Reviewerly/exHarmony huggingface dataset:
Securing IoT Applications with Smart Objects: Framework and a Socially Assistive Robots Case Study

The expanded deployment of smart objects in IoT applications is pushing existing IoT platform architectures and their security functionalities to their limits. Indeed, smart objects exhibit semi-autonomous behaviours, are not centrally controlled all the time and therefore need more dynamic approaches in protecting them against vulnerabilities and security incidents. In this paper, we introduce a novel framework for securing the latest generation of IoT applications that involve smart objects, while illustrating its application in securing an Ambient Assisted Living (AAL) system that comprises socially assistive robots. The framework's innovative aspects lie in the use of predictive analytics for anticipating the behaviour of smart objects, including abnormalities in their security behaviour. The importance of anticipating such abnormalities is validated, demonstrated and discussed in the context of the AAL application.
Towards Blockchain-Based GDPR-Compliant Online Social Networks: Challenges, Opportunities and Way Forward

Online Social Networks (OSNs) are very popular and widely adopted by the vast majority of Internet users across the globe. Recent scandals on the abuse of users’ personal information via these platforms have raised serious concerns about the trustworthiness of OSN service providers. The unprecedented collection of personal data by OSN service providers poses one of the greatest threats to users’ privacy and their right to be left alone. The recent approval of the GDPR (General Data Protection Regulation) presents OSN service providers with great compliance challenges. A set of new data protection requirements are imposed on data controllers (OSN service providers) by GDPR that offer greater control to data subjects (OSN users) over their personal data. This position paper investigates the link between GDPR provisions and the use of blockchain technology for solving the consent management problem in online social networks. We also describe challenges and opportunities in designing a GDPR-compliant consent management mechanism for online social networks. Key characteristics of blockchain technology that facilitate regulatory compliance were identified. The legal and technological state of play of the blockchain-GDPR relationship is reviewed and possible ways to reconcile blockchain technology with the GDPR requirements are demonstrated. This paper opens up new research directions on the use of the disruptive innovation of blockchain to achieve regulatory compliance in the application domain of online social networks.
Image Encryption Algorithm Based on a Novel Six-Dimensional Hyper- Chaotic System

Due to the rapid evaluation in the field of communications and multimedia and the increasing use of the Internet, multimedia data security has become very urgent. of the best alternative way to achieve multimedia data security is encryption, which prevents unauthorized entities from accessing confidential data. In recent years, the chaotic system of image encryption becomes an efficient way to encrypt images due to its high security. It has certain special properties like sensitivity to initial conditions, and control parameters, pseudorandom, ergodicity, and non-convergence, etc. chaotic dynamics systems became a promising alternative to traditional encryption algorithms. In this paper, This paper presents a new algorithm for the image encryption/decryption scheme depended on a novel six-dimensional hyper-chaotic system to achieve a high level of security, the chaotic sequence generated from system employ for permutation and diffusion the original image to create an encrypted image. The performance of the algorithm has been analyzed through analyzes statistical such as Histogram Analysis, Correlation Coefficient Analysis, Information Entropy Analysis, Key Space Analysis, Key Sensitivity Analysis, Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio, The experimental results show that the algorithm has good encryption performance, large keyspace equals to and the high sensitivity for small changes in secret key which makes the algorithm immune to Brute force attacks, and it can resist the statistical attacks, therefore, the presented encryption algorithm depends on a novel hyperchaotic system is more secure against the statistical and differential attacks.
PEDAGOGICAL MODELLING OF DEVELOPMENT OF INFORMATION AND COMMUNICATION COMPETENCE OF PROFESSORS OF DISTANCE LEARNING MILITARY EDUCATION SYSTEM

The present article describes the results of analysis of academic views on such terms as “a pedagogical model”, “the modelling” and the possibilities to implement the pedagogical modelling in respect of a professor of the military education system. The pedagogical model of development of his/her information and communication competence in matters of distance learning was suggested and its main constituent elements as conceptual, insightful, methodic, effective, and, according to the requirements of the leading methodological approaches, informative, competence-based, subject-activity, and contextual aspects were substantiated. The content of the key constituent elements of the present model was also described.
Image steganography based on SVD and DWT techniques

Steganography is an invisible communication. It embeds the secret details into cover to generate the stego information. A novel image steganography approach based on Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) is proposed. Firstly, secret and cover images are divided into sub bands using DWT. Most of the information is obtained in LL band. Hence, SVD performed on LL sub band of secret image to get three individual matrices U, S, and V. Add these individual matrices to LL band of the cover image to generate three stego images. The quality of the stego images are evaluated by image metrics like PSNR, SC etc., the proposed algorithm gives best results compared with existing algorithms.
ALCN: Adaptive Local Contrast Normalization

To make Robotics and Augmented Reality applications robust to illumination changes, the current trend is to train a Deep Network with training images captured under many different lighting conditions. Unfortunately, creating such a training set is a very unwieldy and complex task. We therefore propose a novel illumination normalization method that can easily be used for different problems with challenging illumination conditions. Our preliminary experiments show that among current normalization methods, the Difference-of-Gaussians method remains a very good baseline, and we introduce a novel illumination normalization model that generalizes it. Our key insight is then that the normalization parameters should depend on the input image, and we aim to train a Convolutional Neural Network to predict these parameters from the input image. This, however, cannot be done in a supervised manner, as the optimal parameters are not known a priori. We thus designed a method to train this network jointly with another network that aims to recognize objects under different illuminations: The latter network performs well when the former network predicts good values for the normalization parameters. We show that our method significantly outperforms standard normalization methods and would also be appear to be universal since it does not have to be re-trained for each new application. Our method improves the robustness to light changes of state-of-the-art 3D object detection and face recognition methods.
Temporal Parallelization of Bayesian Smoothers

This paper presents algorithms for temporal parallelization of Bayesian smoothers. We define the elements and the operators to pose these problems as the solutions to all-prefix-sums operations for which efficient parallel scan-algorithms are available. We present the temporal parallelization of the general Bayesian filtering and smoothing equations and specialize them to linear/Gaussian models. The advantage of the proposed algorithms is that they reduce the linear complexity of standard smoothing algorithms with respect to time to logarithmic.
High Performance Code Generation in MLIR: An Early Case Study with GEMM

This article is primarily meant to present an early case study on using MLIR, a new compiler intermediate representation infrastructure, for high-performance code generation. Aspects of MLIR covered in particular include memrefs, the affine dialect, and polyhedral utilities and pass infrastructure surrounding those. This article is also aimed at showing the role compiler infrastructure could play in generating code that is competitive with highly tuned manually developed libraries, albeit in a more modular, reusable, and automatable way.
An improved deep convolutional neural network model with kernel loss function in image classification

To further enhance the performance of the current convolutional neural network, an improved deep convolutional neural network model is shown in this paper. Different from the traditional network structure, in our proposed method the pooling layer is replaced by two continuous convolutional layers with $ 3 \times 3 $ convolution kernel between which a dropout layer is added to reduce overfitting, and cross entropy kernel is used as loss function. Experimental results on Mnist and Cifar-10 data sets for image classification show that, compared to several classical neural networks such as Alexnet, VGGNet and GoogleNet, the improved network achieve better performance in learning efficiency and recognition accuracy at relatively shallow network depths.
Neural Re-Simulation for Generating Bounces in Single Images

We introduce a method to generate videos of dynamic virtual objects plausibly interacting via collisions with a still image's environment. Given a starting trajectory, physically simulated with the estimated geometry of a single, static input image, we learn to 'correct' this trajectory to a visually plausible one via a neural network. The neural network can then be seen as learning to `correct' traditional simulation output, generated with incomplete and imprecise world information, to obtain context-specific, visually plausible re-simulated output - a process we call neural re-simulation. We train our system on a set of 50k synthetic scenes where a virtual moving object (ball) has been physically simulated. We demonstrate our approach on both our synthetic dataset and a collection of real-life images depicting everyday scenes, obtaining consistent improvement over baseline alternatives throughout.
Gender and sexual minorities in astronomy and planetary science face increased risks of harassment and assault

Lesbian, gay, bisexual, transgender, queer/questioning, pansexual, asexual, and/or nonbinary (LGBTQPAN) people are a vulnerable, yet understudied group in the American STEM workplace.Only recently have efforts been made to assess their workplace experiences and levels of representation in STEM.In this paper, we sought to explore the experiences of LGBTQPAN individuals in the astronomy and planetary science community.We conducted an internet-based survey of the workplace experiences of 474 astronomers and planetary scientists.We hypothesized LGBTQPAN women and gender minority respondents in our sample would observe negative remarks or directly experience verbal or physical harassment in higher proportion compared to cisgender, straight women, a more commonly studied group.We found support for this hypothesis.LGBTQPAN women and gender minorities also observed more homophobic and transphobic remarks from their peers, and were more likely to feel unsafe at work due to their race, sexual orientation, and gender identity, compared to cisgender, straight women.They were more likely to be verbally harassed due to sexual orientation and gender identity, and were fully twice as likely to be physically harassed due to gender or sex.Based on our sample, we suggest that LGBTQPAN women and gender minorities experience a more hostile workplace climate in astronomy and planetary science compared to cisgender, straight women.
QUALITY AS THE BASIS OF EFFECTIVE MANAGEMENT OF THE EDUCATIONAL MARKET AND A GOAL OF DEVELOPMENT OF UNIVERSITIES IN THE CONDITIONS OF INDUSTRY 4.0

The purpose of the article is to evaluate the effectiveness of managing the educational markets in the countries of the world that have the highest global competitiveness in the sphere of education, to determine and compare the factors of development of universities in the conditions of Industry 4.0 in these countries, and to develop recommendations for increasing the effectiveness of managing the educational market.For evaluating the effectiveness of managing the educational market, the authors' method is used, which is based on the classical formula of measuring effectiveness as a ratio of results and costs.For evaluating the susceptibility of the indicators of development of universities in the conditions of Industry 4.0 to the influence of various factors, the authors use the method of correlation analysis and the method of calculation of direct average.The information and analytical basis of the research includes the analytical and rating materials of CWUR, Legatum Institute, Online learning consortium, QS, Times Higher Education (THE) and World Bank for 2019.It is substantiated that quality is the basis of effective management of the educational market and is a goal of development of universities in the conditions of Industry 4.0.Growth of the volume of state financing of education and improvement of the macro-economic environment will allow achieveing internationalization of education, but they have small influence on its results.Quality of education determines the effectiveness of the national market of education, but is hardly managed at the level of macro-economy.
Finding online neural update rules by learning to remember

We investigate learning of the online local update rules for neural activations (bodies) and weights (synapses) from scratch. We represent the states of each weight and activation by small vectors, and parameterize their updates using (meta-) neural networks. Different neuron types are represented by different embedding vectors which allows the same two functions to be used for all neurons. Instead of training directly for the objective using evolution or long term back-propagation, as is commonly done in similar systems, we motivate and study a different objective: That of remembering past snippets of experience. We explain how this objective relates to standard back-propagation training and other forms of learning. We train for this objective using short term back-propagation and analyze the performance as a function of both the different network types and the difficulty of the problem. We find that this analysis gives interesting insights onto what constitutes a learning rule. We also discuss how such system could form a natural substrate for addressing topics such as episodic memories, meta-learning and auxiliary objectives.
SOIC: Semantic Online Initialization and Calibration for LiDAR and Camera

This paper presents a novel semantic-based online extrinsic calibration approach, SOIC (so, I see), for Light Detection and Ranging (LiDAR) and camera sensors. Previous online calibration methods usually need prior knowledge of rough initial values for optimization. The proposed approach removes this limitation by converting the initialization problem to a Perspective-n-Point (PnP) problem with the introduction of semantic centroids (SCs). The closed-form solution of this PnP problem has been well researched and can be found with existing PnP methods. Since the semantic centroid of the point cloud usually does not accurately match with that of the corresponding image, the accuracy of parameters are not improved even after a nonlinear refinement process. Thus, a cost function based on the constraint of the correspondence between semantic elements from both point cloud and image data is formulated. Subsequently, optimal extrinsic parameters are estimated by minimizing the cost function. We evaluate the proposed method either with GT or predicted semantics on KITTI dataset. Experimental results and comparisons with the baseline method verify the feasibility of the initialization strategy and the accuracy of the calibration approach. In addition, we release the source code at https://github.com/--/SOIC.
Optical Character Recognition in Banking Sectors Using Convolutional Neural Network

Optical Character Recognition is to changing computerized pictures or content into machine editable content and also recognizes the text within a digital image. It is very useful in banking sectors. Till now, Bank deposit slips are processed manually every day in all the banks, it takes a long time. The proposed approach is to recognize the handwritten digits and characters from the bank deposit slip using deep learning techniques. It makes all the banking processes and transactions faster. The handwritten digits and characters such as account holder name, account number and date are segmented by image processing techniques. The segmented character and digits are fed to the CNN model. The model recognizes the digit and character from the deposit slip. The Proposed model delivers better recognition accuracy.
Synthetic Samples Generation for Imbalance Class Distribution with LSTM Recurrent Neural Networks

Developing a classifier model is difficult to properly classify rare items or minority data as long as the training data distribution is imbalanced. Existing resampling issues to remedy this problem, often causes loss of information or are depended on proper computation of neighborhood information to generate new samples. On the other hand, Long Short Term Memory has been used as successful generative models for time series and sequence generation tasks. This article proposes a resampling approach to synthesize new minority samples leveraging Long Short Term Memory based Deep Learning technique to capture association information from manifolds of minority samples and generates new minority sequences based on the conditional probability on the parent sequences. The obtained experimental results on benchmark imbalance model testing datasets revealed that the proposed approach outperforms baseline classifiers by up to 5% as compared to their performance while trained on imbalanced data and outperforms one of the benchmark imbalance reduction method SMOTE by up to 34% in specific cases.
One-Way Hash Function Based on Delay-Induced Hyperchaos

A scheme for constructing one-way Hash function based on hyperchaos induced by time delay and key-stream function iteration is proposed in this paper. In this scheme, the plaintext and secret key are used as the initial value in two hyperchaotic Chen systems; these values are evolved in a hyperchaotic way during a predefined period. The results of the evolution are quantified and iterated using key-stream function iteration to confuse and diffuse the plaintext and secret key. The cipher block chaining mode is used to generate a 128 bits Hash value for a plaintext of arbitrary length. Theoretical analysis and simulation results indicate that the proposed algorithm has satisfactory performance, such as value compression function, irreversibility, initial value sensitivity, forgery resistance and collision resistance.
Imbalance Data Classification Using Local Mahalanobis Distance Learning Based on Nearest Neighbor

Abstract In the dataset, any one of its classes is normally outnumbered by other classes and is known as class imbalance data. Many standard learning algorithms face the classification problem in performance due to imbalance data. The issues can be solved by many existing conventional methods such as cost-sensitive, sampling or ensemble methods. But these methods alter the original data distribution, which leads to loss of useful information of the users and it may cause unexpected errors or increase the problem of overfitting. In this research, local Mahalanobis distance learning (LMDL) method is applied in the nearest neighbor (NN) for improving the performance of the classification in the imbalance dataset. The multiple distance metrics are used in the LMDL to investigate the data effectively and obtain the relevant features based on the analysis. The distance metric uses the original data for learning the prototype and support the NN. A number of experiments on various datasets are conducted for validating the quality as well as the efficiency of the proposed LMDL method. The experimental results stated that the proposed LMDL achieved nearly 82% in E-coli dataset, 94% in breast cancer dataset and 98% in Iris dataset for all metrics such as accuracy, precision, recall and F -measure.
Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates

To deflect adversarial attacks, a range of "certified" classifiers have been proposed. In addition to labeling an image, certified classifiers produce (when possible) a certificate guaranteeing that the input image is not an $\ell_p$-bounded adversarial example. We present a new attack that exploits not only the labelling function of a classifier, but also the certificate generator. The proposed method applies large perturbations that place images far from a class boundary while maintaining the imperceptibility property of adversarial examples. The proposed "Shadow Attack" causes certifiably robust networks to mislabel an image and simultaneously produce a "spoofed" certificate of robustness.
Nonlinear Decentralized Data Fusion with Generalized Inverse Covariance Intersection

Decentralized data fusion is a challenging task even for linear estimation problems. Nonlinear estimation renders data fusion even more difficult as dependencies among the nonlinear estimates require complicated parameterizations. It is nearly impossible to reconstruct or keep track of dependencies. Therefore, conservative approaches have become a popular solution to nonlinear data fusion. As a generalization of Covariance Intersection, exponential mixture densities have been widely applied for nonlinear fusion. However, this approach inherits the conservativeness of Covariance Intersection. For this reason, the less conservative fusion rule Inverse Covariance Intersection is studied in this paper and also generalized to nonlinear data fusion. This generalization employs a conservative approximation of the common information shared by the estimates to be fused. This bound of the common information is subtracted from the fusion result. In doing so, less conservative fusion results can be attained as an empirical analysis demonstrates.
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions

The lack of interpretability is an inevitable problem when using neural network models in real applications. In this paper, an explainable neural network based on generalized additive models with structured interactions (GAMI-Net) is proposed to pursue a good balance between prediction accuracy and model interpretability. GAMI-Net is a disentangled feedforward network with multiple additive subnetworks; each subnetwork consists of multiple hidden layers and is designed for capturing one main effect or one pairwise interaction. Three interpretability aspects are further considered, including a) sparsity, to select the most significant effects for parsimonious representations; b) heredity, a pairwise interaction could only be included when at least one of its parent main effects exists; and c) marginal clarity, to make main effects and pairwise interactions mutually distinguishable. An adaptive training algorithm is developed, where main effects are first trained and then pairwise interactions are fitted to the residuals. Numerical experiments on both synthetic functions and real-world datasets show that the proposed model enjoys superior interpretability and it maintains competitive prediction accuracy in comparison to the explainable boosting machine and other classic machine learning models.