Reviewerly/exHarmony huggingface dataset:
Combining AI and AM – Improving approximate matching through transformer networks

Approximate matching is a well-known concept in digital forensics to determine the similarity between digital artifacts. An important use case of approximate matching is the reliable and efficient detection of case-relevant data structures on a blacklist (e.g., malware or corporate secrets), if only fragments of the original are available. For instance, if only a cluster of indexed malware is still present during the digital forensic investigation, the approximate matching algorithm shall be able to assign the fragment to the blacklisted malware. However, traditional approximate matching functions like TLSH and ssdeep fail to detect files based on their fragments if the presented piece is relatively small compared to the overall file size (e.g., like one-third of the total file). A second well-known issue with traditional approximate matching algorithms is the lack of scaling due to the ever-increasing lookup databases. In this paper, we propose an improved matching algorithm based on transformer-based models from the field of natural language processing. We call our approach Deep Learning Approximate Matching (DLAM). As a concept from artificial intelligence, DLAM gets knowledge of characteristic blacklisted patterns during its training phase. Then DLAM is able to detect the patterns in a typically much larger file, that is DLAM focuses on the use case of fragment detection. Our evaluation is inspired by two widespread blacklist use cases: the detection of malware (e.g., in JavaScript) and corporate secrets (e.g., pdf or office documents). We reveal that DLAM has three key advantages compared to the prominent conventional approaches TLSH and ssdeep. First, it makes the tedious extraction of known to be bad parts obsolete, which is necessary until now before any search for them with approximate matching algorithms. This allows efficient classification of files on a much larger scale, which is important due to exponentially increasing data to be investigated. Second, depending on the use case, DLAM achieves a similar (in case of mrsh-cf and mrsh-v2) or even significantly higher accuracy (in case of ssdeep and TLSH) in recovering fragments of blacklisted files. For instance, in the case of JavaScript files, our assessment shows that DLAM provides an accuracy of 93% on our test corpus, while TLSH and ssdeep show a classification accuracy of only 50%. Third, we show that DLAM enables the detection of file correlations in the output of TLSH and ssdeep even for fragment sizes, where the respective matching function of TLSH and ssdeep fails.
Dashcam forensic investigation guidelines

Dashboard cameras (“dashcams”) have become an important in-car accessory used to record audio and visual footage of car journeys. Dashcams appear more often in digital investigations and will become more prevalent with the growth of autonomous vehicles. The audio/video footage produced by dashcams contain important items of evidence, including the routes followed by the motor vehicle, video footage of the road ahead, the road behind and the cabin, as well as audio footage of conversations within the car. However, there exist no tools or guidelines on how these devices should be investigated, this could lead to cases of miscarriage of justice. This paper provides an overview of the key features of forensic interest within a dashcam device, followed by guidance on how to respond at a crime scene which involves a dashcam device and in particular how to preserve the evidence found therein. This is followed by with guidelines on how to acquire the evidence in a dashcam device, the problems and challenges involved in the examination and analysis of dashcams, before finishing with a discussion around the reporting and presentation of dashcam evidence.
High-efficiency photon-number-resolving detector for improving heralded single-photon sources

Abstract Heralded single-photon sources (HSPS) intrinsically suffer from multiphoton emission, leading to a trade-off between the source’s single-photon quality and the heralding rate. A solution to this problem is to use photon-number-resolving (PNR) detectors to filter out the heralding events where more than one photon pair is created. Here, we demonstrate an improvement of a HSPS by heralding photons using a high-efficiency parallel superconducting nanowire single-photon detector (P-SNSPD) with PNR power. Specifically, we show a reduction in the <?CDATA $g^{(2)}(0)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mi>g</mml:mi> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mn>2</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:msup> <mml:mo stretchy="false">(</mml:mo> <mml:mn>0</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> of the heralded single photon by <?CDATA $(26.9 \pm 0.1)\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">(</mml:mo> <mml:mn>26.9</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.1</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> for a fixed pump power, or alternatively, an increase in the heralding rate by a factor of <?CDATA $1.368 \pm 0.002$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>1.368</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.002</mml:mn> </mml:math> for a fixed <?CDATA $g^{(2)}(0)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mi>g</mml:mi> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mn>2</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:msup> <mml:mo stretchy="false">(</mml:mo> <mml:mn>0</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> . We also demonstrate that such a PNR device can reveal thermal photon-number statistics of unheralded photons, which is enabled by our ability to construct its full input–output response function. These results are possible thanks to our P-SNSPD architecture that ensures non-latching operation with no electrical crosstalk, which are essential conditions necessary to obtain the correct photon-number statistics and also faster recovery times, therefore enabling fast heralding rates. These results show that our efficient PNR P-SNSPD architecture can significantly improve the performance of HSPSs and can precisely characterize them, making these detectors a useful tool for a wide range of optical quantum information protocols.
Logic reasoning under data veracity concerns

Logic reasoning involving big data often requires the proper handling of data veracity. Indeed with data that cannot be trusted to the same extent, users should at least be aware on the trust they can have in the obtained reasoning. In this paper, we propose a novel logic framework that is based on so-called L-grades. L-grades are a special case of Zadeh's Z-numbers, consisting of a pair (s,c)∈[0,1]2 in which s is a suitability grade (or satisfaction grade) and c is a confidence grade denoting how confident we can be on s. Both grades are further processed using fuzzy logic. A novel negation operator and novel conjunction and disjunction operators satisfying commutativity, associativity, but not distributivity properties are proposed. Furthermore, so-called sibling aggregators for L-grades are introduced and studied. With this framework we aim to contribute to explainable computational intelligence. The practical applicability of L-grades is discussed based on the demand for more informative, explainable results in the TILES decision support system for sand extraction from the North Sea seabed.
Sharing datasets for digital forensic: A novel taxonomy and legal concerns

During the last few years, there have been numerous changes concerning datasets for digital forensics like the development of data generation frameworks or the newly released CFReDS website by NIST. In addition, it becomes mandatory (e.g., by funding agencies) to share datasets and publish them in a manner that they can be found and processed. The core of this article is a novel taxonomy that should be used to structure the data commonly used in the domain, complementing the existing methods. Based on the taxonomy, we discuss that it is not always necessary to release the dataset, e.g., in the case of random data. In addition, we address the legal aspects of sharing data. Lastly, as a minor contribution, we provide a separation of the terms structured, semi-structured, and unstructured data where there is currently no consent in the community.
Impact of an Emergency Remote Teaching Model on Students’ Academic Performance During COVID-19

Abstract The COVID-19 pandemic posed a major challenge to universities. It forced them to face the urgent need to rapidly transform their traditional onsite teaching into an emergency remote teaching (ERT) model rather than being able to gradually introduce an effective transition to an online model. Based on a sample of 505 students enrolled in the course on Work Organization at the University of Murcia in Spain, this study analyzes the impact of implementing an ERT model on students’ academic performance. Results show that students display superior academic performance in an onsite teaching–learning model compared to the online ones adopted during COVID-19. Findings also reveal that students’ self-assessment activities enhance their academic performance—both in onsite and online teaching contexts—which implies that ERT model performance can be alleviated by adequately planning self-assessment activities during the course.
Conditional Particle Filters with Bridge Backward Sampling

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Augmented Agile: Human-Centered AI-Assisted Software Management

Agile methods have served software engineering well for over two decades, improving responsiveness to change, empowering teams, and facilitating better communication among various project stakeholders. But is it enough to lead us through the next era where balancing business value with human values has become more relevant than ever, especially in an increasingly artificial intelligence (AI)-assisted, hybrid world? We do not think so, and, in this article, we present our vision of “augmented agile” where agile practices are augmented with new capabilities made possible by AI while incorporating human-centered values.
Deformable medical image registration with global–local transformation network and region similarity constraint

Deformable medical image registration can achieve fast and accurate alignment between two images, enabling medical professionals to analyze images of different subjects in a unified anatomical space. As such, it plays an important role in many medical image studies. Current deep learning (DL)-based approaches for image registration directly learn spatial transformation from one image to another, relying on a convolutional neural network and ground truth or similarity metrics. However, these methods only use a global similarity energy function to evaluate the similarity of a pair of images, which ignores the similarity of regions of interest (ROIs) within the images. This can limit the accuracy of the image registration and affect the analysis of specific ROIs. Additionally, DL-based methods often estimate global spatial transformations of images directly, without considering local spatial transformations of ROIs within the images. To address this issue, we propose a novel global-local transformation network with a region similarity constraint that maximizes the similarity of ROIs within the images and estimates both global and local spatial transformations simultaneously. Experiments conducted on four public 3D MRI datasets demonstrate that the proposed method achieves the highest registration performance in terms of accuracy and generalization compared to other state-of-the-art methods.
Survey on Face Recognition using an Improved VGGNET Convolutional Neural Network

Many well-known convolutional neural networks are now based on huge training samples and super computers. It makes lot of issue to overcome this, so this research study develops a deep learning algorithm in VGGNET for Figure classifying and put forward a person’s face to recognition system that called Micro Face. Micro Face employs the CASIA Web Face storage for testing and training samples. This study describes that, when differentiate to the initial algorithm, the upgrade algorithm brings down the parameters, and make that image visible in improving the pooling function, and it would increase the more number of convolution networks for the kernels, which not only decrease the weakness on huge training samples and super computers, it can also carry off 96% identification rate with acceptable identification on performance to achieve and determined value.
Deepfake: An Endanger to Cyber Security

‘Deepfake’ got originated from the technology ‘deep learning’ working behind it and the type of information it generates ‘fake’ after manipulating the original information. It’s an AI-based innovation used to make counterfeit recordings and sound that look and sound genuine. The inventions and implication of digital technologies in all spheres of mankind, has posed a great challenge to mankind to come up with secure solutions against a digital problem called Deepfake resulting from the application of deep learning thereby compromising authentication or originality. The said technology creates digital images and videos all new and totally fake. But the consequence it creates on society is totally a negative impact. With the aid of AI in developing hyper realistic videos, Deepfake has extended its giant wings to harm societal health and resulting in a critical challenge against the authenticity of the source. The Internet has become the platform to deliver these Deepfakes to unlimited destinations within no time. There are lot many researches have been carried on how to detect this deep fakes. Most of the research works have used deep learning models like Convolution Neural Network (CNN) for analyzing the convolution traces in deepfakes. Some of the research works have used Recurrent Neural Networks (RNN) by combining the Long Short-Term Memory (LSTM) with Blockchain. This research study has presented the comprehensive literature study, which highlights the various approaches used in generation and detection of deepfakes.
Breast Cancer Survival Prediction and Analysis

The root cause of breast cancer is due to an inherited mutation in the BRCA (Breast Cancer) gene. The mutated types of the genes can cause abnormal cell growth in the breast which ultimately spreads to other parts as well. This type of cancer is the major source of death amongst women. Swelling in the breast part, breast skin irritation, and lumps in the breast are some of the symptoms of breast cancer. Even though multiple machine learning models have been developed, they are not accurate enough. Most of the existing breast cancer prediction algorithms did not include gene-related data during the training phases. Most recent models faced challenges while handling missing values and selecting the best features from the available large feature set. There is no proper evidence to conclude that one algorithm is better than another. This paper aims to predict the survival of people against breast cancer with help of machine learning and ensemble techniques by using medical data, gene expression profiles, and treatment types while focusing on choosing better feature selection approaches and effective handling of missing values. It aims to combine various algorithms present using voting techniques and provide better results. It also includes performing analysis and visualizations of the results through charts and graphs. It aims to provide visualizations regarding various treatments available and their effectiveness, the correlation of different treatment types, and the accuracy trends of different algorithms and their combinations.
Student Attendance Recognition using the Haar Cascade Algorithm

Attendance systems are very important in schools, colleges, and many organizations. It tells about whether a student is regular to the school/college or not, an employee is regular to the work or not. The traditional method of attendance is to take manually by using roll calls and notes in the attendance register given by the organization. In some organizations, students need to sign in the register for attendance. But it is a difficult process, there may be a chance for the occurrence of proxy. It creates a lot of disturbances in class and a waste of time. Proxy leads to the irregularity of the students and decreases performance in academics. It may affect their goals and deviates into other activities. Alternating methods need to be implemented that should rectify the issues that exist with the traditional method, and save both time and manual work.
Earthquake Magnitude Prediction using Deep Learning Techniques

Earth’s surface starts shaking due to unforeseen release of energy in its lithosphere causing strong seismic waves. This phenomenon is called as earthquake. Natural disasters like this can cause loss of invaluable lives as well as the infrastructure of the country. In recent times the earthquake that happened in Turkey-Syria was the most prominent with a magnitude of 7.8. It cost thousands of lives, destroying their families, livelihood, homes, and buildings causing a drawback in the country’s economic growth. Timely and accurate prediction of earthquake is necessary to reduce the impact of its damage to the country. Earthquakes should be predicted in order to ensure the country’s preparedness to face it and take appropriate measures to safeguard and reduce the damages. The proposed approach uses neural network technique to make predictions. Model is trained and tested on seismic dataset. The performance parameters in the proposed technique outperforms the conventional methods by 89%, 0.2213, 0.4165 concerning accuracy, mean absolute error (MAE) and root mean squared error (RMSE) respectively.
MedNcrypt: A Blockchain based Decentralised Health Record Storage System using IPFS

The use of blockchain technology has shown great potential in various fields, including healthcare. This paper proposes a decentralized medical record storage system that utilizes Inter Planetary File System (IPFS) to store and retrieve medical records. The system uses blockchain technology to ensure secure and tamper-proof storage and retrieval of medical records while allowing patients to have full control over their own data. Implementation of smart contracts can be done to automate and streamline the process of granting access to medical records to authorized parties. The proposed system has several advantages over traditional medical record storage systems, including improved security, increased accessibility, and reduced costs. The proposed system has the potential to revolutionize the way medical records are stored and managed, and can greatly benefit patients, healthcare providers, and other stakeholders in the healthcare industry.
Blockchain based Music Streaming Platform using NFTs

In terms of music development, dissemination, and audience interaction, the music business has seen several improvements. However, in existing music platforms, a significant flaw regarding unfair revenue distribution policy by intermediaries puts artists at disadvantage. The challenges that listeners confront include frequent interrupting ads and lower-quality features and music for various subscription packs. There is no transparency in transactions and revenue models in the existing systems. The objective of the platform is to reform income distribution practices that disfavor artists and to provide a fair share of the revenue to the artist for their work. The study looks at how blockchain technology with IPFS protocol and non-fungible tokens may be used to construct a decentralized application (dApp) that maximizes artist’s income by removing centralized authority. Additionally, users are provided with the ability to trade music NFTs and stream high-quality, uninterrupted music, at zero subscription fee.
A Differential Privacy-based System for Efficiently Protecting Data Privacy

The daily services people use, such as search engines, mobile services, and online social activity, all have vast sensitive personal information in today’s information realm. The question of how to aggregate sensitive user data without compromising individual privacy is a fundamental obstacle to the increasing data accessibility. Differential privacy has become a widely accepted method for disclosing sensitive information while ensuring statistical privacy. There are numerous algorithms that can be used to address various target functions. However, the research gap identified in the existing works is that the noise-added data used in the algorithm is in decimal values, and in some cases, it is negative as well. In general, count or frequency can neither be negative nor decimal, but should rather be a whole number. This research focuses on developing a unique mechanism to protect the sensitive information with the use of differential privacy. Laplace noise addition is frequently advanced as a method for satisfying differential privacy. A dataset is curated by collecting user responses by a survey form and stored as an actual dataset to which the random noise is added to produce the noise added dataset using the differential privacy algorithm i.e., Laplace algorithm. The randomized response algorithm is used to create the noise added dataset. Laplace Mechanism Algorithm is implemented by C++ and Rust, and the result is compared with the existing algorithms. The algorithm only produces whole number that is integers as the count values and produced results with better accuracy than the existing Laplace algorithm.
Low-Power Convolutional Neural Network Accelerator on FPGA

Convolutional Neural Network (CNN) accelerator is highly beneficial for mobile and resource-constrained devices. One of the research challenges is to design a power-economic accelerator. This paper proposes a CNN accelerator with low power consumption and acceptable performance. The proposed method uses pipelining between the used kernels for the convolution process and a shared multiplication and accumulation block. The available kernels work consequently while each one performs a different operation in sequence. The proposed method utilizes a series of operations between the kernels and memory weights to speed up the convolution process. The proposed accelerator is implemented using VHDL and FPGA Altera Arria 10 GX. The results show that the proposed method achieves 26.37 GOPS/W of energy consumption, which is lower than the existing method, with acceptable resource usage and performance. The proposed method is ideally suited for small and constrained devices.