Reviewerly/exHarmony huggingface dataset:
A Guide to Digital Forensic “Theoretical to Software-Based Investigations”

A branch of forensic science called “digital forensics” deals with the utilization of digital data received, maintained, and conveyed by electronic devices as evidence in inquiries and legal proceedings. It is a growing field in computing that frequently necessitates the intelligent analysis of large amounts of complex data. Rapid advancements in computer science and information technology enable the development of novel techniques and software for digital investigations. Initially, much of the analysis software was unique and proprietary, but over time, specialised analysis software for both the private and governmental sectors became available. The aim of this chapter is to deliver a comprehensive overview of digital forensics phases, applications, merits, and demerits and widely used software of the domain. The chapter also discusses legitimate and legal considerations, followed by the scope and role of artificial intelligence for solving complex problems of digital forensics.
Augmented Reality in Portuguese Museums: A Grounded Theory Study on the Museum Professionals’ Perspectives

Augmented Reality (AR) is increasingly present in several fields, including the museological space, where the challenges of presenting objects interactively and attractively are constant, especially with the sociocultural changes of recent decades. Although there are numerous studies on AR in museums, the perspective of museum professionals on the technology still needs to be explored. Thus, in this study, we use a qualitative design and conduct in-depth interviews with professionals from 10 Portuguese museums involved in creating or applying AR within these environments. Applying the grounded theory, the researchers propose a framework to understand Portuguese museum professionals’ practices, perceptions, and experiences with AR in museum environments. The findings allow the creation of a theoretical framework divided into four levels, namely the perceptions of museum professionals on the role and use of AR, the understanding of departments, museum teams, and digital strategies, the perceived challenges, limitations, and advantages in the use of augmented reality technologies, and the future perspectives of AR in museums. The theory resulting from this study may also contribute suggestions for the design and implementation of AR in museums, which both museum professionals and designers can use.
Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring

Detecting malicious activities in Vehicular Ad hoc Networks (VANETs) is an important research field as it can prevent serious damage within the network and enhance security and privacy. In this regard, a number of approaches based on machine learning (ML) algorithms have been proposed. However, they encounter several challenges due to data being constantly generated over time; this can impact the performance of models trained on fixed datasets as well as cause the need for real-time data analysis to obtain timely responses to potential threats in the network. Therefore, it is crucial for machine learning models to learn and improve their predictions or decisions in real time as new data become available. In this paper, we propose a new approach for attack detection in VANETs based on incremental online machine learning. This approach uses data collected from the monitoring of the VANET nodes’ behavior in real time and trains an online model using incremental online learning algorithms. More specifically, this research addresses the detection of black hole attacks that pose a significant threat to the Ad hoc On Demand Distance Vector (AODV) routing protocol. The data used for attack detection are gathered from simulating realistic VANET scenarios using the well-known simulators Simulation of Urban Mobility (SUMO) and Network Simulator (NS-3). Further, key features which are relevant in capturing the behavior of VANET nodes under black hole attack are monitored over time. The performance of two online incremental classifiers, Adaptive Random Forest (ARF) and K-Nearest Neighbors (KNN), are assessed in terms of Accuracy, Recall, Precision, and F1-score metrics, as well as training and testing time. The results show that ARF can be successfully applied to classify and detect black hole nodes in VANETs. ARF outperformed KNN in all performance measures but required more time to train and test compared to KNN. Our findings indicate that incremental online learning, which enables continuous and real-time learning, can be a potential method for identifying attacks in VANETs.
Orthogonal Families of Bicircular Quartics, Quadratic Differentials, and Edwards Normal Form

Orthogonal families of bicircular quartics are naturally viewed as pairs of singular foliations of C^ by vertical and horizontal trajectories of a non-vanishing quadratic differential. Yet the identification of these trajectories with real quartics in CP2 is subtle. Here, we give an efficient, geometric argument in the course of updating the classical theory of confocal families in the modern language of quadratic differentials and the Edwards normal form for elliptic curves. In particular, we define a parameterized Edwards transformation, providing explicit birational equivalence between each curve in a confocal family and a fixed curve in normal form.
Attacking the tracker with a universal and attractive patch as fake target

Adversarial attacks in visual object tracking aim to drop tracking performance through injecting imperceptible perturbations to the input of the tracker. Current methods usually superimpose perturbation maps on the input images, and advocate blinding the tracker via occluding the real targets to achieve attack effect. From the perspective of attraction, we alternatively propose a novel idea of attacking the tracker, which advocates using perturbation patches to act as fake targets to attract the tracker's attention. For this purpose, we establish a multi-conditional objective function to generate our ideal patch in an offline iterative manner. For invisibility, we integrate the constraint of patch value into the function for unified optimization. For universality, in addition to adopting large-scale and high-diversity training samples, we also incorporate the video-agnostic condition into this function. To make the patch attractive like a fake target, we elaborately design the non-overlapping area to determine the patch position, and generate matched fake labels to mislead the tracker to track the patch. In online attacking, it only needs to paste the optimized patch onto the video frames, the tracker will be successfully attracted by our patch, achieving attack effect. Extensive experimental results on 8 popular tracking datasets demonstrate that our method can obtain exceptional attack performance in both non-targeted and targeted attack. Additionally, the experiments on transferability illustrate our optimized patches can be directly applied to other trackers with different architectures.
Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review

Anonymization techniques are widely used to make personal data broadly available for analytics/data-mining purposes while preserving the privacy of the personal information enclosed in it. In the past decades, a substantial number of anonymization techniques were developed based on the famous four privacy models such as k-anonymity, ℓ-diversity, t-closeness, and differential privacy. In recent years, there has been an increasing focus on developing attribute-centric anonymization methods, i.e., methods that exploit the properties of the underlying data to be anonymized to improve privacy, utility, and/or computing overheads. In addition, synthetic data are also widely used to preserve privacy (privacy-enhancing technologies), as well as to meet the growing demand for data. To the best of the authors’ knowledge, none of the previous studies have covered the distinctive features of attribute-centric anonymization methods and synthetic data based developments. To cover this research gap, this paper summarizes the recent state-of-the-art (SOTA) attribute-centric anonymization methods and synthetic data based developments, along with the experimental details. We report various innovative privacy-enhancing technologies that are used to protect the privacy of personal data enclosed in various forms. We discuss the challenges and the way forward in this line of work to effectively preserve both utility and privacy. This is the first work that systematically covers the recent development in attribute-centric and synthetic-data-based privacy-preserving methods and provides a broader overview of the recent developments in the privacy domain.
OHO: A Multi-Modal, Multi-Purpose Dataset for Human-Robot Object Hand-Over

In the context of collaborative robotics, handing over hand-held objects to a robot is a safety-critical task. Therefore, a robust distinction between human hands and presented objects in image data is essential to avoid contact with robotic grippers. To be able to develop machine learning methods for solving this problem, we created the OHO (Object Hand-Over) dataset of tools and other everyday objects being held by human hands. Our dataset consists of color, depth, and thermal images with the addition of pose and shape information about the objects in a real-world scenario. Although the focus of this paper is on instance segmentation, our dataset also enables training for different tasks such as 3D pose estimation or shape estimation of objects. For the instance segmentation task, we present a pipeline for automated label generation in point clouds, as well as image data. Through baseline experiments, we show that these labels are suitable for training an instance segmentation to distinguish hands from objects on a per-pixel basis. Moreover, we present qualitative results for applying our trained model in a real-world application.
An Adaptive Multi-Scale Network Based on Depth Information for Crowd Counting

Crowd counting, as a basic computer vision task, plays an important role in many fields such as video surveillance, accident prediction, public security, and intelligent transportation. At present, crowd counting tasks face various challenges. Firstly, due to the diversity of crowd distribution and increasing population density, there is a phenomenon of large-scale crowd aggregation in public places, sports stadiums, and stations, resulting in very serious occlusion. Secondly, when annotating large-scale datasets, positioning errors can also easily affect training results. In addition, the size of human head targets in dense images is not consistent, making it difficult to identify both near and far targets using only one network simultaneously. The existing crowd counting methods mainly use density plot regression methods. However, this framework does not distinguish the features between distant and near targets and cannot adaptively respond to scale changes. Therefore, the detection performance in areas with sparse population distribution is not good. To solve such problems, we propose an adaptive multi-scale far and near distance network based on the convolutional neural network (CNN) framework for counting dense populations and achieving a good balance between accuracy, inference speed, and performance. However, on the feature level, in order to enable the model to distinguish the differences between near and far features, we use stacked convolution layers to deepen the depth of the network, allocate different receptive fields according to the distance between the target and the camera, and fuse the features between nearby targets to enhance the feature extraction ability of pedestrians under nearby targets. Secondly, depth information is used to distinguish distant and near targets of different scales and the original image is cut into four different patches to perform pixel-level adaptive modeling on the population. In addition, we add density normalized average precision (nAP) indicators to analyze the accuracy of our method in spatial positioning. This paper validates the effectiveness of NF-Net on three challenging benchmarks in Shanghai Tech Part A and B, UCF_ CC_50, and UCF-QNRF datasets. Compared with SOTA, it has more significant performance in various scenarios. In the UCF-QNRF dataset, it is further validated that our method effectively solves the interference of complex backgrounds.
Document Fraud Detecting System

“Document Fraud Detecting System” deals with detection of fraud documents as well as copied contents from the existing documents. The system has a wide range of applications. This can be implemented in various fields. The proposed system is implemented for colleges , where documents submitted by students are checked for fraudulent activities. Whenever a fraudulent activity is detected a warning is send to the student. The existing system contains lots of paper work. It is difficult to detect any fraudulent documents and also it is a tedious work to find copied contents from the internet. In the proposed system the admin can register the staffs and students. The students can submit their assignments to the staff. The staff verifies the assignments by comparing existing assignment and the system detects whether the assignments submitted by the students are copied or not. Whenever a fraudulent activity is identified a warning is send to the student. If the document is genuine the staff can give suggestions about the document to the students. Then the student can rewrite their assignments according to the suggestions and submit their hardcopy. Document fraud is an intentional act designed to obtain a financial advantage or an undue service. To be efficient as possible, fraud detection must combine several complementary analysis methods, which together allow an optimal appreciation of the authenticity of the elements. Document fraud has become the third largest criminal industry world. Prevention of fraud and abuse has become a major concern of many organizations .Fraud detection involves a static approach, which searches for inconsistencies in the compositional profile of the object, and a dynamic approach which examines the aging process.
CHANGELIVESORG

A website called "CHANGELIVESORG" was created to make volunteering and donating to charities easier. People can browse a range of charity causes and organizations on the platform's user-friendly website, where they can also securely make online donations. Additionally, it offers a platform for people to join up to donate their time and talents. The proposed method makes it simpler to coordinate and manage philanthropic activities because it is quick and responsive. Because of the platform's transparency, contributors and event planners can see how their money is being spent. It is made to reach more people who are in need, enhancing the impact of humanitarian endeavors. The software gives event planners a means to manage volunteers and donations, streamline the application process, and request assistance for events.
A Posterior p-Value for Homogeneity Testing of the Three-Sample Problem

In this paper, we study a special kind of finite mixture model. The sample drawn from the model consists of three parts. The first two parts are drawn from specified density functions, f1 and f2, while the third one is drawn from the mixture. A problem of interest is whether the two functions, f1 and f2, are the same. To test this hypothesis, we first define the regular location and scale family of distributions and assume that f1 and f2 are regular density functions. Then the hypothesis transforms to the equalities of the location and scale parameters, respectively. To utilize the information in the sample, we use Bayes’ theorem to obtain the posterior distribution and give the sampling method. We then propose the posterior p-value to test the hypothesis. The simulation studies show that our posterior p-value largely improves the power in both normal and logistic cases and nicely controls the Type-I error. A real halibut dataset is used to illustrate the validity of our method.
Precise Short-Term Small-Area Sunshine Forecasting for Optimal Seedbed Scheduling in Plant Factories

Photosynthesis is one of the key issues for vertical cultivation in plant factories, and efficient natural sunlight utilization requires predicting the light falling on each seedbed in a real-time manner. However, public weather services neither provide sunshine data nor meet spatial resolution requirement. Facing these short-term and small-area weather forecasting challenges, we propose a cross-scale approach to infer seedbed-sized areas of sunshine from the city-level public weather services, and then design a seedbed rotation scheduling system for optimal natural sunlight utilization. First, an end-edge-cloud coordinated computing architecture was employed to concurrently aggregate the multi-scale data from weather satellites to sunshine sensors in the plant factory. Second, the small area of sunshine deterministically depends on the meteorological data given a fixed environment, and this correlation was described by a hybrid mapping model, which combined the long short-term memory (LSTM) and gradient boosting decision tree (GBDT) algorithms to form the LSTM-GBDT hybrid prediction algorithm (LGHPA). By training the LGHPA with historical local sensory sunshine and the city-scale meteorological data, the hourly sunshine on a seedbed can be predicted from the public weather forecasting service. Finally, a dynamic seedbed scheduling scheme was constructed to provide uniform solar energy absorption according to the one-hour-ahead radiation estimation. Experiment results show that the hourly sunshine prediction error was less than 18.44% over a seasonal period and the deviation for different solar absorption by seedbeds with rotation capability is less than 7.1%. Consequently, it was demonstrated that the application of short-term, small-area sunshine forecasting improved the performance of seedbed rotation for uniformly absorbed solar radiation. The proposed method verifies the feasibility of precisely predicting small-area sunshine down to the seedbed scale by leveraging a model-based approach and a cloud-edge-end merged cybernetic computing paradigm.
Challenges and Opportunities in Quantum Software Architecture

Quantum computing is a relatively new paradigm that has raised considerable interest in physics and computer science in general but has so far received little attention in software engineering and architecture. Hybrid applications that consist of both quantum and classical components require the development of appropriate quantum software architectures. However, given that quantum software engineering (QSE) in general is a new research area, quantum software architecture—a sub-research area in QSE—is also understudied. The goal of this chapter is to provide a list of research challenges and opportunities for such architectures. In addition, to make the content understandable to a broader computer science audience, we provide a brief overview of quantum computing and explain the essential technical foundations.
Target-Aware Feature Bottleneck for Real-Time Visual Tracking

Recent Siamese network-based visual tracking approaches have achieved high performance metrics on numerous recent visual tracking benchmarks, where most of these trackers employ a backbone feature extractor network with a prediction head network for classification and regression tasks. However, there has been a constant trend of employing a larger and complex backbone network and prediction head networks for improved performance, where increased computational load can slow down the overall speed of the tracking algorithm. To address the aforementioned issues, we propose a novel target-aware feature bottleneck module for trackers, where the proposed bottleneck can elicit a target-aware feature in order to obtain a compact feature representation from the backbone network for improved speed and robustness. Our lightweight target-aware bottleneck module attends to the feature representation of the target region to elicit scene-specific information and generate feature-wise modulation weights that can adaptively change the importance of each feature. The proposed tracker is evaluated on large-scale visual tracking datasets, GOT-10k and LaSOT, and we achieve real-time speed in terms of computation and obtain improved accuracy over the baseline tracker algorithm with high performance metrics.
Continuous Alignment Between Software Architecture Design and Development in CI/CD Pipelines

Oftentimes, architectural information is not well-maintained, while software is developed and thus is out of sync with the product. Development of architectural models and documentation is therefore frequently done (i) before development starts and quickly becomes outdated, or (ii) as an afterthought or step taken when, e.g., significant refactoring becomes necessary, or (iii) a combination of the two. In order to resolve this dilemma and maintain better clarity and reasoning on the system, we propose to ensure continuous alignment between software architecture design and the development process itself, by suitable extension and use of CI/CD pipelines. Specifically, methods from architecture recovery should serve as a good basis for achieving such continuous alignment, e.g., when focused or projected on specific critical user journeys. The alignment will also allow the detection of architectural decay and deviation from targeted non-functional properties. In this chapter, we outline the vision and open research topics toward continuous alignment.
Adaptive Age Estimation towards Imbalanced Datasets

Current age estimation datasets often have a skewed long-tail distribution with significant data imbalance, rather than an ideal uniform distribution for each category. The existing age estimation algorithms that rely on label distribution do not leverage data density information to address the issue of data imbalance. To solve the aforementioned problem, this paper proposes a novel method based on cost-sensitive learning, namely Data-Imbalance Adaptive Age Regression (DIAAR), for age estimation. DIAAR consists of two main modules: the adaptive soft label (ASL) module and the Data Density Smoothing (DDS) module. The ASL module embeds soft labels in the form of probability in the age regression. It assigns different degrees of soft labels adaptively to head and tail data based on their density, which helps balance the dataset. The DDS module further addresses data imbalance by revising data density through kernel smoothing and reweighting the loss function accordingly. Experiments on two benchmark datasets show that DIAAR can effectively deal with data imbalance and improve the accuracy of age estimation, achieving an average improvement of 8% over the baseline models. Moreover, this approach can be applied to various methods based on convolutional neural network models.