Reviewerly/exHarmony huggingface dataset:
UAF-GUARD: Defending the use-after-free exploits via fine-grained memory permission management

The defense of Use-After-Free (UAF) exploits generally could be guaranteed via static or dynamic analysis, however, both of which are restricted to intrinsic deficiency. The static analysis has limitations in loop handling, optimization of memory representation and constructing a satisfactory test input to cover all execution paths. While the lack of maintenance of pointer information in dynamic analysis may lead to defects that cannot accurately identify the relationship between pointers and memory. In order to successfully exploit a UAF vulnerability, attackers need to reference freed memory. However, main existing schemes barely defend all types of UAF exploits because of the incomplete check of pointers. To solve this problem, we propose UAF-GUARD to defend against the UAF exploits via fine-grained memory permission management. Specially, we design two key data structures to enable the fine-grained memory permission management to support efficient relationship search for pointers and memory, which is the key design of our defending scheme against UAF exploits. In addition, UAF-GUARD can precisely locate the position of UAF vulnerabilities, so that malicious programs can be terminated in the place where the abnormality is discovered. We implement UAF-GUARD on a 64-bit Linux system, and further use UAF-GUARD to transform a program into a suitable version that can defend against UAF vulnerabilities exploits. Compared with main existing schemes UAF-GUARD is able to effectively and efficiently defend against all the three types of UAF exploits with acceptable space overhead (26.4% for small programs and 0.3% for large programs) and time complexity (21.9%).
In-plane coplanarity measurement by Talbot interferometer

The monitoring and measurement of in-plane coplanarity is important in the production and assembly of smaller and more fine-pitched optical and mechanical systems. Owing to its features such as greater accuracy, resolution, instantaneous response and non-intrusive nature, interferometry is advantageous and extensively utilized in a broad spectrum of applications. Interferometers possess high resolution, long measurement range and fast response times. Infra-Red diffraction interferometer is broadly used for coplanarity measurement [1-2]. Recently grating interferometer has been also used for the coplanarity measurement [3]. Circular grating Talbot interferometry has been widely used in many metrological as well as microscopic applications [4-10]. The benefits of using circular gratings are that they are easy to align, and no prior knowledge is needed for the measuring direction [10]. In this paper, a fast and cost-effective 3D (x, y, z- axis) measurement system for in-plane coplanarity inspection of an optical table is proposed. Different from the line-scanning measurement of traditional laser-based systems, the proposed, circular grating Talbot interferometer system with fringe shift measurement can provide the 3D (x, y, z -axis) measurement of coplanarity. With the conventional practice of using the zero Talbot area, it is not possible to obtain fringes of the non-coplanar surfaces unless the Talbot distance is much greater than the distance between the surfaces. The Talbot area is employed to measure the deformation of the two non-coplanar surfaces simultaneously. But this approach has a critical limitation, and the optical setup will be extremely case-sensitive. In the proposed system, Talbot interferometric fringes are extracted from the background image using image processing operations of background normalization to reduce the unevenness in the background intensity and the morphological grayscale dilation operation to eliminate the grating lines [9-10]. In the extracted Talbot interferometric fringes of these interferograms, the intensity distribution profile is plotted along the center of the grating. The center of the shifted fringe in intensity distribution profile is obtained after averaging the position of fringe. The shift between the center of two gratings determines the coplanarity. The resolution of this method is limited by the grating pitch and the capabilities of the imaging equipment.
Cross-domain heterogeneous signcryption with keyword search for wireless body area network

The Wireless body area network (WBAN) is a network composed of sensors communicating through wireless technology, these sensors can capture and encrypt the physiological data of the human body and transmit it to a remote cloud server for use by authorized users. However, how to retrieve and obtain the encrypted data has become a problem that must be solved. Nowadays, searchable encryption with keywords (SEK) is a widely used technology to solve this problem. Nevertheless, there are some problems that need to be noted. First, SEK is vulnerable to keyword guessing attack (KGA) and inside keyword guessing attack (IKGA). Second, since the sender and receiver are likely to work under different cryptosystems, the designed scheme should satisfy heterogeneity. Third, the communication parties in heterogeneous domain usually use different cryptographic system parameters, achieving cross-domain authentication between these communication parties can greatly improve the practicability of the scheme. To address these issues, we put forward a new searchable signcryption scheme for WBAN. Under the complexity of computational assumptions, the proposed scheme is proved to simultaneously achieve ciphertext indistinguishability, trapdoor indistinguishability, ciphertext unforgeability and the resistance of KGA and IKGA in the random oracle model (ROM). Further, our scheme allows the WBAN sensors in the certificateless public key cryptography (CLC) environment and the receivers in the public key infrastructure (PKI) environment to realize cross-domain authentication and heterogeneous communication. Compared to the five existing schemes, the total computation cost of our scheme is reduced by at least 59.99%.
Convolutional neural network based object detection system for video surveillance application

Summary Video surveillance is emerging as a promising solution for the humans to lead a peaceful and independent life in their homes. The recognition and localization of moving objects plays a central role in the video surveillance. The manual surveillance is time consuming and tedious. Therefore, novel object detection via optimized deep learning model is developed in this work that supports the video surveillance application. In the initial phase, proposed angle and distance based Local Binary Pattern (LBP) features are extracted. Subsequently, these extracted features are subjected to object detection phase, where optimized Convolutional Neural Network (CNN) will expose the information about the detected object. Further, the learning quality of CNN is decided by the weight parameter, which is responsible to distinguish the objects with high accuracy. Therefore, a hybrid optimization concept referred as Sealion Leader Update with Particles (SLUP) is introduced in this research work to fine‐tune the weight of CNN. Finally, a comparative analysis is made between the proposed and the extant approaches in terms of “positive, negative, and other measures.”
Uncertainty-Aware Scene Graph Generation

As a challenging task in computer vision, Scene graph generation (SGG) aims to model the underlying semantic relationships among objects in a given image for scene understanding. Due to the increasing scale and subjectivity, the annotations of existing SGG benchmarks inevitably suffer from some uncertainty issues, resulting in the models hardly learning the relationships comprehensively. In this work, we address the uncertainty from the perspectives of both classifier parameters and relationship labels. On one hand, we handle the classifier uncertainty via learning a Bayesian classifier reparameterization, of which the weights are sampled from a latent space spanned with a prior distribution. On the other hand, we assume that each relationship label is sampled from a latent label space and mitigate the label uncertainty via estimating the latent relationship distribution. As a result, the distribution of the classifier parameters are comprehensively learned under the supervision of the estimated relationship labels, thus improving the model’s generalization ability. Experimental results on the popular benchmark demonstrate that the proposed strategies significantly improve different baseline models on different SGG tasks.
Comparison of Deep Learning Network for Breast Tumor Segmentation from X-Ray

Over 8% of women will be diagnosed with breast tumors (BT) in their lifetime. Tumors are formed by the uncontrollable development of tissues in a specific area of the body. They can be benign or malignant. The best survival rate can be expected with earlier screening and diagnosis of the tumor. To distinguish between benign and malignant tumors in x-ray images of the breast, segmentation of the tumor is a crucial first step. Screening mammography is an efficient method of detecting BT. As a result, the research presented two distinct deep learning models, termed SegNet and UNet architectures, to segment BT from mammograms. Datasets accessible to the public were utilized in the proposed system, specifically INbreast. Histogram equalization is used on datasets during preprocessing to improve the compressed areas and normalize the pixel dispersion. To avoid overfitting and boost the quantity of training data, augmentation techniques are employed. The metrics like the dice coefficient (DC) and the Intersection of Union (IoU) score are considered to evaluate the model. The metrics of the SegNets model are greater than the U-Net, as demonstrated by the experimental results. For the INbreast dataset, the SegNets achieve a maximum DC of 92.75% and an IoU score of 86.49%.
DecoMine: A Compilation-Based Graph Pattern Mining System with Pattern Decomposition

Graph pattern mining (GPM) is an important application that identifies structures from graphs. Despite the recent progress, the performance gap between the state-of-the-art GPM systems and an efficient algorithm—pattern decomposition—is still at least an order of magnitude. This paper clears the fundamental obstacles of adopting pattern decomposition to a GPM system.
Deep Learning with Blockchain Technology for Secure Data Management in Healthcare Sector using Hybrid Elliptic Curve-Rivest–Shamir–Adleman Cryptography

Healthcare management, it involves various processes such as managing finance, logistics, inventory, staff, and patients. Nowadays, a healthcare system also faces many challenges regarding trust, privacy, security, traceability, immutability, and so forth. Here, the traditional data collection and processing of Electronic Health Records is a centralized technique that faces several risks that may cause system failure and affect the system’s reliability. This article aims to develop secured data management in the healthcare sector using blockchain technology and deep learning. Here, the data is collected in the patient node and encrypting the data by Hybrid Elliptic Curve-Rivest–Shamir–Adleman Cryptography (H-EC-RSAC). The institution nodes handle the data for analysis, in which the concept of an improved deep neural network (DNN) is utilized. In addition, the blockchain sector stores the index and address of health data. The modification is done in both H-EC-RSAC and improved DNN by a hybrid meta-heuristic algorithm called adaptive mixture ratio-based Spider Monkey–Cat Swarm Optimization (AM-SMCSO) for enhancing encryption and authentication performance. Throughout the analysis, the accuracy of the AM-SMCSO-H-EC-RSAC + DNN attains 93.8%. The precision of the designed method achieves 93.2%. The developed approach proves its ability to offer maximum security to healthcare data.
Whale social optimization driven deep recurrent neural network for loan eligibility prediction

Summary People can use bank loans to make investment decisions thanks to technological advancements in the banking sector. The bank, however, only has a limited amount of resources, therefore it must grant to borrowers who can follow to the repayment schedule. Determining the candidate for a loan and finding a secure alternative for a bank are therefore crucial tasks. A deep recurrent neural network based on the whale social optimization algorithm (WSOA) is developed to predict loan eligibility. The WSOA is modified in this case to train Deep RNN for loan eligibility prediction in order to yield the most precise result. The proposed WSOA is developed by combining the social ski‐driver algorithm and the whale optimization algorithm. By selecting the trusted person to save bank assets, the risk factor is also decreased. The loan eligibility prediction is performed by processing massive data with Box‐Cox transformation. Moreover, wrapper feature selection is adapted for choosing imperative features. The proposed WSOA‐based Deep RNN provided increased efficiency with the highest accuracy of 94%, the highest sensitivity of 95.4%, and the highest specificity of 91.3%.
Deep Tasks Summarization for Comprehending Mixed Tasks in a Commit

In Version Control System (VCS), a developer frequently uploads multiple tasks such as adding features, code refactoring, and fixing bugs, into a single commit and crumbles each task’s summary when writing a commit message. It causes code readers to feel challenged in understanding the developer’s past tasks within the commit history. To resolve this issue, we propose an automatic approach to generating a task summary to help comprehend multiple mixed tasks in a commit and developed tool support named Task summary Generator (TsGen). In our approach, we use the commit with a single task as input and identify the task to sort its elements sequentially. Then we generate feature vectors from each sorted element to train the Neural Machine Translation (NMT) model. Based on the trained NMT model, we generate the feature vector from each task of a commit with multiple tasks and put each of them into the model to provide the task summary. In evaluation, we compared the performance of TsGen with two existing methods for nine open-source projects. As a result, TsGen outperformed CoDiSum and Jiang’s NMT by 52.08% and 28.07% in BiLingual Evaluation Understudy (BLEU) scores. In addition, the human evaluation was carried out to demonstrate that TsGen helps understand mixed tasks in a commit and gained a 0.27 higher preference than the actual commit message.
Managing in Writing: Recommendations from Textual Patterns in Managers’ Email Communication

This study draws from personality psychology and linguistics of written communication to explore the characteristics of self-selected well-written email communications (N=273) solicited from Polish managers who organized and supervised the (remote) work of their units during the COVID-19 period. The focus is on the writing of managers with above-average levels of conscientiousness and agreeableness, as these personality factors are predictors of efficacy in the completion of two work-related goals, Achievement and Communion, according to the Theory of Purposeful Work Behavior. The linguistic patterns responsible for effective email communication are identified through both automated and qualitative textual analyses of the email sample. The study has implications for management training via the assumption that linguistic patterns that a reflexive manager uses in writing are subjected to monitoring and can be modeled and adapted to. Specific recommendations for managerial writing styles concern informational, instructional, explanatory, feedback, and query messages.
Neural text summarization for Hungarian

Abstract One of the most important NLP tasks for the industry today is to produce an extract from longer text documents. This task is one of the hottest topics for the researchers and they have created some solutions for English. There are two types of the text summarization called extractive and abstractive. The goal of the first task is to find the relevant sentences from the text, while the second one should generate the extraction based on the original text. In this research I have built the first solutions for Hungarian text summarization systems both for extractive and abstractive subtasks. Different kinds of neural transformer-based methods were used and evaluated. I present in this publication the first Hungarian abstractive summarization tool based on mBART and mT5 models, which gained state-of-the-art results.
Tracking Evolving Geometric Data by Local Graph Laplacian Operators

Geometric data are samples on geometric surfaces of physical or abstract objects. When the underlying objects evolve over time, their geometric data change as well. Tracking their evolution is critical to many emerging areas, such as autonomous driving, edge computing networks, and drug interactions. Lacking well-defined grids in geometric data prohibits easy temporal matching for tracking evolution of geometric data. This paper considers the framework of graph signal processing to exploit spectral analysis on geometric surfaces to achieve the tracking task.This paper begins with modeling an underlying geometric surface by a graph, followed by using the spectra of local graph Laplacians to detect graph patches corresponding to regions of high curvatures on the geometric surfaces. The Laplacian spectra of feature graph patches and the structural information of graphs are analyzed together to perform temporal matching across times. Sparse temporal transforms are reconstructed based on the matched feature graph patches, and extrapolating the transforms to the full-scale graph derives the estimation of geometric evolution.
IntP: Quantifying cross-application interference via system-level instrumentation

Large-scale container datacenters host tens of thousands of diverse container-wrapped applications each day improving resource usage and maintenance costs. However, resource contention-related interference between co-located applications can severely degrade performance, affecting the quality of service at the user level and compromising experience. Understanding the sources of noise that generates this interference and better managing how to consolidate applications to physical hosts can significantly improve resource usage and overall performance reducing costs for providers and users. This paper presents IntP-an open-source system-level monitoring tool, which analyses selected architectural counters and operating systems data structures to estimate the stress an application puts on each hardware's subsystem and consequently infer the potential interference it could generate in other applications hosted in the same physical machine. Different from state-of-the-art tools that apply a more high-level approach using micro benchmarks and application metrics, IntPs low level instrumentation enables a more accurate prediction of the performance degradation that results from contention on shared resources, with less monitoring overhead. This information can be used to optimize scheduling strategies, which will make datacenter more resource-efficient and cost-effective. To show examples on how to use this tool and validate its results we present three cases studies that applied IntP in their interference-aware methodologies to improve resource utilization in distributed architectures that were able to achieve an increase up to 35% in resource efficiency and up to 25% in user level performance.
A Novel Method for Imbalanced Data Classification based on Label Reassignment

Imbalanced data classification is one of the most challenging supervised learning problems in machine learning. Several data-sampling methods have been proposed in the last decade employing over-sampling and under-sampling techniques. Over-sampling technique engenders artificial minority-class instances that creates overfitting problems. However, under-sampling techniques suffer from informative information lost by removing major-part of majority-class instances. In this paper, we have proposed a novel approach for imbalanced data classification using label reassignment of misclassified majority-class instances. The idea is to consider the misclassified majority-class instances as minority-class instances to make the dataset balanced. We conceptualise the reassignment of majority-class instances in overlapping region to minority-class instances in Label Reassignment. We have conducted experiment on 11 imbalanced datasets and compared the performance of proposed method with well-known Synthetic Minority Oversampling Technique (SMOTE) method. The proposed method outperforms SMOTE in most of the experimented datasets with a significant improvement in terms of Recall and F1 Score.
Balancing Privacy Preservation and Utility in Anti-Face Detection Systems

The advancement in facial detection has led to increased privacy concerns as it can potentially lead to ma-licious activities such as identity theft and illegal surveillance. Hence, several anti-face detection systems have been developed to address this concern. However, existing systems are trained end-to-end and do not give information as to how image features e.g. resolution, texture, and color can contribute to detection/misdetection. In this work, we find the significant image features in predicting the minimum Gaussian noise, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$sigma_{min}$</tex> , necessary to avoid face detection. Three regression models were trained to determine the minimum level of perturbation. The multilayer perceptron (MLP) regression model exhibited the best performance, with 38.02 % of the detected faces being effectively concealed. The results from this study show that the image features such as local binary pattern, width, value, and height significantly impacted the predicted values of sigma, and can be used to accurately determine the minimum level of Gaussian noise to prevent face detection systems from detecting faces in images.
Gradual Drift Detection by Computing Outlier in Data Stream using Z-score

In data streaming scenario, data are created successively. And it is very common that the concepts of data may change at any positions in data stream. The properties of the target variables may shift between the static training data to real-world dynamic data. For changing the concepts, the learning performances are affected seriously. In addition, due to storage limitations, it is tough for an online learner to predict original labels of streaming data because of the concept drift as well as the location of drifting for data streaming. Therefore, a method that can detect the exact positions of drifts and quickly able to adapt changes for improving the learning performance is needed. As a solution, this paper presents a Gradual Drift Detection method for Data Stream using Z-score (DDZ) method. This DDZ approach uses Z-score to detect the stable positions of drifts in data stream. The proposed DDZ method adapts the changes very fast. As a result, DDZ is able to predict correct labels of instances and provide better outcomes. Four synthetic data stream generators with abrupt and gradual drifting and two real-world datasets are considered for evaluating the performance of the proposed method. The performance of the proposed DDZ method attains higher accuracy, recall and F-Score as compare to some existing drift detectors.
Child Head Gesture Classification through Transformers

This paper proposes a transformer network for head pose classification (HPC) which outperforms the existing SoA for HPC. This robust model is then extended to overcome the limited child data challenge by applying transfer learning resulting in an accuracy of 95.34% for child HPC in the wild.