Reviewerly/exHarmony huggingface dataset:
A Collaborative Filtering Recommender System using Apache Mahout, Ontology and Dimensionality Reduction Technique

Making improvements in the efficacy of methodology incorporated in the recommender engine is the biggest challenge. It's also vital to think about achieving a balance between two desirable property accuracy and time to find generate recommendation item set when promoting goods via recommender engine because it needs to generate correct suggestions in real-time. This study provides a novel approach for recommendation engine based on Collaborative Filtering (CF) methodologies in this respect. As a result, we use dimensionality reduction approach of linear algebra and ontology approach for semantic similarity to address two major problems in recommendation engines: 1. sparsity and 2. scalability. We incorporated ontology to make improvements in recommendation efficacy and used Singular- Value factorization approach to handle dataset-sparsity and to better scalability of recommendation engine technique. We demonstrate the method's efficiency by evaluating it on real-world Movie-Lens dataset and comparing the findings to those of other approaches in the literature. The solution proposed by us improved the sparsity and scalability concerns in CF, according to the results.
Pseudo-Analysis as a Tool of Information Processing

The theory of the pseudo-analysis is based on a special real semiring (called also tropical semiring).This theory enables a unified approach to three important problems as nonlinearity, uncertainty and optimization, with many applications.There are presented applications in fuzzy logics and fuzzy sets, utility theory, Cumulative Prospect theory of partial differential equations.
Artificial Intelligent Player Character Using Neuroevolution of Augmenting Topologies and Neural Networks

There has been a wide research and development of algorithms and methodologies that have focused on the development of Artificial Intelligence supported applications and game agents to train models in order to clear the game without losing. In context to this area of expertise, this paper proposes a training methodology to augmentvirtual players for the game Flappy Bird, which involves steering clear of thecharacter, in this context, which is a bird, of a series of obstacles. The modelling of the game-scenario has been done to depict theactual game and ensure adequate representation of the problem we tried to solve. We have proposed a neural network structurethat uses Neuroevolution of Augmenting Topologies algorithm, which is a genetic algorithm to learn to play the game. We have also implementedself-built fitness functions for genome evaluation along with further enhancements in the algorithms integrated.
Puzzles of Existential Generalisation from Type-theoretic Perspective

The present paper addresses several puzzles related to the Rule of Existential Generalization, (EG). In solution to these puzzles from the viewpoint of simple type theory, I distinguish (EG) from a modified Rule of Existential Quantifier Introduction which is derivable from (EG). Both these rules are often confused and both are considered as primitive but I show that (EG) itself is derivable from the proper Rule of Existential Quantifier Introduction. Moreover, the latter rule must be primitive in logical systems that treat both total and partial functions, for the universal and the existential quantifiers are not interdefinable in them. An appropriate natural deduction for such a system is deployed. The present logical system is simpler than the system recently proposed and applied by the present author. It utilises an adequate definition of substitution which is capable of handling not only a higher-order quantification, but also (hyper)intensional contexts.
Customer Segmentation Based on Sentimental Analysis

SA is commonly known as Sentimental analysis is a continuous field of research on analysis consumer emotions on purchase of products. This survey paper tackles an inclusive survey of mobile phone reviews. Customer segmentation is a vital part of every company deciding their product outreach. In this machine learning project, we will utilize nltk for clustering, for handling unlabeled datasets. The acquired findings illustrate the efficacy of the solution, which has a high level of accuracy in both mobile classification and user segmentation.
Mathematical Model of Intelligent Monitoring System Based on BPNN and Convolutional Hierarchical Analysis Algorithm

This paper proposes a mathematical model of an intelligent monitoring system composed of a combination of BPNN neural network and convolutional hierarchical analysis algorithm. Using the advantages of neural network, genetic algorithm overcomes the defect that neural network is easy to fall into local minimum error. Using the reconstruction feature based on lifting wavelet to determine whether the dynamic process is abnormal, the BPNN based on the reconstructed mean feature divides the five abnormal patterns into three categories. The three types of abnormal patterns are further identified by LSSVM based on the reconstructed shape features. The numerical simulation of MATLAB shows that the monitoring of various parameters in the well through the monitoring model has remarkable predictive ability, good real-time performance, powerful learning function, and an increase of 9.7% in accuracy, which has strong practicability.
Safety Control for Prime Focus Spectrograph

Prime Focus Spectrograph is a telescope system to be deployed in Hawaii. The system consists of roughly 2400 controllable observation units, which we call the cobras, and the cobra control problem is to reach the targets while avoiding collisions. We decompose this problem into cobra assignment and trajectory planning. The cobra assignment adopts an efficient near-optimal algorithm that maximizes the target acquisition and completely avoids final-position collisions by losing only 0.9&#x0025; of the target acquisition. The trajectory planning uses a collision-based search which lowers the in-transit collisions to almost zero.
Adaptive Tracking Algorithm for Multiperson Motion Targets Based on Local Feature Similarity

The traditional multitarget tracking method has many points of concern. In view of the problems of high target recognition switching rate and high target trajectory false alarm rate in complex scenes and the problem of target loss caused by video tracking in existing video tracking algorithms, a new method based on video tracking is proposed: a multitarget tracking algorithm for local feature similarity, a strong target motion maneuvering or rapid deformation of asymmetric rigid targets. The algorithm builds a depth metric model, which can predict and track the temporal features of the target trajectory frame appearance features and motion features at the same time, which makes the extracted target features more discriminative and reduces the target recognition switching rate. At the same time, the adaptive model tracking algorithm can adjust the model in real time according to the clarity of the target area, effectively ensure the accuracy of the target tracking model, effectively aggregate the characteristics of the trajectory frame, and reduce the false alarm rate. The experimental results show that combining the multiperson moving target adaptive tracking algorithm based on local feature similarity into the DSST model can improve the average accuracy and success rate of the DSST model and has good stability.
Improvement of panoptic segmentation method for urban road

When learning and studying the panoptic segmentation method upsnet, in order to better apply it in the intelligent driving scene, the following improvements are made to the algorithm: 1. Aiming at the problem that the scale difference of different types of targets in the traffic scene is too large, the feature extraction network of the network is improved, and the upsnet panoptic segmentation network combined with recursive feature pyramid is proposed. 2. Aiming at the occlusion problem between different categories in panoptic segmentation task, an occlusion processing model is added to upsnet to solve the occlusion problem. The improved algorithm is compared with upsnet and other excellent panoptic segmentation networks on cityscapes data set and the panoptic segmentation data set labeled in this paper. The experimental results show that the evaluation index PQ (panoptic quality) has been greatly improved, and the improved network is more suitable for intelligent driving scenes.
Curriculum Online + Offline Teaching Evaluation Model and Fuzzy Cluster AHP Evaluation

This article first uses AHP to construct a scientific quality evaluation model, and ranks the scientific quality of the administrative cadres of the 74 units surveyed; then, using the cluster analysis method and combining the ranking situation, the scientific quality is better. The grey fixed-weight clustering evaluation method based on online + offline is proposed, the evaluation model and evaluation method are constructed, and application examples are given. By reasonably determining the weights in the evaluation indicators, the qualitative analysis and quantitative analysis can be better integrated, to a certain extent, it overcomes the subjective arbitrariness of only qualitative teaching quality evaluation work, and has increased by 7.9%.
Assessing Seismic Origin of Geological Features by Fitting Equidistant Parallel Lines

Abstract Some planes in sedimentary rocks contain features that appear to lie near equally spaced parallel lines. Determining whether or not they do so can provide information on possible mechanisms for their formation. The problem is recast here in terms of circular statistics, enabling closeness of candidate sets of lines to the points to be measured by a mean resultant length. This leads to a test of goodness of fit and to estimates of the direction of the lines and of the spacing between them. Two contrasting data sets are analysed.
Sentiment Analysis of Corona Vaccination on Twitter Data using Machine Learning Techniques

Currently, most of the people are stating their opinion through social media. Public opinion stands an important one while reviewing any product, Movie etc. The field of opinion mining has turn out to be the most significant areas of Natural Language Processing. Opinion mining can be used to make decisions about the product or Movie centred on the reviews given by customers in social media. Twitter is one of the most popular social media platforms; Twitter allows users to express their opinions through tweets. It is vital to use these reviews to make decisions and manage the situation. This paper examines public opinions regarding Corona vaccination during pandemic situations with the goal of exploring the opinions of people. In this work, opinion mining of tweets posted by users in Twitter regarding corona vaccination has been performed using Machine Learning Models and Lexicon based approach. Data are collected from Twitter through Tweepy API, and it is pre-processed using NLTK library using python. A comparison of eleven different classification algorithms is made to determine which the best is. As a result, we can conclude that people support neutral decision to take Corona vaccine during the current pandemic.
CFN: A coarse‐to‐fine network for eye fixation prediction

Many image-to-image computer vision approaches have made great progress by an end-to-end framework with the encoder–decoder architecture. However, the same image-to-image eye fixation prediction task is not the same as those computer vision tasks in that it focuses more on salient regions rather than precise predictions for every pixel. Thus, it is not appropriate to directly apply the end-to-end encoder–decoder to the eye fixation prediction task. In addition, although high-level feature is important, the contribution of low-level feature should also be kept and balanced in computational model. Nevertheless, some low-level features that attract attention are easily neglected while transiting through the deep network. Therefore, the effective way to integrate low-level and high-level features for improving eye fixation prediction performance is still a challenging task. In this paper, a coarse-to-fine network (CFN) that encompasses two pathways with different training strategies are proposed: coarse perceiving network (CFN-Coarse) can be a simple encoder network or any of the existing pretrained network to capture the distribution of salient regions and generate high-quality feature maps; fine integrating network (CFN-Fine) uses fixed parameters from the CFN-Coarse and combines features from deep to shallow in the deconvolution process by adding skip connections between down-sampling and up-sampling paths to efficiently integrate deep and shallow features. The saliency map obtained by the method is evaluated over 6 standard benchmark datasets, namely SALICON, MIT1003, MIT300, Toronto, OSIE, and SUN500. The results demonstrate that the method can surpass the state-of-the-art accuracy of eye fixation prediction and achieves the competitive performance to date under most evaluation metrics on SALICON Saliency Prediction Challenge (LSUN2017).
Electric Vehicle Speed Control with Traffic sign Detection using Deep Learning

The prime focus is mainly on e-Vehicles ADAS which will be a future mode of transportation. Road accidents are the major cause of death. The main issues that cause road accidents are ignorance of traffic rules and improper traffic rules following. Where traffic rules have two types traffic signboards and traffic signals. The proposed system, Traffic Sign Detection along with Electric Vehicle Speed Control using Deep Learning and CAN protocol will help assist the driver and reduce the road accidents caused by traffic rules ignorance. In this system, a camera is fixed in the vehicle windshield that captures the traffic signs on the roads. The camera sensor will send an image as a signal to ADAS/AD ECU through Ethernet wire where the captured image undergoes processing and is identified using the Deep Learning CNN technique in ADAS/AD ECU Microcontroller. Then, the ADAS/AD ECU tells the Transmission ECU to reduce the current speed to a specific speed and also to Cluster ECU to display an alert message to the driver in the cluster display using CAN protocol.
Recommendation Based on Graph Neural Network with Structural Identity

With the development of graph neural networks (GNN), some researchers use interaction records to construct graphs and use GNN to model and capture the information on the neighborhood of user nodes or item nodes, so as to make good use of cross-user collaborative signals in the recommendation. However, using GNN to model the graph structure is limited, because it can only capture the information carried by neighbors within a certain range of the central node, called community information, but can hardly capture structural identity such as the roles of the nodes in the graph, leading to sub-optimal recommendation performance. In order to solve the limitation, we design a Structural-Identity-aware Graph Neural Network, called SIGNN, which can capture both the structural identity that independent of nodes’ position in the graph and the community information around the neighbors of nodes, to make full use of the information in the interaction graph and make a better recommendation. The experimental results on three real-world datasets show that SIGNN outperforms existing methods, and the effects of each component are verified by ablation experiments.
Efficient Johnson-SB Mixture Model for Segmentation of CT Liver Image

To overcome the problem that the traditional Gaussian mixture model (GMM) cannot well describe the skewness distribution of the gray-level histogram of a liver CT slice, we propose a novel segmentation method for liver CT images by introducing the Johnson-SB mixture model (JSBMM). The Johnson-SB model not only has a flexible asymmetrical distribution but also covers a variety of other distributions as well. In this article, the parameter optimization formulas for JSBMM were derived by employing the expectation-maximization (EM) algorithm and maximum likelihood. The implementation process of the JSBMM-based segmentation algorithm is provided in detail. To make better use of the skewness of Johnson-SB and improve the segmentation accuracy, we devise an idea to divide the histogram into two parts and calculate the segmentation threshold for each part, respectively, which is called JSBMM-TDH. By analyzing and comparing the segmentation thresholds with different cluster numbers, it is illustrated that the segmentation threshold of JSBMM-TDH will tend to be stable with the increasing of cluster number, while that of GMM is sensitive to different cluster numbers. The proposed JSBMM-TDH is applied to segment four randomly obtained abdominal CT image sequences, and the segmentation results and robustness have been compared between JSBMM-TDH and GMM. It is verified that JSBMM-TDH has preferable segmentation results and better robustness than GMM for the segmentation of liver CT images.
Detecting Obstacle in 3D Space using Monocular Camera

Monocular 3D object detection attempts to predict object location and dimension in 3D space using a single optical camera. The main challenge of monocular 3D object detection is the lack of depth information to infer the object&#x2019;s distance. In this work, we propose a monocular 3D object detection based on CenterNet with discrete depth and encoded orientation angle. Our proposed method is able to achieve 54.6% detection score for car class on the challenging Cityscapes autonomous driving dataset, outperforming prior monocular 3D object detection by a convincing margin.
Data Augmentation for Dysarthric Speech Recognition Based on Text-to-Speech Synthesis

In the field of automatic speech recognition (ASR) for people with dysarthria, it is problematic that not enough training speech data can be collected from people with dysarthria. To solve this problem, we propose a method of data augmentation using text-to-speech (TTS) synthesis. In the proposed data augmentation method, a deep neural network (DNN)-based TTS model is trained by utilizing speech data recorded from a speaker with dysarthria, and the trained TTS model is then used to generate the speaker&#x2019;s speech data for training the ASR model for the speaker. The results of a speech recognition experiment on a person having spinal muscular atrophy (SMA) showed that the speech recognition error rate was improved by using the proposed data augmentation.
Hidden Vulnerabilities in Cosine Similarity based Poisoning Defense

Federated learning is a collaborative learning paradigm that deploys the model to the edge for training over the local data of the participants under the supervision of a trusted server. Despite the fact that this paradigm guarantees privacy, it is vulnerable to poisoning. Malicious participants alter their locally maintained data or model to publish an insidious update, to reduce the accuracy of the global model. Recent byzantine-robust (euclidean or cosine-similarity) based aggregation techniques, claim to protect against data poisoning attacks. On the other hand, model poisoning attacks are more insidious and adaptable to current defenses. Though different local model poisoning attacks are proposed to attack euclidean based defenses, we could not find any work to investigate cosine-similarity based defenses. We examine such defenses (FLTrust and FoolsGold) and find their underlying issues. We also demonstrate an efficient layer replacement attack that is adaptable to FLTrust, impacting to lower the accuracy up to 10%. Further, we propose a cosine-similarity based local model poisoning attack (CSA) on FLTrust and FoolsGold, which generates diverse and poisonous client updates. The later attack maintains a high trust score and a high averaged weighted score for respective defenses. Experiments are carried out on different datasets, with varying attack capabilities and settings, to study the effectiveness of the proposed attack. The results show that the test loss is increased by 10 - 20×.
Statistical assessment of the hazards associated with pyroclastic density currents at the Tacaná Volcanic Complex, México-Guatemala border

The Tacaná Volcanic Complex (TVC) is located on the México- Guatemala border. It is an active volcanic system composed of three volcanic edifices and a dome characterized by a variety of eruptive styles and long repose times between explosive events during the last 41 ky. Recent geological studies improved the stratigraphy and permitted the reconstruction of most of its eruptive history. However, only the types of eruptions have been described, and there are no sufficient data to assign mass magnitudes or VEI values to the events. Therefore, to perform a statistical analysis of this information, we first define a new scaled parameter that quantifies the relative size or dimension of the different eruption types according to the areas covered by potentially damaging pyroclastic density currents. This scale of eruptive categories, referred to as the Eruption Impact Parameter (EIP), is conformed to fulfill the ordered property required for statistical analysis of the sequence of volcanic events. The analysis of the EIP-characterized eruptive history of the TVC shows that eruptions in separate EIP categories predominate over non-coincident time intervals with different durations. Since there is no certainty that the eruptive series is complete and stationary through the whole 41 ky record, we first separately test the non-coincident censored intervals for independence. Then, we analyze the latest, likely complete, censored interval characterized by EIP 2 events using a mixture of exponentials distribution (MOED) to calculate probabilities of future eruptions in that EIP range. To analyze the different EIP > 2 intervals of activity, in which there is not enough evidence to support completeness over the whole 41 ky period for all the EIP categories, we use an extreme value approach using a Non-Homogeneous Generalized Pareto–Poisson Process (NHGPPP) model to estimate probabilities of occurrence and thus assess the volcanic hazard associated to major eruptions. We conclude that although the probabilities of major eruptions in the foreseeable future are not among the highest compared with other active stratovolcanoes of México, moderate eruptions (EIP 2) may represent a significant hazard to the increasing fixed and mobile population in the proximal, fertile, coffee-producing area populating the slopes of the volcano.