Reviewerly/exHarmony huggingface dataset:
General Natural Language Processing Translation Strategy and Simulation Modelling Application Example

In this paper, we describe the fundamental problem of how to translate natural language into computationally guided processes and suggest a straightforward strategy of implementing natural language processing by means of this relating the abstract word topology dimension towards a numerical, orgitonal and restricted or compressed dimensional of an abstract meta-mesh description and hence cybernetic modelling space. By this, natural language is a guide towards a scientific meshed topology of events or room-time configurations in a virtual bifurcating meaning room of natural language. We first sketch the general strategy and then apply the notion of the term ‘motion’ to explore meshed unities of this abstraction dimension space by this compressing meaning and hence information, in our abstract description state space of the orgitonal-osmotic based paradigm denoted by directions, flows and distance-sets, as well as Markovian transition calculus indicating the future use of matrix algebra in a demonstrative descriptive example.
An Improved Random Forest Algorithm Based on Spark

Classification algorithms are an important branch of data mining, and are also of great importance in the era of BD.The random forest algorithm (RFA) is one of the classification algorithms and is widely used in various industries for its good classification performance.However, the performance of RFA is not so good when dealing with high-dimensional data and unbalanced data.The main objective of this paper is to improve the RFA based on Spark.In this paper, we read a large amount of relevant algorithm literature in terms of algorithm research, and gain a comprehensive understanding of what feature selection and unbalanced classification are, as well as what characteristics they have and how these problems should be solved.It then focuses on how some domestic and international scholars have solved these problems.This paper focuses on studying and analysing the strengths and weaknesses of the RFA, and makes relevant improvements to address the two weaknesses of the RFA.In order to solve the problems of the RFA in the field of feature selection and the field of unbalanced classification, the optimization is improved respectively, and the parallelized design of the optimized algorithm is finally implemented on Spark.
A differential correction based shadow removal method for real-time monitoring

Shadow removal is an important issue in the field of motion object surveillance and automatic control. Although many works are concentrated on this issue, the diverse and similar motion patterns between shadows and objects still severely affect the removal performance. Constrained by the computational efficiency in real-time monitoring, the pixel feature based methods are still the main shadow removal methods in practice. Following this idea, this paper proposes a novel and simple shadow removal method based on a differential correction calculation between the pixel values of Red, Green and Blue channels. Specifically, considering the fact that shadows are formed because of the occlusion of light by objects, all the reflected light will be attenuated. Hence there will be a similar weakening trends in all Red, Green and Blue channels of the shadow areas, but not in the object areas. These trends can be caught by differential correction calculation and distinguish the shadow areas from object areas. Based on this feature, our shadow removal method is designed. Experiment results verify that, compared with other state-of-the-art shadow removal methods, our method improves the average of object and shadow detection accuracies by at least 10% in most of the cases.
The development flipbook explainer of transformation geometry

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Suherman, Yarman, Defri Ahmad, Nurul Afifah Rusyda; The development flipbook explainer of transformation geometry. AIP Conference Proceedings 7 February 2023; 2698 (1): 060027. https://doi.org/10.1063/5.0122661 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
3D spatial distortion model based on the Lagrange difference in a binocular visual system

The accuracy of binocular visual system calibration using the traditional method is poor in the depth direction. To enlarge the high-accuracy field of view (FOV) of a binocular visual system, a 3D spatial distortion model (3DSDM) based on the 3D Lagrange difference is proposed to minimize 3D space distortion. In addition, a global binocular visual model (GBVM) is proposed that contains the 3DSDM and a binocular visual system. The GBVM calibration method and 3D reconstruction method are based on the Levenberg–Marquardt method. Experiments were carried out to verify the accuracy of our proposed method by measuring the length of the calibration gauge in a 3D space. Experiments show that compared to traditional methods our method can improve the calibration accuracy of a binocular visual system. Our GBVM has a lower reprojection error, higher accuracy, and a larger working field.
A Voting-Based Poisoning Attack to Federated Learning with Quantization

Federated Learning (FL) enables many clients (e.g., mobile devices) to train a global model collaboratively, while keeping their training data distributed. Despite its effectiveness in preserving the user privacy, FL is known to be vulnerable to poisoning attacks that malicious clients try to damage the global model accuracy by sending malicious gradients during the training process. However, previous poisoning attacks do not consider model compression, which is significant to address the communication bottleneck in FL. In this work, we demonstrate that three existing poisoning attacks are not effective when applying SignSGD, a widely used model compression method. We further propose a new attack called voting attack, which tries to craft gradients that deviate far from the benign gradients in the compressed domain, to compromise FL training with SignSGD. Our empirical results on two real-world datasets show that our new voting attack can substantially reduce the testing accuracy. We show that less than 20% compromised clients are sufficient to degrade the test accuracy of models trained on CIFAR10 or Fashion-MNIST.
Semantic Similarity in a Taxonomy by Evaluating the Relatedness of Concept Senses with the Linked Data Semantic Distance

This paper addresses the information-theoretic definition of semantic similarity based on the notion of information content, and presents an evolution of a novel approach for evaluating semantic similarity in a taxonomy. Such an approach takes into account not only the generic sense of a concept but also its intended sense in a given context. In particular, a method for computing the semantic relatedness of concepts in RDF knowledge graphs is used for evaluating the relevance of the intended sense of a concept with respect to its generic sense. The experiment of this work shows that the relatedness method based on triple patterns adopted in this paper leads to higher correlation values with human judgment with respect to the ones obtained according to the original proposal that is based on a triple weights relatedness measure.
Next POI Recommender System: Multi-view Representation Learning for Outstanding Performance in Various Context

Location-based Social Networks (LBSNs) are software service that enable a user to find knowledge and to socialize with other users by offering other user's contents (e.g. reviews, photos, etc.) to a user. This LBSNs have many sub-fields, but Point-of-Interest (POI) recommendation is the most important. Because it is related to the growth of Small and Medium Enterprise (SME) by increasing visitation rate. Generally, it should be possible to respond to various contexts of users in POI recommendation. These contexts are very various and complex, but we define mainly three contexts based on user behavior in local domain. However, each context is defined by different user behavior, so each model and performance are different on various evaluation criteria. In other words, no model is outstanding in all contexts. Therefore, this paper introduces how to define each context, how to make POI embedding for recommendation in empirical multi-view representation learning technique, and how to make optimized POI embedding which is outstanding performance in all contexts of POI recommendation, for various downstream tasks.
MULTIFACTOR MODEL FOR PRICING CRYPTOCURRENCIES

The main aim of the paper is to contribute to the discussion of cryptocurrency pricing through asset pricing models.The paper develops multifactor models for the cryptocurrency valuation and discusses the elements of these models based on the literature review of scholarly articles, books, and scientific databases.We further developed the models from unit factor to five factors in order to observe whether the development is achieved by adding new factors into the model.Consequently, we pointed out the most influential factor that determines the value of cryptocurrencies.
WAMF-FPI: A Weight-Adaptive Multi-Feature Fusion Network for UAV Localization

UAV localization in denial environments is a hot research topic in the field of cross-view geo-localization. The previous methods tried to find the corresponding position directly in the satellite image through the UAV image, but they lacked the consideration of spatial information and multi-scale information. Based on the method of finding points with an image, we propose a novel architecture—a Weight-Adaptive Multi-Feature fusion network for UAV localization (WAMF-FPI). We treat this positioning as a low-level task and achieve more accurate localization by restoring the feature map to the resolution of the original satellite image. Then, in order to enhance the ability of the model to solve multi-scale problems, we propose a Weight-Adaptive Multi-Feature fusion module (WAMF), which introduces a weighting mechanism to fuse different features. Finally, since all positive samples are treated in the same way in the existing methods, which is very disadvantageous for accurate localization tasks, we introduce Hanning loss to allow the model to pay more attention to the central area of the target. Our model achieves competitive results on the UL14 dataset. When using RDS as the evaluation metric, the performance of the model improves from 57.22 to 65.33 compared to Finding Point with Image (FPI). In addition, we calculate the actual distance errors (meters) to evaluate the model performance, and the localization accuracy at the 20 m level improves from 57.67% to 69.73%, showing the powerful performance of the model. Although the model shows better performance, much remains to be done before it can be applied.
Two-photon absorption dynamics by a rectangular entangled-photon pulse with time delay

Abstract We theoretically analyze the transient dynamics in two-photon absorption (TPA) driven by a rectangular pulse consisting of entangled photons with time delay in terms of verifying the quantum virtual-state spectroscopy (VSS) method. We consider two types of three-level atomic systems: one is the atomic system having an intermediate state between ground and excited states, considered in common TPA, and another is the atomic system having an intermediate state above the excited state, assumed in the original VSS theory. We show that for common TPA configuration VSS does not occur at all, whereas for the original VSS configuration VSS-like signals can be obtained regardless of whether the incident photons are entangled or not, where photon entanglement simply contributes to the enhancement of the signal. In addition, by introducing frequency cutoff to the spectrum of incident photons, we also show that the VSS-like signal disappears even for the original VSS configuration when the intermediate state is completely virtually excited. Thus, the obtained results suggest that both the broad spectrum of incident photons causing the real excitation of intermediate state and energy configuration of intermediate state above the excited state are essential for VSS-like signals and that VSS in itself does not occur in a strict sense at least. The effect of unphysical parameters appearing in the original VSS scheme, recently theoretically predicted, is also discussed.
Drone Detection Using Deep Learning: A Benchmark Study

Since Unmanned Aerial Vehicles (UAVs) became available to the civilian public, it has witnessed dramatic spread and exponential popularity. This escalation gave rise to privacy and security concerns, both on the recreational and institutional levels. Although it is mainly used for leisure and productivity activities, it is evident that UAVs can also be used for malicious purposes. Today, as legislation and law enforcement federations can hardly control every incident, many institutions resort to surveillance systems to prevent hostile drone intrusion. Although drone detection can be carried out using different technologies, such as radar or ultra-sonic, visual detection is arguably the most efficient method. Other than being cheap and readily available, cameras are typically a part of any surveillance system. Moreover, the rise of deep learning and neural network models rendered visual recognition very reliable [9, 21]. In this work, three state-of-the-art object detectors, namely YOLOv4, SSD-MobileNetv1 and SSD-VGG16, are tested and compared to find the best performing detector on our drone data-set of 23,863 collected and annotated images. The main work covers detailed reportage of the results of each model, as well as a comprehensive comparison between them. In terms of accuracy and real-time capability, the best performance was achieved by the SSD-VGG16 model, which scored average precision (AP50) of 90.4%, average recall (AR) of 72.7% and inference speed of 58 frames per second on the NVIDIA Jetson Xavier kit.
LED: Learnable Encryption with Deniability

User privacy is an important issue in the cloud machine learning service. In this paper, we raise a new threat about the online machine learning service, which comes from outside superior authority. The authority may ask the user and the cloud to disclose secrets and the authority can monitor the user behavior. We propose a protection approach called learnable encryption with deniability (LED), which can convince the outsider of the fake data and can protect the user privacy.
Smart Management Attendance System with Facial Recognition Using Computer Vision Techniques on the Raspberry Pi

In this study, a smart attendance system was created using computer vision techniques embedded in the Raspberry Pi device. The initial process is carried out by recording students taking certain courses and taking facial images for the needs of the system database. In the next stage, the system will be regulated according to the time of lecture entry to determine which students will attend the lecture. Every student who wants to enter the classroom is identified by taking facial images with a camera from the Raspberry Pi device to identify and determine the time students enter to attend lectures. Each image taken will be processed to detect the presence of a face using the Viola-Jones method and to extract features using the LBP method to obtain the feature value of each image. The results obtained will be stored in the system for the facial recognition process. The final stage of the system being built is to perform face recognition according to the initial image to carry out the attendance process. This process will be carried out using the normalized cross-correlation (NCC) technique, in which the highest feature similarity obtained between the initial image and the newly captured image is the result of recognition by the system. From the trials that have been carried out, the developed system gives good results in obtaining attendance management in a fairly efficient manner, and the algorithm proposed for facial recognition obtains good results with an accuracy rate of 97.54%.
Integration of a three-colour channel with the four diagonals of the GLCM approach for similar image retrieval

Feature extractions are perceptive problems due to differences of opinion in image assets and contents. An image has strong texture and colour channels, i.e., a gray-level cooccurrence matrix (GLCM) and a Red, Green and Blue (RGB) channel. We proposed an algorithm for the integration of three colours with diagonal approach method (ITDM) in combined colour and GLCM significant image features, which are used for image retrieval and reduced time. It has four steps: 1) Extract the colour channel, which is RGB, YCbCr, and L* a* b*; 2) Extract the colour channel of G, Y, and L*; 3) Apply the GLCM method in the G, Y, and L* channels and 4) Image features are based on the classification of features by the C-means algorithm and re-ranking methods. To develop an image, simplify the computation of image retrieval methods. A series of analyses and comparisons are performed in the experiment and proven to enhance the retrieval rate.
Data workflows and visualization in support of surveillance practice

The Swedish National Veterinary Institute (SVA) is working on implementing reusable and adaptable workflows for epidemiological analysis and dynamic report generation to improve disease surveillance. Important components of this work include: data access, development environment, computational resources and cloud-based management. The development environment relies on Git for code collaboration and version control and the R language for statistical computing and data visualization. The computational resources include both local and cloud-based systems, with automatic workflows managed in the cloud. The workflows are designed to be flexible and adaptable to changing data sources and stakeholder demands, with the ultimate goal to create a robust infrastructure for the delivery of actionable epidemiological information.
Contour extraction method and implementation of active contour model algorithm based on N-order Bezier curve for cardiac medical images

Optimize the active contour model. In the process of optimization, the n-order Bezier model curve is used to smooth the evolution curve of the active contour model to make it more smooth. The optimized curve is applied to the contour extraction and analysis of medical image, so as to promote the processing of medical image. The n-order Bezier curve algorithm is used to optimize the active contour model algorithm. The active contour model is a discrete point curve, and the active contour is smooth and seamless. The active contour obtained by the optimized algorithm is smooth and seamless, which is more suitable for image processing operations such as medical image and contour extraction of medical image. The algorithm of using n-order Bezier curve to optimize the active contour model should be applied to the image analysis of medical images.
Covert Communication over Federated Learning Channel

We propose a novel covert communication technique between the federated learning (FL) server and participants without affecting the FL performance. The FL server superimposes the covert message onto the aggregated gradient and broadcasts the superimposed signal to all FL participants. FL participants decode the covert message treating the aggregated gradient as interference, and restore the original global model after removing the covert message from the superimposed signal. Therefore, the FL performance is not affected by sending the covert message. We analyze the covertness of communication against the adversary that monitors the statistical distribution of model updates. We derive the maximum achievable transmission rate of the covert message without being detected by the adversary and without affecting the federated learning performance.
Analysis of Ethereum versus Bitcoin: The GARCH approach

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Suraya Fadilah Ramli, Zahrul Azmir A. B. S. L. Kamarul Adzhar, Syed Anand Najmi Sayed Abu Bashar, Muhammad Fikri Abdullah; Analysis of Ethereum versus Bitcoin: The GARCH approach. AIP Conference Proceedings 8 February 2023; 2500 (1): 020049. https://doi.org/10.1063/5.0112690 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Validation and implementation of Aperio LV1 remote live view telepathology system for intraoperative frozen section diagnosis

Telepathology (TP) allows for remote slide review with performance comparable to traditional light microscopy. Use of TP in the intraoperative setting allows for faster turnaround and greater user convenience by obviating the physical presence of the attending pathologist. We sought to perform a practical validation of an intraoperative TP system using the Leica Aperio LV1 scanner in tandem with Zoom teleconferencing software. A validation was performed in accordance with recommendations from CAP/ASCP, using a retrospectively identified sample of surgical pathology cases with a 1 year washout period. Only cases with frozen-final concordance were included. Validators underwent training in the operation of the instrument and conferencing interface, then reviewed the blinded slide set annotated with clinical information. Validator diagnoses were compared to original diagnoses for concordance. 60 slides were chosen for inclusion. 8 validators completed the slide review, each requiring 2 h. The validation was completed in 2 weeks. Overall concordance was 96.4%. Intraobserver concordance was 97.3%. No major technical hurdles were encountered. Validation of the intraoperative TP system was completed rapidly and with high concordance, comparable to traditional light microscopy. Institutional teleconferencing implementation driven by the COVID pandemic facilitated ease of adoption.
Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition

Automatic speech recognition models are often adapted to improve their accuracy in a new domain. A potential drawback of model adaptation to new domains is catastrophic forgetting, where the Word Error Rate on the original domain is significantly degraded. This paper addresses the situation when we want to simultaneously adapt automatic speech recognition models to a new domain and limit the degradation of accuracy on the original domain without access to the original training dataset. We propose several techniques such as a limited training strategy and regularized adapter modules for the Transducer encoder, prediction, and joiner network. We apply these methods to the Google Speech Commands and to the UK and Ireland English Dialect speech data set and obtain strong results on the new target domain while limiting the degradation on the original domain.