Reviewerly/exHarmony huggingface dataset:
A Novel four - Wing chaotic system with multiple attractors based on hyperbolic sine: Application to image encryption*

A novel symmetrical four-wing fourth-order chaotic system is constructed. The basic dynamic characteristics of the system were analyzed by phase diagram, bifurcation diagram, Lyapunov index spectrum, Poincare cross section diagram and 0–1 test. In addition, the chaotic attractors under different parameters in the system are analyzed. In the dynamic analysis of the new system, it is found that the new system has some characteristics, such as multi-stability, offset boosting, multi-state transition phenomenon, transient chaos and intermittent chaos, and coexistence of multiple attractors. These features have the value of in-depth analysis compared to previous systems and can make it promising for more applications. Moreover, after calculating the complexity of the system by C0 algorithm at different initial values, it is found that the complexity of the system has been stable. Due to the existence of many characteristics, the new chaotic system has attracted great attention. At the same time, the circuit design of the system is realized by using Multisim simulation software and FPGA digital hardware circuit. Finally, a novel and efficient image encryption algorithm is designed by combining multi-direction pixel scrambling and DNA dynamic encryption. The NIST test, key space, encryption histogram, adjacent pixel correlation, robustness and information entropy are analyzed by encrypting images using chaotic sequences of the new system. In a word, its plentiful characteristics and intricate phenomena have significant reference value in the field of chaotic image encryption.
A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer

Breast cancer is a gigantic burden on humanity, causing the loss of enormous numbers of lives and amounts of money. It is the world's leading type of cancer among women and a leading cause of mortality and morbidity. The histopathological examination of breast tissue biopsies is the gold standard for diagnosis. In this paper, a computer-aided diagnosis (CAD) system based on deep learning is developed to ease the pathologist's mission. For this target, five pre-trained convolutional neural network (CNN) models are analyzed and tested-Xception, DenseNet201, InceptionResNetV2, VGG19, and ResNet152-with the help of data augmentation techniques, and a new approach is introduced for transfer learning. These models are trained and tested with histopathological images obtained from the BreakHis dataset. Multiple experiments are performed to analyze the performance of these models through carrying out magnification-dependent and magnification-independent binary and eight-class classifications. The Xception model has shown promising performance through achieving the highest classification accuracies for all the experiments. It has achieved a range of classification accuracies from 93.32% to 98.99% for magnification-independent experiments and from 90.22% to 100% for magnification-dependent experiments.
Exploratory Research on the Use of Blockchain Technology in Recruitment

In recent years, human resource (HR) professionals have struggled with problems such as reduced efficiency, increased cost, fraud risks, and time-consuming procedures in the process of finding and hiring the right talent for companies. Blockchain technology has emerged as a powerful tool to solve these problems and transform the recruitment process. The aim of this study is to investigate the opportunities, challenges, possible solutions, and prospects for blockchain technology adoption in recruitment. In order to achieve this aim, exploratory research has been conducted through a combination of a comprehensive literature review and a structured interview with experts in the field of human resource management (HRM) and blockchain. Thematic analysis has been used to analyze qualitative data. The findings reveal that blockchain-based recruitment systems enhance the recruitment processes by offering opportunities such as increasing the speed and reliability of the transactions and reducing costs and routine workload. Thus, it provides a sustainable competitive advantage to the companies. However, these systems have some technological, organizational, and environmental challenges that can be addressed in the long term. In the future, it is expected that the use of blockchain technology in recruitment processes will be a gradual process and will change the role of recruiters.
A Path Planning Model for Stock Inventory Using a Drone

In this study, a model and solution are shown for controlling the inventory of a logistics warehouse in which neither satellite positioning nor IoT solutions can be used. Following a review of the literature on path planning, a model is put forward using a drone that can be moved in all directions and is suitable for imaging and transmission. The proposed model involves three steps. In the first step, a traversal path definition provides an optimal solution, which is pre-processing. This is in line with the structure and capabilities of the warehouse. In the second step, the pre-processed path determines the real-time movement of the drone during processing, including camera movements and image capture. The third step is post-processing, i.e., the processing of images for QR code identification, the interpretation of the QR code, and the examination of matches and discrepancies for inventory control. A key benefit for the users of this model is that the result can be achieved without any external orientation tools, relying solely on its own movement and the organization of a pre-planned route. The proposed model can be effective not only for inventory control, but also for exploring the structure of a warehouse shelving system and determining empty cells.
A critical outlook at augmented reality and its adoption in education

Despite a significant volume of empirical research suggesting that augmented reality has a positive impact on student learning outcomes, it has not been widely adopted within education. This review critically analyses the literature to determine why this is the case. Our review methodology was based on the PRISMA strategy. A total of 169 papers were identified for use in this study and this group of papers was evaluated using content analysis. Specifically we analysed the research goals, motivation and the extent of the application of co-design in the research papers. We found that the research in this area is primarily student-centred, and a minority of papers apply co-design as a technique. This is significant because it is well understood that student-centred outcomes have the least effect on technology adoption within education. Based on these findings we make a series of recommendations including a shift away from research focused solely on learning outcomes and towards research which also considers how augmented reality integrates into the teaching environment.
Blockchain Technology as an Ecosystem: Trends and Perspectives in Accounting and Management

A plethora of studies have examined the emerging technology of blockchain and its applications in accounting, management, and enterprise resource planning systems (ERPs). Blockchain technology (BT) can change the architecture of today’s ERPs and overcome the limitations of these centralized systems. The aim of this study is twofold. First, this paper defines and analyzes the deployment of an innovative architecture of a Blockchain as an Ecosystem (BaaE) platform proposing a conceptual model of the Triple Entry Accounting (TEA) transforming the current accounting practices. Second, the paper explores the integration of cost management, supply chain, and inventory management on BT providing the significant challenges and benefits and suggesting an agenda for future research. The authors conduct an exploratory qualitative analysis of an extensive body of literature, from 81 journals. The paper’s innovative contribution and primary objective is to explore, address, and employ this emerging BaaE platform technology that could potentially be integrated with TEA. Further, the study examines the theoretical, technical, and business aspects regarding TEA, since there is limited research evidence in this field. Additionally, the study tries to identify the implications of BaaE in the area of cost management, supply chain, and inventory management from an ecosystem perspective. This effort can assist organizations and practitioners in understanding and further examining this emerging technology.
Dynamic Forecasting of Solar Energy Microgrid Systems Using Feature Engineering

The intermittent and stochastic nature of solar energy generation systems, climate change, and the inefficiency of modern power systems due to zero inertia have created many challenges for on-grid operators. Solar forecasting systems based on machine learning algorithms are an emerging and effective solution that uses Big Data related to weather phenomena. However, the predictive ability of these algorithms is hampered by the sporadic nature of solar energy generation. In this article, a robust hybrid machine learning system that utilizes multiple linear regression (MLR) and a Pearson correlation coefficient (PCC) was tested on solar power plant sites of varying capacities in Germany (100–8500 kW). The volume of Big Data features can be reduced by focusing on the features that significantly improve the reliability of the mid-term forecasting system. In this way, drastic fluctuations in the prediction of photovoltaic (PV) power generation can be avoided. The results of our approach are evaluated regarding real-world data using the extreme gradient boosting (XGBoost) with feature engineering, and principal component analysis (PCA), in order to forecast PV energy, rank, and track the importance of feature engineering for different PV capacities. Furthermore, we found that the need for selectivity and reduction of performance error was supported by ridge regression. In addition, the proposed novel XGBoost forecast system decreased the root-mean-square error (RMSE) and mean absolute error (MAE) by 30% and 18%, respectively, compared to the Autoencoder and long short-term memory (LSTM) for same datasets. Furthermore, the CoD determination coefficient ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> ) increased by 85% compared to the statistical model's autoregressive integrated moving average (ARIMA).
Real-time citywide reconstruction of traffic flow from moving cameras on lightweight edge devices

Traffic flow estimation is required for road infrastructure management tasks such as road development planning, routing, and navigation. Determining traffic flow on a citywide scale is challenging because of the expensive costs and portability of current devices. Portable sensing devices such as drive cams and smartphones are an effective source of monitoring the road infrastructure environment because of their continuous interaction with the surrounding. However, the use of such devices to estimate real-time traffic flow has not been fully explored. In this study, we optimize the vehicle detection neural network for inference on lightweight edge devices and develop a client–server framework to reduce and share the computational load to make accurate real-time traffic flow processing from moving camera videos. We conduct extensive research work for various input network sizes and frame rates combinations for three widely used edge devices – Jetson Xavier AGX, Jetson Xavier NX, and Jetson Nano. We obtain a traffic flow reconstruction accuracy ranging from 73.1% to 80.8% evaluated using ground truth data. With the proliferation of moving cameras in vehicles (dash cams, stereo cams, etc.) and inexpensive edge devices, we expect our real-time traffic flow estimation algorithm to have a very promising future. Our findings may serve as a useful reference for several domains in the area of real-time artificial intelligence applications and emerging edge computing devices.
A Novel Grayscale Image Encryption Scheme Based on the Block-Level Swapping of Pixels and the Chaotic System

Hundreds of image encryption schemes have been conducted (as the literature review indicates). The majority of these schemes use pixels as building blocks for confusion and diffusion operations. Pixel-level operations are time-consuming and, thus, not suitable for many critical applications (e.g., telesurgery). Security is of the utmost importance while writing these schemes. This study aimed to provide a scheme based on block-level scrambling (with increased speed). Three streams of chaotic data were obtained through the intertwining logistic map (ILM). For a given image, the algorithm creates blocks of eight pixels. Two blocks (randomly selected from the long array of blocks) are swapped an arbitrary number of times. Two streams of random numbers facilitate this process. The scrambled image is further XORed with the key image generated through the third stream of random numbers to obtain the final cipher image. Plaintext sensitivity is incorporated through SHA-256 hash codes for the given image. The suggested cipher is subjected to a comprehensive set of security parameters, such as the key space, histogram, correlation coefficient, information entropy, differential attack, peak signal to noise ratio (PSNR), noise, and data loss attack, time complexity, and encryption throughput. In particular, the computational time of 0.1842 s and the throughput of 3.3488 Mbps of this scheme outperforms many published works, which bears immense promise for its real-world application.
Aggregation Strategy on Federated Machine Learning Algorithm for Collaborative Predictive Maintenance

Industry 4.0 lets the industry build compact, precise, and connected assets and also has made modern industrial assets a massive source of data that can be used in process optimization, defining product quality, and predictive maintenance (PM). Large amounts of data are collected from machines, processed, and analyzed by different machine learning (ML) algorithms to achieve effective PM. These machines, assumed as edge devices, transmit their data readings to the cloud for processing and modeling. Transmitting massive amounts of data between edge and cloud is costly, increases latency, and causes privacy concerns. To address this issue, efforts have been made to use edge computing in PM applications., reducing data transmission costs and increasing processing speed. Federated learning (FL) has been proposed a mechanism that provides the ability to create a model from distributed data in edge, fog, and cloud layers without violating privacy and offers new opportunities for a collaborative approach to PM applications. However, FL has challenges in confronting with asset management in the industry, especially in the PM applications, which need to be considered in order to be fully compatible with these applications. This study describes distributed ML for PM applications and proposes two federated algorithms: Federated support vector machine (FedSVM) with memory for anomaly detection and federated long-short term memory (FedLSTM) for remaining useful life (RUL) estimation that enables factories at the fog level to maximize their PM models' accuracy without compromising their privacy. A global model at the cloud level has also been generated based on these algorithms. We have evaluated the approach using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset to predict engines' RUL Experimental results demonstrate the advantage of FedSVM and FedLSTM in terms of model accuracy, model convergence time, and network usage resources.
BT-Unet: A self-supervised learning framework for biomedical image segmentation using barlow twins with U-net models

Deep learning has brought the most profound contribution towards biomedical image segmentation to automate the process of delineation in medical imaging. To accomplish such task, the models are required to be trained using huge amount of annotated or labelled data that highlights the region of interest with a binary mask. However, efficient generation of the annotations for such huge data requires expert biomedical analysts and extensive manual effort. It is a tedious and expensive task, while also being vulnerable to human error. To address this problem, a self-supervised learning framework, BT-Unet is proposed that uses the Barlow Twins approach to pre-train the encoder of a U-Net model via redundancy reduction in an unsupervised manner to learn data representation. Later, complete network is fine-tuned to perform actual segmentation. The BT-Unet framework can be trained with a limited number of annotated samples while having high number of unannotated samples, which is mostly the case in real-world problems. This framework is validated over multiple U-Net models over diverse datasets by generating scenarios of a limited number of labelled samples using standard evaluation metrics. With exhaustive experiment trials, it is observed that the BT-Unet framework enhances the performance of the U-Net models with significant margin under such circumstances.
OutfitTransformer: Outfit Representations for Fashion Recommendation

Predicting outfit compatibility and retrieving complementary items are critical components for a fashion recommendation system. We present a scalable framework, OutfitTransformer, that learns compatibility of the entire out- fit and supports large-scale complementary item retrieval. We model outfits as an unordered set of items and leverage self-attention mechanism to learn the relationships between items. We train the framework using a proposed set-wise outfit ranking loss to generate a target item embedding given an outfit, and a target item specification. The generated target item embedding is then used to retrieve compatible items that match the outfit. Experimental results demonstrate that our approach outperforms state-of-the-art methods on compatibility prediction, fill-in-the-blank, and complementary item retrieval tasks.
CORE: Consistent Representation Learning for Face Forgery Detection

Face manipulation techniques develop rapidly and arouse widespread public concerns. Despite that vanilla convolutional neural networks achieve acceptable performance, they suffer from the overfitting issue. To relieve this issue, there is a trend to introduce some erasing-based augmentations. We find that these methods indeed attempt to implicitly induce more consistent representations for different augmentations via assigning the same label for different augmented images. However, due to the lack of explicit regularization, the consistency between different representations is less satisfactory. Therefore, we constrain the consistency of different representations explicitly and propose a simple yet effective framework, COnsistent REpresentation Learning (CORE). Specifically, we first capture the different representations with different augmentations, then regularize the cosine distance of the representations to enhance the consistency. Extensive experiments (in-dataset and cross-dataset) demonstrate that CORE performs favorably against state-of-the-art face forgery detection methods. Our code is available at https://github.com/niyunsheng/CORE.
Improving Multi-Target Multi-Camera Tracking by Track Refinement and Completion

Multi-camera tracking of vehicles on a city-wide level is a core component of modern traffic monitoring systems. For this task, single-camera tracking failures are the most common causes of errors concerning automatic multi-target multi-camera tracking systems. To address these problems, we propose several modules that aim at improving single-camera tracklets, e.g., appearance-based tracklet splitting, single-camera clustering, and track completion. After these track refinement steps, hierarchical clustering is used to associate the enhanced single-camera tracklets. During this stage, we leverage vehicle re-identification features as well as prior knowledge about the scene's topology. Last, the proposed track completion strategy is adopted for the cross-camera association task to obtain the final multi-camera tracks. Our method proves itself competitive: With it, we achieved 4th place in track 1 of the 2022 AI City Challenge.
Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation

Whole-body 3D human mesh estimation aims to reconstruct the 3D human body, hands, and face simultaneously. Although several methods have been proposed, accurate prediction of 3D hands, which consist of 3D wrist and fingers, still remains challenging due to two reasons. First, the human kinematic chain has not been carefully considered when predicting the 3D wrists. Second, previous works utilize body features for the 3D fingers, where the body feature barely contains finger information. To resolve the limitations, we present Hand4Whole, which has two strong points over previous works. First, we design Pose2Pose, a module that utilizes joint features for 3D joint rotations. Using Pose2Pose, Hand4Whole utilizes hand MCP joint features to predict 3D wrists as MCP joints largely contribute to 3D wrist rotations in the human kinematic chain. Second, Hand4Whole discards the body feature when predicting 3D finger rotations. Our Hand4Whole is trained in an end-to-end manner and produces much better 3D hand results than previous whole-body 3D human mesh estimation methods. The codes are available here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Dress Code: High-Resolution Multi-Category Virtual Try-On

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Existing literature focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. In this research activity, we introduce Dress Code, a novel dataset which contains images of multi-category clothes. Dress Code is more than 3× larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024 × 768) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code.
Prompt–RSVQA: Prompting visual context to a language model for Remote Sensing Visual Question Answering

Remote sensing visual question answering (RQA) was recently proposed with the aim of interfacing natural language and vision to ease the access of information contained in Earth Observation data for a wide audience, which is granted by simple questions in natural language. The traditional vision/language interface is an embedding obtained by fusing features from two deep models, one processing the image and another the question. Despite the success of early VQA models, it remains difficult to control the adequacy of the visual information extracted by its deep model, which should act as a context regularizing the work of the language model. We propose to extract this context information with a visual model, convert it to text and inject it, i.e. prompt it, into a language model. The language model is therefore responsible to process the question with the visual context, and extract features, which are useful to find the answer. We study the effect of prompting with respect to a black-box visual extractor and discuss the importance of training a visual model producing accurate context.