Reviewerly/exHarmony huggingface dataset:
Fraud Detection Using Optimized Machine Learning Tools Under Imbalance Classes

Fraud detection is considered to be a challenging task due to the changing nature of fraud patterns over time and the limited availability of fraud examples to learn such sophisticated patterns. Thus, fraud detection with the aid of smart versions of machine learning (ML) tools is essential to assure safety. Fraud detection is a primary ML classification task; however, the optimum performance of the corresponding ML tool relies on the usage of the best hyperparameter values. Moreover, classification under imbalanced classes is quite challenging as it causes poor performance in minority classes, which most ML classification techniques ignore. Thus, we investigate four ML techniques, namely, logistic regression, decision trees, random forest, and extreme gradient boost, that are suitable for handling imbalance classes to maximize recall and simultaneously reduce false negatives. First, these classifiers are trained on two original benchmark unbalanced fraud detection datasets, namely, phishing website URLs and fraudulent credit card transactions. Then, three synthetically balanced datasets are produced for each original data set by implementing the sampling frameworks, namely, random under sampler, synthetic minority oversampling technique (SMOTE), and SMOTE edited nearest neighbor (SMOTEENN). The optimum hyperparameters for all the 16 experiments are revealed using the method RandomzedSearchCV. The validity of the 16 approaches in the context of fraud detection is compared using two benchmark performance metrics, namely, area under the curve of receiver operating characteristics (AUC ROC) and area under the curve of precision and recall (AUC PR). For both phishing website URLs and credit card fraud transaction datasets, the results indicate that extreme gradient boost trained on the original data shows trustworthy performance in the imbalanced dataset and manages to outperform the other three methods in terms of both AUC ROC and AUC PR.
IEEE BigData Cup 2022 Report Privacy-preserving Matching of Encrypted Images

We summarize the results of IEEE BigData 2022 Cup: Privacy-preserving Matching of Encrypted Images - a data mining challenge organized at the KnowledgePit platform in association with the IEEE BigData 2022 conference. We describe the challenge in the hearth of the competition task, as well as the data acquisition and preparation steps. We also provide a brief overview of the top-performing solutions submitted by participants. Finally, we present results of the post-competition data analysis, in which we consider the similarity of solutions submitted by various teams in terms of their errors on the test data. We conclude this analysis by discussion of the significance and impact of the competition results on the underlying problem of constructing effective and efficient anonymization algorithms for monitoring in the DOOH advertising industry.
Lossless Data Compression with Bit-back Coding on Massive Smart Meter Data

In this paper, lossless time-series data compression scheme with bit-back asymmetric numeral systems (BB-ANS) is proposed for massive smart meter environment. As smart meters increase in deployment and connectivity, an efficient compression method is needed to transmit and save big data. Bit-back coding was introduced as a novel compression method using bayesian inference modeling. Recently, bit-back coding is combined with asymmetric numeral systems (ANS) which are stack-like structures, showing significant compression gains on several cases. ANS is an approach for entropy coding combining Huffman coding and arithmetic coding which has a first-in-last-out (FILO) form suited for bit-back coding. Compared to other compression methods, bit-back coding effectively shares priorly learned probabilistic models for encoder and decoder. In this study, variational auto-encoder (VAE) is customized to jointly learn approximate posterior and likelihood between message and latent variables. Thus, the smart meter data can be efficiently updated within finite length time-intervals. The proposed scheme is evaluated with the actual smart meter dataset and the results demonstrate the superiority of BB-ANS in compression ratios over other state-of-the-art lossless compression schemes. To the best of our knowledge, this study is the first attempt to apply bit-back coding to time-series smart metering data, enabling efficient data compression with deep generative models.
Region Segmentation of Whole-Slide Images for Analyzing Histological Differentiation of Prostate Adenocarcinoma Using Ensemble EfficientNetB2 U-Net with Transfer Learning Mechanism

Recent advances in computer-aided detection via deep learning (DL) now allow for prostate cancer to be detected automatically and recognized with extremely high accuracy, much like other medical diagnoses and prognoses. However, researchers are still limited by the Gleason scoring system. The histopathological analysis involved in assigning the appropriate score is a rigorous, time-consuming manual process that is constrained by the quality of the material and the pathologist's level of expertise. In this research, we implemented a DL model using transfer learning on a set of histopathological images to segment cancerous and noncancerous areas in whole-slide images (WSIs). In this approach, the proposed Ensemble U-net model was applied for the segmentation of stroma, cancerous, and benign areas. The WSI dataset of prostate cancer was collected from the Kaggle repository, which is publicly available online. A total of 1000 WSIs were used for region segmentation. From this, 8100 patch images were used for training, and 900 for testing. The proposed model demonstrated an average dice coefficient (DC), intersection over union (IoU), and Hausdorff distance of 0.891, 0.811, and 15.9, respectively, on the test set, with corresponding masks of patch images. The manipulation of the proposed segmentation model improves the ability of the pathologist to predict disease outcomes, thus enhancing treatment efficacy by isolating the cancerous regions in WSIs.
Authenticity and interest in virtual reality: Findings from an experiment including educational virtual environments created with 3D modeling and photogrammetry

Virtual Reality (VR) and photogrammetry are emerging technologies that facilitate and shape the ongoing digital transformation of education. VR offers new opportunities for creating immersive and interactive educational experiences. Photogrammetry enables new ways to create lifelike educational virtual environments and is becoming an alternative to manual 3D modeling with graphics software. The manner in which VR affects the authenticity of educational experiences has been addressed in previous educational and psychological research. Empirical papers have so far focused on the authenticity of educational VR environments created by 3D modeling. However, little is known about the authenticity of educational VR environments developed with photogrammetry. Given that VR provides rich multi-sensory experiences and interests can be stimulated by engaging contexts, educational VR environments also possess great potential to support interest development. What is still unknown regarding this topic are the beneficial characteristics of VR environments and the individual variables required to trigger and explain interest development. Consequently, we conducted an experiment following up on the mentioned authenticity and interest research questions in the context of higher education. A two-group between-subjects design was used and N = 64 educational science and psychology university students gathered information about a railroad bridge wearing a head-mounted display (HMD). The control group encountered an educational virtual environment created with 3D modeling. The intervention group was presented with the same educational virtual environment but the main object of the railroad bridge was generated by photogrammetry. Situational interest was measured in the pretest and the posttest; authenticity-related variables (i.e., presence and representation fidelity) were assessed in the posttest. Concerning authenticity, there were no significant group differences. Photogrammetry might thus not affect authenticity in educational contexts in which participants focus on gathering information. Regarding interest development, there were two main findings. First, interest in VR for learning increased from pretest to posttest, supporting that interest can be induced in VR. Second, a large share of posttest interest was explained by presence and pretest interest, highlighting the importance of these variables.
Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise

Images taken with different sensors and transmitted through different channels can be noisy. In such conditions, the image most often suffers from random-valued impulse noise. Denoising an image is an important part of image preprocessing before recognition by a neural network. The accuracy of image recognition by a neural network directly depends on the intensity of image noise. This paper presents a three-stage image cleaning and recognition system, which includes a developed detector of pulsed noisy pixels, a filter for cleaning found noisy pixels based on an adaptive median, and a neural network program for recognizing cleaned images. It was noted that at low noise intensities, cleaning is practically not required, but noise with an intensity of more than 10% can seriously damage the image and reduce recognition accuracy. As a training base for noise, cleaning, and recognition, the CIFAR10 digital image database was used, consisting of 60,000 images belonging to 10 classes. The results show that the proposed neural network recognition system for images affected by to random-valued impulse noise effectively finds and corrects damaged pixels. This helped to increase the accuracy of image recognition compared to existing methods for cleaning random-valued impulse noise.
Mesh U-Nets for 3D Cardiac Deformation Modeling

During a cardiac cycle, the heart anatomy undergoes a series of complex 3D deformations, which can be analyzed to diagnose various cardiovascular pathologies including myocardial infarction. While volume-based metrics such as ejection fraction are commonly used in clinical practice to assess these deformations globally, they only provide limited information about localized changes in the 3D cardiac structures. The objective of this work is to develop a novel geometric deep learning approach to capture the mechanical deformation of complete 3D ventricular shapes, offering potential to discover new image-based biomarkers for cardiac disease diagnosis. To this end, we propose the mesh U-Net, which combines mesh-based convolution and pooling operations with U-Net-inspired skip connections in a hierarchical step-wise encoder-decoder architecture, in order to enable accurate and efficient learning directly on 3D anatomical meshes. The proposed network is trained to model both cardiac contraction and relaxation, that is, to predict the 3D cardiac anatomy at the end-systolic phase of the cardiac cycle based on the corresponding anatomy at end-diastole and vice versa. We evaluate our method on a multi-center cardiac magnetic resonance imaging (MRI) dataset of 1021 patients with acute myocardial infarction. We find mean surface distances between the predicted and gold standard anatomical meshes close to the pixel resolution of the underlying images and high similarity in multiple commonly used clinical metrics for both prediction directions. In addition, we show that the mesh U-Net compares favorably to a 3D U-Net benchmark by using 66% fewer network parameters and drastically smaller data sizes, while at the same time improving predictive performance by 14%. We also observe that the mesh U-Net is able to capture subpopulation-specific differences in mechanical deformation patterns between patients with different myocardial infarction types and clinical outcomes.
Factors that Boost and Hinder the Transition from Traditional to Self-managed Teams

Background: Considering the required speed of organizational transitions to keep software development competitive, transitions based on a trial-and-error approach are less and less acceptable. Even so, due to the lack of a systematic process and knowledge capable of guiding the transition of traditional teams to self-managed teams (SMT) companies have faced difficulties in completing this mission, becoming dependent on individual efforts with unpredictable results. Objective: This work aims to analyze available scientific publications in order to characterize the critical factors related to the transition from traditional teams to SMT in a software development context. Method: We used the Scopus scientific database to conduct a Systematic Literature Review on its available publications in the last 5 years. Results: A total of 14 factors capable of boosting or hindering the team transition toward SMT were labeled into 6 interdependent perspectives through analysis of the evidence provided by 28 selected publications. Conclusion: Our findings show that this transition cannot be seen as an isolated process with immediate results and that there is still much to investigate about the transition of traditional teams to SMT in the software development industry.
Volumetric video streaming

The rise of capturing systems for objects and scenes in 3D with increased fidelity and immersion has led to the popularity of volumetric video contents that can be seen from any position and angle in 6 degrees of freedom navigation. Such contents need large volumes of data to accurately represent the real world. Thus novel optimization solutions and delivery systems are needed to enable volumetric video streaming over bandwidth-limited networks. In this chapter, we discuss theoretical approaches to volumetric video streaming optimization, through compression solutions, as well as network and user adaptation, for high-end and low-powered devices. Moreover, we present an overview of existing end-to-end systems, and we point to the future of volumetric video streaming.
Surface normal and Gaussian weight constraints for indoor depth structure completion

Raw depth maps captured by depth sensors generally contain missing contents due to glossy, transparent, and sparsity problems. Recent methods well completed flat regions of raw depth maps; however, ignored the accuracy of depth structures. In this paper, an effective depth structure completion method is developed to infer missing depth structures. First, a raw depth map is divided into flat regions and depth structures based on a structure prediction network. Second, two local features including surface normals and Gaussian weights are extracted from a reference RGB image to impose constraints on flat regions and depth structures, separately. Third, a kernel least-square module is adopted to handle the texture-copy artifacts problem. Finally, an iterative optimization model is developed by embedding the two constraints into a Markov random field. The cost function of the model comprises three terms, which limit data fidelity between completed depth map and raw depth map, smoothness of flat regions, and accuracy of depth structures, respectively. The proposed method is evaluated on four indoor datasets including Matterport3D, RealSense, ScanNet, and NYUv2, and compared with eight recent baselines. Quantitative results demonstrate that RMSE and MAE of completed depth maps are considerably reduced by 22.0% and 45.3%, respectively. Visual results show the superiority in completing depth structures and suppressing texture-copy artifacts. Generalization test verify the effectiveness on unseen datasets.
An automated software failure prediction technique using hybrid machine learning algorithms

Many sophisticated applications have been emerged in various industries due to the rapid growth of software technologies. Especially, the business organizations utilize the services of software-based applications to provide a state-of-the-art service. However, fault prediction in a software is a biggest challenge that needs to be addressed by the industries to improve the growth of their business. Therefore, there is a need for new techniques to perform fault prediction at an early stage of software life cycle so that software defects can be avoided in later stage. To overcome the issues in manual prediction, many prediction techniques are available that can predict the defects automatically. All of the available techniques are based on the pattern learning that finds the fault in the software based on the previously learned similar patterns. Even though many fault findings techniques are available, still there are some challenges to achieve the desired effect in its performance. To overcome the issues in currently available prediction techniques, this paper introduces an efficient software failure prediction technique using hybrid machine learning algorithms. First part of the work performs feature selection with an improved fitness function by utilizing genetic algorithm (GA) to optimize the features in the data set. After selecting the better features, Decision Tree algorithm is used as a classification technique for processing that features. The work compares the GA-DT based hybrid model with the currently available machine learning model such as RCSOLDA-RIR and WPA-PSO for the prediction of software failure. The outcome of the experimental analysis shows that the proposed model achieves better accuracy than the currently available model.
GNNear

Recently, Graph Neural Networks (GNNs) have become state-of-the-art algorithms for analyzing non-euclidean graph data. However, to realize efficient GNN training is challenging, especially on large graphs. The reasons are many-folded: 1) GNN training incurs a substantial memory footprint. Full-batch training on large graphs even requires hundreds to thousands of gigabytes of memory. 2) GNN training involves both memory-intensive and computation-intensive operations, challenging current CPU/GPU platforms. 3) The irregularity of graphs can result in severe resource under-utilization and load-imbalance problems.
Addressing insider attacks via forensic-ready risk management

Cyberattacks perpetrated by insiders are difficult to prevent using traditional security approaches. Often, such attackers misuse legitimate access to the system to conduct an attack, or an external attacker manipulates or masquerades as an insider to gain access, bypassing the security controls. A possible solution to this problem are forensic-ready software systems that support the eventual forensic investigation. For example, assuring that appropriate evidence of an attack would be generated and assessable if needed. While not primarily aimed at prevention, the controls of forensic-ready systems can be used to ensure reliable post-incident investigation in the case of an insider attack. Currently, however, there is a gap in adequate methods for identifying requirements and assessment of such systems. Therefore, we propose FR-ISSRM, a risk management approach to derive the forensic readiness requirements addressing insider attacks. The requirements, once implemented, assist in the reliable uncovering culprit, root cause, damage of the attack, and overall improvement of security posture. The approach is then demonstrated in three cases covering typical insider attacks.
A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters

Model extraction is a major threat for embedded deep neural network models that leverages an extended attack surface. Indeed, by physically accessing a device, an adversary may exploit side-channel leakages to extract critical information of a model (i.e., its architecture or internal parameters). Different adversarial objectives are possible including a fidelity-based scenario where the architecture and parameters are precisely extracted (model cloning). We focus this work on software implementation of deep neural networks embedded in a high-end 32-bit microcontroller (Cortex-M7) and expose several challenges related to fidelity-based parameters extraction through side-channel analysis, from the basic multiplication operation to the feed-forward connection through the layers. To precisely extract the value of parameters represented in the single-precision floating point IEEE-754 standard, we propose an iterative process that is evaluated with both simulations and traces from a Cortex-M7 target. To our knowledge, this work is the first to target such an high-end 32-bit platform. Importantly, we raise and discuss the remaining challenges for the complete extraction of a deep neural network model, more particularly the critical case of biases.
Context-aware style learning and content recovery networks for neural style transfer

Neural text transfer aims to change the style of a text sequence while keeping its original content. Due to the lack of parallel data, unsupervised learning-based approaches have gained considerable development. However, there are still several problems in these approaches: (1) The generated transferred sequences sometimes have inconsistencies between the transferred style and content, and (2) It is difficult to ensure sufficient preservation of the core semantics of original sequences in the transferred sequences. To address these defects, we propose Context-aware Style Learning and Content Recovery networks (CSLCR) for neural text transfer. Specifically, to improve the consistency between the transferred style and content, the designed context-aware style learning layer (CSL) retrieves target style samples with similar semantics to the original sequence, and promotes deep interactive fusion with the original sequence, so as to generate transferred sequence with context-aware style. To tackle the second problem, we explore content constraint recovery layer (CCR) from an indirect perspective, which decodes and recovers the core content semantics of the original sequence and the transferred sequence by both recovery decoding layers, respectively, and intensifies the preservation of the core semantics of both the sequences by a multi-level constraint mechanism. Experiments on two public datasets demonstrate the superiority of our proposed method.
Generalized fringe order correction based on phase shift segmentation for absolute phase retrieval

We present a fringe order correction method for various absolute phase retrieval algorithms. In fringe projection profilometry based on absolute phase retrieval (FPP-APR), the mismatches tend to be easily generated around the 2π discontinuities or edges of the wrapped phase due to the defocus and noise of the system. In this paper, a staggered wrapped phase with a phase shift of 2π / N segments the calculated wrapped phase into a left-wrapped phase and a right-wrapped phase. Then, a statistical method independently determines the fringe order of each closed-wrapped phase without the 2π discontinuities. The experiments demonstrate that the proposed fringe order correction has the generality and simplicity for the existing absolute phase retrieval and performs high robustness to measure the colorfully complex objects of daily life.
BoFF: A bag of fuzzy deep features for texture recognition

Here, we propose a novel method for texture recognition that employs fuzzy modeling over deep learning features. Specifically, the well-established pipeline of deep filter banks for texture description is followed, but using fuzzy equivalence measures for aggregating the deep features. This solution is more robust than a simple “all-or-nothing” assignment used on bag-of-visual-words, and it is less expensive than complex statistical representations such as Fisher vectors. Additionally, it avoids dependence on strong assumptions about specific distributions. The proposed method is evaluated on texture classification tasks, including both benchmark databases and a practical task in botany. In both cases, the results were competitive with state-of-the-art methods and suggest the potential of this combination for texture analysis in general.