Reviewerly/exHarmony huggingface dataset:
A public and private key image encryption by modified approach of Vigener cipher and the chaotic maps

The article presents a real-time image encryption using a modified approach of Vigenère cipher and the chaotic maps with an efficient key management. The modification is provided to Vigenère cipher through the key expansion procedure. The proposed encryption chooses four random keys of 8-tuples to expand into the 8×8 initial key block. Thereafter, the Vigenère cipher encrypts the pixels blocks using the initial key block and the next blocks through key updation using the XOR operation and the Arnold transform. Later, the partially encrypted image is further scrambled through the Baker map to increase the randomness and unpredictability of the data. The chosen secret key parameters are exchanged publicly through the Diffie–Hellman key exchange agreement. Simulation results and comparison analysis of some extant systems ensure that the proposed algorithm is secure against cryptanalytic attacks and efficient in real-time image processing. Further, the average execution time for encryption and decryption of an image with size 256×256 is 0.4926 which is very efficient than a typical and recent encryption scheme.
The Cybersecurity Awareness INventory (CAIN): Early Phases of Development of a Tool for Assessing Cybersecurity Knowledge Based on the ISO/IEC 27032

Knowledge of possible cyber threats as well as awareness of appropriate security measures plays a crucial role in the ability of individuals to not only discriminate between an innocuous versus a dangerous cyber event, but more importantly to initiate appropriate cybersecurity behaviors. The purpose of this study was to construct a Cybersecurity Awareness INventory (CAIN) to be used as an instrument to assess users’ cybersecurity knowledge by providing a proficiency score that could be correlated with cyber security behaviors. A scale consisting of 46 items was derived from ISO/IEC 27032. The questionnaire was administered to a sample of college students (N = 277). Based on cybersecurity behaviors reported to the research team by the college’s IT department, each participant was divided into three groups according to the risk reports they received in the past nine months (no risk, low risk, and medium risk). The ANOVA results showed a statistically significant difference in CAIN scores between those in the no risk and medium-risk groups; as expected, CAIN scores were lower in the medium-risk group. The CAIN has the potential to be a useful assessment tool for cyber training programs as well as future studies investigating individuals’ vulnerability to cyberthreats.
Reversible Data Hiding in Encrypted Images Based on an Adaptive Recognition Strategy for Blocks

As the rapid development of third-party storage and homomorphic encryption have profoundly stimulated the desire for secure communication, reversible data hiding in encrypted images has received widespread attention, since it allows lossless data conveying and perfect image recovery. In order to obtain secure reversible data hiding with high embedding capacity, a novel block embedding method is proposed, based on an adaptive recognition strategy for combined blocks in the binary image, with which the adjacent identical blocks can be integrated into a combination to reserve more spare bits for data accommodation. Furthermore, a fully reversible data hiding method for grayscale images in the encryption domain is designed. The secret data is hidden into lower bit-planes of the image while the original bits of those embedded lower pixels are recorded into the vacated space of higher bit-planes. The original image can be reconstructed flawlessly as well as the secret data being extracted without errors. To reinforce security, the original image and the secret data are encrypted and scrambled based on sequences generated with the high-dimension chaotic system. Due to its high sensitivity of initial values, the performance such as security and robustness is guaranteed. By comparing the PSNR value of the marked decrypted image and evaluating the quality of the extracted secret image, experimental results demonstrate that the proposed method can obtain higher embedding capacity, achieving 0.2700–0.3924 bpp increment over the state-of-the-art methods, and recover the marked decrypted image with high visual symmetry/quality, and efficiently resist against potential attacks, such as the histogram analysis, differential, brute-force, JPEG attacks, and so on.
Cross-Lingual Named Entity Recognition Based on Attention and Adversarial Training

Named entity recognition aims to extract entities with specific meaning from unstructured text. Currently, deep learning methods have been widely used for this task and have achieved remarkable results, but it is often difficult to achieve better results with less labeled data. To address this problem, this paper proposes a method for cross-lingual entity recognition based on an attention mechanism and adversarial training, using resource-rich language annotation data to migrate to low-resource languages for named entity recognition tasks and outputting changing semantic vectors through the attention mechanism to effectively solve the long-sequence semantic dilution problem. To verify the effectiveness of the proposed method, the method in this paper is applied to the English–Chinese cross-lingual named entity recognition task based on the WeiboNER data set and the People-Daily2004 data set. The obtained F1 value of the optimal model is 53.22% (a 6.29% improvement compared to the baseline). The experimental results show that the cross-lingual adversarial named entity recognition method proposed in this paper can significantly improve the results of named entity recognition in low resource languages.
Spatiotemporal Detection and Localization of Object Removal Video Forgery with Multiple Feature Extraction and Optimized Residual Network

Video forgery detection and localization is one of the most important issue due to the advanced editing software that provides strengthen to tools for manipulating the videos. Object based video tampering destroys the originality of the video. The main aim of the video forensic is to eradicate the forgeries from the original video that are useful in various applications. However, the research on detecting and localizing the object based video forgery with advanced techniques still remains the open and challenging issue. Many of the existing techniques have focused only on detecting the forged video under static background that cannot be applicable for detecting the forgery in tampered video. In addition to this, conventional techniques fail to extract the essential features in order to investigate the depth of the video forgery. Hence, this paper brings a novel technique for detecting and localizing the forged video with multiple features. The steps involved in this research are keyframe extraction, pre-processing, feature extraction and finally detection and localization of forged video. Initially, keyframe extraction uses the Gaussian mixture model (GMM) to extract frames from the forged videos. Then, the pre-processing stage is manipulated to convert the RGB frame into a grayscale image. Multi-features need to be extracted from the pre-processed frames to study the nature of the forged videos. In our proposed study, speeded up robust features (SURF), principal compound analysis histogram oriented gradients (PCA-HOG), model based fast digit feature (MBFDF), correlation of adjacent frames (CAF), the prediction residual gradient (PRG) and optical flow gradient (OFG) features are extracted. The dataset used for the proposed approach is collected from REWIND of about 40 forged and 40 authenticated videos. With the help of the DL approach, video forgery can be detected and localized. Thus, this research mainly focuses on detecting and localization of forged video based on the ResNet152V2 model hybrid with the bidirectional gated recurrent unit (Bi-GRU) to attain maximum accuracy and efficiency. The performance of this approach is finally compared with existing approaches in terms of accuracy, precision, F-measure, sensitivity, specificity, false-negative rate (FNR), false discovery rate (FDR), false-positive rate (FPR), Mathew’s correlation coefficient (MCC) and negative predictive value (NPV). The proposed approach assures the performance of 96.17% accuracy, 96% precision, 96.14% F-measure, 96.58% sensitivity, 96.5% specificity, 0.034 FNR, 0.04 FDR, 0.034 FPR, 0.92 MCC and 96% NPV, respectively. Along with is, the mean square error (MSE) and peak-to-signal-noise ratio (PSNR) for the GMM model attained about 104 and 27.95, respectively.
Hagnifinder: Recovering magnification information of digital histological images using deep learning

Training a robust cancer diagnostic or prognostic artificial intelligent model using histology images requires a large number of representative cases with labels or annotations, which are difficult to obtain. The histology snapshots available in published papers or case reports can be used to enrich the training dataset. However, the magnifications of these invaluable snapshots are generally unknown, which limits their usage. Therefore, a robust magnification predictor is required for utilizing those diverse snapshot repositories consisting of different diseases. This paper presents a magnification prediction model named Hagnifinder for H&E-stained histological images.Hagnifinder is a regression model based on a modified convolutional neural network (CNN) that contains 3 modules: Feature Extraction Module, Regression Module, and Adaptive Scaling Module (ASM). In the training phase, the Feature Extraction Module first extracts the image features. Secondly, the ASM is proposed to address the learned feature values uneven distribution problem. Finally, the Regression Module estimates the mapping between the regularized extracted features and the magnifications. We construct a new dataset for training a robust model, named Hagni40, consisting of 94 643 H&E-stained histology image patches at 40 different magnifications of 13 types of cancer based on The Cancer Genome Atlas. To verify the performance of the Hagnifinder, we measure the accuracy of the predictions by setting the maximum allowable difference values (0.5, 1, and 5) between the predicted magnification and the actual magnification. We compare Hagnifinder with state-of-the-art methods on a public dataset BreakHis and the Hagni40.The Hagnifinder provides consistent prediction accuracy, with a mean accuracy of 98.9%, across 40 different magnifications and 13 different cancer types when Resnet50 is used as the feature extractor. Compared with the state-of-the-art methods focusing on 4-5 levels of magnification classification, the Hagnifinder achieves the best and most comparable performance in the BreakHis and Hagni40 datasets.The experimental results suggest that Hagnifinder can be a valuable tool for predicting the associated magnification of any given histology image.
Automated crack detection in laminated composites by optical flow measurements

In this paper, an automated crack detection method for laminated composites of carbon fiber reinforced polymers (CFRP) is presented. Image correlation is performed on micrographs of the unspeckled specimen edge using DeepFlow, an algorithm for optical flow estimation. Cracks are then detected from local maxima of the displacement gradient evaluated along the midline of the plies. The detection strategy is applied to 3 different materials, with increasingly irregular microstructures, and on quasi-isotropic layups (a challenging case for crack detection). The crack density evolution in the 90∘ and ±45∘ plies is presented. The proposed method proves its capacity to detect cracks, even in complex configurations where other methods fail, and can provide an extensive amount of data that are useful for the identification of damage models for laminates. The procedure is largely automated in order to make it suitable for industrial applications.
SODES: Solving ordinary differential equations step by step

In this paper, we introduce SODES (Stepwise Ordinary Differential Equations Solver) a new solver for Ordinary Differential Equations (ODE). SODES can optionally provide the solution displaying all the steps needed to obtain it. This way, SODES is an important tool not only for researchers who need solving ODE but also constitutes an important tool for the teaching and learning process of ODE. SODES has been developed using programming with a Computer Algebra System (CAS). Specifically, we use the CAS Derive but it can be easily adapted to any other CAS supporting programming. SODES provides, step by step, the solution of the following types of ODE: separable, homogeneous, exact, integrating factors, linear, Bernoulli, Riccati, first order ODE of nth degree, Cauchy’s problems of first order ODE, higher order linear homogeneous equations with constant coefficients, Lagrange’s method for particular solutions of higher order linear equations with constant coefficients, higher order linear equations with constant coefficients and Cauchy’s problems of higher order linear equations with constant coefficients. SODES also deals with two generic programs which determine the type or types of a given ODE and provides the solution. In this paper we will also introduce a draft of a Graphical User Interface (GUI) for SODES in a local web application using programming in Python (using its CAS module SymPy) which is a more portable and free CAS. This draft can be used in English, French and Spanish, and can be easily extended to other languages. The code of SODES and the GUI are freely available so that it can be used by users who also will be able to adapt it to their needs.
Energy aware Clustered blockchain data for IoT: An end-to-end lightweight secure &amp; Enroute filtering approach

Internet of things (IoT) include smart homes, smart buildings and smart factories, in which all the devices are connected over the internet through wireless network for data transmission or sharing. IoT faces many security challenges due to sharing the data in an open source. Security and energy are the most focusing challenges in IoT due to its resource constrained nature. IoT devices have low energy that is wasted by the attackers, and the data also gets hacked by the attacker, which leads to poor security. This paper proposed CLIENT (Clustered blockchain IoT with En-route) to provide energy efficient and secure communication. For that, this research proposed five processes. Such as Chaotic map based Elgamal authentication, credit score based clustering, capuchin search optimization based packets routing, signature based Enroute filtering and deep leaning based anomaly packet detection. First process is Chaotic Map based Elgamal Authentication, in which chaotic map is used in Elgamal key generation. Second process is Credit score based clustering which minimizes the complexity and overhead during packet transmission. Third process is Capuchin search optimization based packets routing which is used to select optimal route from cluster member to cluster head (CH). Fourth process is signature based Enroute filtering which used to detect and drop false report from the network. Edwards signature based digital signature algorithm (EdDSA) is used for verification purpose. And final process is deep learning based anomaly packets detection, this process used Katz centrality based feed forward small world neural network (FFSWNN) for packet classification (normal or malicious). All the transactions are stored in blockchain to enhance security. The proposed system implemented by using NS3. The result shows that proposed system achieves superior performance compared to existing works in terms of energy consumption, packet loss rate, end to end delay, routing overhead and network life time, precision, recall, accuracy, false alarm rate, encryption time, decryption time and security strength.
Literature Review on Waste Management Using Blockchain

The ever-evolving blockchain like Ethereum has wide applications in various sectors but is comparatively unknown in the sector of managing waste. If assorted steps within the management method are not meted out correctly, waste management ways can even create a health and environmental risk. Smart cities can subdue ecological issues caused by unethical waste handling to overstate mankind, defend marine life, and scale back contamination. The conventional technologies do not offer adequate levels of transparency and coordination among completely different entities. Tamper-proof technology introduced by blockchain; municipalities can escalate the productivity of their waste management efforts. The projected blockchain technology can attach correct stakeholders toward combining and providing information. We review the various opportunities provided by blockchain Technology in proper handling of the various types of wastes like Solid Waste, Electronic Waste, Medical Waste and Industrial Waste. In this review paper we compare various research available in this domain related to different types of waste on the basis of vital specifications. Moreover, we present perceptive analysis of the available case studies to focus on the feasibility of blockchain in proper waste handling. Ultimately, we put forth challenges that can lead to upcoming research.
Formal System of Categorical Syllogistic Logic Based on the Syllogism &amp;lt;i&amp;gt;AEE-4&amp;lt;/i&amp;gt;

Adopting a different method from the previous scholars, this article deduces the remaining 23 valid syllogisms just taking the syllogism AEE-4 as the basic axiom.The basic idea of this study is as follows: firstly, make full use of the trichotomy structure of categorical propositions to formalize categorical syllogisms.Then, taking advantage of the deductive rules in classical propositional logic and the basic facts in the generalized quantifier theory, we deduce the remaining 23 valid categorical syllogisms by taking just one syllogism (that is, AEE-4) as the basic axiom.This article not only reveals the reducible relations between the syllogism AEE-4 and the other 23 valid syllogisms, but also establishes a concise formal axiomatic system for categorical syllogistic logic.We hope that the results and methods will provide a good mathematical paradigm for studying other kinds of syllogistic logics, and that the project will appeal to specialists in logic, linguistic semantics, computational semantics, cognitive science and artificial intelligence.
Improving the robustness of adversarial attacks using an affine-invariant gradient estimator

As designers of artificial intelligence try to outwit hackers, both sides continue to hone in on AI’s inherent vulnerabilities. Designed and trained from certain statistical distributions of data, deep neural networks (DNNs) remain vulnerable to deceptive inputs that violate a DNN’s statistical, predictive assumptions. Before being fed into a neural network, however, most existing adversarial examples cannot maintain malicious functionality when applied to an affine transformation. For practical purposes, maintaining that malicious functionality serves as an important measure of the robustness of adversarial attacks. To help DNNs learn to defend themselves more thoroughly against attacks, we propose an affine-invariant adversarial attack, which can consistently produce more robust adversarial examples over affine transformations. For efficiency, we propose to disentangle current affine-transformation strategies from the Euclidean geometry coordinate plane with its geometric translations, rotations and dilations; we reformulate the latter two in polar coordinates. Afterwards, we construct an affine-invariant gradient estimator by convolving the gradient at the original image with derived kernels, which can be integrated with any gradient-based attack methods. Extensive experiments on ImageNet, including some experiments under physical condition, demonstrate that our method can significantly improve the affine invariance of adversarial examples and, as a byproduct, improve the transferability of adversarial examples, compared with alternative state-of-the-art methods.
Kurcuma: a kitchen utensil recognition collection for unsupervised domain adaptation

Abstract The use of deep learning makes it possible to achieve extraordinary results in all kinds of tasks related to computer vision. However, this performance is strongly related to the availability of training data and its relationship with the distribution in the eventual application scenario. This question is of vital importance in areas such as robotics, where the targeted environment data are barely available in advance. In this context, domain adaptation (DA) techniques are especially important to building models that deal with new data for which the corresponding label is not available. To promote further research in DA techniques applied to robotics, this work presents Kurcuma (Kitchen Utensil Recognition Collection for Unsupervised doMain Adaptation), an assortment of seven datasets for the classification of kitchen utensils—a task of relevance in home-assistance robotics and a suitable showcase for DA. Along with the data, we provide a broad description of the main characteristics of the dataset, as well as a baseline using the well-known domain-adversarial training of neural networks approach. The results show the challenge posed by DA on these types of tasks, pointing to the need for new approaches in future work.
Complementary adversarial mechanisms for weakly-supervised temporal action localization

Weakly supervised Temporal Action Localization (WTAL) aims to locate the start and end boundaries of action instances and recognize their corresponding categories. Classical methods mainly rely on random erasure mechanisms, attention mechanisms, or imposing loss constraints. Despite their great progress, there are still two challenges of incomplete positioning and context confusion. Therefore, we propose a framework with complementary adversarial mechanisms to address these issues. In the adversarial learning stage, for an input snippet, we roughly determine its proper duration, by matching it with the specified multi-scaled anchors based on CAS score loss; then, it undergoes a frame-level iterative regression to precisely figure out its boundary, which can reject none closely related frames merged in, and ensures no overlapping between different action proposals. Subsequently, the GCN module explicitly enhances the feature representation for this fine localized snippet, aiming to strengthen the exclusiveness between different action snippets. Afterward, our complementary learning module calculates the similarity between the original input video Vg and the video Vr reconstructed with the above localization refined snippets, aiming to ensure no closely relevant frames missing, this checking mechanism works as feedback to guide the regression module for more accurate localization regression. Finally, each refined snippet undergoes multi-instance learning to obtain its classification score, and the top-k strategy is used to aggregate temporally adjacent snippets based on their content similarity, which can avoid fragmentation of an action proposal. This method is tested on datasets of THUMOS14 and ActivityNet1.2, and their average accuracy is 64.68% and 32.94% respectively, its comparisons with other articles prove its effectiveness.