Reviewerly/exHarmony huggingface dataset:
An image encryption scheme based on a seven-dimensional hyperchaotic system and Pascal's matrix

The transmission of an excessive number of images between users via the Internet and other communication media necessitates the use of effective techniques to protect the images from unauthorized usage. Encryption is one of the most effective methods for protecting both images and data. This paper presents a new grayscale image encryption method that integrates a seven-dimensional hyperchaotic system with Pascal's matrix. The algorithm used the hyperchaotic system to first confuse the original image before diffusing the permuted image using Pascal's matrix. To assess the image encryption performance, the suggested algorithm used histogram, correlation coefficient, differential attack, information entropy, and noise attack. Simulation results indicate that the suggested algorithm has a high level of security and is resistant to a variety of attacks.
Cyber Victimisation, Restorative Justice and Victim-Offender Panels

In recent years, individuals study and work from home with some degree of normality. Technology and the Internet have become an essential part of life. This increased reliance on technology and constant engagement with the online world has its negative repercussions. However, it has increased the number of offenders involved in cybercrimes. Considering the aftermath of cybercrimes and the need to address the impact of cybercrimes on victims, this paper reviews the existing mechanisms, such as legislation, international frameworks and conventions. The main purpose of this paper resides in the discussion of the possible use of restorative justice in supporting the needs of the victims. Taking into consideration the cross-border nature of many of these offences, other solutions have to be considered in order to give the victims a chance to be heard and to heal the wounds caused by the crime. This paper argues for the use of victim-offender panels, which are meetings between a group of cyber victims and a group of convicted cyber offenders, allowing victims to express the harm caused by the crime, to be healed and giving room for the offenders to feel remorse, lessening thus the likelihood of recidivism, under the umbrella of reintegrative shaming.
Automatic Determination of Back Azimuth Based on a Single Mini Array for an Earthquake Early Warning System

ABSTRACT Earthquake early warning (EEW) systems systematically monitor ground motion and provide alerts prior to damaging shaking. However, these systems present certain challenges, which include avoiding alerts caused by false triggers or delayed alerts for earthquakes from outside the seismic network. Here, we show that an array processing approach, which is capable of identifying the propagation direction and type of seismic phase, can overcome these challenges. In this study, a new automatic array processing approach was implemented using a single mini array to determine the back azimuth and slowness of the phases for local events. Seismograms of local events with ML 2.0–4.6 that occurred in the Korean Peninsula were analyzed using three array processing methods: beam packing, frequency–wavenumber, and plane-wave fitting. In addition, a combined P-wave picking procedure was introduced, and its performance was evaluated. We developed the criteria associated with slowness range and consistency of back azimuths to determine a stable back azimuth from the three array processing methods, estimating the back azimuth of the seismic phase with more accuracy than any single method. It is expected that seismic mini arrays and the developed automatic array processing approach can be used for network EEW systems to discriminate alarms caused by false triggers. In addition, the developed approach can be used for rapid onsite and front-detection warnings using the array inside or on the outskirts of a network when combined with other estimations, such as an epicentral distance or another back azimuth.
Modeling Within-Basket Auxiliary Item Recommendation with Matchability and Ubiquity

Within-basket recommendation is to recommend suitable items for the current basket with some already known items. The within-basket auxiliary item recommendation ( WBAIR ) is to recommend auxiliary items based on the primary items in the basket. Such a task exists in many real-life scenarios. Unlike the associations between items that can be transmitted in both directions, primary and auxiliary relationships are unidirectional. Then, the suitable matching patterns between primary and auxiliary items cannot be explored by traditional directionless methods. Therefore, we design the Matc4Rec algorithm to integrate the primary and auxiliary factors, and finally recommend items that not only match the interests of users but also satisfy the primary and auxiliary relationships between items. Specifically, we capture the pattern from three aspects: matchability within-basket , matchability between baskets , and ubiquity . By exploiting this pattern, the designed algorithm not only achieves good results on real-world datasets but also improves the interpretability of recommendations. As a result, we can know which commodities are suitable as auxiliary items. The experiment results demonstrate that our algorithm can also alleviate the cold start problem.
Application of A* Algorithm Based on Extended Neighborhood Priority Search in Multi-Scenario Maps

The robustness of the traditional A* algorithm of path planning is poor due to its excessive number of traversal nodes, slow search speed, and large turning angle. Aiming to solve the above problems, a multi-scenario adaptive A* algorithm based on extended neighborhood priority search is proposed. Firstly, this algorithm designs the heuristic function that can adapt to various scene changes by quantifying the scene map information, and the search weight is adjusted adaptively to enhance the robustness and adaptability of the algorithm. Secondly, the search strategy based on extended neighborhood priority is adopted to improve the orientation of the algorithm, and the redundant node removal strategy is used to smooth the path to reduce the number of traversed nodes and the turning angle. Finally, simulation tests are conducted in several representative map environments. The test results show that the proposed algorithm is superior to the traditional A* algorithm due to its stronger robustness and significantly improved performance metrics, with an 84.95% reduction in the number of traversal nodes, an 83.84% reduction in the number of path nodes, a 62.28% reduction in turning points on the path, a 77.38% reduction in the total turning angle, and a 58.47% reduction in the search time.
Product Incremental Security Risk Assessment Using DevSecOps Practices

Security risk assessment is often a heavy manual process, making it expensive to perform. DevOps, that aims at improving software quality and speed of delivery, as well as DevSecOps that augments DevOps with the automation of security activities, provide tools and procedures to automate the risk assessment. We propose a solution to integrate risk assessment with DevSecOps activities and processes in order to make the risk assessment more continuous and automated. The solution is illustrated on a use case where the firewall of a robot vehicles is updated while risk assessment is done in an iterative manner. This approach aims at facilitating assessment (and certification such as EUCC) processes.
SOMPT22: A Surveillance Oriented Multi-pedestrian Tracking Dataset

Multi-object tracking (MOT) has been dominated by the use of track by detection approaches due to the success of convolutional neural networks (CNNs) on detection in the last decade. As the datasets and bench-marking sites are published, research direction has shifted towards yielding best accuracy on generic scenarios including re-identification (reID) of objects while tracking. In this study, we narrow the scope of MOT for surveillance by providing a dedicated dataset of pedestrians and focus on in-depth analyses of well performing multi-object trackers to observe the weak and strong sides of state-of-the-art (SOTA) techniques for real-world applications. For this purpose, we introduce SOMPT22 dataset; a new set for multi person tracking with annotated short videos captured from static cameras located on poles with 6-8 m in height positioned for city surveillance. This provides a more focused and specific benchmarking of MOT for outdoor surveillance compared to public MOT datasets. We analyze MOT trackers classified as one-shot and two-stage with respect to the way of use of detection and reID networks on this new dataset. The experimental results of our new dataset indicate that SOTA is still far from high efficiency, and single-shot trackers are good candidates to unify fast execution and accuracy with competitive performance. The dataset will be available at: sompt22.github.io .
Consistency-Based Self-supervised Learning for Temporal Anomaly Localization

This work tackles Weakly Supervised Anomaly detection, in which a predictor is allowed to learn not only from normal examples but also from a few labeled anomalies made available during training. In particular, we deal with the localization of anomalous activities within the video stream: this is a very challenging scenario, as training examples come only with video-level annotations (and not frame-level). Several recent works have proposed various regularization terms to address it i.e. by enforcing sparsity and smoothness constraints over the weakly-learned frame-level anomaly scores. In this work, we get inspired by recent advances within the field of self-supervised learning and ask the model to yield the same scores for different augmentations of the same video sequence. We show that enforcing such an alignment improves the performance of the model on XD-Violence.
MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, RGBW Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of RGBW CFA to Bayer at full-resolution is introduced. The participants were provided with a new dataset including 70 (training) and 15 (validation) scenes of high-quality RGBW and Bayer pair. In addition, for each scene, RGBW of different noise level were provided at 0 dB, 24 dB and 42 dB. All the data were captured using a RGBW sensor in both outdoor and indoor conditions. The final results are evaluated using objective metrics including PSNR, SSIM [5], LPIPS [7] and KLD. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found in https://github.com/mipi-challenge/MIPI2022 .
MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and Report

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, RGB+ToF Depth Completion, one of the five tracks, working on the fusion of RGB sensor and ToF sensor (with spot illumination) is introduced. The participants were provided with a new dataset called TetrasRGBD, which contains 18k pairs of high-quality synthetic RGB+Depth training data and 2.3k pairs of testing data from mixed sources. All the data are collected in an indoor scenario. We require that the running time of all methods should be real-time on desktop GPUs. The final results are evaluated using objective metrics and Mean Opinion Score (MOS) subjectively. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022
Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI &amp; AIM 2022 Challenge: Report

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20–50 ms while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.
Simultaneous Detection and Classification of Partially and Weakly Supervised Cells

Detection and classification of cells in immunohistochemistry (IHC) images play a vital role in modern computational pathology pipelines. Biopsy scoring and grading at the slide level is routinely performed by pathologists, but analysis at the cell level, often desired in personalized cancer treatment, is both impractical and non-comprehensive. With its remarkable success in natural images, deep learning is already the gold standard in computational pathology. Currently, some learning-based methods of biopsy analysis are performed at the tile level, thereby disregarding intra-tile cell variability; while others do focus on accurate cell segmentation, but do not address possible downstream tasks. Due to the shared low and high-level features in the tasks of cell detection and classification, these can be treated jointly using a single deep neural network, minimizing cumulative errors and improving the efficiency of both training and inference. We construct a novel dataset of Proteasome-stained Multiple Myeloma (MM) bone marrow slides, containing nine categories with unique morphological traits. With the relative difficulty of acquiring high-quality annotations in the medical-imaging domain, the proposed dataset is intentionally annotated with only $$5\%$$ of the cells in each tile. To tackle both cell detection and classification within a single network, we model these as a multi-class segmentation task, and train the network with a combination of partial cross-entropy and energy-driven losses. However, as full segmentation masks are unavailable during both training and validation, we perform evaluation on the combined detection and classification performance. Our strategy, uniting both tasks within the same network, achieves a better combined Fscore, at faster training and inference times, as compared to similar disjoint approaches.
Cyberattacks on local governments 2020: findings from a key informant survey

Based on empirical data from a survey that we conducted in 2020 of key informants in local governments (CIOs, CISOs, and IT Directors), this paper examines patterns of cyberattacks, types of attackers, the frequencies of incidents and breaches of local government IT systems, and purposes of attacks. The paper also examines whether and to what extend local governments offer cybersecurity awareness training to their officials and staff and whether a nexus exists between training and these persons support for cybersecurity in their governments. Throughout the paper, we compare data from the 2020 survey with data from a nationwide local government cybersecurity survey that a team that included the authors conducted in 2016. We conclude with recommendations to local governments to improve their practice and management of cybersecurity in their organisations.
Automated variable renaming: are we there yet?

Abstract Identifiers, such as method and variable names, form a large portion of source code. Therefore, low-quality identifiers can substantially hinder code comprehension. To support developers in using meaningful identifiers, several (semi-)automatic techniques have been proposed, mostly being data-driven ( e.g., statistical language models, deep learning models) or relying on static code analysis. Still, limited empirical investigations have been performed on the effectiveness of such techniques for recommending developers with meaningful identifiers, possibly resulting in rename refactoring operations. We present a large-scale study investigating the potential of data-driven approaches to support automated variable renaming. We experiment with three state-of-the-art techniques: a statistical language model and two DL-based models. The three approaches have been trained and tested on three datasets we built with the goal of evaluating their ability to recommend meaningful variable identifiers. Our quantitative and qualitative analyses show the potential of such techniques that, under specific conditions, can provide valuable recommendations and are ready to be integrated in rename refactoring tools. Nonetheless, our results also highlight limitations of the experimented approaches that call for further research in this field.
2D Camera-Based Air-Writing Recognition Using Hand Pose Estimation and Hybrid Deep Learning Model

Air-writing is a modern human–computer interaction technology that allows participants to write words or letters with finger or hand movements in free space in a simple and intuitive manner. Air-writing recognition is a particular case of gesture recognition in which gestures can be matched to write characters and digits in the air. Air-written characters show extensive variations depending on the various writing styles of participants and their speed of articulation, which presents quite a difficult task for effective character recognition. In order to solve these difficulties, this current work proposes an air-writing system using a web camera. The proposed system consists of two parts: alphabetic recognition and digit recognition. In order to assess our proposed system, two character datasets were used: an alphabetic dataset and a numeric dataset. We collected samples from 17 participants and asked each participant to write alphabetic characters (A to Z) and numeric digits (0 to 9) about 5–10 times. At the same time, we recorded the position of the fingertips using MediaPipe. As a result, we collected 3166 samples for the alphabetic dataset and 1212 samples for the digit dataset. First, we preprocessed the dataset and then created two datasets: image data and padding sequential data. The image data were fed into the convolution neural networks (CNN) model, whereas the sequential data were fed into bidirectional long short-term memory (BiLSTM). After that, we combined these two models and trained again with 5-fold cross-validation in order to increase the character recognition accuracy. In this work, this combined model is referred to as a hybrid deep learning model. Finally, the experimental results showed that our proposed system achieved an alphabet recognition accuracy of 99.3% and a digit recognition accuracy of 99.5%. We also validated our proposed system using another publicly available 6DMG dataset. Our proposed system provided better recognition accuracy compared to the existing system.
Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems

In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or marginalized groups. Hence, there is concern about the detection and mitigation of these biases, which may increase the discriminatory treatments of some demographic groups. Recommender systems, used today by millions of users, are not exempt from this drawback. The influence of these systems on so many user decisions, which in turn are taken as the basis for future recommendations, contributes to exacerbating this problem. Furthermore, there is evidence that some of the most recent and successful recommendation methods, such as those based on graphical neural networks (GNNs), are more sensitive to bias. The evaluation approaches of some of these biases, as those involving protected demographic groups, may not be suitable for recommender systems since their results are the preferences of the users and these do not necessarily have to be the same for the different groups. Other assessment metrics are aimed at evaluating biases that have no impact on the user. In this work, the suitability of different user-centered bias metrics in the context of GNN-based recommender systems are analyzed, as well as the response of recommendation methods with respect to the different types of biases to which these measures are addressed.
A Lecture Centric Automated Distractor Generation for Post-Graduate Software Engineering Courses

Critical circumstances, natural disasters or pandemics like COVID 19 gave rise to the wide applicability of E-learning into education system. Efficient and fair online assessment is very important to utilize the inevitable benefits of E-learning.. In order to make it efficient, the trend of assessment has shifted from the subjective type to the objective type assessments which is mainly based on Multiple Choice Questions (MCQs), generation of MCQs is a tedious, tiresome and time consuming task. To cater this dire need, this study proposes an automated Multiple Choice Question (MCQ) generation by utilizing state of the art transformer based model T5 for the task of question generation and a lexicon based approach Sense2vec for the task of distractor generation. It also presented a domain specific lecture text based test data for performing evaluation on the task of domain specific lecture text based MCQ generation.