Reviewerly/exHarmony huggingface dataset:
Enhanced Digital Image Watermarking System using Manta Ray Foraging Comparison with Bi-directional ELM for Data Security

The main objective of this study is to enhance data security in digital image watermarking systems using Manta Ray Foraging Optimization (MRFO) and compare Peak Signal-to-Noise Ratio (PSNR) with Bidirectional ELM (BELM). The dataset in this paper utilises the publicly available Kaggle database. The sample size for analysing the data security in a digital image watermarking system with enhanced PSNR was 20 (Group <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1=10$</tex> and Group 2 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$=10)$</tex> and calculations were conducted using G-power 0.8 alpha and beta values of 0.05 and 0.2 and a 95% confidence interval. The evaluation of the digital image watermarking system with increased PSNR is performed by Manta Ray Foraging Optimization (BLEM) and whereas number of samples <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{N}=10)$</tex> and Bi-directional ELM (BELM) where number of samples <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{N}=10)$</tex> . The PSNR of Manta Ray Foraging Optimization (MRFO) is 60.30 percent greater than that of Bi-directional ELM (BELM) which is 44.50 percent. The significance level of the study is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{p} &lt; 0.05$</tex> or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{p}=0.025$</tex> . Manta Ray Foraging Optimization (MRFO) achieves a higher PSNR than Bi-directional ELM (BELM) when it relates to improving digital image watermarking systems and protecting the data.
Hybrid K-Means Clustering for Training Special Children using Utility Pattern Mining

In this Hybrid K-Means Clustering for Grouping research work, the k-means methodology is a well-known process for grouping things together. Most of the time, this algorithm sorts the objects into a set number of clusters, but in this case, the user gives the number k. At first, it picks cluster centres at random and measures how far apart k points are. This kind of cluster centre is called k centroids, and it will keep changing until there are no more changes. When making applications that use machine intelligence, a machine should be able to think like a person and make the right choices. In this case, it's not possible to get k-points from the user. So, the Genetic Algorithm (GA) is used to search with heuristics to find the initial cluster centres. The goal of this research work is to look at how k-means clustering with GA can be used to optimise. The performance evaluation of hybrid k means method illustrates the precision and accuracy of the selected clustering methods. The result states that precision was 78.35% and its accuracy was 72.67% found while using the approach. The ROC curve analysis is performed with sensitivity and specificity data. The result states that the area under curve of the approach is 84.0%.
Probability of a Device Failure using Support Vector Machine by comparing with Random Forest Algorithm to improve the accuracy

Aim: The main purpose of this study is to compare the effectiveness of two methods for predicting a device's failure: the Innovative Support Vector Machine (SVM) and the Random Forest (RF). Materials and Methods: From the Kaggle dataset, 800 samples of device failures were collected. These samples were split into two groups: 560 for training (70 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) and 240 for testing (30%). To determine the performance of the SVM algorithm, accuracy, precision, and specificity values were calculated.Results:Based on the overall performance analysis of independent samples t-test on the two groups, the SVM algorithm achieved accuracy, precision, and specificity of 86.6%, 96.20%, and 83.5%, respectively, compared to 78.40%, 77.68%, and 95.60% for the RF algorithm. These models were significant (p 0.05), and G power was found to be 0.8.Conclusion: In this study, the SVM algorithm outperforms the RF algorithm in detecting probability device failure.
Innovative Machine Learning method for the creation of Power-Maximizing Solar Trackers

Energy consumption is generally high in summer, today there are no homes without air conditioning. The Power consumption is naturally higher in summer. At the same time power generation is not going to increase. Solar power is a natural way for us to meet our home electricity needs, not just this summer. Inverters are generally fitted in many homes today. They manage to keep the inverter on when there is no electricity. But the electricity stored in this inverter is the electricity available to us through the power board. That is what they are storing and using. But this solar panel will also help to overcome the power shortage. In this paper, an innovative machine learning model was proposed to track solar panels to obtain maximum energy optimization. We can produce a portion of our electricity needs from sunlight if we do not buy only the solar panel and its charging controller. The proposed model regularly tracks the solar panel direction and identifies the maximum optimized location while the solar light was regularly fallen in the panel. The proposed model was compared with existing models in various aspects and the results were discussed.
Prakruthi Sedhya: A Natural Farming Mobile App

Andhra Pradesh Community Natural Farming (APCNF) is an organization that promotes natural farming. Their farming techniques are low-cost and eco-friendly and include the use of local resources. Natural farming has the ability to achieve the twin objectives of ensuring global food security and environmental preservation because it does not rely on agrochemicals. The Andhra Pradesh government has initiated Rythu Sadhikara Samstha (RySS), which occasionally conducts village-level training on natural farming techniques through SHG and RySS groups. But, it is still unknown to many people and has no support to assist them. We have designed this Prakruthi Sedhya application, a complete Natural Farming Mobile app. Our motto is to help people by assisting them in farming and creating a network among them. This app provides details about cultivating different crops, using natural farming techniques, and crop care information, including preparing natural fertilizers and using them at appropriate times. Furthermore, it indicates the weather forecast in each region. It also includes details about government schemes and minimum support prices(MSP) of all crops, fixed by the central government of India. This application connects farmers and buyers so that farmers can sell their yield directly to customers. Building a network among farmers through discussion forums can also be met. This app was created using real data that was gathered from APCNF specialists, agriculture officers, and active farmers. It is a complete Telugu language-based app and anyone can use this app because it is made to be user-friendly.
Why and How Should We Explain AI?

Why should we explain opaque algorithms? Here four papers are discussed that argue that, in fact, we don’t have to. Explainability, according to them, isn’t needed for trust in algorithms, nor is it needed for other goals we might have. I give a critical overview of these arguments, showing that there is still room to think that explainability is required for responsible AI. With that in mind, the second part of the paper looks at how we might achieve this end goal. I proceed not from technical tools in explainability, but rather highlight accounts of explanation in philosophy that might inform what those technical tools should ultimately deliver. While there is disagreement here on what constitutes an explanation, the three accounts surveyed offer a good overview of the current theoretical landscape in philosophy and of what information might constitute an explanation. As such, they can hopefully inspire improvements to the technical explainability tools.
Software Vulnerability Detection via Multimodal Deep Learning

Vulnerabilities in software are like ticking time bombs, but it is difficult to completely eliminate them. For example, buffer overflow is a quite common vulnerability that occurs when a program receives too much data that can corrupt nearby space in memory and manipulate other data for malicious actions. To detect potential vulnerabilities in source code, we consider the code as multisource data by extracting semantically meaningful sub-graphs: Abstract Syntax Tree Graph (ASTG) and Tokenized Data Flow Graph (TDFG). We combine these with the original sequence of tokens and 49 heuristic features to train and leverage a multimodal deep learning network to detect vulnerable statements. We propose a Multisource Deep Learner (MDL) with joint representations based on the pretrained attention-based Bidirectional Gated Recurrent Unit (BGRU) neural networks for vulnerability detection in source code. Our framework not only detects potential vulnerabilities but also locates and ranks the vulnerable statements according to their importance based on the Program Dependence Graph (PDG). Our results show that an MDL-based model using multiple modalities is significantly better than a single modality based model. We also present comparisons with state-of-the-art methods.
Server-Supported Decryption for Mobile Devices

We propose a threshold encryption scheme with two-party decryption, where one of the keyshares may be stored and used in a device that is able to provide only weak security for it. We state the security properties the scheme needs to have to support such use-cases, and construct a scheme with these properties. Our construction is based on the ElGamal cryptosystem, with additional zero-knowledge proofs that can provide IND-CCA security, and resistance to offline guessing attacks.
M$$^2$$M: A General Method to Perform Various Data Analysis Tasks from a Differentially Private Sketch

Differential privacy is the standard privacy definition for performing analyses over sensitive data. Yet, its privacy budget bounds the number of tasks an analyst can perform with reasonable accuracy, which makes it challenging to deploy in practice. This can be alleviated by private sketching, where the dataset is compressed into a single noisy sketch vector which can be shared with the analysts and used to perform arbitrarily many analyses. However, the algorithms to perform specific tasks from sketches must be developed on a case-by-case basis, which is a major impediment to their use. In this paper, we introduce the generic moment-to-moment ( $$\textrm{M}^2\textrm{M}$$ ) method to perform a wide range of data exploration tasks from a single private sketch. Among other things, this method can be used to estimate empirical moments of attributes, the covariance matrix, counting queries (including histograms), and regression models. Our method treats the sketching mechanism as a black-box operation, and can thus be applied to a wide variety of sketches from the literature, widening their ranges of applications without further engineering or privacy loss, and removing some of the technical barriers to the wider adoption of sketches for data exploration under differential privacy. We validate our method with data exploration tasks on artificial and real-world data, and show that it can be used to reliably estimate statistics and train classification models from private sketches.
SIMple ID: QR Codes for Authentication Using Basic Mobile Phones in Developing Countries

Modern foundational electronic IDentity (eID) systems commonly rely on biometric authentication so as to reduce both their deployment costs and the need for cryptographically capable end-user devices (e.g., smartcards, smartphones). However, this exposes the users to significant security and privacy risks. We introduce SIMple ID which uses existing infrastructure, Subscriber Identity Module (SIM) cards and basic feature phones, to realise modern authentication protocols without the use of biometrics. Towards this goal, we extend the international standard for displaying images stored in SIM cards and show how this can be used to generate QR codes on even basic no-frills devices. Then, we introduce a suite of lightweight eID authentication protocols designed for on-SIM execution. Finally, we discuss SIMple ID’s security, benchmark its performance and explain how it can enhance the security and privacy offered by widespread foundational eID platforms such as India’s Aadhaar.
FlowADGAN: Adversarial Learning for Deep Anomaly Network Intrusion Detection

Due to the increasingly evolved attacks on the Internet, especially IoT, 5G, and vehicle networking, a robust Network Intrusion Detection System (NIDS) has gained increasing attention from academic and industrial communities. Anomaly-based intrusion detection algorithms aim to detect unexpected deviations in the expected network behaviour, thus detecting unknown or novel attacks compared to signature-based methods. Deep Anomaly Detection (DAD) technologies have attracted much attention for their ability to detect unknown attacks without manually building the traffic behaviours profile. However, low recall rates and high dependencies on data labels still hinder the development of DAD technologies. Inspired by the successes of Generative Adversarial Networks (GANs) for detecting anomalies in the area of Computer Vision and Images, we have proposed a deep end-to-end architecture called FlowADGAN for detecting anomalies in NIDS. Unlike traditional GAN-based NIDS methods that usually construct Generator (G) and Discriminator (D) based on vanilla GAN, the proposed architecture is composed of a flow encoder-decoder-encoder for G, and a flow encoder for D. FlowADGAN can learn a latent flow feature space of G so that the latent space better captures the normality underlying the network traffic data. We conduct several experimental comparisons with existing machine learning algorithms like One-Class SVM, LOF, and PCA and existing deep learning methods, including AutoEncoder and VAE, on three public datasets, NSL-KDD CICIDS2017 and UNSW-NB15. The evaluation results show that FlowADGAN can significantly improve the performance of the anomaly-based NIDS.
A Product Owner’s Navigation in Power Imbalance Between Business and IT: An Experience Report

[Context and motivation] We consider a company where software development was previously a minor activity and today is a major activity with high priority and attention. Software is now developed according to Scrum, and the company can be seen as being in an agile transition. [Question/problem] What are the relevant specifics of this organization and which product owner behaviors appear to be valuable or problematic, respectively? [Principal ideas/results] A fear of disruption put the development teams under pressures that led to low efficiency in an interesting way. The introduction of an IT product owner to assist a business product owner reduced this effect, but a problematic power imbalance still remains. [Contribution] Not only do agile technical teams need feedback from their product owner, the product owner also needs meaningful, effective feedback from the teams. Our experience report shows how this can be improved by the introduction of an IT product owner when the organization otherwise has insufficient focus on certain important dynamics of software engineering.
Why Can't I Have a Robot Lawyer? Limits on the Right to Appear Pro Se

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JChainz: Automatic Detection of Deserialization Vulnerabilities for the Java Language

In the last decade, we have seen the proliferation of code-reuse attacks that rely on deserialization of untrusted data in the context of web applications. The impact of these attacks is really important since they can be used for exposing private information of the users. In this paper, we design a tool for automatic discovery of deserialization vulnerabilities for the Java language. Our purpose is to devise an automatic methodology that use a set of program analysis techniques and is able to output a deserialization attack chain. We test our techniques against common Java libraries used in web technology. The execution of our tool on such a dataset was able to validate the attack chains for the majority of already known vulnerabilities, and it was also able to discover multiple novel chains that represent new types of attack vectors.
Automatically Classifying Kano Model Factors in App Reviews

[Context and motivation] Requirements assessment by means of the Kano model is common practice. As suggested by the original authors, these assessments are done by interviewing stakeholders and asking them about the level of satisfaction if a certain feature is well implemented and the level of dissatisfaction if a feature is not or not well implemented. [Question/problem] Assessments via interviews are time-consuming, expensive, and can only capture the opinion of a limited set of stakeholders. [Principal ideas/results] We investigate the possibility to extract Kano model factors (basic needs, performance factors, delighters, irrelevant) from a large set of user feedback (i.e., app reviews). We implemented, trained, and tested several classifiers on a set of 2,592 reviews. In a 10-fold cross-validation, a BERT-based classifier performed best with an accuracy of 92.8%. To assess the classifiers' generalization, we additionally tested them on another independent set of 1,622 app reviews. The accuracy of the best classifier dropped to 72.5%. We also show that misclassifications correlate with human disagreement on the labels. [Contribution] Our approach is a lightweight and automated alternative for identifying Kano model factors from a large set of user feedback. The limited accuracy of the approach is an inherent problem of missing information about the context in app reviews compared to comprehensive interviews, which also makes it hard for humans to extract the factors correctly.
Knowns and Unknowns: An Experience Report on Discovering Tacit Knowledge of Maritime Surveyors

[Context] Requirements elicitation is an essential activity to ensure that systems provide the necessary functionality to users, and that they are fit for purpose. In addition to traditional 'reductionist' techniques, the use of observations and ethnography-style techniques have been proposed to identify requirements. [Research Problem] One frequently heard issue with observational techniques is that they are costly to use, as developers who would partake, would lose considerable development time. Observation also does not guarantee that all essential requirements are identified, and so luck plays a role. Very few experience reports exist to evaluate observational techniques in practice, and for organizations it is difficult to assess whether observation is a worthwhile activity, given its associated cost. [Results] This report presents experiences from DNV, a global leader providing maritime services who are renewing an information system to support its expert users. We draw on several data sources, covering insights from both developers and users. The data were collected through 9 interviews with users and developers, and over 80 h of observation of prospective users in the maritime domain. We capture 'knowns' and 'unknowns' from both developers and users, and highlight the importance of observational studies. [Contribution] While observational techniques are costly to use, we conclude that essential information is uncovered, which is key for developers to understand system users and their concerns.
Data-Driven Persona Creation, Validation, and Evolution

[Context and motivation] Personas are a well-known technique to represent a particular user type and stimulate software development. [Question/problem] Personas are often based on findings from ethnographic studies, and their creation can be time and effort intensive. Furthermore, validating the correctness of the Personas is an open issue. [Principal ideas/results] We advocate a data-driven approach that relies on analyzing various kinds of user data, in particular, user feedback and monitoring data, to create, validate, and evolve Personas. [Contributions] In this research preview paper, we discuss the problem we want to address with our research, formulate research questions, describe the initial technical solution, and present our planned contributions through a fictional usage scenario. Furthermore, we provide initial research results from an ongoing interview study that analyzes the use of Personas in practice.
Artificial Intelligence Prediction of Urban Spatial Risk Factors from an Epidemic Perspective

Abstract From the epidemiological perspective, previous research methods of COVID-19 are generally based on classical statistical analysis. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. We take the Spatio-temporal data of people infected with new coronary pneumonia before February 28 in Wuhan in 2020 as the research object. We use kriging spatial interpolation technology and core density estimation technology to establish the epidemic heat distribution on fine grid units. We further examine the distribution of nine main spatial risk factors, including agencies, hospitals, park squares, sports fields, banks, hotels, Etc., which are tested for the significant positive correlation with the heat distribution of the epidemic. The weights of the spatial risk factors are used for training Generative Adversarial Network models, which predict the heat distribution of the outbreak in a given area. According to the trained model, optimizing the relevant environment design in urban areas to control risk factors effectively prevents and manages the epidemic from dispersing. The input image of the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area.
HFR-Video-Based Fingertip Velocimeter for Multifinger Tapping Detection

In this study, we propose a novel concept of a software-based fingertip velocimeter using high-frame-rate (HFR) video processing that can simultaneously estimate when and where an operator taps with his/her finger by detecting the high-frequency component that develops when the fingertip actively contacts something. Our software-based fingertip velocimeter can precisely estimate the velocities of multiple fingers through HFR video processing in real time. Digital image correlation (DIC) operating at every frame for sub-pixel-precision velocity estimation is hybridized with convolution neural network (CNN)-based object detection operating at intervals of dozens of frames to robustly update the fingertip ROI regions during the frame-by-frame DIC operation. We developed a real-time multifinger tapping detection system that can execute DIC operation on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${720}\times {540}$ </tex-math></inline-formula> resolution images at 500 frames/s with CNN-based fingertip detection at 30 frames/s. By presenting several experimental results for finger tapping detection, including virtual keyboard interaction with a ten-finger keyboard input, the effectiveness of our fingertip velocimeter as a finger tapping interface was demonstrated, which can simultaneously estimate the tapping positions and moments of multiple fingers when finger tapping is performed ten times or more in a second.