minimization problem and then is solved by iterative convergent schemes to eliminate the requirement to solving quadratic programming problems (QPPs) of its corresponding dual formulation. The ‘plus’ function present in the reformulated dual formulations of the proposed schemes is not differentiable which is handled by applying the generalized derivative approach or smooth approximation techniques and further solved by applying Newton–Armijo stepsize method. To establish the efficacy of the proposed algorithms, numerical experiments are carried out and comparison of results are carried out with support vector machine (SVM), twin support vector machine (TWSVM), kernel ridge regression (KRR), random vector Functional-link (RVFL) net, extreme learning machine (ELM), constrained extreme learning machine (CoELM) and twin extreme learning machine (TELM). The classification results are presented in terms of accuracy and further, statistical comparison of the algorithms’ performance is carried out to verify the significance of the proposed methods.
Design and Investigation of Capsule Networks for Sentence Classification

In recent years, convolutional neural networks (CNNs) have been used as an alternative to recurrent neural networks (RNNs) in text processing with promising results. In this paper, we investigated the newly introduced capsule networks (CapsNets), which are getting a lot of attention due to their great performance gains on image analysis more than CNNs, for sentence classification or sentiment analysis in some cases. The results of our experiment show that the proposed well-tuned CapsNet model can be a good, sometimes better and cheaper, substitute of models based on CNNs and RNNs used in sentence classification. In order to investigate whether CapsNets can learn the sequential order of words or not, we performed a number of experiments by reshuffling the test data. Our CapsNet model shows an overall better classification performance and better resistance to adversarial attacks than CNN and RNN models.
OAuth 2.0 meets Blockchain for Authorization in Constrained IoT Environments

We present models for utilizing blockchain and smart contract technology with the widely used OAuth 2.0 open authorization framework to provide delegated authorization for constrained IoT devices. The models involve different trade-offs in terms of privacy, delay, and cost, while exploiting key advantages of blockchains and smart contracts. These include linking payments to authorization grants, immutably recording authorization information and policies in smart contracts, and offering resilience through the execution of smart contract code on all blockchain nodes.
Synthesis and machine learning for heterogeneous extraction

We present a way to combine techniques from the program synthesis and machine learning communities to extract structured information from heterogeneous data. Such problems arise in several situations such as extracting attributes from web pages, machine-generated emails, or from data obtained from multiple sources. Our goal is to extract a set of structured attributes from such data.
Multi-objective evolution of oblique decision trees for imbalanced data binary classification

Imbalanced data classification is one of the most challenging problems in data mining. In this kind of problems, we have two types of classes: the majority class and the minority one. The former has a relatively high number of instances while the latter contains a much less number of instances. As most traditional classifiers usually assume that data is evenly distributed for all classes, they may considerably fail in recognizing instances in the minority class due to the imbalance problem. Several interesting approaches have been proposed to handle the class imbalance issue in the literature and the Oblique Decision Tree (ODT) is one of them. Nevertheless, most standard ODT construction algorithms use a greedy search process; while only very few works have addressed this induction problem using an evolutionary approach and this is done without really considering the class imbalance issue. To cope with this limitation, we propose in this paper a multi-objective evolutionary approach to find optimized ODTs for imbalanced binary classification. Our approach, called ODT-Θ-NSGA-III (ODT-based-Θ-Nondominated Sorting Genetic Algorithm-III), is motivated by its abilities: (a) to escape local optima in the ODT search space and (b) to maximize simultaneously both Precision and Recall. Thanks to these two features, ODT-Θ-NSGA-III provides competitive and better results when compared to many state-of-the-art classification algorithms on commonly used imbalanced benchmark data sets.