Reviewerly/exHarmony huggingface dataset:
RSA and AES Based Hybrid Encryption Technique for Enhancing Data Security in Cloud Computing

Cloud computing security has become the most crucial problem in its growth. Encryption has been developed as a solution and plays a vital role in the security of information systems in cloud computing. Many methods are required to secure the shared data. The advanced internet, networking firms, health information, and cloud applications have significantly increased our data every minute. The current work focuses on cryptography to protect sensitive data exchanged between personal users, companies, organizations, or in the cloud applications and others during data transfer across the network. Firstly, data sent from the sender to the network receiver must be encrypted using the cryptographic algorithm. Secondly, the recipient shows the original data using the decryption technique. In this paper, we have proposed a model to use a hybrid encryption and decryption process based on AES-128 and the RSA algorithm. Furthermore, we used the HMAC algorithm to ensure the integrity and authenticity of data. Our experiment work has been done to explain the time required and throughput for encryption and decryption of three encrypting algorithms, AES, RSA, and hybrid algorithms, based on different data sizes, and their efficiency is compared. The results of the experiments show that the hybrid algorithm is better in terms of security.
Construction of small confusion component based on logarithmic permutation for hybrid information hiding scheme

The design and development of secure nonlinear cryptographic Boolean function plays an unavoidable measure for modern information confidentiality schemes. This ensure the importance and applicability of nonlinear cryptographic Boolean functions. The current communication is about to suggest an innovative and energy efficient lightweight nonlinear multivalued cryptographic Boolean function of modern block ciphers. The proposed nonlinear confusion element is used in image encryption of secret images and information hiding techniques. We have suggested a robust LSB steganography structure for the secret hiding in the cover image. The suggested approach provides an effective and efficient storage security mechanism for digital image protection. The technique is evaluated against various cryptographic analyses which authenticated our proposed mechanism.
Open fringes interferograms generation through the phase contrast technique

Phase contrast imaging is a proven technique for sensing optical wavefronts and obtaining qualitative and quantitative insights into phase disturbances. Traditionally, this method involves introducing a phase mask at the Fourier transform plane of the phase object to create a synthetic reference wave that interferes with the analyzed phase disturbance. In this study, we incorporate an intensity-dependent photorefractive material, facilitating the self-induction of a Zernike-type phase filter. We introduce an innovative modification by inducing an angular offset between the synthetic reference wave and the phase distribution of the object. Effective implementation necessitates an illumination area larger than the phase object's support. This method stands out due to its resilience to environmental factors, shared by all common-path interferometers. Furthermore, it leverages open fringe interferograms, consolidating all essential phase information into a single image. This innovation streamlines phase retrieval, eliminating the need for multiple images, and extends applicability beyond small-phase object perturbations. We present results demonstrating the recovered phase distribution using the well-established Takeda technique for open fringe interferograms. This novel approach promises to enhance phase distortion analysis, offering versatility and simplicity in a variety of applications.
Learned Image Transmission Toward Machine-Type Semantic Communications

Humans tend to focus on only a few regions of interest (ROI) rather than perceiving the entire scene. This insight is also useful for machine tasks. Built upon the properties of ROI, in this paper, we propose a learned image transmission framework toward machine tasks, which ensures both the image reconstruction quality and the task accuracy. The whole system is optimized under a tripartite RDA tradeoff across the channel bandwidth cost (rate, R), the signal reconstruction quality (distortion, D), and the machine task performance (accuracy, A). According to the image content complexity distribution and the specific task, we incorporate both the entropy model and the ROI map to guide the source-channel coding rate allocation. As a result, we obtain the system coding gain. During this process, we develop two types of real-time ROI generation methods, suitable for high and low bandwidth cost regions, respectively. Experimental results show that our approach vastly outperforms state-of-the-art engineered image transmission methods and emerging image transmission methods. Moreover, we conduct an extensive ablation study to demonstrate the importance of individual components in our method, by which we expect to facilitate future research on this novel approach for machine-type semantic communications.
3D Scene Perception for Autonomous Driving

Autonomous driving has achieved a great development in recent years. In order to perceive the road and the distance of objects on the road for automatic path planning, 3D scene perception is required. Depth acquisition, as a fundamental part of 3D vision, is urgently needed and attracting increasing interests. With the rapid development of deep learning based vision processing techniques, lots of efforts have been put into the investigation of deep learning based depth estimation. This chapter analyses the existing depth acquisition methods, especially the deep learning based depth estimation with vision. Methodologies of different vision based depth estimation approaches, including depth estimation with a single image, a stereo pair and a video, have been explained, and their similarities and differences have been summarized and analyzed. This chapter also discusses the future depth acquisition/estimation direction with enhanced temporal information exploration and multimodal information fusion.
The Te Transform: A High-Resolution Integral Transform and Its Key Properties

In this paper, we present six new contributions: two novel definitions and four groundbreaking theorems related to the theoretical foundations of the integral Te transform, with a specific focus on analyzing functions with integrable modulus. The definitions referred to the Te window and the Te transform in two parameters, respectively. The theorems provide the main theoretical basis for the Te transform: the existence of the Te transform in two parameters, the Te transform ∈L1(R), the existence of the inverse Te transform, and uniqueness of the Te transform. These results reveal the importance of the fact that the Te transform only depends on two parameters (translation and dyadic frequency), obtaining its inverse transformation more directly; hence, breaking through a new approach in function analysis by representing a function in the scale-frequency plane. The theoretical results presented in this paper are supported by the previous works of the authors.
A Secure and Trusted Capital Verification Model Based on Blockchain and Zero Knowledge Proof

Data leakage, result tampering, and oversight issues are issues with traditional capital verification. These issues can be successfully resolved using a combination of blockchain technology, smart contracts, cryptography, and other technologies because it is a traceable and tamper-proof data structure. In order to prevent data leaking, this article suggests a blockchain-based capital verification methodology that uses zero-knowledge proof technology. Using smart contracts, the possibility of result fabrication is automatically verified and removed. Additionally, this approach establishes a penetrating supervision scheme, and regulators are heavily involved in every facet of capital verification. The results of the experiment demonstrate that the plan is reliable and effective, has low storage costs, and can successfully safeguard personal data.
Color image encryption using a Zigzag Transformation and sine–cosine maps

This research endeavor is dedicated to enhancing data confidentiality in internet transmissions by strategically employing Hash functions and harnessing the advantageous attributes of chaos. These attributes, characterized by inherent complexity, sensitivity to initial conditions, and resilience to attacks, are foundational elements in the realm of image encryption. The proposed encryption algorithm firmly adheres to the principles of confusion and diffusion, thereby constructing a secure framework. Within the permutation phase of the algorithm, a meticulously crafted scrambling technique takes center stage. This technique adeptly disperses adjacent pixels within an image across the red, green, and blue channels through both horizontal and vertical zigzag transformations. This intricate process significantly elevates the level of intricacy and unpredictability within the encryption procedure, bolstering security measures. To further fortify security, the substitution stage integrates two one-dimensional chaotic systems, specifically 1-DSP and 1-DCP. These systems serve as mechanisms to introduce subtle alterations to the original image, affecting all pixels within the encrypted image. The extent of these alterations hinges on the values of the three least significant bits (LSB) of each pixel. The incorporation of this dual chaotic system approach introduces an additional layer of complexity and security into the encryption methodology. The effectiveness of the proposed system is subjected to rigorous evaluation through a comprehensive security analysis. This analysis encompasses an array of performance metrics, including NPCR (Normalized Pixel Change Rate), UACI (Unified Average Changing Intensity), correlation coefficient, histogram analysis, and key sensitivity analysis. These metrics collectively serve to assess the viability and feasibility of the suggested approach, functioning as a litmus test for the system’s resilience and efficiency. The results of this evaluation underscore the robust security and superior performance of the proposed image encryption method, positioning it favorably when compared to various advanced image encryption algorithms.
A Simplified Variant of Gödel’s Ontological Argument

A simplified variant of Gödel’s ontological argument is presented. The simplified argument is valid already in basic modal logics K or KT, it does not suffer from modal collapse, and it avoids the rather complex predicates of essence (Ess.) and necessary existence (NE) as used by Gödel. The variant presented has been obtained as a side result of a series of theory simplification experiments conducted in interaction with a modern proof assistant system. The starting point for these experiments was the computer encoding of Gödel’s argument, and then automated reasoning techniques were systematically applied to arrive at the simplified variant presented. The presented work thus exemplifies a fruitful human-computer interaction in computational metaphysics. Whether the presented result increases or decreases the attractiveness and persuasiveness of the ontological argument is a question I would like to pass on to philosophy and theology.
Region Assisted Sketch Colorization

Automatic sketch colorization is a challenging task that aims to generate a color image from a sketch, primarily due to its inherently ill-posed nature. While many approaches have shown promising results, two significant challenges remain: limited color patterns and a wide range of artifacts such as color bleeding and semantic inconsistencies among relevant regions. These issues stem from the operation of traditional convolutional structures, which capture structural features in a pixel-wise manner, resulting in inadequate utilization of regional information within the sketch. Therefore, we propose the Region-Assisted Sketch Coloring (RASC) method, which introduces an intermediate representation called the 'Region Map' to explicitly characterize the regional information of the sketch. This Region Map is derived from the input sketch and is effectively formulated by our RASC architecture, enhancing the perception of region-wise features beyond the original pixel-wise features. Specifically, we start by employing the sketch encoder to extract hierarchical feature maps from the input sketches. Subsequently, we introduce a coarse-to-fine decoder comprising a series of Region-based Modulation (RM) blocks. This decoder modulates features that combine the modulation results of its previous block and the sketch features of the corresponding encoder block with our Region Formulation module. Each module explicitly formulates the sketch features in a region-wise manner. This accurately captures both the inner-region local style and inter-region global context dependency, resulting in various color patterns and fewer synthesis artifacts. Our experimental results show that our proposed method surpasses state-of-the-art methods in both synthetic and real sketch datasets.
Research Trends in Software Development Effort Estimation

Developing a software project without the appropriate amount of effort would significantly impede and even fail the project, putting the software developer's quality at risk. Therefore, software development effort estimation (SDEE) is the most critical activity in software engineering. SDEE has seen extensive research, resulting in a massive rise in the literature in a relatively short period. In this regard, it is crucial to identify the significant study topics in software development effort estimation that will assist researchers in understanding and recognizing research trends. This research applied a systematic literature review (SLR) to compile all journals from the predefined search directory about software development effort estimation thoroughly and unbiasedly from 2018 to 2022. This review was a prelude to further research activities in software development effort estimation. Five research topics out of 71 papers have been revealed, including the machine learning approach, algorithmic technique, expert judgement, dataset analysis, and evaluation metric. With 27 journals, deploying a machine learning approach for SDEE is the most discussed research topic. The potential research described in this study can be investigated further in software development effort estimation field.
‘Practically useful, scientifically important, and to the honour of the country’: geological maps and services provided by the Geological Survey of Norway these past 165 years

Abstract Geological maps document knowledge of considerable value (economic, cultural and aesthetic) for societies, enterprises and people in general. Knowledge illustrated in geological maps also helps us to foresee how landscape interventions and climate change may affect our living ground and make us vulnerable to geohazards. The present paper presents an overview of geological mapping carried out by the Geological Survey of Norway since 1858 while also focusing on mapping activities and products produced in the present millennium. Key issues to consider are how we are moving into the digital world, how we use geological mapping to follow up on national agendas like Green Shift and Blue Growth and how national agreements and cooperation help us to make geological and other geographic information available to everyone through open access web portals and services.
An Effective Knowledgeable Label-Aware Approach for Sentential Relation Extraction

In recent years, sentential relation extraction has made remarkable progress with text and knowledge graphs (KGs). However, existing architectures ignore the valuable information contained in relationship labels, which KGs provide and can complement the model with additional signals and prior knowledge. To address this limitation, we propose a neural architecture that leverages knowledgeable labels to enhance sentential relation extraction. We name our proposed method knowledge label-aware sensitive relation extraction (KLA-SRE). To achieve this, we combine pre-trained static knowledge graph embeddings with learned semantic embeddings from other tokens to efficiently represent relation labels. By combining static pre-trained graph embeddings with learned word embeddings, we mitigate the inconsistency between predicted relations and given entities. Experimental results on various relation extraction benchmarks in different fields show that knowledge labels improve the F1 score by 1.6% and 1.1% on average over the baseline on standard- and minority-shot benchmarks, respectively.
Vision Based Automatic Translation for South African Sign Language

Deaf and hard of hearing (DHH) people are estimated to exceed 5% of the total population of South Africa and the world. Additionally, majority of people with hearing abilities are not proficient in sign language. This creates a communication divide between the DHH and hearing communities, to the disadvantage of the DHH. Although some provisions are made to cater to the needs of DHH people in the form of sign language education and interpreters, such interpreters are severely scarce and often costly. In this research, we developed a first vision based neural sign language translation (NSLT) model for South African Sign Language (SASL) as an initial step towards bridging this communication barrier. To this end, we recorded a parallel SASL and English corpus with the help of six sign language interpreters. The dataset comprises of 5047 sentences in the domain of government and politics. We conducted comprehensive experiments using several visual feature extraction architectures as well as translation architectures. The best results were obtained using a 3D-CNN and recurrent models (0.51 BLEU-4), followed by transformer models (0.43 BLEU-4). These are lower than results obtained using the RWTH-PHOENIX-Weather 2014T dataset (11.73 BLEU-4) and comparable to those using the How2Sign (1.73 BLEU-4). We also investigated the impact of pretraining the model on a dataset that consists of gloss annotations and fine-tuning on the SASL dataset and increase the BLEU-4 score to 1.3. Our results on text-based translation (8.52 BLEU-4) suggest that there is a need for better feature extraction methods for sign language translation.
Machine Learning Classification Algorithms for Accurate Breast Cancer Diagnosis

The introduction of algorithms based on machine learning (ML) has revolutionized computer science by allowing machines to learn without explicit programming. These algorithms automate operations, like categorization and clustering, that formerly required human interaction. In the medical field, classification algorithms based on machine learning have been used extensively to diagnose various diseases, including breast cancer, which remains one of the leading causes of death among women. This study describes an experiment that analyzes the effectiveness of machine learning-based algorithms for breast cancer diagnosis. Among the techniques evaluated are Logistic Regression, Decision Tree, Neural Network, Naive Bayes, The kNN, support vector machines (SVM), AdaBoost, Stochastic gradient descent (SGD), CN2 rule inducer, Constant, and Random Forest. The investigation used the renowned Wisconsin Diagnosis Breast Cancer dataset (WDBC). The primary objective of this experiment is to evaluate the Precision and accuracy of these algorithms in discriminating between malignant and benign tumors. The SVM model has the best accuracy at 0.975%, suggesting that Random Forest is a viable breast cancer diagnostic tool. These findings indicate that algorithms based on machine learning can potentially increase the accuracy and speed of breast cancer diagnosis. This research can inform the development of more precise and influential breast cancer diagnostic tools, enabling physicians to administer quick and appropriate therapy to patients.
Heuristic Methods for Solving the Traveling Salesman Problem (TSP): A Comparative Study

The Traveling Salesman Problem (TSP) is an important NP-Hard combinatorial problem worth studying in Computer Science, Mathematical Optimization, and Operations Research. Heuristic methods are often employed in looking for a nearly-optimized solution for TSP. Common heuristic methods for solving the TSP are the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), and the Lin-Kernighan-Helsgaun (LKH) algorithm. Recent studies have combined the Reinforcement Learning (RL) methods with LKH to boost performance. However, little is known about the performance differences between the many heuristic methods given. Also, little is understood about the impact of the TSP landscape on the performance of the heuristic methods. In this paper, five heuristic methods of GA, ACO, SA, LKH, VSR-LKH are discussed. We employed four types of landscape instances from the TSPLIB to perform an empirical analysis of the five heuristics. For the TSP landscape instances tested, we find that the loss and convergence speed of the algorithms is not directly related to the dimension of the TSP instances but the landscape of the TSP instances instead.
Fast Grayscale Morphology for Circular Window

Abstract Morphological operations are among the most popular classic image filters. The filter assumes the maximum or minimum value within a window and is often used for light object thickening and thinning operations, which are important components of various workflows, such as object recognition and stylization. Circular windows are preferred over rectangular windows for obtaining isotropic filter results. However, the existing efficient algorithms focus on rectangular or binary input images. Efficient morphological operations with circular windows for grayscale images remain challenging. In this study, we present a fast grayscale morphology heuristic computation algorithm that decomposes circular windows using the convex hull of circles. We significantly accelerate traditional methods based on Minkowski addition by introducing new decomposition rules specialized for circular windows. As our morphological operation using a convex hull can be computed independently for each pixel, the algorithm is efficient for modern multithreaded hardware.
Computer Aided Diagnosis (CAD) and Classification of Microbial and other Wide Range of Dermal Diseases using AI and Medical Image Processing

The human body's largest and most defensive organ is its skin. It covers internal organs and shields the human body from extraneous substances outside of it. Numerous illnesses can harm a person's skin caused by microbes such as bacteria, fungi, and viruses; for example, MRSA (methicillin-resistant Staphylococcus aureus) infection, Herpes zoster, Acne vulgaris, warts, eczema, psoriasis, and the fifth disease. It can also be damaged by carcinogenic and tumor-inducing agents, leading to skin cancer such as melanoma, which is more fatal and life-threatening to human life. Skin diseases can be diagnosed by blood tests, tissue sample collection (biopsy), and skin examination by dermatologists and experts. If non-expert doctors or laboratory technicians examine the skin, it can lead to medical errors and misdiagnosis. A proper and precise diagnosis and detection are required to treat the specific disease. This research aims to detect dermal diseases through sample images and classify and identify the cause of disease with greater accuracy and precision in a time- and cost-efficient way. This research uses medical processing algorithms such as pre-processing and segmentation of the diseased image and image classification algorithms such as deep learning, part of a neural network, to classify the diseased medical images.