Reviewerly/exHarmony huggingface dataset:
Restaurant Queuing Time Prediction Using Random Forest Regression

Unknown queuing time often brings negative consumer experience in restaurant service. Prediction of queuing time has been a critical task to benefit both consumers and restaurants. However, current research mainly focuses on the queuing theory, which requires collecting prior knowledge and survey. This paper proposes a queuing time prediction model based on Random Forest Regression with features selection method to ease the limitations of previous research. Random Forest Algorithm have achieved enormous success in various fields. Experiments on a real-life restaurant queuing database validate the practicality of the model. The proposed method outperforms the baseline model on the experimental data. We conclude the intrinsic information of the queuing model with the feature selection method, which will help improve other application models in queuing scenarios.
Realization of elderly fall integration monitoring system based on AlphaPose and YOLOV4

In order to be in a long-term monitoring state when the elderly are not falling, and to save hardware resources as much as possible, and to quickly identify and give an alarm when the elderly have a tendency to fall, this article chooses to use an improved YOLOV4 laid on Jeston nano for rough detection under daily circumstances, and to transmit real-time images to the PC host under abnormal conditions, it uses an Alphapose-based algorithm laid inside it to monitor the details through the instantaneous state angles of the cervical spine and the spine with the xy axis plane.Compared with the current detection system, the proposed multiple Jeston nano+PC cooperation modes save more operating resources. At the same time, the judgment logic for judging falls will have better stability and higher recognition rate than the judgment logic that has been more respected in recent years. At the same time, the speed has also increased many times, confirming the possibility of fall detection integrated into the monitoring system.
End to End Multi-object Tracking Algorithm Applied to Vehicle Tracking

At present, most of the existing multi-object tracking algorithms use the tracking-by-detection structure. On the one hand, these methods can not make full use of the intermediate features of the detector, on the other hand, the way to solve the similarity does not take into account the correlation between objects. At the same time, the existing multi-object tracking methods do not deal with the occluded object features. Based on the above problems, this paper proposes an end-to-end multi-object tracking algorithm, which uses the object deep features transmitted by the detector to directly generate the incidence matrix through the end-to-end association network; At the same time, considering the interference in occlusion, the self attention mechanism is used to enhance the features of the object. In terms of association strategy, this paper uses Hungarian matching algorithm to associate according to the association matrix. The algorithm has carried out a large number of experiments on KITTI data set, achieved 51.80&#x0025; HOTA (high-order tracking accuracy) and 53.77&#x0025; MOTA (multi-object tracking accuracy), and achieved considerable results compared with some existing mainstream methods.
Investigation of the effects of temperature variations on Inertial Measurement Units for UAV

The wide expansion of Unmanned Aerial Vehicles (UAVs) is leading to an increased number of failures and accidents. Sensors and measurement platforms represent some of the most critical items from a reliability point of view because redundancy strategies to avoid failures are usually not applicable due to cost, size, and weight limitations. Furthermore, the operating conditions that the UAV could endure during a single profile mission could be highly variable and highly severe. For these reasons, it is fundamental to investigate the metrological performances and the system reliability of measurement platforms installed on UAVs taking into account the actual environmental conditions during the flight. In this work, a customized measurement setup has been developed to test the response of a set of low-cost Inertial Measurement Units (IMUs) varying the operating temperature of the devices. Temperature dependencies of the IMU’s output have been investigated, taking into account a slow temperature variation within a climatic chamber up to 100 °C. The results emphasize the presence of significant drifts of the sensor’s output even within a limited temperature range, highlighting the effectiveness of a possible error compensation procedure.
Improved garbage image classification for inception-Resnet-V2

In view of the current situation that the development of deep learning convolutional neural network models tends to be high in depth, the number of model parameters is large and the training time is continuously increasing. A structure based on the combination of Inception-ResNet-V2 and depthwise separable convolution is proposed, using depthwise separable volume. The integration separates channels and regions, thereby reducing a large number of parameters; at the same time, in order to extract more critical information in the feature map and improve the classification effect of the model, the attention mechanism CBAM module is introduced. In the experiment, the image of the garbage dataset is used as the experimental object, and the model is trained by data enhancement methods such as image cropping, rotation, and flipping of the original data. In order to verify the best performance of the improved model in the deep network, the experiment conducted model tests of different depths. The experimental results show that as the network continues to deepen, the classification effect of the model shows a trend of increasing first and then decreasing. When the number of Inception-ResNet-A, B, and C modules is 7,14, and 7, the model of the improved Inception ResN et- V2has the highest classification accuracy, reaching 95.80&#x0025;, which is 4.58&#x0025; higher than the Inception-Resnet-V2 classification accuracy, and has less parameters and better classification effect, which has higher practical value.
Face alignment by learning from small real datasets and large synthetic datasets

In recent years, face-related research had a wide variety of real-life applications. However, issues such as privacy violations and data abuse caused by its applications have also triggered global controversy. It is undeniable that face-related technology is efficient and convenient, but the dangers and risks are hidden by face technology should be comprehensively considered. Current face algorithms are still challenging in complex and challenging environments (e.g., large angles or expressions). Firstly the existing public training datasets are mostly frontal faces, which have an unbalanced distribution of challenging data. Secondly, the collected real datasets require explicit user consent, and the annotation process is time-consuming and expensive. In this paper, we open a new research direction through synthetic datasets. We try to use synthetic datasets to reduce the dependence of the model on the real-world data set. The face alignment experiments explore the synthetic dataset&#x0027;s complementarity and availability.
The Quantitative Evaluation of the Pathos to Ethos Ratio in Scientific Texts

In this paper we present the annotation-based argumentation analysis of scientific texts with the aim of quantitatively evaluating their persuasiveness in terms of two infrequently studied persuasion modes: ethos and pathos. The expert annotation of texts is performed manually through the use of web-tools for visualizing argumentative statements and reasoning schemes, along with representing the argumentation structure of a text in form of a directed graph. In these graphs, argumentative statements and connecting arguments (reasoning models) are correspondingly denoted by information nodes and scheme nodes. We describe a method to evaluate the argumentation in a text for ethos and pathos distinctly, by analyzing both the content of information nodes (premises and conclusions) and the functionality of corresponding reasoning models. For evaluating information nodes, we have constructed two dictionaries of argumentation markers that are represented by lexical-syntactical patterns and respectively identify ethos and pathos in collection texts. Quantitative values are calculated for 30 annotated texts of the collection. We employ these values to group these texts into several sets based on the proportion of pathos and ethos.
Cryptocurrency Price forecasting: A Comparative Study of Machine Learning Model in Short-Term Trading

In recent years, the expansion of the cryptocurrency market has received significant attention among investors, studies of cryptocurrency price predictions have been conducted in various fields. With the enhancement of machine learning algorithms and increased computational capabilities, machine learning has proved one of the most efficient cryptocurrency prediction methods. However, most studies focused on single digital currency prediction or small-scale algorithm comparison for multiple currencies. This study aims to present a comparative performance of large-scale selected Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting time series data for a short-term trading period in ten cryptocurrencies (BTC, ETH, ADA, BNB, XRP, DOGE, LUNA, LINK, LTC, and BCH) with ten selected machine learning algorithms (Decision Tree, Linear Regression, Ridge Regression, Lasso Regression, Bayesian Regression, Random Forest, K-Nearest Neighbors, Neural Networks, Gradient Boosting, and Support Vector Machine). Our experiment results show that the Gradient Boosting with the mean square error criterion is superior in predicting most major cryptocurrencies by performing statistical analysis and data visualizations. Additionally, the Random Forest and Decision Tree model built by the Classification and Regression Tree algorithm also shows outstanding performance in certain currencies such as ETH, XRP, LUNA, and LTC. Thus, all three algorithms can help anticipate the short-term evolutions of the cryptocurrency market.
GGAT: Knowledge Graph Embedding Model via Global Information

Recently, knowledge graph embedding model based on Graph Attention Network (GAT) has shown great potential in link prediction task. However, the existing GAT based models ignore the global information in the neighborhood. We propose GGAT, a knowledge graph embedding model based on global information. The encoding ability of GGAT is enhanced by using global information. Meanwhile, we employ multi-head attention mechanism to improve GGAT&#x0027;s perception of the interaction between entities in the neighborhood. In addition, GGAT uses residual structure to improve the stability of the model and the ability to perceive remote semantic connections. Experiments on two link prediction benchmarks demonstrate the proposed key capabilities of GGAT. Moreover, we set a new state-of-the-art on a knowledge graph dataset.
Time-aware Multi-layer Interest Extraction Network for Click-Through Rate Prediction

Click-through rate (CTR) prediction, which is used to estimate the probability of a user clicking on a candidate item, acts as a core task in recommender system. Previous researchers model user&#x0027;s historical behaviors as a sequence and apply sequential models to extract user interests. However, user behaviors result from multiple factors, including not only their interests but also the time, especially in time-sensitive scenes. While a few researchers have considered behavior time in sequential modeling, the target predicted time is still ignored. In this paper, we propose a novel network for CTR prediction dubbed Time-aware Multi-layer Interest Extraction network (TMIE), which considers the influence imposed by user behavior time and the target predicted time along-side with modeling user interests. Specifically, we design and employ time-aware GRU as low-layer interest extractor to capture primary interests. Then simplified transformer is applied as high-layer extractor to further explore the mutual relevance among user&#x0027;s interests. We perform abundant comparative experiments on both public and industrial datasets and the excellent results demonstrate the rationality and effectiveness of our methods. Notably, our heuristic work is an exciting attempt to catch up the synergistic impact of behavior time and multi-layer user interests in CTR prediction.
Fall Detection and Warning System for Nursing Homes based on Bluetooth Low Energy

Fall accidents are a frequent problem with the elderly and lead to severe injuries and/or could have a lethal ending. To prevent these potential deaths the nursing personnel visits the elderly on a regular basis. This has an enormous influence on the mental and physical capabilities of the nursing personnel. In combination with the ever-growing presence of Internet of Things (IoT) applications, this paper proposes a low-power wireless fall detection and warning system based on Bluetooth Low Energy (BLE). The aim of the system is to lower the workload of the nursing personnel and prevent elderly from dying from hypothermia. The system consists of a patient wearable (P) monitoring the movement of the elderly, a Detection Node (DN) scanning the room of the elderly to pinpoint the position of the fallen elderly, multiple Network Nodes (NNs) in the hallways sending the alert messages to the closest caretaker wearing a Caretaker Node (CN). This node visualizes all vital parameters, so the nursing personnel can help in the fastest way possible. A proof-of-concept is proposed in this paper, together with measurements and power analysis.
Secure and Efficient Web of Things Digital Twins using Permissioned Blockchains

The growing popularity and features of digital twins have made them key elements in Industry 4.0 and the Internet of Things (IoT). In this paper, we propose the use of digital twins as an indirection mechanism, instead of accessing directly the actual IoT devices, in order to offer secure sensing and actuation in IoT systems through Distributed Ledger Technologies (DLTs), which can enhance the security and transparency of such digital twins. In particular, we leverage advances in Web of Things (WoT) and permissioned blockchains to build flexible and secure digital twins. Specifically, we build smart contract-based digital twins of IoT devices that follow W3C's WoT standards. This architecture offers decentralization, immutability, auditability, and enhanced availability and reliability.
Research on Adversarial Attack Technology for Object Detection in Physical World Based on Vision

Deep learning has achieved excellent results in various tasks of computer vision and has surpassed human performance even in some specific scenarios. Object detection is an important task in computer vision and widely used in fields of autonomous driving, precision medicine, and military object detection. However, the existence of adversarial perturbations and patches expose the object detection model to serious security threats. This brings difficulties to the wide application of the object detection model. In this paper, we talk about physical world attack methods in object detection in three lines: background introduction, research progress and future directions, in order to provide references for subsequent research.
Crowd and Group Detection in Shopping Centre Using Mask R-CNN

This paper presents a mask R-CNN model and a dataset to detect crowd and group of people in one of the shopping areas in Indonesia. The dataset presented is the real condition of people behavior during Covid-19 pandemic. The dataset created have a total of 760 Pictures with more than 4500 total annotated objects classified in three classes (Person, Group, and Crowd) that is divided into train, validation, and test dataset. The model then created using Mask R-CNN with Resnet 101 Backbone using COCO Pre-weight. Using a dynamic learning rate, the model has an accuracy of 88.35% and 0.772 validation-loss while tested on test dataset. Our contribution includes creating a workflow of mask R-CNN implementation that could be implemented in various shopping centers and a Crowd detection model that can be use to create an automatic monitoring system.
Performance analysis of SQL Prepared Statements in CRUD operations

In a web application, communication between the application and the database server is one of the most resource intensive operations which therefore requires optimization. Database optimization is a challenging task where even a minor change can have a big impact on overall application performance. Prepared statements bring two main advantages: security and consistency. They are often considered to also have a performance advantage but, in this paper, it will be shown how prepared statements can have decremental performance impact, and when they should and should not be used if resources and performance is a concern.
An Auditory Measure for Anomaly Detection based on Auto-encoders

We aim at detecting anomalies of several hydro-turbines and electric generators in a power plant based on their auditory signals. Auto-encoders implemented with artificial neural networks are used for this task. For each device, an auto-encoder is trained to describe the audio properties of normal signals of the device. For inference, conventionally, residual spectra between input and prediction produced by auto-encoders are used for anomalies detection. The frame energies of the residual spectra are used for such detection; higher energies are used as indications of presence of anomalies. This approach does not fit well with the industry environment of this work. Audio signals of the devices have quite large variances. Frame energies of the residual spectra are influenced by those variances dramatically, making the conventional approach unable to make robust detection. To deal with this problem, we propose a measure called Peaks-to-Noise Ratio(PNR) to estimate the auditory energies(instead of the physical energies) of residual spectra to determine confidences of anomaly occurrences. Experiments showed that this measure is more robust than conventional ones against the energy variances of the residuals.
A quantum learning approach based on Hidden Markov Models for failure scenarios generation

Finding the failure scenarios of a system is a very complex problem in the field of Probabilistic Safety Assessment (PSA). In order to solve this problem we will use the Hidden Quantum Markov Models (HQMMs) to create a generative model. Therefore, in this paper, we will study and compare the results of HQMMs and classical Hidden Markov Models HMM on a real datasets generated from real small systems in the field of PSA. As a quality metric we will use Description accuracy DA and we will show that the quantum approach gives better results compared with the classical approach, and we will give a strategy to identify the probable and no-probable failure scenarios of a system.
Performance Modelling of Renewable Energy Systems Using kNN Algorithm for Smart Grid Applications

Share of energy from renewable source like solar and wind in the electric grids are steadily increasing in the recent years. Understanding the performance of power systems based on these stochastic energy sources, under various operating environments, is essential for the management of renewable integrated smart grids. In this paper, we propose such performance models for wind and solar energy systems, based on k Nearest Neighbor (kNN) algorithm. Operational data from a 2.3 MW wind turbine were used for the wind power model whereas the solar PV performance was modelled based on the data from a 800 kW <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> monocrystalline solar PV system. The data were cleaned for possible outliers and noises using Z score and DBSCAN clustering method. Model features were identified based on the Pearson and Spearman Correlations. The kNN models for the solar and wind power systems were then developed with optimal k values of 30 and 28 respectively. Performance of the models were evaluated using different error matrices. With RMSEs of 0.03 and 0.06 respectively for the wind and solar performance models, the proposed method could accurately quantify the generation from these systems under varying operating environments.
PV-YOLO: An Object Detection Model for Panoramic Video based on YOLOv4

Most existing object detection methods are applied in ordinary video of limited view. This significantly limits their usefulness and efficiency in real-world large scale deployments with the need for detecting across many views. To address this efficiency issue, we develop a novel object detection model suitable for detection in panoramic videos to achieve detection within a 360-degree panorama without the need to repeat detection in each view. Specifically, we make improvements on YOLOv4 and propose PV-YOLO, using deformable convolution in the backbone network to prevent the geometric deformation problem of targets and adding transverse skip connection in the feature fusion part of the model to enhance feature fusion. Extensive comparative evaluations validate the superiority of this new PV-YOLO model for object detection in panoramic video over a wide range of state-of-art methods on several challenging benchmarks including YOLOv4, YOLOv3, Faster-RCNN, and EfficientDet-D3, etc.
Survey on Task Scheduling Methods in Cloud RPS System

The cloud platform is a popular technology because of its prominent feature, scalability, which allows users to easily adjust the usage of cloud resources. Cloud Service Provider (CSP) uses cloud provisioning methods to provide a sufficient amount of virtual resources the user needs on time, then uses a task scheduling algorithm to process the workload within the deadline or budget constraint. Ineffective scheduling may lead to underutilization (which wastage of cloud resources) or overutilization (which degrades cloud performance). This paper presents the optimization metric and comparison of task scheduling algorithms in the Cloud Resource Provisioning with Scheduling (Cloud RPS) system. We recommend using the hybrid algorithm to achieve the desired objective based on the algorithm that has been compared in this paper.
Visibility Graph-Based Path Planning Algorithm Safety Evaluation and Optimization

The main objective of the current research is to create an algorithm that will evaluate the safety of maze solutions produced by Visibility Graph (VG) based maze solving algorithms. The proposed algorithm suggests a method to improve safety at the expense of path length increase.The software was developed as part of the research to perform the calculation and provide a Graphical User Interface.The results showed that the currently suggested algorithm was able to detect and significantly reduce autonomous robot/vehicle safety concerns.