Reviewerly/exHarmony huggingface dataset:
Information consolidation and repackaging for augmented reality library service: a special reference to the Layar app

Purpose The purpose of this research is to introduce the Layar augmented reality (AR) application among library users and to understand the user’s satisfaction towards the information services provided by the Layar application with the help of the structural equation model (SEM). Design/methodology/approach According to Thomas (2016), action research is mainly undertaken to develop new skills or new approaches and to solve issues and problems with direct application to any applied setting. The present study helps to develop new skills and approaches to repackaging information using AR applications. Researchers have identified the question of what could be done to increase the awareness of Layar AR applications among students. Because the Layar augmented application is one of the new tools for an academic library to repackage information for mass accessibility. Therefore, in the present action research approach, researchers encompass two activities action and research. Researchers have used participatory action research methods by collecting data from 17 MBA institute libraries affiliated with Savitribai Phule Pune University. Researchers have systematically used the Layar application in the library by obtaining permission from each higher authority. Researchers have designed a Layar satisfaction model using the SEM with AMOS and SPSS. Findings The researcher found that the relationship between experience, performance and service quality is positively significant. The user’s experience is satisfied with the Layar application, but users are not satisfied with the service quality and performance of the Layar application. Research limitations/implications This study tested Layar AR application in MBA libraries affiliated with Savitribai Phule Pune University in the Pune and Pimpri Chinchwad areas. Practical implications The Layar app helps the academic library to convert selected print collections into an AR feel for library users. This is an additional method of providing information services to users through mobile devices. A total of 157 students downloaded the Layar application from their handsets and provided feedback through a questionnaire. Researchers have found that the relationships between users and Layar experience, performance and service quality are positively significant. The user experience is satisfied with the Layar application, but users are not satisfied with the service quality and performance of the Layar application. Originality/value This study examined the performance, service quality and user experience of Layar applications. Structural equation and Modelling theories were used to examine the relationship between user satisfaction and information services using the Layar application.
Gaussian Wasserstein distance based ship target detection algorithm

In remote sensing images, ship images are characterized by small target size, irregular arrangement and large scale difference, and the detection accuracy of existing target detection algorithms for small targets of ships is low. In order to extract the information in remote sensing target images more effectively and improve the detection accuracy of small targets, four models are constructed on the framework of GWD based on the dataset of HRSC16 in this paper. Firstly, the initial model based on GWD is trained, secondly, a cutout data enhancement strategy is added to GWD to further improve the robustness and overall performance of the convolutional neural network in the training process, then a ConvNeXt backbone network is introduced on the basis of GWD to improve the scalability and convergence of the network, and finally, the third model is based on the network is deepened, which can extract small target features more fully. The image recognition accuracy in HRSC16 dataset is improved from 54.1% at the beginning to 61.65%. Experiments show that the target detection algorithm proposed in this paper has good detection accuracy for small target detection of ships, and it is improved by 7.55% for small target detection after the improvement.
The Improvement of the Discrete Wavelet Transform

Discrete wavelet transforms are widely used in signal processing, data compression and spectral analysis. For discrete data with finite sizes, one always pads the data with zeros or extends the data into periodic data before performing the discrete periodic wavelet transform. Due to discontinuity on the boundaries of the original data, the obtained wavelet coefficients always decay slowly, leading to data compression ratios that are significantly lower. In order to solve this issue, in this study, we coupled polynomial fitting into classic discrete periodic wavelet transforms to mitigate these boundary effects.
Predicting Abnormal User Behaviour Patterns in Social Media Platforms based on Process Mining

Cyberbullying has been one of the adverse repercussions of social media these days. The intensity of cyberbullying is risen considerably as a due to the increased use of image sharing and textual comments. Cyberbullying can be defined as transmitting, publishing, or circulating unbearable, harmful, false, or cruel content about some other individual. To keep the site safe and secure, automated processes for detecting certain instances are now vital. Process mining seems to be a combination of methodologies that integrate data science with management system to aid in the evaluation of organizational functions using log information. Whenever images and language that appear to be benign are combined, they might send bullying texts. As a result, different methods for analysing text and photos may fail to detect all instances of cyberbullying. In this study, we attempted to detect various examples of cyberbullying by combining textual data. The proposed system discovers hidden connections between individuals and members of a group who have similar behaviours. We achieve this through a novel strategy that incorporates techniques including data mining, analyzation of business procedures, and much more. Our study summarizes complete methodological process involved in the proposed system from the computer-generated data source in which the data pertaining user behavior, actions, and processes in an OS, software, website, or other sources to provide the visual representation of the abnormalities. Moreover, phase of identifying user behavioral patterns is the one which deserves attention.
Secret Sharing Scheme: Based on LCD Code

Data privacy is one of the main issues in communication technology and affects our daily lives. Secret sharing is essential in secure communication and has numerous applications in information security. Secret sharing is a methodology for accessing sensitive data by a few approved members. It shares the secret key in different structures among a few approved members, a specific number expected to recover the secret. There are many ways to deal with the development of secret sharing schemes. One of them depends on coding theory. There are many secret sharing schemes based on linear codes, but it is complicated to determine the access structure. We present a perfect scheme using linear complementary dual (LCD) codes to give more proficient and adaptable choices for secret sharing. We use the properties of LCD codes involving generator matrix in secret distribution and secret recovery phases. Likewise, we use a one-way hash function to verify the authorized participants by the dealer in the recovery phase. The performance analysis indicates that the proposed scheme is suitable for implementation in secret sharing.
Entanglement Generation with Schrödinger Kitten States

We present a protocol to generate quantum entanglement with Schrödinger Kitten States. Stronger quantum entanglement than photon-subtracted two-mode squeezed vacuum states (PSTMSV) and TMSV are obtained for squeezing level below -5.12 dB.
Attention Mechanism Improves YOLOv5x for Detecting Vehicles on Surveillance Videos

Vehicle detection accuracy on surveillance videos is heavily restricted by camera angles, low lighting conditions, low visibility caused by harsh weather, and serious occlusions. For the full 24/7 operation, the Intelligent Transportation Services (ITS) are expected to perform well on all the categories of the target detections in the environment. Unfortunately, most existing datasets do not cover all these difficult conditions. Moreover, the state-of-the-art Deep Learning detector performance decreases for these difficult conditions. This paper reports on the training of an object detection system using a range of traffic scenarios: sunny, rainy, snowy, one-side road, two-side road, complex road structures with occlusions, heavy traffic with congestion, light traffic, and reduced traffic at night. The state-of-the-art object detector of YOLOv5x is used for vehicle detection and is fine-tuned on this new diverse dataset through transfer learning. Transfer learning freezes the backbone network while training the remaining fully connected network. To further improve the detection performance, we added two convolutional block attention modules (CBAM) to the neck as our proposed system: 2xCBAM-YOLOv5. Several experiments refined the number of CBAMs and the placement of these modules to optimize performance. Doing transfer learning alone, the mean Average Precision(mAP) on the test data improves from 75.9% to 78.9%. After transfer learning, ablations were done on YOLOv5x combined with the new CBAMs. The resulting mAP reaches 85.0%, while precision improves from 82.3% to 88.2%, recall improves from 72.3% to 80.4% and F1-score improves from 0.77 to 0.841 compared with transfer learning alone. This new architecture provides an overall improvement for ITS traffic surveillance applications.
A reference model for software product line capability assessment

Abstract The Software Product Line (SPL) approach receives more attention due to the observed benefits, such as cost reduction, quality improvements, and reduced delivery time. Although organizations are aware of its potential benefits, they face some challenges while creating a clear road map to adopt this approach. Capability maturity models are developed to guide organizations by providing an extensive road map for improvement. Accordingly, we developed a capability maturity model, entitled SPL Capability Maturity Model (SPL‐CMM), to improve an organization's SPL capabilities by enabling the assessment of SPL‐specific processes and providing a guideline for process improvement. SPL‐CMM, developed based on SPICE‐ISO/IEC 330xx standard, has two dimensions of process and capability. The process dimension consists of 16 SPL‐specific processes defined under four process areas: business, architecture, technical, and organization, and the capability dimension has six capability and maturity levels from levels 0 to 5. It is a structured approach assisted by adequate measures with guidance on actions for improvement. A case study was conducted to check the applicability and usefulness of the proposed approach in a software company. The case study results show that SPL‐CMM is applicable for identifying the current state of the SPL process capability and the gaps for process improvement to the next level.
Fairness-aware Multi-view Clustering

In the era of big data, we are often facing the challenge of data heterogeneity and the lack of label information simultaneously. In the financial domain (e.g., fraud detection), the heterogeneous data may include not only numerical data (e.g., total debt and yearly income), but also text and images (e.g., financial statement and invoice images). At the same time, the label information (e.g., fraud transactions) may be missing for building predictive models. To address these challenges, many state-of-the-art multi-view clustering methods have been proposed and achieved outstanding performance. However, these methods typically do not take into consideration the fairness aspect and are likely to generate biased results using sensitive information such as race and gender. Therefore, in this paper, we propose a fairness-aware multi-view clustering method named Fair-MVC. It incorporates the group fairness constraint into the soft membership assignment for each cluster to ensure that the fraction of different groups in each cluster is approximately identical to the entire data set. Meanwhile, we adopt the idea of both contrastive learning and non-contrastive learning and propose novel regularizers to handle heterogeneous data in complex scenarios with missing data or noisy features. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of the proposed framework. We also derive insights regarding the relative performance of the proposed regularizers in various scenarios.KeywordsMulti-view LearningContrastive LearningClustering
Vulnerability of CNNs against Multi-Patch Attacks

Convolutional Neural Networks have become an integral part of anomaly detection in Cyber-Physical Systems (CPS). Although highly accurate, the advent of adversarial patches exposed the vulnerability of CNNs, posing a security concern for safety-critical CPS. The current form of patch attacks often involves only a single adversarial patch. Using multiple patches enables the attacker to craft a stronger adversary by utilizing various combinations of the patches and their respective locations. Moreover, mitigating multiple patches is a challenging task in practice due to the nascence of the domain. In this work, we present three novel ways to perform an attack with multiple patches: Split, Mono-Multi, and Poly-Multi attacks. We also propose a search method named 'Boundary Space Search (BSS)' for the placement of patches to enhance the attack's efficacy further, experimenting on EuroSAT, Imagenette, and CIFAR10 datasets for various perturbation levels across diverse model architectures. The results show that the Poly-Multi attack outperforms other multi-patch and single-patch attacks and the best perception stealth to surpass the detection. We also highlight the trade-off between the number of patches and the patch size in a Multi-Patch attack. In the end, we analyze the ability of the Multi-Patch attack to overcome state-of-the-art defenses designed for single patch attacks.
Resilient and Communication Efficient Learning for Heterogeneous Federated Systems.

The rise of Federated Learning (FL) is bringing machine learning to edge computing by utilizing data scattered across edge devices. However, the heterogeneity of edge network topologies and the uncertainty of wireless transmission are two major obstructions of FL's wide application in edge computing, leading to prohibitive convergence time and high communication cost. In this work, we propose an FL scheme to address both challenges simultaneously. Specifically, we enable edge devices to learn self-distilled neural networks that are readily prunable to arbitrary sizes, which capture the knowledge of the learning domain in a nested and progressive manner. Not only does our approach tackle system heterogeneity by serving edge devices with varying model architectures, but it also alleviates the issue of connection uncertainty by allowing transmitting part of the model parameters under faulty network connections, without wasting the contributing knowledge of the transmitted parameters. Extensive empirical studies show that under system heterogeneity and network instability, our approach demonstrates significant resilience and higher communication efficiency compared to the state-of-the-art.
Energy minimization segmentation model based on MRI images

Medical image segmentation is an important tool for doctors to accurately analyze the volume of brain tissue and lesions, which is important for the correct diagnosis of brain diseases. However, manual image segmentation methods are time-consuming, subjective and lack of repeatability, it needs to develop automatic and reliable methods for image segmentation.Magnetic Resonance Imaging (MRI), a non-invasive imaging technique, is commonly used to detect, characterize and quantify tissues and lesions in the brain. Partial volume effect, gray scale in homogeneity, and lesions presents a great challenge for automatic medical image segmentation methods. So, the paper is dedicated to address the impact of partial volume effect and multiple sclerosis lesions on the segmentation accuracy in MRI. The objective function of the improved model and the post-processing method of lesion filling are researched based on the fuzzy clustering space and energy model.In particular, an energy-minimized segmentation algorithm is proposed. Through experimental verification, the AR-FCM algorithm can better overcome the problem of low segmentation accuracy of the RFCM algorithm for tissue boundary voxels and improve the segmentation accuracy of this algorithm. Meanwhile, a multi-channel input energy-minimization segmentation method with lesion filling and anatomical mapping is further proposed.The feasibility of the lesion filling strategy using post-processing can be confirmed and the segmentation accuracy is increased by comparison experiments.
Dall’area di Broca al sensorio digitale, trasformazioni antropologiche in atto e ‘cervelli in movimento’: una mente incorporata in un mondo digitalizzato.

The new forms of social communication, with the increasing presence of images within the text, make us necessary to reconsider the cerebral basis of literacy and theyr reconfiguration via neural recycling. In the theoretical framework of Embodied philosophy of mind, we propose an analysis of the ‘extended writing”, actually using emoticon and emoji, in the light of our contemporary neuropsychological knowledge (hemispheric specialization and the functional circuit of literacy).
Multi-Task Explainable Quality Networks for Large-Scale Forensic Facial Recognition

Identifying suspects from surveillance footage is a crucial task in forensic investigations, but it is often hindered by the variable conditions of observation and the large amounts of data. Face image quality (FIQ) is a metric that measures the usefulness of a face sample for facial recognition. Existing methods for automated FIQ assessment only provide a scalar value for quality, and do not indicate which factors are causing low quality. Additionally, these methods are computationally expensive, which makes current FIQ assessment methods unsuitable for large numbers of images. To address these issues, we introduce multi-task explainable quality networks (XQNets). XQNets provide both the quality value and the associated facial and environmental attributes, and automatically learn how each attribute contributes to the quality value during the training process. We also propose a dataset-agnostic quality pairing protocol to ensure that sample pairs are balanced across datasets and evaluations are fair. Our experiments on the LFW and SCface benchmarks show that our approach generalizes well across different datasets and outperforms state-of-the-art methods. Our method offers a fast, explainable approach to FIQ assessment, making it suitable for large-scale forensic applications.
A New Algebraic Approach to the Regular Syndrome Decoding Problem and Implications for PCG Constructions

The Regular Syndrome Decoding (RSD) problem, a variant of the Syndrome Decoding problem with a particular error distribution, was introduced almost 20 years ago by Augot et al.. In this problem, the error vector is divided into equally sized blocks, each containing a single noisy coordinate. More recently, the last five years have seen increased interest in this assumption due to its use in MPC and ZK applications. Generally referred to as “LPN with regular noise" in this context, the assumption allows to achieve better efficiency when compared to plain LPN. In all previous works of cryptanalysis, it has not been shown how to exploit the special feature of this problem in an attack. We present the first algebraic attack on RSD. Based on a careful theoretical analysis of the underlying polynomial system, we propose concrete attacks that are able to take advantage of the regular noise distribution. In particular, we can identify several examples of concrete parameters where our techniques outperform other algorithms.
A survey on adversarial attack and defense of deep learning models for medical image recognition

The advancement of hardware and computing power has enabled deep learning to be used in a variety of fields, particularly in AI medical applications in intelligent medicine and medical metaverse. Deep learning models are aiding in many clinical medical image analysis tasks, including fusion, registration, detection, classification and segmentation. In recent years, many deep learning-based approaches have been developed for medical image recognition, including classification and segmentation. However, these models are susceptible to adversarial samples, posing a threat to their real world application and making them unsuitable for clinical use. This paper provides an overview of adversarial attack strategies that have been proposed against medical image models and the defense methods used to protect them. We assessed the advantages and disadvantages of these strategies and compared their efficiency. We then examined the existing state and restrictions of research methods involving the adversarial attack and defense of deep learning models for medical image recognition. Additionally, several suggestions were given on how to enhance the robustness of medical image deep learning models in intelligent medicine and medical metaverse.
Movie Recommendation System Using Filtering Techniques

In today’s world many prestigious applications used recommendation systems. Some of the information filtering algorithms are used to predict preferences as per user requirements. This system saves users time in searching and provides the best user experience. Books, news, music, articles, movies, videos, etc. are the most popular areas where recommendation systems are used. When we take reference from several research papers, one thing is clear Content-Based Filtering Algorithm is used for the recommendation system. In this paper, we work on several algorithms and manipulation of results of this we get a final recommendation list based on ratings like dislike content, related content, etc. Keywords: -Contest Based Filtering, Movie Recommendation System, Vector Similarity, Text to vector, K NN Algorithm, Cosine Similarity.
Differentially private tree-based redescription mining

Abstract Differential privacy provides a strong form of privacy and allows preserving most of the original characteristics of the dataset. Utilizing these benefits requires one to design specific differentially private data analysis algorithms. In this work, we present three tree-based algorithms for mining redescriptions while preserving differential privacy. Redescription mining is an exploratory data analysis method for finding connections between two views over the same entities, such as phenotypes and genotypes of medical patients, for example. It has applications in many fields, including some, like health care informatics, where privacy-preserving access to data is desired. Our algorithms are the first tree-based differentially private redescription mining algorithms, and we show via experiments that, despite the inherent noise in differential privacy, it can return trustworthy results even in smaller datasets where noise typically has a stronger effect.