Reviewerly/exHarmony huggingface dataset:
Smart Factory Using Virtual Reality and Online Multi-User: Towards a Metaverse for Experimental Frameworks

Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging “hands-on” training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing collaboration in virtual environments through VR-powered metaverses. The VR system includes functional components, object-oriented configurations, advanced core, interfaces, and an online multi-user system. We present the study of the first application case of the framework with VR in a metaverse, focused on the smart factory, that shows the most relevant technologies of Industry 4.0. Functionality tests were carried out and evaluated with users through usability metrics that showed the satisfactory results of its potential educational and commercial use. Finally, the experimental results show that a commercial software framework for VR games can accelerate the development of experiments in the metaverse to connect users from different parts of the world in real time.
A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering

Food recommender-systems are considered an effective tool to help users adjust their eating habits and achieve a healthier diet. This paper aims to develop a new hybrid food recommender-system to overcome the shortcomings of previous systems, such as ignoring food ingredients, time factor, cold start users, cold start food items and community aspects. The proposed method involves two phases: food content-based recommendation and user-based recommendation. Graph clustering is used in the first phase, and a deep-learning based approach is used in the second phase to cluster both users and food items. Besides a holistic-like approach is employed to account for time and user-community related issues in a way that improves the quality of the recommendation provided to the user. We compared our model with a set of state-of-the-art recommender-systems using five distinct performance metrics: Precision, Recall, F1, AUC and NDCG. Experiments using dataset extracted from "Allrecipes.com" demonstrated that the developed food recommender-system performed best.
Density‐based clustering

Abstract Clustering refers to the task of identifying groups or clusters in a data set. In density‐based clustering , a cluster is a set of data objects spread in the data space over a contiguous region of high density of objects. Density‐based clusters are separated from each other by contiguous regions of low density of objects. Data objects located in low‐density regions are typically considered noise or outliers. In this review article we discuss the statistical notion of density‐based clusters, classic algorithms for deriving a flat partitioning of density‐based clusters, methods for hierarchical density‐based clustering, and methods for semi‐supervised clustering. We conclude with some open challenges related to density‐based clustering. This article is categorized under: Technologies &gt; Data Preprocessing Ensemble Methods &gt; Structure Discovery Algorithmic Development &gt; Hierarchies and Trees
A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia

Automatic Leukemia or blood cancer detection is a challenging job and is very much required in healthcare centers.It has a significant role in early diagnosis and treatment planning.Leukemia is a hematological disorder that starts from the bone marrow and affects white blood cells (WBCs).Microscopic analysis of WBCs is a preferred approach for an early detection of Leukemia since it is cost-effective and less painful.Very few literature reviews have been done to demonstrate a comprehensive analysis of deep and machine learning-based Acute Lymphoblastic Leukemia (ALL) detection.This article presents a systematic review of the recent advancements in this knowledge domain.Here, various artificial intelligence-based ALL detection approaches are analyzed in a systematic manner with merits and demits.The review of these schemes is conducted in a structured manner.For this purpose, segmentation schemes are broadly categorized into signal and image processing-based techniques, conventional machine learning-based techniques, and deep learning-based techniques.Conventional machine learning-based ALL classification approaches are categorized into supervised and unsupervised machine learning is presented.In addition, deep learning-based classification methods are categorized into Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and the Autoencoder.Then, CNN-based classification schemes are further categorized into conventional CNN, transfer learning, and other advancements in CNN.A brief discussion of these schemes and their importance in ALL classification are also presented.Moreover, a critical analysis is performed to present a clear idea about the recent research in this field.Finally, various challenging issues and future scopes are discussed that may assist readers in formulating new research problems in this domain.
A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition

Most supervised learning-based approaches follow the assumptions that offline data and online data must obey a similar distribution, which is difficult to satisfy in realistic remaining useful life (RUL) prediction. To solve the problem, domain adaptation (DA) learning-oriented transfer learning (TL) was proposed. Nevertheless, only adopting a conventional global DA approach may confuse the fine-grained features between subdomains represented by different degenerate stages. Consequently, a novel variational auto-encoder-long–short-term memory network-local weighted deep sub-domain adaptation network (VLSTM-LWSAN) is proposed for RUL prediction. Specifically, the input data are compressed into the interpretable latent space, from which the fine-grained features between subdomains are local alignment through local weighted deep sub-domain adaptation network. In this sense, the discrepancy between the unlabeled target domain and the source domain is decreased. The proposed VLSTM-LWSAN is verified by an aircraft turbofan engine dataset. The research results represent that the VLSTM-LWSAN outperforms some deep learning approaches without transfer learning and conventional transfer learning approaches.
Intelligent transportation system for internet of vehicles based vehicular networks for smart cities

A popular research area is internet traffic analysis as it has many applications, mainly for classifying internet traffic. Innovative technologies have been developed for predicting and identifying traffic congestion in the intelligent Internet of vehicles (IOVs). In this paper, an intelligent transport system for the IOVs-based vehicular network traffic for smart city scenario is proposed based on tree-based Decision Tree (DT), Random Forest (RF), and Extra Tree (ET), and XGBoost machine learning (ML) models. Simulation results indicate that the proposed system can provide high detection accuracy and low computational costs thanks to ensemble learning and averaging important feature selection. The tree-based ML techniques with feature selection performed better than those without feature selection for IOV-based vehicular network traffic. The Stacking model shows higher classification accuracy, 99.05%, compared to the lowest KNN accuracy, 96.6%, and SVM accuracy, 98.01%.
Detecting Deepfakes with Self-Blended Images

In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes. SBIs are generated by blending pseudo source and target images from single pristine images, reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images). The key idea behind SBIs is that more general and hardly recognizable fake samples encourage classifiers to learn generic and robust representations without overfitting to manipulation-specific artifacts. We compare our approach with state-of-the-art methods on FF++, CDF, DFD, DFDC, DFDCP, and FFIW datasets by following the standard cross-dataset and cross-manipulation protocols. Extensive experiments show that our method improves the model generalization to unknown manipulations and scenes. In particular, on DFDC and DFDCP where existing methods suffer from the domain gap between the training and test sets, our approach outperforms the baseline by 4.90% and 11.78% points in the cross-dataset evaluation, respectively. Code is available at https://github.com/mapooon/SelfBlendedImages.
An Adaptive Neuro-Fuzzy System With Integrated Feature Selection and Rule Extraction for High-Dimensional Classification Problems

A major limitation of fuzzy or neuro-fuzzy systems is their failure to deal with high-dimensional datasets. This happens primarily due to the use of T-norm, particularly, product or minimum (or a softer version of it). Thus, there are hardly any work dealing with datasets having features more than hundred or so. Here, we propose a neuro-fuzzy framework that can handle datasets with even more than 7000 features! In this context, we propose an adaptive softmin (Ada-softmin) which effectively overcomes the drawbacks of “numeric underflow” and “fake minimum” that arise for existing fuzzy systems while dealing with high-dimensional problems. We call it an adaptive Takagi–Sugeno–Kang (AdaTSK) fuzzy system. We then equip the AdaTSK system to perform feature selection and rule extraction in an integrated manner. In this context, a novel gate function is introduced and embedded only in the consequent parts, which can determine the useful features and rules, in two successive phases of learning. Unlike conventional fuzzy rule bases, we design an enhanced fuzzy rule base, which maintains adequate rules but does not grow the number of rules exponentially with features that typically happens for fuzzy neural networks. The integrated feature selection and rule extraction AdaTSK (FSRE-AdaTSK) system consists of three sequential phases: 1) feature selection; 2) rule extraction; and 3) fine tuning. The effectiveness of the FSRE-AdaTSK is demonstrated on 19 datasets of which five are in more than 2000 dimension including two with more than 7000 features. This may be the first attempt to develop fuzzy rule-based classifiers that can directly deal with more than 7000 features without requiring separate selection of features or any other dimensionality reduction method.
Brain Imaging Generation with Latent Diffusion Models

Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data provides a promising alternative, allowing to complement training datasets and conducting medical image research at a larger scale. Diffusion models recently have caught the attention of the computer vision community by producing photorealistic synthetic images. In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images. We used T1w MRI images from the UK Biobank dataset (N = 31,740) to train our models to learn about the probabilistic distribution of brain images, conditioned on covariates, such as age, sex, and brain structure volumes. We found that our models created realistic data, and we could use the conditioning variables to control the data generation effectively. Besides that, we created a synthetic dataset with 100,000 brain images and made it openly available to the scientific community.
An Efficient Key Recovery Attack on SIDH

We present an efficient key recovery attack on the Supersingular Isogeny Diffie–Hellman protocol (SIDH). The attack is based on Kani’s “reducibility criterion” for isogenies from products of elliptic curves and strongly relies on the torsion point images that Alice and Bob exchange during the protocol. If we assume knowledge of the endomorphism ring of the starting curve then the classical running time is polynomial in the input size (heuristically), apart from the factorization of a small number of integers that only depend on the system parameters. The attack is particularly fast and easy to implement if one of the parties uses 2-isogenies and the starting curve comes equipped with a non-scalar endomorphism of very small degree; this is the case for SIKE, the instantiation of SIDH that recently advanced to the fourth round of NIST’s standardization effort for post-quantum cryptography. Our Magma implementation breaks SIKEp434, which aims at security level 1, in about ten minutes on a single core.
Segment anything model for medical image analysis: An experimental study

Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations. Segment Anything Model (SAM) is a foundation model trained on over 1 billion annotations, predominantly for natural images, that is intended to segment user-defined objects of interest in an interactive manner. While the model performance on natural images is impressive, medical image domains pose their own set of challenges. Here, we perform an extensive evaluation of SAM’s ability to segment medical images on a collection of 19 medical imaging datasets from various modalities and anatomies. In our experiments, we generated point and box prompts for SAM using a standard method that simulates interactive segmentation. We report the following findings: (1) SAM’s performance based on single prompts highly varies depending on the dataset and the task, from IoU=0.1135 for spine MRI to IoU=0.8650 for hip X-ray. (2) Segmentation performance appears to be better for well-circumscribed objects with prompts with less ambiguity such as the segmentation of organs in computed tomography and poorer in various other scenarios such as the segmentation of brain tumors. (3) SAM performs notably better with box prompts than with point prompts. (4) SAM outperforms similar methods RITM, SimpleClick, and FocalClick in almost all single-point prompt settings. (5) When multiple-point prompts are provided iteratively, SAM’s performance generally improves only slightly while other methods’ performance improves to the level that surpasses SAM’s point-based performance. We also provide several illustrations for SAM’s performance on all tested datasets, iterative segmentation, and SAM’s behavior given prompt ambiguity. We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others. SAM has the potential to make a significant impact in automated medical image segmentation in medical imaging, but appropriate care needs to be applied when using it. Code for evaluation SAM is made publicly available at https://github.com/mazurowski-lab/segment-anything-medical-evaluation.
Relationships between IT department culture and agile software development practices: An empirical investigation

IT department culture has been widely recognized as an important factor that influences the adoption of agile practices. Yet, the research pertaining to the relationship between IT department culture and agile practices usage remains underexplored. This study proposes and tests the relationships between four competing cultural forms and two types of agile practices - social and technical. The findings contribute to the extant literature by integrating the competing values model of culture into the literature on factors affecting agile development at the IT department level.
Interpreting Adversarially Trained Convolutional Neural Networks

We attempt to interpret how adversarially trained convolutional neural networks (AT-CNNs) recognize objects. We design systematic approaches to interpret AT-CNNs in both qualitative and quantitative ways and compare them with normally trained models. Surprisingly, we find that adversarial training alleviates the texture bias of standard CNNs when trained on object recognition tasks, and helps CNNs learn a more shape-biased representation. We validate our hypothesis from two aspects. First, we compare the salience maps of AT-CNNs and standard CNNs on clean images and images under different transformations. The comparison could visually show that the prediction of the two types of CNNs is sensitive to dramatically different types of features. Second, to achieve quantitative verification, we construct additional test datasets that destroy either textures or shapes, such as style-transferred version of clean data, saturated images and patch-shuffled ones, and then evaluate the classification accuracy of AT-CNNs and normal CNNs on these datasets. Our findings shed some light on why AT-CNNs are more robust than those normally trained ones and contribute to a better understanding of adversarial training over CNNs from an interpretation perspective.
Deep-learning-powered photonic analog-to-digital conversion

Analog-to-digital converters (ADCs) must be high speed, broadband, and accurate for the development of modern information systems, such as radar, imaging, and communications systems; photonic technologies are regarded as promising technologies for realizing these advanced requirements. Here, we present a deep-learning-powered photonic ADC architecture that simultaneously exploits the advantages of electronics and photonics and overcomes the bottlenecks of the two technologies, thereby overcoming the ADC tradeoff among speed, bandwidth, and accuracy. Via supervised training, the adopted deep neural networks learn the patterns of photonic system defects and recover the distorted data, thereby maintaining the high quality of the electronic quantized data succinctly and adaptively. The numerical and experimental results demonstrate that the proposed architecture outperforms state-of-the-art ADCs with developable high throughput; hence, deep learning performs well in photonic ADC systems. We anticipate that the proposed architecture will inspire future high-performance photonic ADC design and provide opportunities for substantial performance enhancement for the next-generation information systems.
End-to-End DSM Fusion Networks for Semantic Segmentation in High-Resolution Aerial Images

Semantic segmentation in high-resolution aerial images is a fundamental research problem in remote sensing field for its wide range of applications. However, it is difficult to distinguish regions with similar spectral features using only multispectral data. Recent research studies have indicated that the introduction of multisource information can effectively improve the robustness of segmentation method. In this letter, we use digital surface models (DSMs) information as a complementary feature to further improve the semantic segmentation results. To this end, we propose a lightweight and simple DSM fusion (DSMF) branch structure module. Compared with the existing feature extraction structures, proposed DSMF module is simple and can be easily applied to other networks. In addition, we investigate four fusion strategies based on DSMF module to explore the optimal feature fusion strategy and four end-to-end DSMFNets are designed according to the corresponding strategies. We evaluate our models on International Society for Photogrammetry and Remote Sensing Vaihingen data set and all DSMFNets achieve promising results. In particular, DSMFNet-1 achieves an overall accuracy of 91.5% on the test data set.
FUTUREGAN: ANTICIPATING THE FUTURE FRAMES OF VIDEO SEQUENCES USING SPATIO-TEMPORAL 3D CONVOLUTIONS IN PROGRESSIVELY GROWING GANS

Abstract. We introduce a new encoder-decoder GAN model, FutureGAN, that predicts future frames of a video sequence conditioned on a sequence of past frames. During training, the networks solely receive the raw pixel values as an input, without relying on additional constraints or dataset specific conditions. To capture both the spatial and temporal components of a video sequence, spatio-temporal 3d convolutions are used in all encoder and decoder modules. Further, we utilize concepts of the existing progressively growing GAN (PGGAN) that achieves high-quality results on generating high-resolution single images. The FutureGAN model extends this concept to the complex task of video prediction. We conducted experiments on three different datasets, MovingMNIST, KTH Action, and Cityscapes. Our results show that the model learned representations to transform the information of an input sequence into a plausible future sequence effectively for all three datasets. The main advantage of the FutureGAN framework is that it is applicable to various different datasets without additional changes, whilst achieving stable results that are competitive to the state-of-the-art in video prediction. The code to reproduce the results of this paper is publicly available at https://github.com/TUM-LMF/FutureGAN.
Literature review of locomotion techniques in virtual reality

This study represents a systematic literature review in which we explored the locomotion techniques in virtual reality between 2012 and 2019. We analysed and compared 22 locomotion methods that we have identified in 26 papers included in our review. The objective is to better understand relevant locomotion techniques and their impact on the user experience. The review of the literature has shown a wide range of different locomotion techniques which each technique is characterized by different advantages and drawbacks, but classic locomotion techniques such as joystick outperformed all the proposed technique in the reviewed studies. We also proposed a taxonomy and two types of evaluation for locomotion techniques in virtual environment. These elements provide guidelines that may help researchers to choose the most adapted locomotion technique according to the aim of their study.