Reviewerly/exHarmony huggingface dataset:
Scaling Large RDF Archives To Very Long Histories

In recent years, research in RDF archiving has gained traction due to the ever-growing nature of semantic data and the emergence of community-maintained knowledge bases.Several solutions have been proposed to manage the history of large RDF graphs, including approaches based on independent copies, time-based indexes, and change-based schemes.In particular, aggregated changesets have been shown to be relatively efficient at handling very large datasets.However, ingestion time can still become prohibitive as the revision history increases.To tackle this challenge, we propose a hybrid storage approach based on aggregated changesets, snapshots, and multiple delta chains.We evaluate different snapshot creation strategies on the BEAR benchmark for RDF archives, and show that our techniques can speed up ingestion time up to two orders of magnitude while keeping competitive performance for version materialization and delta queries.This allows us to support revision histories of lengths that are beyond reach with existing approaches.
Hamming distributions of popular perceptual hashing techniques

Content-based file matching has been widely deployed for decades, largely for the detection of sources of copyright infringement, extremist materials, and abusive sexual media. Perceptual hashes, such as Microsoft’s PhotoDNA, are one automated mechanism for facilitating detection, allowing for machines to approximately match visual features of an image or video in a robust manner. However, there does not appear to be much public evaluation of such approaches, particularly when it comes to how effective they are against content-preserving modifications to media files. In this paper we present a million-image scale evaluation of several perceptual hashing archetypes for popular algorithms (including Facebook’s PDQ, Apple’s Neuralhash, and the popular pHash library) against seven image variants. The focal point is the distribution of Hamming distance scores between both unrelated images and image variants to better understand the problems faced by each approach.
Federated Learning With Personalized Differential Privacy Combining Client Selection

Federated Learning(FL) enables clients to construct a global model collaboratively while protecting the security and privacy of clients' datasets. Differential privacy(DP) is a common method to protect clients' data privacy in FL. However, most of existing works assume the privacy needs of all clients are the same, which is rare in reality, making it difficult to apply their approaches to realistic scenarios. What's more, existing works considering DP protection are not combined with client selection, which will result in clients with the same dataset quality but different added noise introduced by DP mechanism having the same chance of being selected, which is likely to harm model performance. In this work, we consider personalized DP(PDP) setting, i.e. each client has personal privacy need. We conduct a convergence analysis of FL with PDP and client selection. We find that for fast convergence and small bias, we prefer clients with high local loss and small added noise. Moreover, the influence of noise on the later stage of training is greater than that in the earlier stage. According to the analysis result, we propose a novel mechanism, containing a PDP mechanism combining sampling for small added noise while meeting the privacy needs of clients and a client selection mechanism with a novel metric score which considers the local loss and privacy needs of clients at the same time. Our experiments demonstrate that the performance of our mechanism is better than the baseline mechanism.
System Identification Using Kalman Filter and Extended Kalman Filter

This chapter presents the standard Kalman filter (KF) and the extended Kalman filter (EKF). The detailed derivations of the discrete-time KF and EKF are presented from a Bayesian perspective. In order to formulate the KF algorithm, the state space model of a linear dynamical system is introduced. By using the Bayes’ theorem, the conditional probability density function for prediction can be obtained in a recursive manner and the analytical solutions can be obtained. Applications for a vehicle tracking system and a structural state estimation problem are provided to demonstrate the state tracking capability of the KF algorithm. Afterwards, the nonlinear state space model for general nonlinear dynamical systems is introduced and it will be utilized in the EKF. By expanding the nonlinear equations using the Taylor series expansion, the locally linearized state space model can be obtained and the procedures of the EKF algorithm are formulated in the same manner as the standard KF algorithm. The EKF with fading memory is introduced to enhance the tracking capability for time-varying systems. Applications to simultaneous states and model parameters estimation are presented. The KF algorithm and its variants provide an effective and efficient way for recursive estimation. More importantly, the EKF is the fundamental algorithm in this book.
An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter

Automated human tracking in real time has been applied in many areas such as security, surveillance, traffic control, and robots. In this paper, an improvement of the Camshift human tracking algorithm based on deep learning and the Kalman filter is proposed. To detail an approach by using YOLOv4-tiny to detect a human in real time, Camshift is used to track a particular person and the Kalman filter is applied to enhance the performance of this algorithm in case of occlusion, noise, and different light conditions. The experiments show that the combination of YOLOv4-tiny and the improved Camshift algorithm raises the standard of speed as well as robustness. The proposed algorithm is suitable for running in real time and adapts well to the same color and different light conditions.
The perspectives for mobile application use in media education

The article shows the results of a situational analysis of a case study with the elements of an experiment to find out the expediency of mobile application use in media education. The case study is aimed at researching the process of mobile learning within a single social object – a group of 32 students, members of the workshop “Young Journalists”, aged 8–14. We used a beta version of the application, which is now about to go into open access on the platform GooglePlay. In the course of development, the function was tested involving the focus group. According to the testing results, adjustments and upgrades to the application services were made. The results prove that the effective version of the mobile application is a communicative platform that can perform the functions of a presenter of theoretical information, a simulator of practical skills, and a space for checking the gained knowledge. This platform can also provide wide opportunities for interaction between teachers and members of the educational process. The authors created a communicative environment as a closed social network, which will encourage students to learn, give parents information about their children’s achievements, and spread the material created by the students.
Research on Chinese content monitoring technology of darknet based on Scrapy

With the development of anonymous network technology and the intensification of fighting domestic cyber crimes, more criminals tend to use the anonymity of Tor network to trade illegal and contraband goods and release reactionary remarks on the darknet in order to evade the supervision of public security departments, seriously endangering national security. Efficient monitoring of Chinese content on the darknet is of practical significance for obtaining investigation clues on the darknet and monitoring online public opinions. This study designs a batch extraction technology of Chinese darknet content based on Scrapy and obtaining the identification code of dark websites. Methods successfully extracts data from several Chinese Darknet online shops as well as Chinese forums, and makes further statistical analysis of the extracted data. Compared with other data monitoring methods on dark websites, this method can improve the extraction efficiency of target sites and has certain versatility, providing effective monitoring methods for fighting against darknet crimes.
Interaction and psychological characteristics of art teaching based on Openpose and Long Short-Term Memory network

As living standards improve, people's demand for appreciation and learning of art is growing gradually. Unlike the traditional learning model, art teaching requires a specific understanding of learners' psychology and controlling what they have learned so that they can create new ideas. This article combines the current deep learning technology with heart rate to complete the action recognition of art dance teaching. The video data processing and recognition are conducted through the Openpose network and graph convolution network. The heart rate data recognition is completed through the Long Short-Term Memory (LSTM) network. The optimal recognition model is established through the data fusion of the two decision levels through the adaptive weight analysis method. The experimental results show that the accuracy of the classification fusion model is better than that of the single-mode recognition method, which is improved from 85.0% to 97.5%. The proposed method can evaluate the heart rate while ensuring high accuracy recognition. The proposed research can help analyze dance teaching and provide a new idea for future combined research on teaching interaction.
Non-fungible Token Price Prediction with Multivariate LSTM Neural Networks

In this paper, we investigate how to forecast Non-Fungible Token (NFT) sale prices by using multiple multivariate time series datasets containing features related to the NFT market space. We examined eight recent studies regarding the forecasting and valuation of NFTs and compared their most important findings. This laid the fundamental work for two separate machine learning prototypes based on Long Short-Term Memory (LSTM) which are able to forecast the sale price history of an individual NFT asset. Root Mean Squared Errors (RMSE) of 0.2975 and 0.24 were obtained which appears to be promising.
ADSSD: Improved Single-Shot Detector with Attention Mechanism and Dilated Convolution

The detection of small objects is easily affected by background information, and a lack of context information makes detection difficult. Therefore, small object detection has become an extremely challenging task. Based on the above problems, we proposed a Single-Shot MultiBox Detector with an attention mechanism and dilated convolution (ADSSD). In the attention module, we strengthened the connection between information in space and channels while using cross-layer connections to accelerate training. In the multi-branch dilated convolution module, we combined three expansion convolutions with different dilated ratios to obtain multi-scale context information and used hierarchical feature fusion to reduce the gridding effect. The results show that on PASCAL VOC2007 and VOC2012 datasets, our 300 × 300 input ADSSD model reaches 78.4% mAP and 76.1% mAP. The results outperform those of SSD and other advanced detectors; the effect of some small object detection is significantly improved. Moreover, the performance of the ADSSD in object detection affected by factors such as dense occlusion is better than that of the traditional SSD.
Applications of Artificial Intelligence (AI) in Marketing Management

Artificial intelligence (AI) in marketing is getting popularity due to the practical impact it will have in both the current business and the industry of the future. It is essential to conduct research studies with a wider scope and coverage on the application of AI in marketing, and it is also essential to conduct meta-analyses of previously conducted studies in order to determine the routes that future research should go. In the most recent decade, the development of artificial intelligence has contributed to an increase in the complexity of marketing. The utilisation of technology to its fullest potential in order to provide a superior experience for customers is the goal of artificial intelligence marketing. In the past several years, it has become increasingly important for companies to identify and comprehend the demands and expectations of their customers in regard to the products and services they provide. Artificial intelligence might be used by marketers to process vast amounts of data, carry out individualised sales, and match the expectations of customers. This research aims to provide a fundamental overview of AI in marketing management, which is the goal of the project.
Adaptive 3D and VFX Films Virtual Learning Environments

In this study, the analysis of the topic “Adaptive 3D and VFX Films Virtual Learning” has been provided. As virtual learning and 3D technologies use are increasing, the interest in their learning in academic discussion is increasing daily. However, there are various drawbacks to the use of3D for learning environments. To solve this drawback, the use of adaptive learning environments is increasing more, such as an environment that can dynamically adapt to the learner and the activities that can be performed by that specific learner. As the new ways of learning have been increasing over the past years (in the times of the COVID-19 Pandemic) through the use of computers in the educational sector. The learning environment has been widely adopted by the educational sectors in the case of obtaining promising outcomes. In recent years, these environments have evolved into more advanced environments with the implication of3D technology. With the help of 3D, these adaptive environments are helping learners according to their preferences.
In silico simulation of hepatic arteries: An open‐source algorithm for efficient synthetic data generation

Abstract Background In silico testing of novel image reconstruction and quantitative algorithms designed for interventional imaging requires realistic high‐resolution modeling of arterial trees with contrast dynamics. Furthermore, data synthesis for training of deep learning algorithms requires that an arterial tree generation algorithm be computationally efficient and sufficiently random. Purpose The purpose of this paper is to provide a method for anatomically and physiologically motivated, computationally efficient, random hepatic arterial tree generation. Methods The vessel generation algorithm uses a constrained constructive optimization approach with a volume minimization‐based cost function. The optimization is constrained by the Couinaud liver classification system to assure a main feeding artery to each Couinaud segment. An intersection check is included to guarantee non‐intersecting vasculature and cubic polynomial fits are used to optimize bifurcation angles and to generate smoothly curved segments. Furthermore, an approach to simulate contrast dynamics and respiratory and cardiac motion is also presented. Results : The proposed algorithm can generate a synthetic hepatic arterial tree with 40 000 branches in 11 s. The high‐resolution arterial trees have realistic morphological features such as branching angles (MAD with Murray's law ), radii (median Murray deviation ), and smoothly curved, non‐intersecting vessels. Furthermore, the algorithm assures a main feeding artery to each Couinaud segment and is random (variability = 0.98 ± 0.01). Conclusions This method facilitates the generation of large datasets of high‐resolution, unique hepatic angiograms for the training of deep learning algorithms and initial testing of novel 3D reconstruction and quantitative algorithms designed for interventional imaging.
Frequency scanning interferometry based deformation monitoring system for the alignment of the FCC-ee machine detector interface

Abstract In a particle accelerator, two particle beams are accelerated and collided at one or several physics detectors. The machine detector interface (MDI) is the place where the accelerator ring is linked to the physics detector. It is the area where the alignment requirements are the most stringent, and the MDI of the lepton version of the future circular collider (FCC-ee) is no exception. It will implement a complex design and alignment requirement for typical components in the MDI are of the order of 30 μm. Until now, no working solutions have been proposed for the alignment of such MDI. In this contribution, two different systems are studied: the Surveillance d’Ouvrages par Fibre Optique (Structural Monitoring by Optical Fibers, SOFO) and the in-lined multiplexed and distributed frequency scanning interferometry (FSI). Both systems allow individual and simultaneous distance measurements of multiple portions along a fiber, and a new system using the FSI technology is proposed for the alignment monitoring. Using a network of helical shaped fiber allows the measurement of deformations if a sufficient amount of fibers is placed on the cylinder. With a sensor measurement accuracy of 1 μm, 3D coordinates of points at the end of a 4 m long and 0.25 m radius cylinder (FCC-ee MDI dimensions) can be determined at an accuracy under 5 μm. This system will provide a link between the alignment of the final focusing quadrupoles and the rest of the machine.
Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection

As a biomarker of depression, speech signal has attracted the interest of many researchers due to its characteristics of easy collection and non-invasive. However, subjects' speech variation under different scenes and emotional stimuli, the insufficient amount of depression speech data for deep learning, and the variable length of speech frame-level features have an impact on the recognition performance.The above problems, this study proposes a multi-task ensemble learning method based on speaker embeddings for depression classification. First, we extract the Mel Frequency Cepstral Coefficients (MFCC), the Perceptual Linear Predictive Coefficients (PLP), and the Filter Bank (FBANK) from the out-domain dataset (CN-Celeb) and train the Resnet x-vector extractor, Time delay neural network (TDNN) x-vector extractor, and i-vector extractor. Then, we extract the corresponding speaker embeddings of fixed length from the depression speech database of the Gansu Provincial Key Laboratory of Wearable Computing. Support Vector Machine (SVM) and Random Forest (RF) are used to obtain the classification results of speaker embeddings in nine speech tasks. To make full use of the information of speech tasks with different scenes and emotions, we aggregate the classification results of nine tasks into new features and then obtain the final classification results by using Multilayer Perceptron (MLP). In order to take advantage of the complementary effects of different features, Resnet x-vectors based on different acoustic features are fused in the ensemble learning method.Experimental results demonstrate that (1) MFCC-based Resnet x-vectors perform best among the nine speaker embeddings for depression detection; (2) interview speech is better than picture descriptions speech, and neutral stimulus is the best among the three emotional valences in the depression recognition task; (3) our multi-task ensemble learning method with MFCC-based Resnet x-vectors can effectively identify depressed patients; (4) in all cases, the combination of MFCC-based Resnet x-vectors and PLP-based Resnet x-vectors in our ensemble learning method achieves the best results, outperforming other literature studies using the depression speech database.Our multi-task ensemble learning method with MFCC-based Resnet x-vectors can fuse the depression related information of different stimuli effectively, which provides a new approach for depression detection. The limitation of this method is that speaker embeddings extractors were pre-trained on the out-domain dataset. We will consider using the augmented in-domain dataset for pre-training to improve the depression recognition performance further.
Turbo: SmartNIC-enabled Dynamic Load Balancing of µs-scale RPCs

Online services are decomposed into fine-grained software components that communicate over the network using fine-grained Remote Procedure Calls (RPCs). Inter-server communication often exhibits patterns of wide RPC fan-outs between software tiers, raising the well-known tail at scale effect and necessitating mechanisms that curb long response tail latencies. When handling µs-scale RPCs, request distribution across the cores of multicore servers is a major determinant of the resulting tail latency. Software approaches for inter-core RPC balancing introduce considerable overheads, throttling a server’s peak throughput. On the other hand, existing NIC-based hardware mechanisms ameliorate software and inter-core synchronization overheads, but result in inter-core load imbalance that leaves significant performance improvement headroom.We introduce Turbo, a hardware on-NIC load-balancing mechanism that achieves near-optimal inter-core load distribution for the most fine-grained, light-tailed RPCs with service times of only a couple of µs. We implement Turbo on a programmable NIC and evaluate it on a range of different service time distributions and with a high-performance Key-Value store. Compared to hardware NIC-based mechanisms that statically spread load across cores, Turbo boosts throughput under a 99% latency Service Level Objective (SLO) of 30× the service time by up to 5×, and by up to 95× for a more aggressive 10× SLO target.
A Two-Stage Voting-Boosting Technique for Ensemble Learning in Social Network Sentiment Classification

In recent years, social network sentiment classification has been extensively researched and applied in various fields, such as opinion monitoring, market analysis, and commodity feedback. The ensemble approach has achieved remarkable results in sentiment classification tasks due to its superior performance. The primary reason behind the success of ensemble methods is the enhanced diversity of the base classifiers. The boosting method employs a sequential ensemble structure to construct diverse data while also utilizing erroneous data by assigning higher weights to misclassified samples in the next training round. However, this method tends to use a sequential ensemble structure, resulting in a long computation time. Conversely, the voting method employs a concurrent ensemble structure to reduce computation time but neglects the utilization of erroneous data. To address this issue, this study combines the advantages of voting and boosting methods and proposes a new two-stage voting boosting (2SVB) concurrent ensemble learning method for social network sentiment classification. This novel method not only establishes a concurrent ensemble framework to decrease computation time but also optimizes the utilization of erroneous data and enhances ensemble performance. To optimize the utilization of erroneous data, a two-stage training approach is implemented. Stage-1 training is performed on the datasets by employing a 3-fold cross-segmentation approach. Stage-2 training is carried out on datasets that have been augmented with the erroneous data predicted by stage 1. To augment the diversity of base classifiers, the training stage employs five pre-trained deep learning (PDL) models with heterogeneous pre-training frameworks as base classifiers. To reduce the computation time, a two-stage concurrent ensemble framework was established. The experimental results demonstrate that the proposed method achieves an F1 score of 0.8942 on the coronavirus tweet sentiment dataset, surpassing other comparable ensemble methods.