Reviewerly/exHarmony huggingface dataset:
PriSIEM: Enabling privacy-preserving Managed Security Services

Security monitoring is invariably enabled by Security Information and Event Management (SIEM) technology. A major problem with SIEM is that in house deployment and operation are costly in terms of purchase, human resources, and IT infrastructure. Managed Security Services (MSS) offerings can provide high quality security monitoring solutions at a fraction of the cost. However, outsourcing security monitoring might entail data confidentiality and integrity risks and current MSS solutions are unable to meet the stringent privacy requirements posed by a wide range of applications. We present PriSIEM, an efficient distributed computing model which enables privacy-preserving MSS, by leveraging two of the most promising techniques for confidential computing, namely hardware-assisted Trusted Execution (TE) and Homomorphic Encryption (HE). TE is used to create a shielded computing environment in the provider's domain, which can be trusted by the data owner. In this trusted environment, potentially sensitive data is encrypted using HE, before it is moved and processed in the rest of the provider's domain (i.e. externally to the TE environment), which cannot be trusted by the data owner. An experimental campaign has been conducted on a proof-of-concept implementation to validate the effectiveness of the hardening mechanisms and to evaluate the performance of the PriSIEM distributed environment.
Image splicing tamper detection based on two-channel dilated convolution

Image splicing tampering detection can provide technical support for the identification of authenticity and integrity of images in various fields by using specific methods to quickly detect whether tampering has occurred in images. Most of the existing splicing tamper detection methods pay more attention to the detection accuracy of tamper image, ignore the importance of tamper region location, and can only provide rough tamper region location map. To solve the above problems, we proposed an image splicing tamper detection model based on dual-channel dilated convolution, which combined the deep features and shallow features of the image to accurately locate the tamper area. In this model, the first channel extracts noise features through a set of high-pass filters to find noise inconsistencies between the real area and the tampered area. The second channel extracts RBG image features through dilated convolution and locates the tampered area in combination with the attention mechanism. Then, for each region of interest, the features generated by the two channels are fused together through bilinear pooling layer. Finally, tamper classification and boundary box regression are performed. We performed experimental analysis on NIST16 and CASIA datasets, and compared with the other five models, this model has better positioning performance, with the F1 index increased by 0.026 and 0.051, respectively, compared with other advanced methods.
Search and Rescue AR Visualization Environment (SAVE): Designing an AR Application for Use with Search and Rescue Personnel

Search and Rescue (SAR) operations are often stressful, complex, and time-sensitive operations that require an efficient flow of information between many different teams to operate effectively to save lives. Current augmented reality (AR) technology can be utilized to assist with these operations by helping facilitate information flow while simultaneously making common operation tasks easier to complete. In this paper, we present an early AR prototype, the Search and Rescue AR Visualization Environment (SAVE), which serves as a headworn AR testbed for various SAR features specifically designed for use with ground-based personnel. Throughout the paper, we describe the process of engaging subject matter experts, producing our working testbed prototype, as well as the next steps needed to continue our research.
Adaptive fuzzy logic with self-adjusting membership functions based tracking control of surface vessels

Tracking control of surface vessels is aimed where the dynamical model includes uncertainties. In an attempt to design a broadly applicable control methodology, a model independent strategy is pursued. The dynamical uncertainties are modeled via a fuzzy logic network. In the design of the controller, proportional derivative feedback was made use of in conjunction with self-adjusting fuzzy logic compensation term obtained by adaptively updating control representative value matrix along with centers and widths of the membership value vector. Stability of the closed loop system is investigated through novel Lyapunov-based arguments and semi-global practical tracking is guaranteed. Numerical simulation studies are performed that support theoretical findings and demonstrate the effectiveness of the proposed method.
Application of Machine Learning Approaches in Health Care Sector to The Diagnosis of Breast Cancer

Abstract Breast cancer (BC) is a kind of malignant disease that represents the primary reason of women’s death around the world, cancer cells form tumors which lead to weakening the functioning of the immune system. If the main risk factors are known and detected correctly, the cure rate becomes higher, and the inappropriate treatments which are the main cause of death will be avoided. Today, several avenues for advancing breast cancer classification research are being studied, in particular to strengthen screening and develop an early diagnosis plan. The purpose of this paper is to approach the unfolding of machine learning techniques in the clinical field to categorize and discriminate patients between malignant and benign groups. Modeling of cytological characteristics based on machine learning is proposed to improve predictive performance. In this work, three proposed algorithms of machine learning techniques have been used for the analyze and classification of Wisconsin breast cancer database, k Nearest Neighbors (k-NN), Naive Bayes (NB) and Support Vector Machine (SVM). We will compare learning metrics of both, using train/test split and cross validation. The obtained results shows that KNN offers the best accuracy (97.07%), NB classifier (94.15 %) and SVM classifier (94.73%).
Video Person Re-Identification with Frame Sampling–Random Erasure and Mutual Information–Temporal Weight Aggregation

Partial occlusion and background clutter in camera video surveillance affect the accuracy of video-based person re-identification (re-ID). To address these problems, we propose a person re-ID method based on random erasure of frame sampling and temporal weight aggregation of mutual information of partial and global features. First, for the case in which the target person is interfered or partially occluded, the frame sampling-random erasure (FSE) method is used for data enhancement to effectively alleviate the occlusion problem, improve the generalization ability of the model, and match persons more accurately. Second, to further improve the re-ID accuracy of video-based persons and learn more discriminative feature representations, we use a ResNet-50 network to extract global and partial features and fuse these features to obtain frame-level features. In the time dimension, based on a mutual information-temporal weight aggregation (MI-TWA) module, the partial features are added according to different weights and the global features are added according to equal weights and connected to output sequence features. The proposed method is extensively experimented on three public video datasets, MARS, DukeMTMC-VideoReID, and PRID-2011; the mean average precision (mAP) values are 82.4%, 94.1%, and 95.3% and Rank-1 values are 86.4%, 94.8%, and 95.2%, respectively.
Fast face detection using a unified architecture for unconstrained and infrared face images

Human face detection along with its localization is a difficult task when the face is presented in the cluttered scene in an unconstrained scenario that might be with arbitrary pose variations, occlusions, random backgrounds and infrared (IR) environment. This paper proposes a novel face detection method which can address some of these issues and challenges quite successfully during face detection in unconstrained as well as infrared environments. It makes use of Fast Successive Mean Quantization Transform (FastSMQT) features for image enhancement and feature representation to deal with illumination and sensor insensitiveness. A split up Sparse Network of Winnows (SNoW) with Winnow updating rule is then exploited to speed up the original SNoW classifier. Finally, the features and classifiers are combined together with skin detection algorithm for face detection in crowd image and head orientation correction of near infrared faces. The proposed face detector is robust in handling pose, occlusion, illumination, blur and low image resolution. The experiment is performed on six challenging and publicly available databases, viz. BIOID, LFW, FDDB, UFI, WIDER FACE and IIT Delhi near infrared. The experimental results depict that the proposed method outperforms traditional as well as some advanced methods in detecting unconstrained and infrared faces under challenging situations.
Active learning approach for inappropriate information classification in social networks

This paper describes an original approach of classification with active learning for inappropriate information detection and its application for the text posts from the VKontakte social network. The novelty of the approach lies in the constantly growing dataset, while the classifiers training process takes place during the operator's work. The approach works with texts of any size and content and applicable for Russian social networks. The research contribution lies in the original approach for inappropriate information detection, while practical significance lies in the automation of routine tasks to reduce the burden on specialists in the area of protection from information. Experimental evaluation of the approach is focused on its iterative retraining part. For the experiment, text posts of different topics from the VKontakte social network were collected and labeled. After that, we have evaluated F-measure and ROC-AUC metrics for classifiers trained on random subsamples of different sizes and different topics. Moreover, the advantages and disadvantages of the approach, as well as future work directions, were indicated.
An Anonymous Verifiable Random Function with Applications in Blockchain

Verifiable random function is a powerful function that provides a noninteractively public verifiable proof for its output. Recently, verifiable random function has found essential applications in designing secure consensus protocols in blockchain. How to construct secure and practical verifiable random functions has also attracted more and more attention. In this paper, we propose a practical anonymous verifiable random function. Security proofs show that the proposed anonymous verifiable random function achieves correctness, anonymity, uniqueness, and pseudorandomness. In addition, we show a concrete application of our proposed anonymous verifiable random function in blockchain to improve the consensus mechanism for Hyperledger fabric. Finally, we implement the proposed anonymous verifiable random function and evaluate its performance. Test results show that the proposed anonymous verifiable random function supports faster computing operations and has a smaller proof size.
Intrinsic graph topological correlation for graph convolutional network propagation

Recently, Graph Convolutional Networks (GCNs) and their variants become popular to learn graph-related tasks. These tasks include link prediction, node classification, and node embedding, among many others. In the node classification problem, the input is a graph with some labeled nodes and the features associated with these nodes and the objective is to predict the unlabeled nodes. While the GCNs have been successfully applied to this problem, some caveats that are inherited from classical deep learning remain unsolved. One such inherited caveat is that, during classification, GCNs only consider the nodes that are a few neighbors away from the labeled nodes. However, considering only a few steps away nodes could not effectively exploit the underlying graph topological information. To remedy this problem, the state-of-the-art methods leverage the network diffusion approaches, such as personalized PageRank and its variants, to fully account for the graph topology. However, these approaches overlook the fact that the network diffusion methods favour high degree nodes in the graph, resulting in the propagation of the labels to the unlabeled,hub nodes. In order to overcome bias, in this paper, we propose to utilize a dimensionality reduction technique, which is conjugate with personalized PageRank. Testing on four real-world networks that are commonly used in benchmarking GCNs' performance for the node classification task, we systematically evaluate the performance of the proposed methodology and show that our approach outperforms existing methods for wide ranges of parameter values. Since our method requires only a few training epochs, it releases the heavy training burden of GCNs. The source code of the proposed method is freely available at https://github.com/mustafaCoskunAgu/ScNP/blob/master/TRJMain.m.
Effects of Distance Metrics on Color Recognition using Color Histogram and KNN Algorithm

Color recognition is crucial in image processing for color-based applications such as object recognition, face recognition, skin color recognition, traffic signal color detection, food color code detection, and so on. The application of classification algorithms, such as the KNN algorithm, facilitates the ability to distinguish between different colors. However, the accuracy of the classification may vary based on the similarity metrics employed. The effect of four different similarity metrics - Euclidean, Chi Square, Chebyshev, and Hamming distance metrics - on the accuracy of color recognition using the RGB Color Histogram for feature extraction and the KNN algorithm for classification among six distinct colors for values of K= 3, 5, and 7 is compared in this paper.
Types of Racism and Twitter Users’ Responses Amid the COVID-19 Outbreak: Content Analysis

When the first COVID-19 cases were noticed in China, many racist comments against Chinese individuals spread. As there is a huge need to better comprehend why all of these targeted comments and opinions developed specifically at the start of the outbreak, we sought to carefully examine racism and advocacy efforts on Twitter in the first quarter of 2020 (January 15 to March 3, 2020).The first research question aimed to understand the main type of racism displayed on Twitter during the first quarter of 2020. The second research question focused on evaluating Twitter users' positive and negative responses regarding racism toward Chinese individuals.Content analysis of tweets was utilized to address the two research questions. Using the NCapture browser link and NVivo software, tweets in English and Spanish were pulled from the Twitter data stream from January 15 to March 3, 2020. A total of 19,150 tweets were captured using the advanced Twitter search engine with the keywords and hashtags #nosoyunvirus, #imNotAVirus, #ChineseDon'tComeToJapan, #racism, "No soy un virus," and "Racismo Coronavirus." After cleaning the data, a total of 402 tweets were codified and analyzed.The data confirmed clear sentiments of racism against Chinese individuals during the first quarter of 2020. The tweets displayed individual, cultural, and institutional racism. Individual racism was the most commonly reported form of racism, specifically displaying physical and verbal aggression. As a form of resistance, Twitter users created spaces for advocacy and activism. The hashtag "I am not a virus" helped to break stereotypes, prejudice, and discrimination on Twitter.Advocacy efforts were enormous both inside and outside the Chinese community; an allyship sentiment was fostered by some white users, and an identification with the oppression experienced by the Chinese population was expressed in the Black and Muslim worldwide communities. Activism through social media manifested through art, food sharing, and community support.
Adaptive projection moiré method for arbitrarily arranged light path system

In the projection moiré method, the projected fringe with a constant period usually generates a moiré pattern with nonuniform fringe spacing, which increases the low modulation regions and local measurement error. In this work, an adaptive moiré pattern generation method for the projection moiré system is developed. By formulating the relationship between the surface contour and moiré fringe spacing, the period of the projected fringe is modulated to acquire a uniform moiré pattern and highly improve the full-field measurement precision. The corresponding calculation algorithm is developed to obtain the 3D morphology. The proposed method is applicable to an arbitrarily arranged projection moiré system on any continuous surface without the phase jump error. A series of experiments are carried out, and the results are discussed in detail.
An Enhanced Cloud-Based Healthcare System for Patient Data Privacy and Security Using Hybrid Encryption

The IT sector has taken a keen interest in recent improvements in remote healthcare systems can provide healthcare systems that are both ubiquitous and easily deployable. Cloud computing is the utilization of computer resources as a service delivered through the internet on demand. These systems offer a stage for sharing medical information, data, applications, and underpinning in a fully automated and omnipresent way. Users&#x0027; confidence in such remote healthcare systems would be enhanced by communication security and patient data privacy. This paper describes a secure mobile healthcare framework based on cloud environment that makes use of wireless area networks. The research effort described here is divided into two categories: first, it tries to protect inter-sensor communication in WANs for body using an approach to key generation based on numerous biometrics and second, the electronic medical reports (EMRs) are securely maintained in the hospital or clinical community cloud, ensuring patient privacy. Because of its very efficient key generation mechanism, the suggested multi-biometric based and Hybrid Security methods provides significant security measures, according to the assessment and analysis.
Objective Video Quality Assessment Method for Face Recognition Tasks

Nowadays, there are many metrics for overall Quality of Experience (QoE), both those with Full Reference (FR), such as Peak Signal-to-Noise Ratio (PSNR) or Structural Similarity (SSIM), and those with No Reference (NR), such as Video Quality Indicators (VQI), which are successfully used in video processing systems to evaluate videos whose quality is degraded by different processing scenarios. However, they are not suitable for video sequences used for recognition tasks (Target Recognition Videos, TRV). Therefore, correctly estimating the performance of the video processing pipeline in both manual and Computer Vision (CV) recognition tasks is still a major research challenge. There is a need for objective methods to evaluate video quality for recognition tasks. In response to this need, we show in this paper that it is possible to develop the new concept of an objective model for evaluating video quality for face recognition tasks. The model is trained, tested and validated on a representative set of image sequences. The set of degradation scenarios is based on the model of a digital camera and how the luminous flux reflected from the scene eventually becomes a digital image. The resulting degraded images are evaluated using a CV library for face recognition as well as VQI. The measured accuracy of a model, expressed as the value of the F-measure parameter, is 0.87.
Indirect Robot Manipulation using Eye Gazing and Head Movement For Future of Work in Mixed Reality

Most robot manipulations are guided by the worker&#x0027;s both hands using a teach-pendant or other gadgets. However, manipulating the robot is more difficult when both hands are occupied, which requires a new manipulation method. This study proposes a novel indirect robot interaction and manipulation method using eye gaze and head movement in mixed reality. The proposed approach has been implemented to provide hands-free robot interaction for pick-and-place tasks. The results show a promising direction for the future of work in human-robot collaboration.
“Put your feet in open pit” - a WebXR Unity application for learning about the technological processes in the open pit mine

In the mining industry, accessing the locations and observing processes for educational purposes can be problematic due to safety reasons or high cost of experiencing it in person. Immersive technologies, especially applied in an accessible and responsive design such as WebXR, have great potential to make such educational and training applications easy to access and effective for a wide audience. In this work-in-progress paper, we present the design process, the detailed production pipeline and the challenges we have met during the implementation of the WebXR interactive environment based on the 360 video of the mining technologies pipeline captured at the open pit mine.