Reviewerly/exHarmony huggingface dataset:
A Detection Approach for Floating Debris Using Ground Images Based on Deep Learning

Floating debris has a negative impact on the quality of the water as well as the aesthetics of surface waters. Traditional image processing techniques struggle to adapt to the complexity of water due to factors such as complex lighting conditions, significant scale disparities between far and near objects, and the abundance of small-scale floating debris in real existence. This makes the detection of floating debris extremely difficult. This study proposed a brand-new, effective floating debris detection approach based on YOLOv5. Specifically, the coordinate attention module is added into the YOLOv5 backbone network to help the model detect and recognize objects of interest more precisely so that feature information of small-sized and dense floating debris may be efficiently extracted. The previous feature pyramid network, on the other hand, summarizes the input features without taking into account their individual importance when fusing features. To address this issue, the YOLOv5 feature pyramidal network is changed to a bidirectional feature pyramid network with effective bidirectional cross-scale connection and weighted feature fusion, which enhances the model’s performance in terms of feature extraction. The method has been evaluated using a dataset of floating debris that we built ourselves (SWFD). Experiments show that the proposed method detects floating objects more precisely than earlier methods.
Imbalanced Traffic Accident Text Classification Based on Bert-RCNN

Abstract Traffic is the signs of urban development, and traffic accidents have a particularly large impact on road conditions. Because traffic accidents are recorded in text, classifying the severity of traffic accidents can not only unify the classification standards, but also greatly help query and prediction of traffic accidents. The frequency of daily minor traffic accidents is much higher than that of serious traffic accidents, so this paper proposes an effective algorithm and model for categorizing imbalanced Chinese traffic accident texts based on severity. Firstly, we establish a standard for refined classification granularity combining expert knowledge, and propose unique accuracy standards to verify the impact of the processed dataset on the model. Then the noise on the dataset is removed, and finally input the text into Bidirectional Encoder Representations from Transformers (BERT) model uses the Recurrent Convolutional Neural Network (RCNN) model as a classifier for fine-tuning. By comparing with the results of other classification methods, it is verified that our algorithm and model have the best accuracy.
Safety Helmet Wearing Recognition Based on Improved YOLOv5

In the industrial production of digital workshops, workers need to wear safety helmets at all times. However, the accuracy of target detection is not high enough due to the characteristics of different light, angle of view, and people easily obstructing each other. To solve this problem, the real-time detection of helmets is realized by improving the YOLOv5 algorithm. The Dahua spherical camera is used to collect the data set, and the network is trained on the self-made data set through manual annotation. Pooling is carried out through softpool, so that it can retain more information of the feature map; meanwhile, improve the network structure of YOLOv5, add a layer of 9*9 feature layer, improve the recognition rate of the detection target, and use the DIoU loss function. According to the experimental results, the following results can be obtained. The average accuracy of the improved YoloV5algorithm in self-made data sets has improved a lot, above 97.3&#x0025;. which is 4 &#x0025; higher than the original algorithm, and the target detection speed is also correspondingly improved. It can effectively and real-time detect the wearing of helmets Condition.
Supporting the creation of non-linear everyday AR experiences in exhibitions and museums: An authoring process based on self-contained building blocks

The use of Augmented Reality (AR) has the potential to make everyday experiences exciting and educational. For example, AR can augment exhibits in museums with animated and interactive content. The creation of this content, however, is still facing challenges. To meet these, we employ reusable, pattern-based building blocks called AR nuggets. An AR nugget implements one application pattern in a small and self-contained piece of software to provide a complete solution for recurrent AR-based experiences. For example, in the application context of museums and exhibitions, we identify superimposition or visualization of echolocation as general patterns for AR use cases. AR nuggets encapsulate AR-specific knowledge and sophisticated functionalities to support authors and reduce the authoring task to tweaking existing templates to individual exhibits. To connect AR nuggets used in different exhibition rooms, we present novel AR nuggets that encapsulate the functionalities needed for creating a path between the exhibits. Additionally, we provide examples of AR nuggets that implement a virtual character that guides visitors to exhibits of interest. With this new set of AR nuggets, spatial connections can be authored, e.g., in the form of a guided tour with interactive narration. For this authoring task, we introduce an AR nugget manager that supports authors in creating and adapting multiple non-linear AR experiences. We illustrate our approach with the creation of an everyday AR application for a museum of natural history, share our experiences and discuss to what extent our approach can mitigate authoring challenges for everyday AR applications from a museum’s point of view. This work contributes to the field of everyday AR with 1) a pattern-based authoring concept to create complex everyday AR experiences based on self-contained building blocks, 2) a set of patterns that allows for spatial connections of these to create non-linear AR content, 3) means for supporting this authoring process in the form of an AR nugget manager, 4) lessons learned in applying our authoring concept in a real application case in a museum, 5) our observation of hurdles that still prevent more widespread use of AR in everyday applications during the realization of this application case.
ANALYSIS OF CRYPTOCURRENCY: AN ETHICAL CONJECTURE WITH REFERENCE TO INDIAN SCENARIO

A cryptocurrency is a form of digital payment that does not rely on banks to validate transactions. It’s a peer-to-peer payment system that allows anyone from anywhere to send and receive money. Digital currency is the future of finance. India has huge rapid growth opportunities in cryptocurrency adoption. The objective of the study is to understand the growth of cryptocurrency over the year and to compare the conventional investment avenues with the cryptocurrency and SWOT analysis for future prospective of cryptocurrency in India. In India around 20 million active users of cryptocurrency. From July 2020 to June 2021 cryptocurrency market raised 640%in India. During this period from total digital currency transactions from Southern Asia, 42% is coming from the Indian market which is about $10 million. A comparative analysis of conventional investment avenues with cryptocurrency shows that Indian investors still prefer to invest in conventional investment avenues. The reason behind this, a lack of information, security issues, and no regulations for cryptocurrency are seen. SWOT analysis shows that in the Indian market due to a decentralized system, no mediate intervention, low transaction cost, the worldwide accessibility adoption rate is high. The major barrier for cryptocurrency's major weaknesses and threats are highly volatile market, security threats, black marketing, no regulations, threats associated with unknown identity. Still Indian cryptocurrency market is huge, and growth is high in the future.
A combined approach of base and meta learners for hybrid system

The ensemble learning method is considered a meaningful yet challenging task. To enhance the performance of binary classification and predictive analysis, this paper proposes an effective ensemble learning approach by applying multiple models to produce efficient and effective outcomes. In these experimental studies, three base learners, J48, Multilayer Perceptron (MP), and Support Vector Machine (SVM) are being utilized. Moreover, two meta-learners, Bagging and Rotation Forest are being used in this analysis. Firstly, to produce effective results and capture productive data, the base learner, the J48 decision tree is aggregated with the rotation forest. Secondly, machine learning and ensemble learning classification algorithms along with the five UCI Datasets are being applied to progress the robustness of the system. Whereas, the recommended mechanism is evaluated by implementing five performance standards concerning the accuracy, AUC (Area Under Curve), precision, recall and F-measure values. In this regard, extensive strategies and various approaches were being studied and applied to obtain improved results from the current literature; however, they were insufficient to provide successful results. We present experimental results which demonstrate the efficiency of our approach to well-known competitive approaches. This method can be applied to image identification and machine learning problems, such as binary classification.
Using a Multi-GPU node to accelerate the training of Pix2Pix neural networks

Abstract Generative adversarial networks are gaining importance in problems such as image conversion, cross-domain translation and fast styling. However, the training of these networks remains unclear because it often results in unexpected behavior caused by non-convergence, model collapse or overly long training, causing the training task to have to be supervised by the user and vary with each dataset. To increase the speed of training in Pix2Pix (image-to-image translation) networks, this work incorporates multi-GPU training using mixed precision, along with optimizations in the GPU image input process. In addition, in order to make the training unsupervised and to terminate it when the best transformations are performed, an early stopping method using the peak signal noise ratio (PSNR) metric is proposed.
Research on methods of enhancing physical layer security of optical fiber communication system in the smart grid

Abstract We investigate numerically the security enhanced optical communication which can be used in smart grid, while the time-delay signature suppression, intelligent control and high quality communication are reached at the same time. In this paper, the communication system includes the driven part, national dispatching control centre (NDCC) and the controlled substations (CSs). To enhance the security of communication system, the driven part which includes two semiconductor lasers, is employed to suppress the time-delay signature. Also, the chaos synchronization between NDCC and different CSs can be selectively realized by controlling the injection parameters. Finally, the high quality communication between NDCC and CS1 is discussed. This research paves the way to the application of chaotic communication in smart grid.
Using instant messaging applications to promote clinical teaching of medical students

The main purpose of this study was to evaluate the use of Telegram as an Instant messaging application in clinical teaching of medical students, especially clinical ward rounds.In this quasi-experimental study, Telegram as an Instant messaging application was employed. To do this, a Telegram group entitled as "Lavender" was created and then selected patients were divided between students and externs daily. After that, students and externs were asked to take medical histories, perform complete physical examinations, and then share their data in the Lavender group. Ultimately, to evaluate the effects of this intervention, two quantitative and qualitative approaches were used.From the students' point of view the highest average was associated with "Readiness for teaching round" (3.71 ± 1.34) and the lowest average was related to "Time-saving for studying about illnesses" (2.08 ± 1.44). In addition, medical trainees mentioned that reporting patients' daily medical histories and progress notes could create a reflective process. The suggestion made by many medical trainees was to select and discuss simpler cases who have more teaching points for them, while other trainees recommended focusing on common illnesses in the Lavender group.The use of messaging software applications such as Telegram in medical education has created a new paradigm, which could facilitate interpersonal interactions among trainees.
A Novel Approach to Minimize the Mean Square Error in Predicting Stock Price Index using Linear Regression in Comparison with LSTM Model

The focus of the proposed framework is to perform novel stock price prediction and forecast trends in changes of stock market price index for selective three stocks in Stock Market. This framework attempts to develop two groups based on classification techniques: Linear Regression (LR) and Long S hort- Term Memory (LS TM). Performance of both models are compared in terms of their capability to predict direction of movement in Bombay Stock Exchange (BSE) index. Proposed framework has been tested for the prediction of three stocks (AAPL, MSFT, and AMZN) with an estimated sample size of 30. Experimental results demonstrate that, average performance of LR model (98.2 &#x0025;) is found significantly better than that of LS TM model (72.3&#x0025;) with (p&#x003C;0.05). Evaluation of parameters for stock index prediction suggests that, LR model depicted promising performance compared to the LS TM model.
Double Security Guarantee: Protecting User Privacy and Model Security in QoS Prediction

Quality of Service (QoS) prediction has played an important role in selecting the optimal cloud service for users, and how to protect users’ privacy with high prediction accuracy has become the focus of attention in service computing. Although federated learning (FL) methods have been widely applied to protect user privacy, when a federated learning model is attacked by malicious users, it may lead to wrong prediction results. In order to protect both user privacy security and prediction model security, we propose a double security guaranteed matrix factorization model named DSGMF. In this model, we design a global gradient allocation method though contribution-based rewards. Meanwhile, to identify and remove potential free-riders, we explore free-rider attack and employ reputation-based detection method. Our proposed model is evaluated on a real-world QoS dataset, and the experimental results validate the effectiveness of our approach.
Task Offloading Method of Internet of Vehicles Based on Cloud-Edge Computing

Along with the development of technology and transportation, the internet of vehicles (IoV) has emerged. However, as the number of vehicles on the road increases, the number of computational tasks that need to be processed increases, and thus energy consumption and time latency are a number of relevant factors that we need to take into account in this process. The combination of vehicle-to-everything (V2X) communication technology and mobile edge computing in IoV provides a feasible solution for offloading and processing of computing tasks in vehicles. In this paper, a predictive vehicle task offloading method (PVTO) is proposed to offload computing tasks to the edge server with vehicle-to-vehicle communication (V2V) and vehicle-to- infrastructure communication (V2I), followed by a multi-objective optimization using genetic algorithm, and then a simple additive weighting algorithm (SAW) and a multiple criteria decision making (MCDM) to solve the optimal offloading strategy. Finally, the effectiveness of PVTO is demonstrated by experimental comparison.
Secure Blockchain Model for Vehicles toll Collection by GPS tracking: A case study of India

This paper aims to implement a blockchain-based, fully decentralized vehicle toll collection model. The resultant scheme uses a Global positioning system to track the vehicles, and the payment module is fully decentralized and uses Ethereum blockchain. Ethereum blockchain uses immutable documents called smart contracts to carry out business logic. It ensures that every transaction between vehicle owners and the authorities is traceable, immutable, transparent, and trustworthy. The Global Positioning System provides the location of an object using coordinates measured using satellites. The data from GPS is recorded constantly to ensure that the vehicle has traveled to the correct location and is stored in a publicly verifiable blockchain. The other vehicle owners work as location verifiers except for the particular vehicle. An automatic payment system is to be developed, which gets triggered when necessary conditions are met by the vehicle. Our proposed model is authentic and secure, ensuring penalties for malicious participants. The experimental results are based on the Gas consumption of all the smart contracts. This model can improve the current toll collection model of the National Highway Authority of India to make it more profitable and corruption-free.
Optimal Data Reduction of Training Data in Machine Learning-Based Modelling: A Multidimensional Bin Packing Approach

In these days, when complex, IT-controlled systems have found their way into many areas, models and the data on which they are based are playing an increasingly important role. Due to the constantly growing possibilities of collecting data through sensor technology, extensive data sets are created that need to be mastered. In concrete terms, this means extracting the information required for a specific problem from the data in a high quality. For example, in the field of condition monitoring, this includes relevant system states. Especially in the application field of machine learning, the quality of the data is of significant importance. Here, different methods already exist to reduce the size of data sets without reducing the information value. In this paper, the multidimensional binned reduction (MdBR) method is presented as an approach that has a much lower complexity in comparison on the one hand and deals with regression, instead of classification as most other approaches do, on the other. The approach merges discretization approaches with non-parametric numerosity reduction via histograms. MdBR has linear complexity and can be facilitated to reduce large multivariate data sets to smaller subsets, which could be used for model training. The evaluation, based on a dataset from the photovoltaic sector with approximately 92 million samples, aims to train a multilayer perceptron (MLP) model to estimate the output power of the system. The results show that using the approach, the number of samples for training could be reduced by more than 99%, while also increasing the model’s performance. It works best with large data sets of low-dimensional data. Although periodic data often include the most redundant samples and thus provide the best reduction capabilities, the presented approach can only handle time-invariant data and not sequences of samples, as often done in time series.
Distance and Direction Based Deep Discriminant Metric Learning for Kinship Verification

Image-based kinship verification is an important task in computer vision and has many applications in practice, such as missing children search and family album construction, among others. Due to the differences in age, gender, expression and appearance, there usually exists a large discrepancy between the facial images of parent and child. This makes kinship verification a challenging task. In this article, we propose a Distance and Direction Based Deep Discriminant Metric Learning (D 4 ML) approach for kinship verification. The basic idea of D 4 ML is to make full use of the discriminant information contained in the facial images of parent and child such that the network can learn more a discriminating distance metric. Specifically, D 4 ML learns the metric by utilizing the discriminant information from two perspectives: distance-based perspective and direction-based perspective. From the distance-based perspective, the designed loss function is used to minimize the distance between images having kinship and maximize the distance between images without kinship. In practice, the gender difference and large age gap may significantly increase the distance between facial images of parent and child. Therefore, learning the metric only from a distance-based perspective is insufficient. Considering that two vectors with a large distance may appear with high similarity in direction, D 4 ML also employs the direction-based loss function in the training process. Both kinds of loss function work together to improve the discriminability of the learned metric. Experimental results on four small size publicly available datasets demonstrate the effectiveness of our approach. Source code of our approach can be found at https://github.com/lclhenu/D4ML .
Research on Named Entity Recognition Based on Multi-Task Learning and Biaffine Mechanism

Commonly used nested entity recognition methods are span-based entity recognition methods, which focus on learning the head and tail representations of entities. This method lacks obvious boundary supervision, which leads to the failure of the correct candidate entities to be predicted, resulting in the problem of high precision and low recall. To solve the above problems, this paper proposes a named entity recognition method based on multi-task learning and biaffine mechanism, introduces the idea of multi-task learning, and divides the task into two subtasks, entity span classification and boundary detection. The entity span classification task uses biaffine mechanism to score the resulting spans and select the most likely entity class. The boundary detection task mainly solves the problem of low recall caused by the lack of boundary supervision in span classification. It captures the relationship between adjacent words in the input text according to the context, indicates the boundary range of entities, and enhances the span representation through additional boundary supervision. The experimental results show that the named entity recognition method based on multi-task learning and biaffine mechanism can improve the F1 value by up to 7.05%, 12.63%, and 14.68% on the GENIA, ACE2004, and ACE2005 nested datasets compared with other methods, which verifies that this method has better performance on the nested entity recognition task.
Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction

Intermittency of electrical power in developing countries, as well as some European countries such as Turkey, can be eluded by taking advantage of solar energy. Correct prediction of solar radiation constitutes a very important step to take advantage of PV solar panels. We propose an experimental study to predict the amount of solar radiation using a classical artificial neural network (ANN) and deep learning methods. PV panel and solar radiation data were collected at Duzce University in Turkey. Moreover, we included meteorological data collected from the Meteorological Ministry of Turkey in Duzce. Data were collected on a daily basis with a 5-min interval. Data were cleaned and preprocessed to train long-short-term memory (LSTM) and ANN models to predict the solar radiation amount of one day ahead. Models were evaluated using coefficient of determination (R2), mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean biased error (MBE). LSTM outperformed ANN with R2, MSE, RMSE, MAE, and MBE of 0.93, 0.008, 0.089, 0.17, and 0.09, respectively. Moreover, we compared our results with two similar studies in the literature. The proposed study paves the way for utilizing renewable energy by leveraging the usage of PV panels.