Reviewerly/exHarmony huggingface dataset:
Start thinking in graphs: using graphs to address critical attack paths in a Microsoft cloud tenant

Abstract The challenge of securing IT environments has reached a new complexity level as a growing number of organisations adopt cloud solutions. This trend increases the possibility of overseen attack paths in an organisation’s IT infrastructure. This paper proposes a methodology for assessing the security of a Microsoft cloud tenant based on the relationships between different cloud entities through the use of graphs. This paper argues for using graph theory as an effective method to understand and uncover complex entity attack paths. To achieve this, we implemented a graph analytics platform using data from a Microsoft cloud test tenant. Methods based on graph theory proved to measurably reduce possible attack paths. Our research can support defenders who want to better understand the interrelationships of Microsoft cloud entities as well as identify and remediate possible attack paths.
Error metric for non-trace-preserving quantum operations

We study the problem of measuring errors in non-trace-preserving quantum operations, with a focus on their impact on quantum computing. We propose an error metric that efficiently provides an upper bound on the trace distance between the normalized output states from imperfect and ideal operations, while remaining compatible with the diamond distance. As a demonstration of its application, we apply our metric in the analysis of a lossy beam splitter and a nondeterministic conditional sign-flip gate, two primary non-trace-preserving operations in the Knill-Laflamme-Milburn protocol. We then turn to the leakage errors of neutral-atom quantum computers, finding that these errors scale worse than previously anticipated, implying a more stringent fault-tolerant threshold. We also assess the quantum Zeno gate's error using our metric. In a broader context, we discuss the potential of our metric to analyze general postselected protocols, where it can be employed to study error propagation and estimate thresholds in fault-tolerant quantum computing. The results highlight the critical role of our proposed error metric in understanding and addressing challenges in practical quantum information processing.
Setting up SLAs using a dynamic pricing model and behavior analytics in business and marketing strategies in cloud computing

Abstract Increasing amounts of data are being generated every year. Sustainable computing systems have become capable of extracting and learning information from the underlying data. Edge and AI (artificial intelligence) are expanding into industrial systems requiring new computing and networking infrastructure. Due to this, SLA computing is becoming increasingly challenging to handle in these emerging cloud environments. The cloud is a service that provides virtual resources to users. Qualitative and quantitative findings in market-oriented approaches are one of the most common methods for managing virtual and physical machines in a network. When allocating services, price is an important factor to consider. In this study, we aim to determine the initial price of VMs while considering the dynamic pricing model in a competitive, sustainable computing system. Besides negotiation-based trading, a multifactor architecture is used for trading in the marketplace. Based on the simulation results, it was found that the performance could be improved by categorizing the VMs based on regression. According to the simulation results, the cloud market system provides a better service-level agreement (SLA) and response time when assigning virtual machines to the market. Based on the results, we found that using the regression method for categorizing the VMs to manage the market improved the SLA.
A Personalized Recommendation System for Academic Events

Academic events are growing in numbers worldwide annually for researchers to discuss their work. The research on recommendation systems in academic domains has high significance for researchers. The classical approach to the recommender system uses content-based and collaborative filtering that tends to produce poor results. The focus of the study is to determine the factors involving the selection of academic events and create a user-based personalised recommender system for academic events. A survey will be conducted to identify the factors affecting the choice of events. The system will filter the results of the events using a matching matrix by conducting a factor analysis and receiving input to find the most relevant academic events from the database. The study's approach evaluates the result based on the pre-processed data and the similarity measures between a similar user (Top-n) and an active user for events with a higher probability of participation. The weighted average of the neighbour’s ratings will be generated for the predictions of the events. The study’s outcome will prove that the personalised recommendation system is better than the classical approach in finding the most relevant events. The recommendation system can be optimised in domains.
APPLICATIONS OF NON-SINGULAR MATRICES IN CRYPTOGRAPHY

The aim of this paper is security for large or short messages by using 4x4 non- singular matrices modulo 27 as the key to transfer plain text to cipher text and its invertible matrices modulo 27 of order 4x4 as the key to transfer cipher text to plain text. Cryptography is the science of using mathematics to encrypt and decrypt messages. It is the art of keeping information secret and safe.
An Object Detection Method Using Probability Maps for Instance Segmentation to Mask Background

This paper proposes a two-step detector called segmented object detection, whose performance is improved by masking the background region. Previous single-stage object detection methods suffer from the problem of imbalance between foreground and background classes, where the background occupies more regions in the image than the foreground. Thus, the loss from the background is firmly incorporated into the training. RetinaNet addresses this problem with Focal Loss, which focuses on foreground loss. Therefore, we propose a method that generates probability maps using instance segmentation in the first step and feeds back the generated maps as background masks in the second step as prior knowledge to reduce the influence of the background and enhance foreground training. We confirm that the detector can improve the accuracy by adding instance segmentation information to both the input and output rather than only to the output results. On the Cityscapes dataset, our method outperforms the state-of-the-art methods.
Low-illumination image contrast enhancement using adaptive gamma correction and deep learning model for person identification and verification

Person verification based on face detection and face recognition is a very important research area in the field of computer vision as it provides authentication before permitting access to resources ensuring safety and security. It is a challenging task to identify and verify a person in low-illumination images, this is because the facial features of a person in a low-illumination image are not clear as the image is of poorer quality than that of an image taken with good illumination. The existing hand-crafted feature-based approaches and deep learning models for low-illumination image contrast enhancement are typically unsatisfactory in the applications of person verification either due to over-enhancing of the image or restricting the contrast of the image while dealing with light illumination. To achieve more accurate face detection and face recognition in low light images, a new approach based on adaptive gamma correction and deep learning model is proposed in this research paper. In this work, two methods: feature-based adaptive gamma correction (FAGC) and deep learning-based adaptive gamma correction (DLAGC) are proposed for contrast enhancement. The proposed approach uses the new adaptive gamma correction-based methods (FAGC, DLAGC) for the image contrast enhancement and applies deep learning models to detect and recognize the face in the enhanced image. The enhancement of the brightness difference between objects and their backgrounds achieved by the proposed adaptive gamma correction-based methods enables the deep learning model to extract the quality semantic information, which improves the accuracy of person verification. The proposed approach is evaluated on Extended Yale Face (EYF) dataset, which is a low-illumination image dataset. The proposed framework with FAGC and DLAGC for person verification achieves an improvement of 24% and 30%, respectively, on EYF dataset and 2.5% and 10%, respectively, on Specs on Faces dataset when compared to the existing techniques.
STDC-Net: A spatial-temporal deformable convolution network for conference video frame interpolation

Abstract Video conference communication can be seriously affected by dropped frames or reduced frame rates due to network or hardware restrictions. Video frame interpolation techniques can interpolate the dropped frames and generate smoother videos. However, existing methods can not generate plausible results in video conferences due to the large motions of the eyes, mouth and head. To address this issue, we propose a Spatial-Temporal Deformable Convolution Network (STDC-Net) for conference video frame interpolation. The STDC-Net first extracts shallow spatial-temporal features by an embedding layer. Secondly, it extracts multi-scale deep spatial-temporal features through Spatial-Temporal Representation Learning (STRL) module, which contains several Spatial-Temporal Feature Extracting (STFE) blocks and downsample layers. To extract the temporal features, each STFE block splits feature maps along the temporal pathway and processes them with Multi-Layer Perceptron (MLP). Similarly, the STFE block splits the temporal features along horizontal and vertical pathways and processes them by another two MLPs to get spatial features. By splitting the feature maps into segments of varying lengths in different scales, the STDC-Net can extract both local details and global spatial features, allowing it to effectively handle large motions. Finally, Frame Synthesis (FS) module predicts weights, offsets and masks using the spatial-temporal features, which are used in deformable convolution to generate the intermediate frames. Experimental results demonstrate the STDC-Net outperforms state-of-the-art methods in both quantitative and qualitative evaluations. Compared to the baseline, the proposed method achieved a PSNR improvement of 0.13 dB and 0.17 dB on the Voxceleb2 and HDTF datasets, respectively.
Using Different Types of Artificial Neural Networks to Classify 2D Matrix Codes and Their Rotations—A Comparative Study

Artificial neural networks can solve various tasks in computer vision, such as image classification, object detection, and general recognition. Our comparative study deals with four types of artificial neural networks-multilayer perceptrons, probabilistic neural networks, radial basis function neural networks, and convolutional neural networks-and investigates their ability to classify 2D matrix codes (Data Matrix codes, QR codes, and Aztec codes) as well as their rotation. The paper presents the basic building blocks of these artificial neural networks and their architecture and compares the classification accuracy of 2D matrix codes under different configurations of these neural networks. A dataset of 3000 synthetic code samples was used to train and test the neural networks. When the neural networks were trained on the full dataset, the convolutional neural network showed its superiority, followed by the RBF neural network and the multilayer perceptron.
Does contextual information within a sentence affect the relationship between word level intelligibility and the Functional Load principle among ELF users? A preliminary study

ABSTRACTThe Functional Load (FL) principle predicts that a high FL phonemic substitution lowers the intelligbility more than a low FL phonemic substitution. However, as yet no study has comprehensively investigated whether contextual information could attenuate any loss of intelligibility due to FL phonemic substitutions. As such, this study investigates the triangular relationship among the FL principle, word level intelligibility, and contextual information within a sentence. A Chinese speaker of English recorded sentences with more and with less contextual information that also had words with no FL substitutions, low FL substitutions, and high FL substitutions. Japanese university students transcribed the missing words from each sentence. The central hypothesis of this study is that missing words with more contextual information within their sentences will be more intelligible than missing words with less contextual information, regardless of any FL substitution. The results supported some of the hypotheses of this study, but not all.KEYWORDS: Functional LoadintelligibilityEnglish as a lingua francapronunciationcontext Disclosure statementNo potential conflict of interest was reported by the authors.Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/13488678.2023.2251731Additional informationFundingThis work was supported by the Japan Society for the Promotion of Science [JSPS KAKENHI Grant Number 19K13220].
Blueprints: Systematizing Behavior Change Designs—The Case of Social Comparison Theory

To improve people’s lives, human-computer interaction researchers are increasingly designing technological solutions based on behavior change theory, such as social comparison theory (SCT). However, how researchers operationalize such a theory as a design remains largely unclear. One way to clarify this methodological step is to clearly state which functional elements of a design are aimed at operationalizing a specific behavior change theory construct to evaluate if such aims were successful. In this article, we investigate how the operationalization of functional elements of theories and designs can be more easily conveyed. First, we present a scoping review of the literature to determine the state of operationalizations of SCT as behavior change designs. Second, we introduce a new tool to facilitate the operationalization process. We term the tool blueprints . A blueprint explicates essential functional elements of a behavior change theory by describing it in relation to necessary and sufficient building blocks incorporated in a design. We describe the process of developing a blueprint for SCT. Last, we illustrate how the blueprint can be used during the design refinement and reflection process.
Surrogate Lagrangian Relaxation: A Path to Retrain-Free Deep Neural Network Pruning

Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline (i.e., training, pruning, and retraining (fine-tuning)) significantly increases the overall training time. In this article, we develop a systematic weight-pruning optimization approach based on surrogate Lagrangian relaxation (SLR), which is tailored to overcome difficulties caused by the discrete nature of the weight-pruning problem. We further prove that our method ensures fast convergence of the model compression problem, and the convergence of the SLR is accelerated by using quadratic penalties. Model parameters obtained by SLR during the training phase are much closer to their optimal values as compared to those obtained by other state-of-the-art methods. We evaluate our method on image classification tasks using CIFAR-10 and ImageNet with state-of-the-art multi-layer perceptron based networks such as MLP-Mixer; attention-based networks such as Swin Transformer; and convolutional neural network based models such as VGG-16, ResNet-18, ResNet-50, ResNet-110, and MobileNetV2. We also evaluate object detection and segmentation tasks on COCO, the KITTI benchmark, and the TuSimple lane detection dataset using a variety of models. Experimental results demonstrate that our SLR-based weight-pruning optimization approach achieves a higher compression rate than state-of-the-art methods under the same accuracy requirement and also can achieve higher accuracy under the same compression rate requirement. Under classification tasks, our SLR approach converges to the desired accuracy × faster on both of the datasets. Under object detection and segmentation tasks, SLR also converges 2× faster to the desired accuracy. Further, our SLR achieves high model accuracy even at the hardpruning stage without retraining, which reduces the traditional three-stage pruning into a two-stage process. Given a limited budget of retraining epochs, our approach quickly recovers the model’s accuracy.
Measures of Information Leakage for Incomplete Statistical Information: Application to a Binary Privacy Mechanism

Information leakage is usually defined as the logarithmic increment in the adversary’s probability of correctly guessing the legitimate user’s private data or some arbitrary function of the private data when presented with the legitimate user’s publicly disclosed information. However, this definition of information leakage implicitly assumes that both the privacy mechanism and the prior probability of the original data are entirely known to the attacker. In reality, the assumption of complete knowledge of the privacy mechanism for an attacker is often impractical. The attacker can usually have access to only an approximate version of the correct privacy mechanism, computed from a limited set of the disclosed data, for which they can access the corresponding un-distorted data. In this scenario, the conventional definition of leakage no longer has an operational meaning. To address this problem, in this article, we propose novel meaningful information-theoretic metrics for information leakage when the attacker has incomplete information about the privacy mechanism—we call them average subjective leakage , average confidence boost , and average objective leakage , respectively. For the simplest, binary scenario, we demonstrate how to find an optimized privacy mechanism that minimizes the worst-case value of either of these leakages.
Post-growth Human–Computer Interaction

Human–Computer Interaction (HCI) researchers have increasingly been questioning computing’s engagement with unsustainable and unjust economic growth, pushing for identifying alternatives. Incorporating degrowth, post-development, and steady-state approaches, post-growth philosophy offers an alternative not rooted in growth but in improving quality of life. It recommends an equitable reduction in resource use through sensible distributive practices where fulfillment is based on values including solidarity, cooperation, care, social justice, and localized development. In this paper, we describe opportunities for HCI to take a post-growth orientation in research, design, and practice to reimagine the design of sociotechnical systems toward advancing sustainable, just, and humane futures. We aim for the critiques, concerns, and recommendations offered by post-growth to be integrated into transformative HCI practices for technology-mediated change.
Acquisition of temporal patterns from electronic health records: an application to multimorbid patients

Abstract Background The exponential growth of digital healthcare data is fueling the development of Knowledge Discovery in Databases (KDD). Extracting temporal relationships between medical events is essential to reveal hidden patterns that can help physicians find optimal treatments, diagnose illnesses, detect drug adverse reactions, and more. This paper presents an approach for the extraction of patient evolution patterns from electronic health records written in Catalan and/or Spanish. Methods We propose a robust formulation for extracting Temporal Association Rules (TARs) that goes beyond simple rule extraction by considering the sequence of multiple visits. Our highly configurable algorithm leverages this formulation to extract Temporal Association Rules from sequences of medical instances. We can generate rules in the desired format, content, and temporal factors while accounting for different levels of abstraction of medical instances. To demonstrate the effectiveness of our methodology, we applied it to extract patient evolution patterns from clinical histories of multimorbid patients suffering from heart disease and stroke who visited Primary Care Centers (CAP) in Catalonia. Our main objective is to uncover complex rules with multiple temporal steps, that comprise a set of medical instances. Results As we are working with real-world, error-prone data, we propose a process of validation of the results by expert practitioners in primary care. Despite our limited dataset, the high percentage of patterns deemed correct and relevant by the experts is promising. The insights gained from these patterns can inform preventive measures and help detect risk factors, ultimately leading to better treatments and outcomes for patients. Conclusion Our algorithm successfully extracted a set of meaningful and relevant temporal patterns, especially for the specific type of multimorbid patients considered. These patterns were evaluated by experts and demonstrated the ability to predict risk factors that are commonly associated with certain diseases. Moreover, the average time gap between the occurrence of medical events provided critical insight into the term of these risk factors. This information holds significant value in the context of primary healthcare and preventive medicine, highlighting the potential of our method to serve as a valuable medical tool.
Self-adjusting population sizes for the (1,λ)-EA on monotone functions

We study the (1,λ)-EA with mutation rate c/n for c≤1, where the population size is adaptively controlled with the (1:s+1)-success rule. Recently, Hevia Fajardo and Sudholt have shown that this setup with c=1 is efficient on OneMax for s<1, but inefficient if s≥18. Surprisingly, the hardest part is not close to the optimum, but rather at linear distance. We show that this behavior is not specific to OneMax. If s is small, then the algorithm is efficient on all monotone functions, and if s is large, then it needs super-polynomial time on all monotone functions. In the former case, for c<1 we show a O(n) upper bound for the number of generations and O(nlog⁡n) for the number of function evaluations, and for c=1 we show O(nlog⁡n) generations and O(n2log⁡log⁡n) evaluations. We also show formally that optimization is always fast, regardless of s, if the algorithm starts in proximity of the optimum. All results also hold in a dynamic environment where the fitness function changes in each generation. An extended abstract, containing only the results without proofs, has been published at the PPSN conference [1].
Development and application of creepage distance measurement system for zinc oxide arrester

Abstract Arrester is used to protect various electrical equipment in the power system from lightning overvoltage, working overvoltage, power frequency transient overvoltage impact and damage. Zinc oxide arrester is widely used in power system. Creepage distance is a necessary item of arrester performance test. It is a necessary project for lightning arrester factory testing and centralized network access testing. At present, the measurement of arrester creepage distance mainly adopts the artificial method of paper tape measurement or wire measurement. In this paper, a measurement system for arrester creepage distance is developed by using three-dimensional reconstruction and laser vision detection, which realizes one-click and fully automatic detection of creepage distance. The field application results show that the system not only solves the problems of heavy workload and low measurement accuracy of manual measurement methods, but also displays the measurement results in real time, which significantly improves the reliability and flexibility of measurement work, shortens the measurement time and improves the work efficiency.