Reviewerly/exHarmony huggingface dataset:
A Multilayered Secure Image Steganography Technique for Resisting Regular-Singular Steganalysis Attacks Using Elliptic Curve Cryptography and Genetic Algorithm

Increasing cyberattacks and data breaches have called for innovative approaches to secure data communication. Although many methods have been proposed, cryptography and steganography have gained the most popularity for protecting the security of secret information. While cryptography works by scrambling the content of a secret message to provide data privacy, steganography focuses on masking secret message existence to ensure secrecy. Fundamentally, cryptography reinforces the data protection capabilities of steganography. However, the ability of steganography to provide data protection for secure data communication has been undermined by steganalysis. Statistical steganalysis mechanisms identify, detect, and accurately estimate the presence of steganography-based hidden information. Regular-singular (RS) analysis scheme is one of the well-known statistical analysis methods that is used to detect steganographic techniques that employs least significant bit (LSB) insertion algorithm for secret message embedding. This paper’s objective is geared towards improving the robustness, imperceptibility, and visual quality of embedded image using secure methods by minimizing MSE values and maximizing PNSR values. Genetic algorithm (GA) is utilized to adjust the stego-image pixel values by identifying appropriate bits within the cover image pixel values through the selection of fittest best chromosome. The security is achieved by encrypting and decrypting keys generated using elliptic curve cryptography (ECC). The technique proposed in this paper proved effective and robust against RS statistical steganalysis attacks as it provides multilayered secure data protection capable of resisting image distortion and statistical detection as shown by RS steganalysis and PSNR results.
Efficient Hyperbolic Perceptron for Image Classification

Deep neural networks, often equipped with powerful auto-optimization tools, find widespread use in diverse domains like NLP and computer vision. However, traditional neural architectures come with specific inductive biases, designed to reduce parameter search space, cut computational costs, or introduce domain expertise into the network design. In contrast, multilayer perceptrons (MLPs) offer greater freedom and lower inductive bias than convolutional neural networks (CNNs), making them versatile for learning complex patterns. Despite their flexibility, most neural architectures operate in a flat Euclidean space, which may not be optimal for various data types, particularly those with hierarchical correlations. In this paper, we move one step further by introducing the hyperbolic Res-MLP (HR-MLP), an architecture extending the attention-free MLP to a non-Euclidean space. HR-MLP leverages fully hyperbolic layers for feature embeddings and end-to-end image classification. Our novel Lorentz cross-patch and cross-channel layers enable direct hyperbolic operations with fewer parameters, facilitating faster training and superior performance compared to Euclidean counterparts. Experimental results on CIFAR10, CIFAR100, and MiniImageNet confirm HR-MLP’s competitive and improved performance.
TO–YOLOX: a pure CNN tiny object detection model for remote sensing images

Remote sensing and deep learning are being widely combined in tasks such as urban planning and disaster prevention. However, due to interference occasioned by density, overlap, and coverage, the tiny object detection in remote sensing images has always been a difficult problem. Therefore, we propose a novel TO–YOLOX(Tiny Object–You Only Look Once) model. TO–YOLOX possesses a MiSo(Multiple-in-Single-out) feature fusion structure, which exhibits a spatial-shift structure, and the model balances positive and negative samples and enhances the information interaction pertaining to the local patch of remote sensing images. TO–YOLOX utilizes an adaptive IOU-T (Intersection Over Uni-Tiny) loss to enhance the localization accuracy of tiny objects, and it applies attention mechanism Group-CBAM (group-convolutional block attention module) to enhance the perception of tiny objects in remote sensing images. To verify the effectiveness and efficiency of TO–YOLOX, we utilized three aerial-photography tiny object detection datasets, namely VisDrone2021, Tiny Person, and DOTA–HBB, and the following mean average precision (mAP) values were recorded, respectively: 45.31% (+10.03%), 28.9% (+9.36%), and 63.02% (+9.62%). With respect to recognizing tiny objects, TO–YOLOX exhibits a stronger ability compared with Faster R-CNN, RetinaNet, YOLOv5, YOLOv6, YOLOv7, and YOLOX, and the proposed model exhibits fast computation.
Progressive matching method of aerial-ground remote sensing image via multi-scale context feature coding

ABSTRACTThe fine 3D model is the essential spatial information for the construction of a smart city. UAV aerial images with large-scale scene perception ability are common data sources for 3D modelling of cities at present. However, in some complex urban areas, a single aerial image is difficult to capture the 3D scene information because of the existence of some problems such as inaccurate edges, holes, and blurred building facade textures due to changes in perspective and area occlusion. Therefore, how to solve perspective changes and area occlusion of the aerial image quickly and efficiently has become an important problem. The ground image can be used as an important supplement to solve the problem of missing bottom and area occlusion in oblique photography modelling. Thus, this article proposes a progressive matching method via multi-scale context feature coding network to achieve robust matching of aerial-ground remote sensing images, which provides better technical support for urban modelling. The main idea consists of three parts: (1) a multi-scale context feature coding network is designed to extract feature on aerial-ground images efficiently; (2) a block-based matching strategy is proposed to pay more attention to local features of the aerial-ground images; (3) a progressive matching method is applied in block matching stage to obtain more accurate features. We used eight sets of typical data, such as aerial images captured by the drone DJI-MAVIC2 and ground images captured by handheld devices as experimental objects, and compared them with algorithms such as SIFT, D2-net, DFM and SuperGlue. Experimental results show that our proposed aerial-ground image matching method has a good performance that the average NCM has improved 2.1–8.2 times, and the average rate of correct matching has an average increase of 26% points with the average root of mean square error is only 1.48 pixels.KEYWORDS: 3D modelaerial-ground remote sensing imagelarge buildingsfeature matchingdeep learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the National Innovation and Entrepreneurship Training Project for University (China) [202210500028].
Harnessing distributional semantics to build context-aware justifications for recommender systems

Abstract This paper introduces a methodology to generate review-based natural language justifications supporting personalized suggestions returned by a recommender system. The hallmark of our strategy lies in the fact that natural language justifications are adapted to the different contextual situations in which the items will be consumed. In particular, our strategy relies on the following intuition: Just like the selection of the most suitable item is influenced by the contexts of usage, a justification that supports a recommendation should vary as well. As an example, depending on whether a person is going out with her friends or her family, a justification that supports a restaurant recommendation should include different concepts and aspects . Accordingly, we designed a pipeline based on distributional semantics models to generate a vector space representation of each context. Such a representation, which relies on a term-context matrix, is used to identify the most suitable review excerpts that discuss aspects that are particularly relevant for a certain context. The methodology was validated by means of two user studies, carried out in two different domains (i.e., movies and restaurants). Moreover, we also analyzed whether and how our justifications impact on the perceived transparency of the recommendation process and allow the user to make more informed choices. As shown by the results, our intuitions were supported by the user studies.
WB Score: A Novel Methodology for Visual Classifier Selection in Increasingly Noisy Datasets

This article addresses the challenges of selecting robust classifiers with increasing noise levels in real-world scenarios. We propose the WB Score methodology, which enables the identification of reliable classifiers for deployment in noisy environments. The methodology addresses four significant challenges that are commonly encountered: (i) Ensuring classifiers possess robustness to noise; (ii) Overcoming the difficulty of obtaining representative data that captures real-world noise; (iii) Addressing the complexity of detecting noise, making it challenging to differentiate it from natural variations in the data; and (iv) Meeting the requirement for classifiers capable of efficiently handling noise, allowing prompt responses for decision-making. WB Score provides a comprehensive approach for classifier assessment and selection to address these challenges. We analyze five classic datasets and one customized flooding dataset in São Paulo. The results demonstrate the practical effect of using the WB Score methodology is the enhanced ability to select robust classifiers for datasets in noisy real-world scenarios. Compared with similar techniques, the improvement centers around providing a visual and intuitive output, enhancing the understanding of classifier resilience against noise, and streamlining the decision-making process.
Joint Extraction of Entities and Relations Based on Enhanced Span and Gate Mechanism

Although entity and relation joint extraction can obtain relational triples efficiently and accurately, there are a number of problems; for instance, the information between entity relations could be transferred better, entity extraction based on span is inefficient, and it is difficult to identify nested entities. In this paper, a joint entity and relation extraction model based on an Enhanced Span and Gate Mechanism (ESGM) is proposed to solve the above problems. We design a new span device to solve the problem of entity nesting and inefficiency. We use the pointer network method to predict the beginning and end of the span, and combine them through the one-to-many matching principle. A binary classification model is then trained to predict whether the span of the combination is the subject. In the object prediction stage, a gating unit is added to fuse the subject information with the sentence information and strengthen the information transfer between the entity and the relationship. Finally, the relationship is used as the mapping function to predict the tail entity related to the head entity. Our experimental results prove the effectiveness of this model. The precision of the proposed model reached 93.8% on the NYT dataset, which was 0.4% higher than that of the comparison model. Moreover, when the same experiment was conducted in a nested entity scenario, the accuracy of the proposed model was 4.4% higher than that of the comparison model.
How can historical disciplines help a STEM student?(features of conducting practical classes)

The article talks about the distinctive features of teaching history disciplines in a technological university. As a rule, students are offered Humanities courses at the beginning of their program. On the one hand, this makes it easier for the instructor as first-year students are likely to remember something from what they studied at school. On the other hand, it makes the instructor’s task more difficult since students often have no interest in studying such disciplines. To ensure effective course delivery, several methods encouraging student involvement are proposed. The main methods include: cohesive presentation; lively, clear and informative visual content; use of interactive materials in class and alternation of different types of practical sessions. One can also find a detailed description of classes that received a positive response from the audience. The novelty of the research lies in the selection of tasks based on historical materials for practicals in a way that provides students with useful skills for further studies and work. The tasks given at the beginning of the course train students to plan their work, making a report or a brief project presentation allows them to improve public speaking skills as well as self-presentation. Participation in debates is important for learning not only how to argue one's point of view, but also how to work in a team. Also, creating one’s own infographics allows a student to master new programs and try to present a huge amount of information in a concise and vivid way. Such a skill set will prove useful for students when working in business, advertising, graphic design or IT while exploration of various aspects of history will broaden their horizons.
Carnap’s Problem for Intuitionistic Propositional Logic

We show that intuitionistic propositional logic is Carnap categorical: the only interpretation of the connectives consistent with the intuitionistic consequence relation is the standard interpretation. This holds with respect to the most well-known semantics relative to which intuitionistic logic is sound and complete; among them Kripke semantics, Beth semantics, Dragalin semantics, topological semantics, and algebraic semantics. These facts turn out to be consequences of an observation about interpretations in Heyting algebras.
Petrography, Geochemistry and Mineralogy of Serpentinite Rocks Exploited in the Ophiolite Units at the Calabria-Basilicata Boundary, Southern Apennine (Italy)

A multi-analytical study on serpentinites in the ophiolite units (Calabria-Basilicata boundary, southern Apennines) was carried out on samples collected from a serpentinite quarry, locally called “Pietrapica”, which sitsin the Pollino UNESCO Global Geopark. Optical microscopy observations revealed the petrographic characteristics, ICP-MS was used to assess the chemical composition while EMPA mineral chemistry, Raman spectroscopy and X-Ray Powder Diffraction and were used altogether to trace the mineral composition of the rocks. Petrography revealed that serpentinites from Pietrapica quarry are essentially composed of serpentine group minerals, amphibole and carbonate minerals with lower abundances of talc and Cr-spinel. Raman spectroscopy and X-ray powder diffraction analysis clearly allowed to establish that carbonate minerals, serpentine and amphibole-like minerals, are the dominant phases, followed by 2:1 phyllosilicate. Electron probe microanalyses were carried out on different minerals in serpentinites samples including serpentine, amphibole, chlorite, clinopyroxene, magnetite, talc, quartz and titanite which are often associated with carbonate veins. Bulk geochemistry is dominated by major oxides SiO2, MgO and Fe2O3 while the most abundant trace elements are Ni and Cr. Chemical analysis showed that some heavy metals in the studied serpentinites such as Ni and Cr, are beyond the maximum admissible limits for Italian normative for public, private and residential green as well as for commercial and industrial use representing a potential environmental concern. Anyway, some of these heavy metals have been recently listed by Europe as critical raw materials and therefore, the Pietrapica abandoned quarry could represent a new resource considering their economic potentiality.
ICT and Digital Revolution: Implications for Teacher Education

Talking about education or teacher education was different before COVID-19. Post pandemic scenario has placed a new kind of challenge in front of the entire humanity. Each sector related to human population is diabolically affected by this virus, the human race struggled to defeat the pandemic. When nobody is left from its affect how the teaching learning process in schools, colleges and universities can be. This disastrous virus has taught us a very important lesson. Now we are coming out of the difficult situation and resuming the normal life patterns, but now all of us have internalised the importance of ICT. In the difficult times most of the authorities and even the government advisories suggested the teachers and students to go online by using various ICT tools and platforms. Many suggestions are received from stakeholders regarding using different ways and means of the online education. ICT skills have emerged to be one of the twenty-first century teaching–learning skill. The emergence and acceptance of online courses is the best example. The areas of education at every level are trying to minimise the damage and resurrect in the best possible way by adopting to various ICT solutions. This chapter attempts to present discreet information on some of the initiatives taken by the Government of India for enabling the stakeholders, providing assistance in terms of study materials, resource, platforms and different ICT based solutions free of cost to both school education and the higher education sectors including teacher education, the parents and administrators can also use them for their collaboration in the education of our future generation. Possible suggestions for the use and significance of all the discussed online solutions are given based on empirical efforts so that a more practical implementation practice can be adjudged.
In-service Teacher Education in India: Enhancing Professionalism Among Working Teachers

In-service teacher education plays a pivotal role in contributing assiduous and committed teachers to society. The NCFTE (National curriculum framework for teacher education: towards preparing professional and humane teacher. NCTE, 2009) has very well defined the aims and objectives of in-service teacher education to be followed while designing and implementing these programmes in India. Broadly, in-service teacher training can be categorised into three types of approaches, namely: Standardised approach, Site-based approach, and Self-directed approach. Teachers can enhance their professionalism through participation in various types of academic activities and in-service programmes like seminars, conferences, workshops, refresher courses, orientation programmes, and induction programmes. Professionalism among working teachers in our country can be enhanced by improving the quality of in-service teacher education programmes, promoting awareness of the professional code of ethics, and making them oblige to it, providing professional development programmes through online mode, improving their work conditions and environment, promoting, and supporting the use of ICT and providing life skills education. There are also various State and National level agencies working for strengthening teacher education in the country like NCTE, NCERT, SCERT, DIET, NIEPA, and UGC-HRDCs which are working towards promoting and inculcating professionalism among working teachers. Thus, through the continual efforts of all stakeholders, the quality of in-service teacher training can definitely be improved and we can build diligent and professional teachers for our educational institutions.
Multilingual video dubbing—a technology review and current challenges

The proliferation of multi-lingual content on today’s streaming services has created a need for automated multi-lingual dubbing tools. In this article, current state-of-the-art approaches are discussed with reference to recent works in automatic dubbing and the closely related field of talking head generation. A taxonomy of papers within both fields is presented, and the main challenges of both speech-driven automatic dubbing, and talking head generation are discussed and outlined, together with proposals for future research to tackle these issues.
Secure outsourcing of manufacturing compliance checks

Abstract Compliance data consists of manufacturing quality measures collected in the production process. Quality checks are most of the times computationally expensive to perform, mainly due to the amount of collected data. Having trusted solutions for outsourcing analyses to the Cloud is an opportunity for reducing costs of operation. However, the adoption of the Cloud computation paradigm is delayed for the many security risks associated with it. In the use case we consider in this paper, compliance data is very sensitive, because it may contain IP-critical information, or it may be related to safety-critical operations or products. While the technological solutions that protect data in-transit or at rest have reached a satisfying level of maturity, there is a huge demand for securing data in-use. Homomorphic Encryption (HE) is one of the main technological enablers for secure computation outsourcing. In the last decade, HE has reached maturity with remarkable pace. However, using HE is still far from being an automated process and each use case introduces different challenges. In this paper, we investigate application of HE to the described scenario. In particular, we redesign the compliance check algorithm to a HE-friendly equivalent. We propose efficient data input encoding that takes advantage of SIMD type of computations supported by the CKKS HE scheme. Moreover, we introduce security/performance trade-offs by proposing limited but acceptable information leakage. We have implemented our solution using SEAL HE library and evaluated our results in terms of time complexity and accuracy. Finally, we analyze the benefits and limitations of integration of a Trusted Execution Environment for secure execution of some computations that are overly expensive for the chosen HE scheme.
An effective classification approach to categorize the breast cancer using modified support vector machine as a classifier

Abstract Breast cancer is a type of cancer which threatens the health of women and occupies first in female cancer mortality. In recent days, the machine learning techniques are widely utilized in various medical fields particularly in the application related to detection and classification. However, the accuracy obtained from the existing machine learning techniques was reliably low and results in poor classification. To overcome the issues in existing algorithms, this research introduced a Modified Support Vector Machine (MSVM) to classify the breast cancer as benign and malignant. The input data is obtained from Wisconsin Breast Cancer Dataset (WBCD) and the pre‐processing is performed to remove the inappropriate information from the raw data. Then, Principle Component Analysis (PCA) is used in the process of dimensionality reduction. The reduced dimensions are subjected into the stage of feature extraction using linear Discriminant analysis (LDA) and finally the classification is performed using the proposed MSVM. The experimental results show that the proposed approach achieved better classification accuracy of 99.34% which is comparatively higher than the existing Deer canid based Deep Convolutional Neural Network (DCNN), LDA‐SVM, Fuzzy rule based system and Intelligent Ensemble Classification method on the basis of Multi‐Layer Perceptron (IEC‐MLP) with classification accuracy of 98.12%, 97.65%, 97.22% and 98.74% respectively.