Reviewerly/exHarmony huggingface dataset:
A novel ensemble <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e735" altimg="si3.svg"><mml:mi>H</mml:mi></mml:math>-infinity proposal density based particle filtering to improve flux estimation in nonlinear point kinetic model

This paper describes a novel Particle Filter algorithm that is both efficient and accurate in terms of computing and employs Ensemble H∞ Filter as the proposed density function to tackles the issue of degeneracy. The problem is considered for flux measurement in a Pressurized Water Reactor described by non-linear point kinetic model. A proposal density based on Ensemble H∞ is considered which uses empirical sample mean and variance to generate samples. The proposed algorithm is numerically less computationally intensive than the techniques currently available in the literature. The strength and suitability of proposed algorithm is compared with existing techniques in a variety of settings.
A Novel Method for Path Planning of Mobile Robots via Fuzzy Logic and ant Colony Algorithem in Complex Daynamic Environments

Researches on mobile robot path planning with meta-heuristic methods to improve classical approaches have grown dramatically in the recent 35 years. Because routing is one of the NP-hard problems, an ant colony algorithm that is a meta-heuristic method has had no table success in this area. In this paper, a new approach for solving mobile robot navigation in dynamic environments, based on the heuristic feature of an optimized ant colony algorithm is proposed. Decision-making influenced by the distances between the origin and destination points and the angle variance to the nearest obstacles. Ideal paths are selected by the fuzzy logic. The proposed ant colony algorithm will optimize the fuzzy rules’ parameters that have been using to On-line (instant) path planning in dynamic environments. This paper presents a new method that can plan local routs all over the area and to guide the moving robot toward the final track. Using this algorithm, mobile robots can move along the ideal path to the target based on the optimal fuzzy control systems in different environments, especially in dynamic and unknown environments.
Combination of visual and semantic criteria for automated selection of region proposals in a bounding box

Deep learning based techniques have been widely used for semantic segmentation. The underlying voluminous DNN models are trained on large datasets that have been annotated at the pixel level by humans. Such low-level annotation tasks are expensive to obtain for newly collected datasets. Alternatively, we propose ComViSe, a segmentation pipeline that requires only high-level annotations that remain relatively accessible (e.g., bounding boxes and labels of a detection, labels of a legend) to segment a given image. ComViSe embeds a segmentation framework, pre-trained on a semantically different dataset, to generate image region proposals. The pipeline relies then on several semantic, visual and geometric criteria to characterize each proposed region, and combines them to select the optimal segmentation mask, comparing diverse aggregation strategies from handcrafted formula to automatic ones, supervised or not. An experimental study conducted on the PASCAL VOC dataset shows that these effectively combined criteria are enough to select the mask proposals with the best IoU score in most cases, and that the aggregation can be done automatically.
Graph Compression for Adjacency-Matrix Multiplication

Abstract Computing the product of the (binary) adjacency matrix of a large graph with a real-valued vector is an important operation that lies at the heart of various graph analysis tasks, such as computing PageRank. In this paper, we show that some well-known webgraph and social graph compression formats are computation-friendly , in the sense that they allow boosting the computation. We focus on the compressed representations of (a) Boldi and Vigna and (b) Hernández and Navarro, and show that the product computation can be conducted in time proportional to the compressed graph size. Our experimental results show speedups of at least 2 on graphs that were compressed at least 5 times with respect to the original.
Sul valore condizionale-restrittivo di <i>ne (= dum ne)</i>

This essay examines a series of examples in which the negative particle ne is used with the conditional-restrictive meaning of dum(modo) ne. Employed mainly as adverb in nominal (mostly comparative) phrases, it seems to be a typical feature of technical prose and therefore generally excluded from the language of high literature. Its productivity runs out during the Julio-Claudian age and survives almost exclusively in locutions as ne minus et sim. where the conditional-restrictive sense is no longer or barely perceptible.
On the Configuration of More and Less Expressive Logic Programs

Abstract The decoupling between the representation of a certain problem, that is, its knowledge model, and the reasoning side is one of main strong points of model-based artificial intelligence (AI). This allows, for example, to focus on improving the reasoning side by having advantages on the whole solving process. Further, it is also well known that many solvers are very sensitive to even syntactic changes in the input. In this paper, we focus on improving the reasoning side by taking advantages of such sensitivity. We consider two well-known model-based AI methodologies, SAT and ASP, define a number of syntactic features that may characterise their inputs, and use automated configuration tools to reformulate the input formula or program. Results of a wide experimental analysis involving SAT and ASP domains, taken from respective competitions, show the different advantages that can be obtained by using input reformulation and configuration.
Yolanda Fernández-Pena, Reconciling synchrony, diachrony and usage in verb number agreement with complex collective subjects. New York and London: Routledge, 2020. Pp xv + 226. ISBN 9780367417154.

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Animation image transfer using CycleGAN

Deep learning based generative models can generate pictures with desirable fidelity and quality. In this paper, we implemented CycleGAN that doesn’t rely on paired datasets in animation industry to transform natural landscape pictures into Japanese animation style pictures. As demonstrated by a set of comprehensive benchmarks, we assume CycleGAN may have the potential to upend the whole animation industry. Numerous results on our dataset show the effectiveness of the proposed method. Our method finally obtains 0.9877 of PSNR and 17.1522 of SSIM, and we also visualize the output results of our images. Our method can give a brief attempt of image style transfer, which may be widely applied to many other different areas.
Experimental analysis of maze solving robot using LSRB algorithm

Sentiment analysis is now widely used in the creation of recommender systems in a variety of areas, as well as the restaurant and food service industries. Most restaurant recommender systems based on sentiment analysis, on the other hand, depend only on static data such food quality, cost, and service quality. Personalised suggestions are generated by analysing user views and extracting their dietary preferences, which is a study need in the literature. A context-aware recommender system is suggested in this study, which extracts people&#x0027;s food preferences from their comments and proposes restaurants based on those choices. Finally, adjacent open eateries are suggested depending on how well they match the user&#x0027;s choices. The Kaggle platform was utilised for review, and feedback from various users was gathered. The system&#x0027;s In three scenarios, accuracy, recall, and f-measure are evaluated for the Wu-palmer module: top1, top3, and top5. The same dataset was used to measure the cosine similarity and the Jaccard similarity, and their accuracy and precision were calculated, and these three modules were compared. The findings show that the suggested systems can deliver suggestions with 92.8 percent (Wu-Palmer) accuracy, 87 percent for cosine similarity, and 82 percent for Jaccard similarity. Giving users a high level of precision, Wu-Palmer has the greatest accuracy compared to the other two Cosine and Jaccard Similarity algorithms. Furthermore, in these areas the system exceeds past studies.
Turn Concrete Jungle into Flower Garden: Transforming Reality based on Semantic Image Synthesis

In this paper, we present an image manipulation system that enables a user to change the scene of an input image by simple semantic conversion of label, which can &#x201C;transform&#x201D; reality. To achieve this, we employed semantic segmentation method and trained a semantic image synthesis model on our landscape dataset of 18 classes. Qualitative results demonstrate the effectiveness of our proposed method.
Developing MATLAB-based application to detect position and size of lung nodule

A lung nodule is a small and round shaped growth in the lung found in the result of radiology examination. Lung nodules can be malignant or cancerous, the risk of lung cancer increases as the nodules get bigger. Lung cancer is the most dangerous cancer, it holds 18.4% of all cancer deaths. Therefore, it is essential to detect the position and size of lung nodules to make sure the presence of lung nodules that may lead to lung cancer. An application for detecting the position and size of lung nodules based on MATLAB has been developed. The application can perform image processing and it is begun from the stage of image quality enhancement by converting the image into grey-scale and median filter. Then it is followed by image segmentation process using Otsu thresholding and watershed. And the last is feature extraction using shape, size, and geometric extraction. The application has successfully detected the position and size of lung nodule based on image data of lung cancer from TCIA database. After being compared to dataset from TCIA database, the accuracy to detect the presence of lung nodules is 100%, while the accuracy to detect lung nodules’ position is 95%. Moreover, the application is also able to determine the lung nodule size.
On the Construction of Symmetries and Retaining Lifted Representations in Dynamic Probabilistic Relational Models

Our world is characterised by uncertainty and complex, relational structures that carry temporal information, yielding large dynamic probabilistic relational models at the centre of many applications.We consider an example from logistics in which the transportation of cargoes using vessels (objects) driven by the amount of supply and the potential to generate revenue (relational) changes over time (temporal or dynamic).If a model includes only a few objects, the model is still considerably small, but once including more objects, i.e., with increasing domain size, the complexity of the model increases.However, with an increase in the domain size, the likelihood of keeping redundant information in the model also increases.In the research field of lifted probabilistic inference, redundant information is referred to as symmetries, which, informally speaking, are exploited in query answering by using one object from a group of symmetrical objects as a representative in order to reduce computational complexity.In existing research, lifted graphical models are assumed to already contain symmetries, which do not need to be constructed in the first place.To the best of our knowledge, we are the first to propose symmetry construction a priori through a symbolisation scheme to approximate temporal symmetries, i.e., objects that tend to behave the same over time.Even if groups of objects show symmetrical behaviour in the long term, temporal deviations in the behaviour of objects that are actually considered symmetrical can lead to splitting a symmetry group, which is called grounding.A split requires to treat objects individually from that point on, which affects the efficiency in answering queries.According to the open-world assumption, we use symmetry groups to prevent groundings whenever objects deviate in behaviour, either due to missing or contrary observations.
Single image dehazing network based on inception module

Indoor and outdoor images captured by the camera are affected by atmospheric dust, haze, sand and other factors, resulting in graying of the images. Although existing image dehazing algorithms can achieve removal of haze, they suffer from incomplete dehazing and color distortion to some extent. To address the above problems, this paper proposes a singleimage dehazing network with multi-scale feature extraction. The network algorithm is based on recurrent generative adversarial network, and a feature extraction network model incorporating Inception module is proposed. The multiscale feature extraction network is incorporated into the generator of CycleGAN. And the residual network is combined to expand the perceptual field. Finally, the fused feature maps are reconstructed and restored to fog-free images by image restoration network. Compared with existing dehazing networks, the proposed network in this paper is more natural in the processing of image dehazing and has better results in image details as well as colors.
A new data mining-based framework to predict the success of private participation in infrastructure projects

The study aims to propose a data mining-based framework to predict the success of private participation in infrastructure projects in developing countries. Data have been collected from the World Bank’s maintained PPI projects database. The proposed framework in this study consists of imputation of missing values, selection of significant features method, resampling imbalanced classes, and application of classification algorithms, including random forest, logistic regression, and support vector machines to predict the binary classes (project success). The results suggest multivariate imputation by chained equations(MICE) as the best method for the imputation, Boruta for the feature selection method, and logistic regression for the classification to predict binary classes in PPI project dataset. The major contribution of this study is that it builds a new data mining-based framework, which considers different feature selection methods and classification techniques. This study will help the practitioners to predict the success of projects carried out under different contractual arrangements and adopt different proactive project management approaches.
Bat Particle Swarm Optimization Modeling Based on Optimization of Knowledge Transfer Time in Big Data Environment

With the advent of the big data era, the factors affecting knowledge transfer have undergone tremendous changes. Through in-depth analysis of the characteristics of knowledge transfer in the big data environment, the bat particle swarm optimization algorithm is analyzed. In the local search, the algorithm proposed in this paper embeds the particle swarm algorithm to generate candidate optimal bats, and re-competes with the random bats generated by the basic bat algorithm to optimize the population, enriching the diversity of the population. On this basis, fully consider Changes in factors such as the subject of knowledge transfer and the type of knowledge in the big data environment have significantly increased the time spent on knowledge transfer, and the optimal transfer time obtained by the solution has been shortened by 15%
The research and implementation of keyword extraction algorithm based on LDA

In this paper, the basic principle of LDA （Latent Dirichlet Allocation）Algorithm is studied and the posterior probability distribution of topics in the document &theta;<sub>m</sub> and the posterior probability distribution of words in the topic &phi;<sub>zm,n</sub> is deduced from the Dirichlet distribution with parameter alpha and beta. And the parameters of LDA model are generated by Gibbs sampling results &theta; and &phi;。 Then, the accuracy of the final subject words under different iteration times is compared, and the whole keyword extraction algorithm process based on LDA is realized in Python. Finally, the results are visually displayed with keyword cloud.
The Use of a Real-Time COVID-19 Standalone Device in an Emergency Department of a Tertiary Hospital in Singapore: A Pilot Observational Study

This study describes the implementation and utility of a standalone device designed, developed, and 3D-printed by PwC Singapore and Southeast Asia Consulting as a response to Corona Virus Disease 2019 (COVID-19), in the Emergency Department (ED) of the National University Hospital in Singapore. Over a 2-week period, all staff used the devices for the duration of their shifts, with the device additionally tagged to patients who were swabbed on suspicion of or surveillance for COVID-19 in the subsequent two weeks. Additional control hardware was placed in the ED to analyze (1) time-intervals of greatest interaction, (2) clusters of close physical distance among staff, (3) areas with high traffic, and (4) potential use of a rapid contact tracing capability. Time-day trends indicated the greatest interaction time-intervals during the beginning of the day, with Monday hosting the greatest average daily interactions across the first two weeks. Social cluster trends indicated the greatest average daily interactions between nurses–nurses during Phase 1, and patients–patients during Phase 2. User-location trends revealed the greatest average daily interaction counts at the intermediate care areas, isolation outdoor tent, pantry, and isolation holding units relative to other areas. Individual-level visualization and contact tracing capabilities were not utilized as nobody contracted COVID-19 during either phase. While congregation in intermediate and resuscitation areas are unavoidable within the ED context, the findings of this study were acted upon, improving social distancing within the pantry and between healthcare groups. This real-time solution addresses multiple privacy concerns while rapidly facilitating contact tracing.
Key Update and Device Location of Internet of Things Based on Smart Contract

In order to reduce the key update time delay of the Internet of Things and reduce the device location error rate and location time, a new key update and device location of Internet of Things based on smart contract was designed. Adopt improved MVIF security mechanism of both parties to build smart contract security mechanism, and the key update of the Internet of Things is realized through key predistribution, user registration and login, and key update. On this basis, multidimensional scaling technology is used to abstract Internet of Things devices into relative coordinates in multidimensional space, and the absolute coordinates are obtained by eliminating the distance estimation error to achieve Internet of Things device positioning. Experimental results show that the proposed method has a lower key update time delay, a lower amount of data used in the key update process, a lower positioning error rate of Internet of Things devices, and a shorter positioning time, which fully verifies the effectiveness of the proposed method.
Virtual 3D Game-on simulation: An immersive learning framework for assisted driving

This paper demonstrates the design and architecture of a virtual driving simulation game. Virtual reality is an alternative world filled with computer-generated images that respond to human movements. Thus, early learners can drive without facing the real-world consequences. The outcome of this research is that it helps early driving learners to overcome the fear of driving. Early learners can easily get over panic and unwanted anxiety. The theoretical framework of this driving game simulation has been designed by using Unity 3D game engine and C# scripting. Gaming controls are given to the player either through the keyboard or through a joystick. The game is visually seen on the monitor screen and headsets are used for achieving realism. Maya and Blender software was used for modelling the framework of the game. We have achieved the output controls of the game by interfacing custom-designed electronic components such as the steering and the accelerator. The goal of this game is to create a virtual learning environment in which early learners can feel at ease while playing. The outcome of this game is to achieve self-confidence among the early learners that help them take away the fear built within them. Even though more women are entering the workforce, there is still a vulnerable segment of the population that is fearful of driving. This game may assist them in overcoming their anxiety and fear.
Computing task offloading based on multi-hop V2V optimization algorithm research

In today’s era when the original data is huge, useful information can only be extracted from the data through calculations and corresponding processing. Nowadays, the computing power of wireless access points, laptops, and mobile phones is comparable to that of computers more than a decade ago. However, most of the time, these computing resources are often idle, which greatly causes a waste of resources. In the Internet of Vehicles scenario, this problem has always been the focus of attention by scholars. The V2V (Vehicle to Vehicle) technology has solved this problem to a certain extent. This article considers the link failure time LET (Link Expiration Time) and the limit of the task allowable delay. Most of the equipment on the road uses external power supply and user equipment is powered by energy. If the transmission of the task cannot be completed within the effective time of the link, it will cause more energy consumption. This article aims at link failure time and task allowable delay Under the limitation of V2V multi-hop task, the total energy loss in the unloading process of the V2V multi-hop task is minimized.
Detecting Indiscriminate Disposal of Waste Using Computer Vision

The waste problem has become a serious issue experienced in almost all parts of the world. With a fairly large population, Indonesia has been one of the countries that contribute the most to waste every year. Most people have been aware of the shortcoming impacts caused by waste such as floods, diseases, and odors that can interfere with life. However, those bad effects of garbage are not well adjusted with the community behavior. The lack of public awareness in handling waste leads to a demand to find solutions in managing waste issues, especially solutions for the community. This study utilized computer vision techniques in detecting objects. Computer vision worked in detecting human objects who commit violations of littering. The process of making the system works started by using the Histogram of Oriented Gradients (HOG) method to perform feature extraction on the dataset. Furthermore, the Support Vector Machine (SVM) method was employed to detect the presence of objects in the dataset, and finally, the Channel and Spatial Reliability Tracking (CSRT) method were used to track objects that had been detected. The tracking process was used to analyze the behavior of the object. The results of the analysis obtained could provide a decision regarding whether or not the violator had thrown garbage in a certain area. The system testing process had been carried out on 100 data samples in the form of video and obtained precision of 90%, recall of 93%, and accuracy of 85%.