Reviewerly/exHarmony huggingface dataset:
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals

We propose a PiggyBack, a Visual Question Answering platform that allows users to apply the state-of-the-art visual-language pretrained models easily. We integrate visual-language models, pretrained by HuggingFace, an open-source API platform of deep learning technologies; however, it cannot be runnable without programming skills or deep learning understanding. Hence, our PiggyBack supports an easy-to-use browser-based user interface with several deep-learning visual language pretrained models for general users and domain experts. The PiggyBack includes the following benefits: Portability due to web-based and thus runs on almost any platform, A comprehensive data creation and processing technique, and ease of use on visual language pretrained models. The demo video can be found at https://youtu.be/iz44RZ1lF4s.
Little Memory Editors Living Inside Your Brain

We interact with the world continuously. However, memories of our experiences are stored as individual events. For example, when we go on a road trip, we do not remember what happens second by second. Instead, we remember only a few special moments or events from a trip, such as dancing around the campfire. Our brains constantly extract memorable events while we interact with the world, and we organize those events based on their relevance. This process is like grouping road trip photos under different folders on the computer, so we can efficiently and accurately retrieve those memories in the future. How does the brain create these memorable events? In this article, you will learn about two groups of neurons inside the brain that help achieve this remarkable feat. You will also learn about how the activation of these neurons shapes the formation and retrieval of memories.
Construction and evaluation of networks among multiple postoperative complications

Postoperative complications confer an increased risk of reoperation, prolonged length of hospital stay, and increased mortality. Many studies have attempted to identify the complex associations among complications to preemptively interrupt their progression, but few studies have looked at complications as a whole to reveal and quantify their possible trajectories of progression. The main objective of this study was to construct and quantify the association network among multiple postoperative complications from a comprehensive perspective to elucidate the possible evolution trajectories.In this study, a Bayesian network model was proposed to analyze the associations among 15 complications. Prior evidence and score-based hill-climbing algorithms were used to build the structure. The severity of complications was graded according to their connection to death, with the association between them quantified using conditional probabilities. The data of surgical inpatients used in this study were collected from four regionally representative academic/teaching hospitals in a prospective cohort study in China.In the network obtained, 15 nodes represented complications or death, and 35 arcs with arrows represented the directly dependent relationship between them. With three grades classified on that basis, the correlation coefficients of complications within grades increased with increased grade, ranging from -0.11 to -0.06, 0.16, and 0.21 to 0.4 in grade 1 to grade 3, respectively. Moreover, the probability of each complication in the network increased with the occurrence of any other complication, even mild complications. Most seriously, once cardiac arrest requiring cardiopulmonary resuscitation occurs, the probability of death will be up to 88.1%.The present evolving network can facilitate the identification of strong associations among specific complications and provides a basis for the development of targeted measures to prevent further deterioration in high-risk patients.
High-precision single-pixel 3D calibration method using pseudo-phase matching

Compressive sensing makes it possible to explore two-dimensional spatial information using a single-point detector. However, the reconstruction of the three-dimensional (3D) morphology using a single-point sensor is largely limited by the calibration. Here we demonstrate a pseudo-single-pixel camera calibration (PSPC) method using pseudo phase matching in stereo, which can perform 3D calibration of low-resolution images with the help of a high-resolution digital micromirror device (DMD) in the system. In this paper, we use a high-resolution CMOS to pre-image the DMD surface and successfully calibrate the spatial position of a single-point detector and the projector with the support of binocular stereo matching. Our system achieved sub-millimeter reconstructions of spheres, steps, and plaster portraits at low compression ratios with a high-speed digital light projector (DLP) and a highly sensitive single-point detector.
A variance-reduced stochastic gradient tracking algorithm for decentralized optimization with orthogonality constraints

Decentralized optimization with orthogonality constraints is found widely in scientific computing and data science. Since the orthogonality constraints are nonconvex, it is quite challenging to design efficient algorithms. Existing approaches leverage the geometric tools from Riemannian optimization to solve this problem at the cost of high sampling and communication complexities. To relieve this difficulty, based on two novel techniques that can waive the orthogonality constraints, we propose a variance-reduced stochastic gradient tracking (VRSGT) algorithm with the convergence rate of $ O(1 / k) $ to a stationary point. To the best of our knowledge, VRSGT is the first algorithm for decentralized optimization with orthogonality constraints that reduces both sampling and communication complexities simultaneously. In the numerical experiments, VRSGT has a promising performance in a real-world autonomous driving application.
A Case-Based Reasoning System-Based Random Forest for Classification

The huge amount of health data attracts machine learning (ML) techniques to medical classification, and, through learning strategies, obtain remarkable results. Some techniques are used to classify and predict data to make accurate decisions, especially case-based reasoning (CBR), which is considered a reasonable technique in medicine, based on past experiences for problem solving. This chapter studies the case-based reasoning approach and its use in the medical field. In the analysis, the authors identify hybridization as a major trend in CBR. Secondly, random forests (RF) as a very popular tool in machine learning is also suggested and is presented as a new way to improve the recall phase of CBR in order to further improve it for medical data. Thus, the authors present hybrid systems between case-based reasoning and random forests. The authors show that combining ideas from some classifiers can lead to better performance.
Multichannel Six‐Wave Mixing with Spatial Multimode and Squeezing via Atomic Coherence

Abstract Multimode properties, based on nonlinear parametric processes, have potential research value on entanglement imaging. This paper studies the spatial and frequency multimode from dressing parametric‐amplified six‐wave mixing (PA‐SWM) process with three entangled signals. The S1 signal contains fifth‐order nonlinearity, the S2 signal contains electromagnetically induced absorption and fifth‐order nonlinearity, and the S3 signal contains Autler–Townes splitting (AT), second‐ and fifth‐order nonlinearity. The frequency multimode is attributed to dressing energy level splitting in spontaneous SWM (SSWM) process. The spatial multimode is due to frequency multimode and beam splitting via Kerr nonlinearity. In the spatial domain, it finds the optimal conditions of intensity difference squeezing (IDS) and spatial imaging along with their competitive relationship. On the one hand, the light spots with single spatial mode are contributed to the maximum IDS but not conducive to entanglement imaging; on the other hand, the light spots with multiple spatial modes are instrumental in entanglement imaging but not appropriate for the IDS. This research has potential applications in squeezed light sources for quantum metrology, and correlated imaging of spatial signals.
An Extractive Question Answering System for the Tamil Language

In the field of Natural Language Processing, Question Answering is a cardinal task that has garnered a lot of attention. With the development of multiple language models, question answering systems have been developed and deployed to facilitate enhanced information retrieval. These systems, however, have been implemented to a large extent only in English. Our objective was to create such a question answering system for the Tamil Language. We decided to use XLM-RoBERTa as our language model, which has been trained on a variety of datasets. We have also employed a hand-annotated dataset for the purpose of validation. We trained the model on two types of datasets, the first one being only in Tamil, whereas the other one being a mixture of Indian languages along with Tamil. The results were satisfactory in both cases. Given the huge amount of computational power the model required for training, we utilized the Colab Pro Plus cloud GPU from Google to satisfy our demands. We will also be publishing our dataset on huggingface so that fellow researchers can use it for further analysis.
Color Visual Cryptography Based on Three Dimensional Chaotic Map

Cryptographic approaches based on chaos theory provide a several new and promising avenues for developing safe picture encryption solutions. This paper aims to complicate the process of decrypting images by adding encryption with keys, this was achieved by applying the principle of the 3D-chaotic system with the encryption algorithm, so we present an image encryption algorithm called black mask by using an efficient Multidimensional Chaotic Map represented by Lorenz system. For the confusion process, the suggested approach is based on a keys stream generator. The process of confusion is initiated by a 256-bit secret keys, which is produced by a logistic maps. To make the cipher more dynamic in the face of any threat. The suggested digital image encryption technique, as well as its security analysis and implementation, are discussed in depth. The experimental results suggest that the proposed method for image encryption and transmission is both efficient and safe. Index Terms— Chaotic Maps, visual Cryptography, confusion, diffusion.
Survey: Recent Techniques of Image Fragile Watermarking

Ease of access to digital images and the many images editing programs available, like photoshop. All this makes the Issue of protecting images against modification becomes essential. Some images contain crucial information that can risk a patient's life, such as medical images and e-government images that relate to citizen information and state or ministry security. The watermark was one of the essential methods for this type of protection, especially the fragile watermark, which is very sensitive to any attack. Because of its other characteristics, it was one of the techniques that proved its efficiency in detecting tampering and the authenticity of images—also, watermarking focuses on protecting the image itself, not about protecting the secret message. A fragile watermark is a watermarking which inserts some information to cover an image to secure it .fragile watermarking could use in such a way and implement in spatial or frequency domain or in both so, making it a hybrid watermarking scheme. The Paper presented set of fragile watermark techniques used by the researchers with the performance metrics of an algorithm used in spatial and frequency domains, also showing how to use artificial intelligence with a watermarking technique to protect Document images from manipulation and forgery. Index Terms— fragile watermark, spatial domain, frequncy domain
Task Scheduling in Cloud Computing Based on The Cuckoo Search Algorithm

Task scheduling is one of the very crucial facets of cloud computing. The task scheduling method must assign jobs to virtual machines. In cloud computing, task scheduling includes a frontal influence on a system's resource utilization and operational costs. Diverse meta-heuristic algorithms, in addition to their modifications, have been developed to improve the efficiency of task executions in the cloud. In this paper, a multiobjective optimization model is applied using the metaheuristics cuckoo search optimization algorithm (MCSO) to enhance the performance of a cloud system with limited computing resources while minimizing the time and cost. Finally, we analyze the performance of the proposed MCSO with the existing methods, such as Bee Life Algorithm (BLA), A Time–Cost aware Scheduling (TCaS) algorithm, Modified Particle Swarm Optimization (MPSO), and Round Robin (RR), for the evaluation metrics makespan and cost. Based on the outcomes of the experiments, it can be inferred that the proposed MCSO provides essential schedule jobs with the shortest makespan and average cost. Index Terms— Task Scheduling, Multiobjective, Cloud Computing, Cost, Makespan.
A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute

This manuscript presents a full duplex communication system for the Deaf and Mute (D-M) based on Machine Learning (ML). These individuals, who generally communicate through sign language, are an integral part of our society, and their contribution is vital. They face communication difficulties mainly because others, who generally do not know sign language, are unable to communicate with them. The work presents a solution to this problem through a system enabling the non-deaf and mute (ND-M) to communicate with the D-M individuals without the need to learn sign language. The system is low-cost, reliable, easy to use, and based on a commercial-off-the-shelf (COTS) Leap Motion Device (LMD). The hand gesture data of D-M individuals is acquired using an LMD device and processed using a Convolutional Neural Network (CNN) algorithm. A supervised ML algorithm completes the processing and converts the hand gesture data into speech. A new dataset for the ML-based algorithm is created and presented in this manuscript. This dataset includes three sign language datasets, i.e., American Sign Language (ASL), Pakistani Sign Language (PSL), and Spanish Sign Language (SSL). The proposed system automatically detects the sign language and converts it into an audio message for the ND-M. Similarities between the three sign languages are also explored, and further research can be carried out in order to help create more datasets, which can be a combination of multiple sign languages. The ND-M can communicate by recording their speech, which is then converted into text and hand gesture images. The system can be upgraded in the future to support more sign language datasets. The system also provides a training mode that can help D-M individuals improve their hand gestures and also understand how accurately the system is detecting these gestures. The proposed system has been validated through a series of experiments resulting in hand gesture detection accuracy exceeding 95%.
A study on the challenges and solutions of blockchain interoperability

With the development of blockchain technology, a variety of mutually independent blockchains such as Bitcoin and Ethereum, as well as private and consortium chains, coexist in large numbers. As a linker of the blockchain network, blockchain interoperability will link more blockchain ecosystems, realize the interconnection of the blockchain and enhance the scalability of the blockchain. This paper summarizes typical blockchain interoperability projects. Firstly, it proposes a blockchain interoperability definition and an architecture model. Secondly, it analyzes technical characteristics of blockchain interoperability, and typical blockchain interoperability cases are compared. Finally, the trend of future development of blockchain interoperability technology is summarized and discussed.
Temporal-Viewpoint Transportation Plan for Skeletal Few-Shot Action Recognition

We propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE). To factor out misalignment between query and support sequences of 3D body joints, we propose an advanced variant of Dynamic Time Warping which jointly models each smooth path between the query and support frames to achieve simultaneously the best alignment in the temporal and simulated camera viewpoint spaces for end-to-end learning under the limited few-shot training data. Sequences are encoded with a temporal block encoder based on Simple Spectral Graph Convolution, a lightweight linear Graph Neural Network backbone. We also include a setting with a transformer. Finally, we propose a similarity-based loss which encourages the alignment of sequences of the same class while preventing the alignment of unrelated sequences. We show state-of-the-art results on NTU-60, NTU-120, Kinetics-skeleton and UWA3D Multiview Activity II.
Rendering massive indoor point clouds in virtual reality

Abstract This paper addresses the challenges of rendering massive indoor point clouds in Virtual Reality. In these kind of visualizations the point of view is never static, imposing the need of a one-shot (i.e. non-iterative) rendering strategy, in contrast with progressive refinement approaches that assume that the camera position does not change between most consecutive frames. Our approach benefits from the static nature of indoor environments to pre-compute a visibility map that enables us to boost real-time rendering performance. The key idea behind our visibility map is to exploit the cluttered topology of buildings in order to effectively cull the regions of the space that are occluded by structural elements such as walls. This does not only improve performance but also the visual quality of the final render, allowing us to display in full detail the space and preventing the user to see the contiguous spaces through the walls. Additionally, we introduce a novel hierarchical data structure that enables us to display the point cloud with a continuous level of detail with a minimal impact on performance. Experimental results show that our approach outperforms state-of-the-art techniques in complex indoor environments and achieves comparable results in outdoor ones, proving the generality of our method.
Adaptive High-order Variation De-noising Method for Edge Detection with Wavelet Coefficients

This study discusses the high-order diffusion method in the wavelet domain.It aims to improve the edge protection capability of the high-order diffusion method using wavelet coefficients that can reflect image information.During the first step of the proposed diffusion method, the wavelet packet decomposition is a more refined decomposition method that can extract the texture and structure information of the image at different resolution levels.The highfrequency wavelet coefficients are then used to construct the edge detection function.Subsequently, because accurate wavelet coefficients can more accurately reflect the edges and details of the image information, by introducing the idea of state weight, a scheme for recovering wavelet coefficients is proposed.Finally, the edge detection function is constructed by the module of the wavelet coefficients to guide high-order diffusion, the denoised image is obtained.The experimental results showed that the method presented in this study improves the denoising ability of the high-order diffusion model, and the edge protection index (SSIM) outperforms the main methods, including the block matching and 3D collaborative filtering (BM3D) and the deep learning-based image processing methods.For images with rich textural details, the present method improves the clarity of the obtained images and the completeness of the edges, demonstrating its advantages in denoising and edge protection.
Trace element geochemical imprints and multi-path health risk assessment of potentially toxic elements in soils from the polymetallic area of tashan-jatau, northwestern nigeria

The occurrence of Potentially toxic elements (PTEs) in the soil phase may portend environmental, ecological and health-related risks. Hence, this study has combined high-precision geochemical analyses (X-ray fluorescence (XRF) and inductively Coupled Plasma Mass Spectrometry (ICP-MS)), quantitative soil pollution indices and health risk assessment modelling for a holistic and precise assessment of the intrinsic vulnerability of the soil system to contamination. Geochemical analysis showed that the soils are highly enriched with PTEs such as V (mean = 449.5 mg/L), Cr (mean = 529.9 mg/L), Cu (mean = 374.4 mg/L), Sr (mean = 1365.3 mg/L), Zr (mean = 2703 mg/L), Zn (mean = 282.5 mg/L), and Pb (mean = 127.3 mg/L). Quantitative soil pollution assessment (contamination factor and enrichment factor) revealed that besides mining, the distribution and association between trace elements and oxides were from surface environmental conditions (including mobility potential, leaching metal-complexation, weathering and oxidation of parent material). Health risk assessment based on hazard quotient and hazard index revealed that the inhabitants are generally more exposed to risks from toxic elements ingestion than dermal contact and inhalation; the children are vulnerable to risks than adults. The cancer risks from ingestion and dermal contact of As and Cr for both children and adults are relatively higher (&gt; 1.0E−04) than the acceptable range; although the children population seems to be more susceptible to cancer risks due to lower body weight.&#x0D;
Novel Robotic Arm Working-Area AI Protection System

From traditionally handmade items to the ability of people to use machines to process and even to human-robot collaboration, there are many risks. Traditional manual lathes and milling machines, sophisticated robotic arms, and computer numerical control (CNC) operations are quite dangerous. To ensure the safety of workers in automated factories, a novel and efficient warning-range algorithm is proposed to determine whether a person is in the warning range, introducing YOLOv4 tiny-object detection algorithms to improve the accuracy of determining objects. The results are displayed on a stack light and sent through an M-JPEG streaming server so that the detected image can be displayed through the browser. According to the experimental results of this system installed on a robotic arm workstation, it is proved that it can ensure recognition reaches 97%. When a person enters the dangerous range of the working robotic arm, the arm can be stopped within about 50 ms, which will effectively improve the safety of its use.