Reviewerly/exHarmony huggingface dataset:
Research on optimization of complex path of inspection robot

Abstract It is a common method to use intelligent robots for automatic inspection in highly automated ships. Path planning and optimization research are an important part of robotics. Intelligent tracking is the key to autonomous movement of mobile robots. In this paper, a grid map is used to establish a simulation model of the robot tracing environment. In a grid map, the more complex the map environment, the slower the algorithm will converge. The -fill-merge method processes the concave obstacles in the environment into regular shapes, reduces the number of unnecessary calculations, thereby greatly optimizing the tracing environment and speeding up the convergence speed. Finally, the MATLAB simulation experiment shows that the method based on environment optimization can effectively accelerate the convergence speed, avoid the local optimum, and quickly find the global optimum when dealing with the problem of autonomous tracking of intelligent robots in a map environment with a large number of concave obstacles. solution.
Web Scraping and Text Mining of Ukrainian News Articles About Ecology

Ecology is a hot topic nowadays. Taking care of it is essential to our existence on Earth. There is plenty of work being done to improve the ecological situation. But a small group of avid supporters can come only so far without a consensus in the society. One way to raise public awareness of the ecology question is through the news. Anybody familiar with the scientific method knows: you cannot improve what you cannot measure. Hence the topic of this paper. We attempt to measure the general awareness of the ecology-related topics through text mining of Ukrainian news articles.
Causal Interventions and Argumentation-Based Frameworks: Formalization of “What If” Scenarios

Argumentation-based frameworks are used as a decision-making mechanism for software agents. This paper aims to investigate how a formal argumentation framework is affected when the underlying causal relationships of its theory is modified in counterfactual situations, the so-called “what if” scenarios. In contrast to previous approaches where causality relationships were derived from static probabilistic distributions, we address scenarios where causal models are intervened. Two novel contributions in the synergy between argumentation and causal theories are presented: 1) we characterize interventions and their consequences in causal argumentation frameworks; and 2) we introduce an account of the so-called sequential interventions that give a characterization of manipulations on time.
Pix2Trips - a system supporting small groups of urban tourists

Group recommendation systems are the subject of many publications, but still is a gap between research results and group decision support systems' needs.Tourists often do not know which attractions they would like to visit.The Pix2Trips system asks the group's members to indicate images that they would like.On this basis, Pix2Trips models the group's preferences and adjusts them to the proposed places' models.Some tourist places in Wroclaw city, Poland, were used in experiments.The paper presents the system's components and discusses the results of the experiments.Conclusions indicate the good overall evaluation of the Pix2Trips system and further research.
The Utility of Arabic Corpus Linguistics

The authors examine the application of electronically searchable corpora, from their own experience, in addressing questions pertinent to linguistics as a whole and to matters internal to Arabic, the while lamenting that the field of Arabic linguistics, in its theoretical and applied orientations alike, has not made use of the rich data source that searchable electronic corpora represent. They show how corpora can be used easily to falsify common assumptions and assertions about the human language capacity in general just as they can be used efficiently to query assumptions and assertions about Arabic itself. So, too, do they hold implications for applied uses such as teaching Arabic as a foreign language and translation between Arabic and other languages. In any of these applications, the use of corpora in the analysis of all varieties of Arabic remains underdeveloped compared to their use in the analysis of other languages, especially English.
Implementation of Mobile App for Laundry “Super-Dry”

In this modernised world, everything is done within a tap of a mobile phone. Each service is offering online services for their customers. Actually saying we became much lazier due to these technologies that's where it comes the ‘digital-world’. Contacting a dhobi, waiting for his arrival and paying him, all these costs valuable time of a working people. And in this pandemic situation physical contact is dangerous. There are laundry services and they do not provide its service online. To avoid this problem we have created a dhobi service app called “Superdry”. One of our motive of building this Laundry Application is to provide employment to those washer-men who are unemployed. Through this app a customer can place order for various laundry services. The dhobi will collect the clothes from the customer house and after washing, will deliver it back. And the customer can make an online payment or even can pay on delivery. Here the time is saved and the contactless service is achieved. Here both the customer and the dhobi are benefited.
Comparative Analysis of Serverless Solutions from Public Cloud Providers

Cloud Providers such as Google Cloud Platform (GCP), Microsoft Azure, Amazon Web Services (AWS) offer have started expanding their serverless platforms, promising higher availability and dynamic auto-scaling while at the same time diminishing operational and maintenance costs. One such is serverless processing, or function-as-a-service (FaaS). Serverless has emerged as a decent contestant for web applications, APIs, backend-services due to their unique event-driven architecture and pay-as-you-go model. In this paper, we discussed the current architecture of serverless functions & compared the strength of serverless offering of two major cloud providers - Google's GCP & Microsoft Azure and conducted a load testing experiment against their node js environment to better understand their capabilities. We conducted 2 different scenarios of user-load testing via the open-source framework, k6. These scenarios included several phases of sudden and spontaneous load increment and decrement, mimicking how it happens in real-world situations. Finally, we observed how quickly and effectively they scale, their cold boot times, and their response times.
Study of robust visual tracking based on traditional denoising methods and CNN

Visual object tracking has been widely used in many computer vision fields, ranging from intelligent video surveillance, autonomous driving, robot navigation, and human-computer interaction. However, complex external environmental interference may cause noise to the video images, during the entire process of data collection, transmission, and reception. In this paper, we aim to combine traditional denoising methods with the state-of-the-art tracker to implement robust tracking in noisy environment. In the first part, we apply salt-and-pepper noise and evaluate the performance of commonly used classical denoising algorithms. In the second part, we combine the denoising methods with Siamese Region Proposal Network to form a new tracker. The precision and success plots of different trackers are presented on noisy OTB100. Our study find that strong noise seriously affects the effect of target tracking and with use of denoising algorithm, the robustness of the tracker is enhanced greatly. In our experiment, for salt-and-pepper noise, median filter has obvious effect and can improve target tracking accuracy to the greatest extent.
Human-machine Co-creation on Artistic Paintings

With the development of machine intelligence, a variety of machine-involved artistic creations have emerged in recent years. There are two critical problems arising, i.e., the relationship between humans and machines in the creative process and the modes of machine-involved artistic creation. This paper disassembles the existing machine-involved creations into the following categories according to the role and depth of the machine's involvement: machine as expression tools and machine as the medium of experience. Then, based on the discussion of the human-machine relationship in the creative process, we propose a human-machine co-creation model with three interactive levels. They correspond to the stimulation of creativity with the help of machines at the tool level, experience level, and service level, respectively. Furthermore, we make a primary practice and exploration of human-machine co-creation on paintings as a case study.
Can Communication Topology Improve a Multi-swarm PSO Algorithms?

Using multiple-swarm PSO is a technique used in recent years to help improve the performance of nature-inspired optimization algorithms. A distributed PSO algorithm can work in every swarm in parallel and asynchronously communicate particles between them. However, the communication design is not a trivial task because any architectural change will affect how it explores the search space and how it exploits the best particles. Nevertheless, it has been reported that the exchange of possible solutions helps optimizations algorithms to improve their performance. This paper focuses on proposing and comparing two communication policies regarding how the communication graph between swarms is organized. These policies intend to limit the communication between populations to increase per-swarm exploration and avoid premature convergence through exploring different parts of the space in different swarms. The proposed policies are chain and hypercube topologies and compare them against a simple cross-over strategy using several continuous optimization benchmark functions to assess the benefits of choosing one communication topology over another. After the experiments, the chain-based topology had a better error performance.
New Educational Objectives and Methodologies for the Information Age

research-article Share on New Educational Objectives and Methodologies for the Information Age Authors: Anna Bobunova Peoples'Friendship University of Russia, Russia Peoples'Friendship University of Russia, RussiaView Profile , Ben Choi Louisiana Tech University, USA Louisiana Tech University, USAView Profile Authors Info & Claims ICFET 2021: 2021 The 7th International Conference on Frontiers of Educational TechnologiesJune 2021 Pages 102–108https://doi.org/10.1145/3473141.3473233Online:05 October 2021Publication History 0citation16DownloadsMetricsTotal Citations0Total Downloads16Last 12 Months16Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Feature Weight Optimization Method Based on t-Memetic Algorithm

Feature weights have a significant effect on the accuracy of case-based reasoning (CBR) prediction models. Therefore, this paper proposes a feature weight optimization method combining a t-distribution mutation operator and a memetic algorithm (t-memetic). In this method, the mean absolute percentage error of the CBR prediction model is defined as a fitness function, and the memetic framework is used to realize the optimization process for case weights. A sparrow search algorithm and adaptive t-distribution mutation operator are used to realize the global search of the case feature weights, and the simulated annealing algorithm is used to perform a local search of the weighted individuals with the best fitness for the current population. Five UCI standard regression datasets are used respectively to test the effectiveness of the proposed method and compare it with classical feature weight optimization algorithms. The results show that the CBR prediction model has the highest accuracy after the weights are optimized by the t-memetic algorithm, which indicates that the proposed weight optimization method can be used effectively in CBR prediction models.
Do CNNs Encode Data Augmentations?

Data augmentations are important ingredients in the recipe for training robust neural networks, especially in computer vision. A fundamental question is whether neural network features encode data augmentation transformations. To answer this question, we introduce a systematic approach to investigate which layers of neural networks are the most predictive of augmentation transformations. Our approach uses features in pre-trained vision models with minimal additional processing to predict common properties transformed by augmentation (scale, aspect ratio, hue, saturation, contrast, and brightness). Surprisingly, neural network features not only predict data augmentation transformations, but they predict many transformations with high accuracy. After validating that neural networks encode features corresponding to augmentation transformations, we show that these features are encoded in the early layers of modern CNNs, though the augmentation signal fades in deeper layers.
Automated Dataset Amplification and its Application to Small Dataset Object Detection Transfer Learning

research-article Share on Automated Dataset Amplification and its Application to Small Dataset Object Detection Transfer Learning Authors: Muhammad R. Abid Florida Polytechnic University, USA Florida Polytechnic University, USAView Profile , Riley Kiefer Florida Polytechnic University, USA Florida Polytechnic University, USAView Profile Authors Info & Claims ICISDM 2021: 2021 the 5th International Conference on Information System and Data MiningMay 2021 Pages 55–61https://doi.org/10.1145/3471287.3471305Online:25 September 2021Publication History 0citation44DownloadsMetricsTotal Citations0Total Downloads44Last 12 Months44Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
MF-Net: Meta Fusion Network for 3D object detection

3D object detection has attracted a significant amount of attention and interest from both academia and industry due to its indispensable role in understanding 3D environments. By fusing the camera and LiDAR sensors, it is expected to improve both the accuracy and robustness of 3D object detection. However, existing fusion approaches are either limited by cascading processing, or easy to be influenced by the interference information in multi-sensors. To this end, this paper incorporates meta learning to fuse the camera and LiDAR data. Specifically, we first extract meta knowledge from images, and then apply it to generate the parameter weights of a set of convolution kernels, which are further exploited for feature extraction on LiDAR point clouds. Furthermore, we propose a meta fusion network (MF -Net), enabling accurate and robust 3D object detection. The superiority and effectiveness of MF -Net have been demonstrated by extensive experiments on KITTI 3D object detection dataset.
Interval modelling in optimization of k‐NN classifiers for large number of attributes in data sets on an example of DNA microarrays

In this contribution there are considered interval-valued methods of improving the quality of classification by the k- N N binary classifiers in the case of large number of attributes in data sets. One of the possible applications of the introduced method are the microarray data so the presented results may be applied in medical diagnosis support, for example, in identification of marker genes. The proposed algorithm involving reduction methods of time complexity and interval modeling of data using interval-valued aggregation functions has a significantly higher classification quality than the other considered algorithm with the aggregation method involving the numerical arithmetic mean. The former algorithm is based on the interval-valued aggregation of uncertainty intervals determined on the basis of certainty coefficients of individual k- N N classifiers while the latter is based on the aggregation of certainty coefficients of individual k- N N classifiers with the use of the numerical arithmetic mean. Moreover, the performance of the new algorithm based on interval-valued methods was compared with the known from the literature classifiers in cancer diagnosis based on gene expression microarrays. The obtained results prove that in practical applications the newly proposed algorithm can be successfully used.
Reduction of open string amplitudes by mostly BRST exact operators

We study two and three-point tree-level amplitudes of open strings. These amplitudes are reduced from higher-point correlation functions by using mostly BRST exact operators for gauge fixing. For simplicity, we focus on an open string tachyon. The two-point amplitude of open string tachyons is reduced from a three-point function of two tachyon vertex operators and one mostly BRST exact operator. Similarly the three-point amplitude is from a four-point function of three tachyon vertex operators and one mostly BRST exact operator. One can also obtain the two-point amplitude from the four-point function of two tachyon vertex operators and two mostly BRST exact operators. In these derivation from four-point functions, moduli integrals are significant. We discuss the overall signs of amplitudes which are indefinite in this formalism.
A Survey of Image Classification Algorithms Based on Graph Neural Networks

Image classification is an image processing method which can distinguish different objects according to their different features reflected in the image information. A graph neural network (GNN) is a connectivity model that captures graph dependencies through messaging between nodes of a graph. After a systematic study of the graph neural network and its application in image processing, this paper presents the mainstream structure model for image classification based on the graph neural network, and synthetically compares the performance of the more popular graph neural network model by analyzing the application scenarios and solving problems of the model.
Story Generation from Images Using Deep Learning

Recently, the problem of creating descriptive captions for images became a significant one. However, human languages' expressivity had been among the challenges that hindered researchers from widely experimenting with creating linguistically rich captions for images. That motivated us to utilize advanced deep learning algorithms to generate captions for images. The researchers proposed an AI model utilizing deep learning and natural language processing algorithms, which has two main components, an image-feature extractor, and a story generator. The researchers trained the first component (image-feature extractor) of the model to predict object names in images. The second component (story-generator) was trained on a custom short descriptive sentence which considered short stories. So, the output from the first component (list of words) will be entered into the second component to generate stories on input images. Thus, when testing the model's performance, a list of names will be entered from the first component so that the second generator arranges them and generates a short story from them. The proposed model developed could generate a short story expressive of an input image as shown by the results of a logical value used on the BLEU scale of 0.59, which further research is planned to improve.
Object Detection in EO/IR and SAR Images Using Low-SWAP Hardware

This paper studies the performance of object detection algorithms applied to electro-optic (EO), infrared (IR), and synthetic aperture radar (SAR) data. First, we describe the simulation of EO and IR images containing vehicles and people and the merging of measured environmental SAR scenes with target image chips. This produces a large data set for training three deep learning algorithms: RetinaNet, EfficientDet, and YOLOv5. The algorithms were trained with a powerful elastic compute cloud (EC2) instance. Performance on simulated data at inference time, in terms of speed and accuracy, was tested on the EC2 instance and a low size weight and power (SWAP) single board computer. YOLOv5 was the most accurate algorithm and the fastest on the EC2 instance but the slowest on the low-SWAP device. RetinaNet and EfficientDet and produced operationally useful throughput on the low-SWAP device for surveillance applications, with RetinaNet having the higher accuracy. This is believed to be the first time the same algorithms have simultaneously been tested on EO, IR, and SAR data, and compared in a unified framework.
Targeted Attacks in Hierarchical Settings via Convex Programming

Adversarial attacks were shown to drastically degrade the performance of one-stage classifiers while being undetectable. In this paper, we examine the susceptibility of both flat and top-down hierarchical classifiers, abbreviated FHCs and TDHCs respectively, to targeted adversarial attacks. Convex programs are formulated to generate input perturbations geared at altering their output predictions according to pre-specified targets (e.g., changing the prediction of a ‘cat’ to a ‘car’ rather than a ‘dog’ or some other pet). A competitive solver based on the Alternating Direction Method of Multipliers (ADMM) is developed and is shown to outperform state-of-the-art solvers. The attacks developed for FHCs and TDHCs are evaluated based on their success rate and imperceptibility. It is shown that FHCs are inherently more robust than TDHCs to said attacks in the sense that fooling their coarse classification generally requires higher levels of perturbation.
Towards More Explicit Interaction Modelling in Agent-Based Simulation Using Affordance Schemata

Modelling agent-environment interactions in an agent-based simulation requires careful design choices. Selecting an interaction partner forms an often neglected, but essential element.
SEED: Semantic Graph Based Deep Detection for Type-4 Clone

Type-4 clones refer to a pair of code snippets with similar semantics but written in different syntax, which challenges the existing code clone detection techniques. Previous studies, however, highly rely on syntactic structures and textual tokens, which cannot precisely represent the semantic information of code and might introduce non-negligible noise into the detection models. To overcome these limitations, we design a novel semantic graph-based deep detection approach, called SEED. For a pair of code snippets, SEED constructs a semantic graph of each code snippet based on intermediate representation to represent the code semantic more precisely compared to the representations based on lexical and syntactic analysis. To accommodate the characteristics of Type-4 clones, a semantic graph is constructed focusing on the operators and API calls instead of all tokens. Then, SEED generates the feature vectors by using the graph match network and performs clone detection based on the similarity among the vectors. Extensive experiments show that our approach significantly outperforms two baseline approaches over two public datasets and one customized dataset. Especially, SEED outperforms other baseline methods by an average of 25.2% in the form of F1-Score. Our experiments demonstrate that SEED can reach state-of-the-art and be useful for Type-4 clone detection in practice.