Reviewerly/exHarmony huggingface dataset:
Evaluation of soil bearing capacity and settlements of soil for various hard rock depths for a 128 m high commercial building with raft foundation

Estimation of appropriate soil bearing capacity and settlement is very important for the design of any structure. There are different techniques available for for assessing bearing capacity and settlement of soil. The present study considers a commercial building with 40 floors resting over cellular raft foundation in layered soils. Location of the structure is in Amaravati, Andhra Pradesh. Bearing Capacity of soil was calculated using Terzaghi bearing capacity equation, Meyerhof bearing capacity, Indian Standard Code method and Teng's bearing capacity equation with SPT N Corrected values. Settlements were calculated using IS Code for same soil modulus and Mayne-Poulos settlement equation for various hard rock depths with soil modulus. Compared to Meyerhof Bearing Capacity, IS Code Method, Terzaghi's bearing capacity values are higher. By using IS 8009 Part-II 1980 equation, the settlement value increased with the increase in the hard rock depth. Mayne-Poulos settlement equation has shown a decrease in settlement with increase in the hard rock depth.
Spatiotemporal Outdoor Lighting Aggregation on Image Sequences

In this work, we focus on outdoor lighting estimation by aggregating individual noisy estimates from images, exploiting the rich image information from wide-angle cameras and/or temporal image sequences. Photographs inherently encode information about the scene’s lighting in the form of shading and shadows. Whereas computer graphic (CG) methods target accurately reproducing the image formation process knowing the exact lighting in the scene, the inverse rendering is an ill-posed problem attempting to estimate the geometry, material, and lighting behind a recorded 2D picture. Recent work based on deep neural networks has shown promising results for single image lighting estimation despite its difficulty. However, the main challenge remains on the stability of measurements. We tackle this problem by combining lighting estimates from many image views sampled in the angular and temporal domain of an image sequence. Thereby, we make efficient use of the camera calibration and camera ego-motion estimation to globally register the individual estimates and apply outlier removal and filtering algorithms. Our method not only improves the stability for rendering applications like virtual object augmentation but also shows higher accuracy for single image based lighting estimation compared to the state-of-the-art.
Automatic Key Recovery of Feistel Ciphers: Application to SIMON and SIMECK

Linear cryptanalysis is one of the most effective statistical analysis methods on symmetric-key ciphers. It has benefited from many improvements since being proposed. Among these works, Antonio et al. proposed a fast arbitrary-round key recovery method based on Fast Walsh-Hadamard Transform (FWHT) in EUROCRYPT 2020. However, they did not promote their method on the Feistel structure, which is used widely. In addition, there are very few automatic methods for the key recovery phase.This paper extends Antonio et al.’s method to the Feistel structure and builds a Mixed-Integer Linear Programming (MILP) model to determine the guessed subkeys automatically. Due to this, we can automatically optimize the time complexity of linear cryptanalysis. Afterward, we apply our method to SIMON and SIMECK and increase the attackable rounds of SIMON64/96, SIMON64/128, SIMON96/96, SIMON96/144, SIMECK48/96, and SIMECK64/128 by one round to 31, 32, 38, 39, 31, and 38, respectively.
A Study On The Systematic Review Of Security Vulnerabilities Of Popular Web Browsers

Internet browser is the most normally utilized customer application and speed and proficiency of our online work rely upon program generally. As the market is immersed with new programs there is a ton of disarray in everybody’s psyche regarding which is the best program. Our task intends to respond to this inquiry. We have done a relative investigation of the most well-known internet browsers specifically Google Chrome, Mozilla Firefox, Internet Explorer, Microsoft Edge, Opera, etc. In the main period of our task different correlation boundaries are chosen which can be comprehensively classified into - General Features, Security highlights, and program extensibility highlights. Utilizing the chose benchmarking instruments every program is tried. The main objective of this study is to identify the security vulnerabilities of popular web browsers. We have also discussed and analyzed each potential security vulnerability found in the web browsers. The study also tries to recommend viable measures to slow down the security breach in web browsers.
Seeing the voice of the student: A pilot study of customer-driven courses

Context: Customer-driven courses have been a classic approach in teaching software engineering for over four decades at the Norwegian University of Science and Technology. The main goal of these courses is close collaboration between students and customers. However, in this scope of work, the intent was to evaluate students&#x0027; perception of skills acquisition, startup formation mindset, and the introduction of external activities. Objectives: Several aspects were investigated, which include how collaboration with external customers affects student skills in a customer-driven course, how collaboration with external customers affects students&#x0027; motivation in startup formation, and how students perceive the introduction of an external Innovation Bootcamp activity at the start of the course. Method: Twelve semi-structured focus group interviews were conducted with 86 students. Furthermore, an inductive thematic analysis was performed, which utilized open coding of the interview transcripts. Results: Students reflected an improvement in technical, soft, and project management skills. It was found that many of the customers&#x0027; projects were not eligible for becoming startups due to the nature of the project niche scope closely related to the customer needs. Motivations in startup formation rely heavily on individual students&#x0027; mindsets, and many of the teams had a weak perception about startup formation within the existing course context. The overall observation from the focus group interviews was that introducing external activities such as Innovation Bootcamp could assist in the process of startup formation and elevate the projects&#x0027; relevance and quality. Contribution: The study&#x0027;s findings contribute to the course&#x0027;s future improvement while introducing external activity. The outcome could promote student innovation and active participation in customer project proposals and end-product features. Educators and researchers can benefit from the findings of our study in regard to making future updates to their customer-driven courses. There is also potential to increase practitioners&#x0027; awareness of positive student input in their project proposals.
Research on Face Recognition Algorithm Based on D-S Evidence Theory and Local Domain Pattern

Local Binary Pattern is a kind of description of the texture within the scope of gray level, but under the influence of illumination and noise, the performance of classification decline rapidly. Therefore, a local feature extraction method is proposed, that is, Local Neighborhood Patterns (LNP). First of all, the image is divided into some local block. Then, treat every pixel and the pixel within the scope of neighborhood as a vector, calculate the distance of each local regional center vector and other pixels vector. Then take the ring extraction features in each local block, it means, using the center of the coded local image as the center of the different radius circle, from the center of the external circular in a certain sequence (clockwise or counterclockwise) extraction characteristics. Make the characteristics of the local image connect one by one as a whole image characteristic vector. At last use the nearest neighbor classifier. This paper also combines data fusion with the LNP, using D-S evidence theory for decision fusion. First of all, it used the gaussian filtering illumination pretreatment method to reduce the influence of the extreme imaging conditions on the face image. Secondly, the image convolution with sobel operator, then get horizontal and vertical edge image. Thirdly, extraction feature vector with LNP and calculate the distance between the test sample and all the classes, by means of the constructor function to realize the conversion of the Euclidean distance and the objective evidence. In the end, to make optimal decision Using D-S evidence theory to objective evidence for fusion. The result in the Extended Yale B database shown that this method can not only get high recognition rate, but also can effectively improve the robustness of illumination, posture, facial expression change.
Towards Scalable Simulation of Federated Learning

Federated learning (FL) allows to train models on decentralized data while maintaining data privacy, which unlocks the availability of large and diverse datasets for many practical applications. The ongoing development of aggregation algorithms, distribution architectures and software implementations aims for enabling federated setups employing thousands of distributed devices, selected from millions. Since the availability of such computing infrastructure is a big barrier to experimenting with new approaches, we claim that efficient simulation of FL is necessary and propose the PaSSiFLora library for simulating FL clients in a cluster environment. In PaSSiFLora, the training algorithm is actually performed on real data, but each cluster node can simulate multiple FL clients. Because uniform random selection of clients results in poor simulation performance due to load imbalance, we propose to use uniform random selection of MultiClients. Each MultiClient runs on a single cluster node and in each training iteration is responsible for simulating several clients, selected from a set of local clients. Our experimental results based on the FEMNIST dataset show that PaSSiFLora is capable of simulating 1536 clients and has a good scalability on 48 cluster nodes, which reduces the average iteration time to 13.57 s, from 330.61 s in the case of one cluster node. The MultiClient architecture allows to improve the average performance by up to 75% while it does not cause significant differences in model accuracy during the training. Additionally, correctness of the training is verified against existing FL frameworks: LEAF and TFF.
Estimation of rock Fe content based on hyperspectral indices

Information on the Fe content of bare rocks is needed for implementing geochemical processes and identifying mines. However, the influence of Fe content on the spectra of bare rocks has not been thoroughly analyzed in previous studies. The Saur Mountain region within the Hoboksar of the Russell Hill depression was selected as the study area. Specifically, we analyzed six hyperspectral indices related to rock Fe content based on laboratory measurements (Dataset I) and field measurements (Dataset II). In situ field measurements were acquired to verify the laboratory measurements. Fe content of the rock samples collected from different fresh and weathered rock surfaces were divided into six levels to reveal the spatial distributions of Fe content of these samples. In addition, we clearly displayed wavelengths with obvious characteristics by analyzing the spectra of these samples. The results of this work indicated that Fe content estimation models based on the fresh rock surface measurements in the laboratory can be applied to in situ field or satellite-based measurements of Fe content of the weathered rock surfaces. It is not the best way to use only the single wavelengths reflectance at all absorption wavelengths or the depth of these absorption features to estimate Fe content. Based on sample data analysis, the comparison with other indices revealed that the performance of the modified normalized difference index is the best indicator for estimating rock Fe content, with R2 values of 0.45 and 0.40 corresponding to datasets I and II, respectively. Hence, the modified normalized difference index (the wavelengths of 2220, 2290, and 2370 nm) identified in this study could contribute considerably to improve the identification accuracy of rock Fe content in the bare rock areas. The method proposed in this study can obviously provide an efficient solution for large-scale rock Fe content measurements in the field.
Measuring Patient Similarity on Multiple Diseases by Joint Learning via a Convolutional Neural Network

Patient similarity research is one of the most fundamental tasks in healthcare, helping to make decisions without incurring additional time and costs in clinical practices. Patient similarity can also apply to various medical fields, such as cohort analysis and personalized treatment recommendations. Because of this importance, patient similarity measurement studies are actively being conducted. However, medical data have complex, irregular, and sequential characteristics, making it challenging to measure similarity. Therefore, measuring accurate similarity is a significant problem. Existing similarity measurement studies use supervised learning to calculate the similarity between patients, with similarity measurement studies conducted only on one specific disease. However, it is not realistic to consider only one kind of disease, because other conditions usually accompany it; a study to measure similarity with multiple diseases is needed. This research proposes a convolution neural network-based model that jointly combines feature learning and similarity learning to define similarity in patients with multiple diseases. We used the cohort data from the National Health Insurance Sharing Service of Korea for the experiment. Experimental results verify that the proposed model has outstanding performance when compared to other existing models for measuring multiple-disease patient similarity.
Blockchain-Based Incentive Mechanism to Combat Fake News

With advancing technology, social media sites have become narratives for news surfacing the digital world. Social media users generally have no qualm for forwarding whatever comes their way. The flexibility of social media platforms offers no way to prove the credibility of a shared piece of information. The authenticity of the news and maintaining netiquette over social media sites have become precedence areas. This has challenged the technology in maintaining the ethics and objectivity of journalism. This paper proposes a shared, decentralized framework that implements an alternate vision to the customary way people share information. In particular, we provide an innovative blockchain and crowd sourcing-based framework for demonstrating the provenance of news surfacing through digital media. Under this platform, the truth is sourced from the crowd, stored, and consequently transformed into a real-time interface. Moreover, using the immutable feature of blockchain, all users are made accountable and valued based on their reputation. Further, nodes are provided with incentives for their rightful behavior and are penalized for their malicious actions. Thus, in this paper, we aim to offer a much-needed layer for secure, reliable information exchange in our present-day social media infrastructure that does not rely on a single source of truth.
Active Contour Model for Image Segmentation With Dilated Convolution Filter

ACMs have been demonstrated to be highly suitable as image segmentation models for computer vision tasks. Among other ACM, the local region-based models show better performance because they extract the local information regarding intensity in the neighborhood and embed it into the energy minimization function to guide the active contour to the boundary of the desired object. However, the online segmentation of noisy and inhomogeneous is still a challenging task for local region-based ACM models. To overcome this challenge, This paper proposes a novel region-based active contour model, named active contour model with local dilated convolution filter (ACLD). The ACLD integrates local image information in the form of a signed pressure force function. Then, a Gaussian kernel is applied using dilated convolution instead of discrete convolution for regularizing the level set formulation. Finally, instead of using a constant stopping condition, the ACLD automatically stops at the object boundaries. The proposed model shows improved image segmentation results visually combined with less computational time in the case of synthetic and natural images compared with the state-of-the-art models. Further, on the ISIC2017 dataset, the ACLD yields segmentation results with the highest accuracy.
An Information Theoretic Defense Algorithm against Adversarial Attacks on Deep Learning

Improving the adversarial robustness of Deep Neural Networks (DNNs) is an important topic in deep learning, and adversarial training is one of the most effective defense algorithm against adversarial attacks. Inspired by the mutual information regularization for solving the worst-case problem, this paper introduces an information theoretic defense algorithm for boosting the adversarial robustness of DNNs via incorporating a novel mutual information regularization into adversarial training. Simulations demonstrate that the proposed information theoretic defense algorithm outperforms the classical adversarial training based on multiple adversarial attacks for different network architectures on benchmark datasets.
An Advanced Integrated Approach in Mobile Forensic Investigation

Rapid advancement of digital technology has encouraged its use in all aspects of life, including the workplace, education, and leisure.As technology advances, so does the number of users, which leads to an increase in criminal activity and demand for a cyber-crime investigation.Mobile phones have been the epicenter of illegal activity in recent years.Sensitive information is transferred due to numerous technical applications available at one's fingertips, which play an essential part in cyber-crime attacks in the mobile environment.Mobile forensic is a technique of recovering or retrieving digital evidence from mobile devices so that it may be submitted in court for legal procedures.As a result, mobile phone data is essential for obtaining evidence in elements of mobile forensic data analysis.So, in this paper, we offer a method for detecting suspect drug-dealing patterns in mobile devices utilizing forensic and Natural Language Processing (NLP) techniques.Machine Learning algorithms are used to uncover the pattern in an original dataset, and performance measurements are used to assess the suggested system.In our approach, Logistic Regression (LR) manifests 95% of the highest accuracy in terms of count vector whereas, the BiLSTM (Bidirectional Long Short Term Memory) also achieved 95% of accuracy in terms of TFIDF.
ESACR: An Energy Saving Approach from Cloud Resources for Green Cloud Environment

Cloud is most recognized environment of computing due to the widely availability of its services to the users. Cloud data centers have adopted virtualization of resources technically known as virtual machines (VMs) for the effective and efficient computation of services. But power consumption through the cloud data centers (CDCs) highlighted as one of the major issues responsible to reduce the quality of services of Cloud computing. Because a large number of electronic resources like processing nodes, servers, disk storage, and networking nodes (i.e. switch/routers) in CDC consume a high degree of energy during computation. Energy is also consumed by cooling plants to cool the data centers as it produces a huge amount of heat (CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) surrounding it. Due to the high energy consumption, the cloud providers pay a high cost of energy computation and it also contributes more carbon emissions to the atmosphere. Reducing energy consumption and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emission is a major challenge in turning the computation from cloud computing to green computing. So, our main focus in this paper is to develop an energy-saving approach for cloud computing based on resource management. Due to variations in the request of services by the users, the demand for resources also varies during the computation. The amount of users’ request are not always same for all 24 hours in a day. Some period of time it is very low and at that period of time, many resources become idle and consume some fixed amount of energy that is a wastage of energy. In this manner, the proposed ESACR approach first define active (required) and idle (non-required) resources as per the users’ request for services at any instance; after that, it converts the idle resources into an energy-saving mode (i.e., turn OFF) unless it is not required for the service of users’ request. So that the saving of energy will increased, if at any instance all non-useable resources is define in OFF mode.
Associated Lattice-BERT for Spoken Language Understanding

Lattices are compact representations that can encode multiple speech recognition hypotheses in spoken language understanding tasks. Previous work has extended the pre-trained transformer to model lattice inputs and achieved significant improvements in natural language processing tasks. However, these models do not consider the global probability distribution of lattices path and the correlation among multiple speech recognition hypotheses. In this paper, we propose an associated Lattice-BERT, an extension of BERT that is tailored for spoken language understanding (SLU). Associated Lattice-BERT augments self-attention with positional relation representations and lattice scores to incorporate lattice structure. We further design a lattice confusion-aware attention mechanism in the prediction layer to push the model to learn from the association information between the lattice confusion paths, which mitigates the impact of the Automatic Speech Recognizer (ASR) errors on the model. We apply the proposed model to a spoken language understanding task, the experiments on the datasets of intention detection recognition show that our proposed method outperforms the strong baselines when evaluated on spoken inputs.
The Use of the Ant Algorithm in the Audit Planning of Multi-Branch Organizations

The study aims to define the important variables to be considered by auditors during the planning phase in multi-branch organizations.The objective of the research determines the possibility of using the ant algorithm as an AI tool suited to plan the audit and identify the audit schedule.The data is based on a survey to collect data to explore the most important factors that influence audit planning.The findings of the study indicate that: 1) the most important variables in audit planning in multi-branch organizations are risk and materiality; 2) the application of AI methods helps reduce bias and judgment of auditors.
Ladder: A Blockchain Model of Low-Overhead Storage

Emerging blockchain accounting mechanism allow mutually distributed parties to transport trusted information and ensure the correctness of data. Every blockchain node stores the complete block locally. Although this measure improves security, it causes huge storage overhead. We present Ladder, a multi-level low-overhead storage model for blockchain, which can greatly reduce the storage overhead of nodes while ensuring that the blockchain cannot be tampered easily and the block information is complete. Ladder distinguishes the retrieved frequency and importance of different blocks by value-density, dividing them into Hot-Blocks, Warm-Blocks and Cold-Blocks adopting different storage strategies respectively. Through experimental verification of time and space cost of different storage strategies, Ladder can greatly reduce the local storage overhead of nodes.