Reviewerly/exHarmony huggingface dataset:
EVIL: Exploiting Software via Natural Language

Writing exploits for security assessment is a challenging task. The writer needs to master programming and obfuscation techniques to develop a successful exploit. To make the task easier, we propose an approach (EVIL) to automatically generate exploits in assembly/Python language from descriptions in natural language. The approach leverages Neural Machine Translation (NMT) techniques and a dataset that we developed for this work. We present an extensive experimental study to evaluate the feasibility of EVIL, using both automatic and manual analysis, and both at generating individual statements and entire exploits. The generated code achieved high accuracy in terms of syntactic and semantic correctness.
Supervised perceptron learning vs unsupervised Hebbian unlearning: Approaching optimal memory retrieval in Hopfield-like networks

The Hebbian unlearning algorithm, i.e., an unsupervised local procedure used to improve the retrieval properties in Hopfield-like neural networks, is numerically compared to a supervised algorithm to train a linear symmetric perceptron. We analyze the stability of the stored memories: basins of attraction obtained by the Hebbian unlearning technique are found to be comparable in size to those obtained in the symmetric perceptron, while the two algorithms are found to converge in the same region of Gardner's space of interactions, having followed similar learning paths. A geometric interpretation of Hebbian unlearning is proposed to explain its optimal performances. Because the Hopfield model is also a prototypical model of the disordered magnetic system, it might be possible to translate our results to other models of interest for memory storage in materials.
A New Study on Optimization of Four-Bar Mechanisms Based on a Hybrid-Combined Differential Evolution and Jaya Algorithm

In mechanism design with symmetrical or asymmetrical motions, obtaining high precision of the input path given by working requirements of mechanisms can be a challenge for dimensional optimization. This study proposed a novel hybrid-combined differential evolution (DE) and Jaya algorithm for the dimensional synthesis of four-bar mechanisms with symmetrical motions, called HCDJ. The suggested algorithm uses modified initialization, a hybrid-combined mutation between the classical DE and Jaya algorithm, and the elitist selection. The modified initialization allows generating initial individuals, which are satisfied with Grashof’s condition and consequential constraints. In the hybrid-combined mutation, three differential groups of mutations are combined. DE/best/1 and DE/best/2, DE/current to best/1 and Jaya operator, and DE/rand/1, and DE/rand/2 belong to the first, second, and third groups, respectively. In the second group, DE/current to best/1 is hybrid with the Jaya operator. Additionally, the elitist selection is also applied in HCDJ to find the best solutions for the next generation. To validate the feasibility of HCDJ, the numerical examples of the symmetrical motion of four-bar mechanisms are investigated. From the results, the proposed algorithm can provide accurate optimal solutions that are better than the original DE and Jaya methods, and its solutions are even better than those of many other algorithms that are available in the literature.
Recognition and incorporation of mineralization-efficient fault systems to produce a strengthened anisotropic geochemical singularity

Identification of geochemical anomalies using singularity theory is a topic of interest in the field of mineral exploration. The ordinary singularity mapping technique does not consider the critical role of geological structures in the ore-forming environment. Therefore, models of spatially weighted singularity have been developed through the incorporation of fault systems, which might play an important role in ore-forming processes, and thus, are key factors in spatial analysis of mineralization indicators. In this study, we proposed a strengthened singularity mapping technique that was spatially enhanced and weighted by mineralization-efficient fault systems. For this, we applied distance distribution analysis to distinguish “efficient” (linked to mineralization) and “inefficient” (not linked to mineralization) fault systems, and then, used the former to generate the strengthened spatially weighted singularity models of geochemical indicator elements. The rationale is that faults with different orientations are the result of various geological processes, of which only some are linked to the ore-forming processes. We also integrated the strengthened models of geochemical anomalies by using random forest (RF) and principal component analysis (PCA) techniques to produce a stronger geochemical clue. Comparison of the integration results demonstrated that the former, a strengthened anisotropic geochemical singularity modeling technique proposed in this paper through the incorporation of efficient fault systems and RF method, is superior to the later, an existing anisotropic geochemical singularity modeling technique through the incorporation of all faults and PCA method. Data related to porphyry copper mineralization in Sarduiyeh district, Iran, was used to illustrate the procedure applied. • Efficient fault systems are incorporated by distance-distribution analysis. • A strengthened anisotropic geochemical singularity is produced. • Spatially weighted singularity mapping technique was improved. • Random forest algorithm can robustly integrate anisotropic geochemical signatures.
Cyber security: Building digital trust

Although information and communication technologies have made communication, entertainment and business easier for people, they have also enabled cybercriminals to carry out their illegal activities in the digital environment. By misusing data, but also by activities that can cause emotional and physical damage to Internet users, cybercriminals have contributed to the erosion of the trust of consumers who have satisfied their needs and desires with digital shopping. In times of global pandemic, when a large number of consumers were forced to use online shopping services, this distrust caused great damage to companies. Strategies to rebuild trust and revitalize consumer loyalty are imperative for many organizations if they want to survive in the marketplace. Of course, legislative support is also needed to ensure cyber security, which is increasingly taking on global dimensions in its scope, presenting imperatives in defense against cyber threats to states themselves, threatening their economic and political stability.
RGRecSys

Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data. Due to the pervasiveness of recommender systems in online technologies, researchers have carried out several robustness studies focusing on data sparsity and profile injection attacks. Instead, we propose a more holistic view of robustness for recommender systems that encompasses multiple dimensions - robustness with respect to sub-populations, transformations, distributional disparity, attack, and data sparsity. While there are several libraries that allow users to compare different recommender system models, there is no software library for comprehensive robustness evaluation of recommender system models under different scenarios. As our main contribution, we present a robustness evaluation toolkit, Robustness Gym for RecSys (RGRecSys -- https://www.github.com/salesforce/RGRecSys), that allows us to quickly and uniformly evaluate the robustness of recommender system models.
The influence of dependability in cloud computing adoption

Abstract Cloud computing (CC) has many benefits, so its use has spread rapidly, particularly in the business sector. An important consideration in the acceptance of CC is whether the CC system is dependable, and it can differ among industry and service type. However, little research has considered the effect of dependability (composed of availability , reliability , security , maintainability ) on CC acceptance. Especially, group comparisons between high IT-intensive (Hi-ITi) and low IT-intensive (Lo-ITi) industries have not been reported, nor have comparisons between software-as-a-service (SaaS) and platform-as-a-service (PaaS)/infrastructure-as-a-service (IaaS). This study aims to explore how the dependability of CC affects users’ intent to accept it, with focus on how this intent is affected by intensity of IT use (by industry) and by the type of CC service used. To validate the proposed model, this study applied structural equation modeling and conducted multi-group analysis. A total of 230 business managers in South Korea represent the sample for our study. For the full dataset , the three dependability attributes ( availability , reliability , security ) do not affect the usefulness of CC, but do affect the ease of use of CC. The usefulness of CC is a determinant for positive intention to accept CC, whereas the ease of use of CC is not. Maintainability is the strongest determinant of CC adoption for the full dataset, and for all individual groups, except those that use SaaS. For Hi-ITi and Lo-ITi industries, results show that managers show no differences in their perceptions of the effect of dependability attributes ( availability , reliability , security ) on the usefulness and the ease of CC. The absence of such a difference in managers’ perception also applies to the relationship between two core variables of TAM (i.e., perceived usefulness , perceived ease of use ) and behavioral intention to accept CC. For SaaS and PaaS/IaaS, managers have different perceptions of security on the usefulness of CC, and the effect of the usefulness of CC on the intention to accept CC. The findings can provide academic researchers and industry practitioners with a differentiated and in-depth perspective on the understanding and the spread of CC.
1-Norm random vector functional link networks for classification problems

Abstract This paper presents a novel random vector functional link (RVFL) formulation called the 1-norm RVFL (1N RVFL) networks, for solving the binary classification problems. The solution to the optimization problem of 1N RVFL is obtained by solving its exterior dual penalty problem using a Newton technique. The 1-norm makes the model robust and delivers sparse outputs, which is the fundamental advantage of this model. The sparse output indicates that most of the elements in the output matrix are zero; hence, the decision function can be achieved by incorporating lesser hidden nodes compared to the conventional RVFL model. 1N RVFL produces a classifier that is based on a smaller number of input features. To put it another way, this method will suppress the neurons in the hidden layer. Statistical analyses have been carried out on several real-world benchmark datasets. The proposed 1N RVFL with two activation functions viz., ReLU and sine are used in this work. The classification accuracies of 1N RVFL are compared with the extreme learning machine (ELM), kernel ridge regression (KRR), RVFL, kernel RVFL (K-RVFL) and generalized Lagrangian twin RVFL (GLTRVFL) networks. The experimental results with comparable or better accuracy indicate the effectiveness and usability of 1N RVFL for solving binary classification problems.
Formation of the spiritual and moral qualities of the personality of students in the conditions of modern education

Introduction. The changes that have occurred in recent years in Russian society have had a negative impact on the formation of spiritual and moral orientations and values of young students, and in general on the development of the personality of a modern growing person. Increasingly, the idols of young people are bloggers who demonstrate and popularize deviant behavior, lack of spirituality, immorality, lack of citizenship. The development of media education, information, spiritual, linguistic security, according to the authors, contribute to civic maturation and the formation of young students in a new, rather aggressive, cultural and information space, which today is becoming one of the most important means of managing the spiritual and moral basis of human life. Materials and Methods. Theoretical methods: comparative analysis of philosophical, psychological, pedagogical, methodological literature; method of scientific synthesis of factual materials. The methodological basis consists of systemic, personal-activity and cultural approaches. Results. The scientific research reveals the goals and functions of the Center for Spiritual and Moral Education and Civic Education and the Information Center. Discussion and Conclusions. The organization of work on the formation of students' spiritual and moral qualities of personality should be carried out in the context of spiritual, media security, psychological, linguistic security.
Robust color image watermarking using multi-core Raspberry pi cluster

Abstract Image authentication approaches have gotten a lot of interest recently as a way to safeguard transmitted images. Watermarking is one of the many ways used to protect transmitted images. Watermarking systems are pc-based that have limited portability that is difficult to use in harsh environments as military use. We employ embedded devices like Raspberry Pi to get around the PC’s mobility limitations. Digital image watermarking technology is used to secure and ensure digital images’ copyright by embedding hidden information that proves its copyright. In this article, the color images Parallel Robust watermarking algorithm using Quaternion Legendre-Fourier Moment (QLFM) in polar coordinates is implemented on Raspberry Pi (RPi) platform with parallel computing and C++ programming language. In the host image, a binary Arnold scrambled image is embedded. Watermarking algorithm is implemented and tested on Raspberry Pi model 4B. We can combine many Raspberry Pi’s into a ‘cluster’ (many computers working together as one) for high-performance computation. Message Passing Interface (MPI) and OpenMP for parallel programming to accelerate the execution time for the color image watermarking algorithm implemented on the Raspberry Pi cluster.
Statistical inference for ordinal predictors in generalized additive models with application to Bronchopulmonary Dysplasia

Discrete but ordered covariates are quite common in applied statistics, and some regularized fitting procedures have been proposed for proper handling of ordinal predictors in statistical modeling. In this study, we show how quadratic penalties on adjacent dummy coefficients of ordinal predictors proposed in the literature can be incorporated in the framework of generalized additive models, making tools for statistical inference developed there available for ordinal predictors as well. Motivated by an application from neonatal medicine, we discuss whether results obtained when constructing confidence intervals and testing significance of smooth terms in generalized additive models are useful with ordinal predictors/penalties as well.
Can Hyperspectral Imaging and Neural Network Classification Be Used for Ore Grade Discrimination at the Point of Excavation?

This work determines whether hyperspectral imaging is suitable for discriminating ore from waste at the point of excavation. A prototype scanning system was developed for this study. This system combined hyperspectral cameras and a three-dimensional LiDAR, mounted on a pan-tilt head, and a positioning system which determined the spatial location of the resultant hyperspectral data cube. This system was used to obtain scans both in the laboratory and at a gold mine in Western Australia. Samples from this mine site were assayed to determine their gold concentration and were scanned using the hyperspectral apparatus in the laboratory to create a library of labelled reference spectra. This library was used as (i) the reference set for spectral angle mapper classification and (ii) a training set for a convolutional neural network classifier. Both classification approaches were found to classify ore and waste on the scanned face with good accuracy when compared to the mine geological model. Greater resolution on the classification of ore grade quality was compromised by the quality and quantity of training data. The work provides evidence that an excavator-mounted hyperspectral system could be used to guide a human or autonomous excavator operator to selectively dig ore and minimise dilution.
Tuning Convolutional Neural Network Hyperparameters by Bare Bones Fireworks Algorithm

Digital image classification is an important component in various applications.Lately, convolutional neural networks have been widely used as a classifier since they achieve superior results, while their application is relatively simple.In order to achieve the best possible results, tuning of the network's hyperparameters is necessary but that represents an exponentially hard optimization problem with computationally very expensive fitness function.The swarm intelligence algorithms have been proven to be effective in solving such exponentially hard optimization problems, however their application to this particular problem has not been sufficiently studied.In this paper, convolutional neural network hyperparameters were tuned by the bare bones fireworks algorithm.The quality of the proposed method was tested on two standard benchmark datasets, CIFAR-10 and MNIST.The results were compared to CIFAR-Net, LeNet-5 and the networks optimized by the harmony search algorithm and the proposed method achieved better results considering the classification accuracy.The proposed method for CNN hyperparameter tuning improved the classification accuracy up to 99.34% on the MNIST dataset and up to 75.51% on the CIFAR-10 dataset compared to 99.25% and 74.76% reported by another method from the specialized literature.
Knowledge distillation circumvents nonlinearity for optical convolutional neural networks

In recent years, convolutional neural networks (CNNs) have enabled ubiquitous image processing applications. As such, CNNs require fast forward propagation runtime to process high-resolution visual streams in real time. This is still a challenging task even with state-of-the-art graphics and tensor processing units. The bottleneck in computational efficiency primarily occurs in the convolutional layers. Performing convolutions in the Fourier domain is a promising way to accelerate forward propagation since it transforms convolutions into elementwise multiplications, which are considerably faster to compute for large kernels. Furthermore, such computation could be implemented using an optical 4f system with orders of magnitude faster operation. However, a major challenge in using this spectral approach, as well as in an optical implementation of CNNs, is the inclusion of a nonlinearity between each convolutional layer, without which CNN performance drops dramatically. Here, we propose a spectral CNN linear counterpart (SCLC) network architecture and its optical implementation. We propose a hybrid platform with an optical front end to perform a large number of linear operations, followed by an electronic back end. The key contribution is to develop a knowledge distillation (KD) approach to circumvent the need for nonlinear layers between the convolutional layers and successfully train such networks. While the KD approach is known in machine learning as an effective process for network pruning, we adapt the approach to transfer the knowledge from a nonlinear network (teacher) to a linear counterpart (student), where we can exploit the inherent parallelism of light. We show that the KD approach can achieve performance that easily surpasses the standard linear version of a CNN and could approach the performance of the nonlinear network. Our simulations show that the possibility of increasing the resolution of the input image allows our proposed 4f optical linear network to perform more efficiently than a nonlinear network with the same accuracy on two fundamental image processing tasks: (i) object classification and (ii) semantic segmentation.
Continual coarse-to-fine domain adaptation in semantic segmentation

Deep neural networks are typically trained in a single shot for a specific task and data distribution, but in real world settings both the task and the domain of application can change. The problem becomes even more challenging in dense predictive tasks, such as semantic segmentation, and furthermore most approaches tackle the two problems separately. In this paper we introduce the novel task of coarse-to-fine learning of semantic segmentation architectures in presence of domain shift. We consider subsequent learning stages progressively refining the task at the semantic level; i.e., the finer set of semantic labels at each learning step is hierarchically derived from the coarser set of the previous step. We propose a new approach (CCDA) to tackle this scenario. First, we employ the maximum squares loss to align source and target domains and, at the same time, to balance the gradients between well-classified and harder samples. Second, we introduce a novel coarse-to-fine knowledge distillation constraint to transfer network capabilities acquired on a coarser set of labels to a set of finer labels. Finally, we design a coarse-to-fine weight initialization rule to spread the importance from each coarse class to the respective finer classes. To evaluate our approach, we design two benchmarks where source knowledge is extracted from the GTA5 dataset and it is transferred to either the Cityscapes or the IDD datasets, and we show how it outperforms the main competitors.
Recursive Multi-Scale Channel-Spatial Attention for Fine-Grained Image Classification

Fine-grained image classification is a difficult problem, and previous studies mainly overcome this problem by locating multiple discriminative regions in different scales and then aggregating complementary information explored from the located regions. However, locating discriminative regions introduces heavy overhead and is not suitable for real-world application. In this paper, we propose the recursive multi-scale channel-spatial attention module (RMCSAM) for addressing this problem. Following the experience of previous research on fine-grained image classification, RMCSAM explores multi-scale attentional information. However, the attentional information is explored by recursively refining the deep feature maps of a convolutional neural network (CNN) to better correspond to multi-scale channel-wise and spatial-wise attention, instead of localizing attention regions. In this way, RMCSAM provides a lightweight module that can be inserted into standard CNNs. Experimental results show that RMCSAM can improve the classification accuracy and attention capturing ability over baselines. Also, RMCSAM performs better than other state-of-the-art attention modules in fine-grained image classification, and is complementary to some state-of-the-art approaches for fine-grained image classification. Code is available at https://github.com/Dichao-Liu/Recursive-Multi-Scale-Channel-Spatial-Attention-Module.