Reviewerly/exHarmony huggingface dataset:
When to Terminate: Path Non-existence Verification Improves Sampling-based Motion Planning

In this work, we combine a sampling based motion planner with path non-existence verification. We show that, using our approach, it is possible to: 1) provide termination criteria to sampling-based motion planners, based on the problem specific constraints; 2) generate a method for rejection sampling that can adapt to the scenario, for instance, leveraging the relative priority between constraints. Furthermore, we describe a language-guided sampling technique based on IRRT*, that utilizes the results from the path non-existence verification procedure to reduce runtime of the underlying motion planner. The approach is studied using route and motion planning for an autonomous vehicle that aims to service transportation tasks while constrained by rules of the road. We evaluate the proposed approach using a set of real-life inspired traffic scenarios. Finally, we show that the resulting runtime of a motion planner with path non-existence verification is significantly shortened when compared to the same motion planner with traditional termination criteria, such as a resource or time limit.
The model of formation of transferable skills of police officers using the case-method in educational institutions of the Ministry of Internal Affairs of Russia

The article considers the formation of transferable skills of police officers using the case method in educational institutions of the Ministry of Internal Affairs of Russia. The purpose of the research is to develop and substantiate the method of formation of transferable skills of police officers during practical lessons at educational institutions of the Ministry of Internal Affairs of Russia using the case-method of education. The research subject is the formation of transferable skills of police officers. The research object is the educational process of police officers based on the case-method in educational institutions of the Ministry of Internal Affairs of Russia. The authors use the methods of participant observation, analysis of the organization of practical lessons, analysis of statutory documents, the method of modelling.&amp;nbsp; The topicality of the research is determined by the necessity to increase the quality of professional training in the Russian institutions of higher education, including those under the Ministry of Internal Affairs. The scientific novelty of the research consists in the development of the model of formation of transferable skills of police officers based on the case-method of education during practical lessons in special disciplines organized at the educational institutions of higher education of the Ministry of Internal Affairs of Russia (table 1), and in the described example of a practical lesson in the discipline &amp;ldquo;Special Equipment of Internal Affairs Bodies&amp;rdquo; with the use of the case-method. The authors consider the case method, one of the methods of active education, as a means of development of transferable skills. The results section contains an example of the dynamics of the level of completeness of transferable skills of the students of educational institutions of the Ministry of Internal Affairs. The authors arrive at the conclusion that the case-method promotes the formation of transferable skills during practical lessons in special disciplines at the educational institutions of the Ministry of Internal Affairs of Russia, as it helps improve the educational process, involve the students in communication, and develop self-sufficiency. &amp;nbsp;
A Novel Methodology for Human Posture Recognition Using CVPI

CVPI (cross-view person identification) from multiple temporally coordinated images captured by a number of different wearable cameras in differing perspectives is a difficult but critical issue that has recently piqued attraction. CVPI's most recent state-of-the-art efficiency is accomplished by comparing presence and movement attributes through videos, but due to the high inconsistency of 3D pose prediction on videos/images taken in the wild, comparing posing characteristics is ineffective. To fix this issue, primarily a new metric of faith for each joints of human body approximate position in 3D Pose prediction for humans is implemented. In the CVPI pose matching, more weight is provided to joints with a higher conviction. Finally, to improve CVPI accuracy, the approximate knowledge about the pose is combined with the presence and motion functions. The proposed approach is tested on three wearable-camera video datasets, and its success is compared to that of many other CVPI approaches. The findings of the experiments demonstrate that the suggested confidence parameter is correct and combining pose, presence, and motion results in a new state-of-the-art CVPI score.
Parametric Optimization for Moisture Infiltration Prevention Into a FOUP (Front Opening Unified Pod)

Contamination control and mitigation is a vital function in the semiconductor manufacturing process, especially with the ever-decreasing feature size of wafers making them more prone and sensitive to contamination. If not properly handled, it could lead to the formation of wafer defects extending to yield reduction and device performance deterioration. Purging the front opening unified pods (FOUP) that carry semiconductor wafers between process steps by the use of diffuser purge and air curtain technology are just two examples of approaches by which contamination could be controlled. With the optimization objective of minimizing both the relative humidity (RH) and total clean dry air (CDA) that would be used by both diffuser-purge and laminar air curtain supply, in this study, the optimum values predicted for the input parameters using the response surface optimization (RSO) tool by Ansys DesignXplorer are as follows: 30 mm air curtain width, 0.3 m/s FFU velocity, 0.3 m/s air curtain velocity and 130.01 l/min flow rate total for the two diffuser-purge. These optimum values of the aforementioned parameters are predicted to maintain the allowable amount of moisture inside the FOUP as specified by the semiconductor industry, while using the least amount of clean dry air.
A Novel Hybrid Methodology for Computing Semantic Similarity between Sentences Through Various Word Senses

Natural Language Processing has been researching and applying sentence similarity for quite a while. Searching for semantic meaning in natural language is an important and related issue. There is variance in semantic similarity according to the operational domain. In order to compute the similarity between sentences, existing approaches have been constructed from approaches for large texts. The problem with these methods is that they process sentences in a really high-dimensional space. They require human input, are inefficient, and are not suited for some applications. In this paper, we present a novel algorithm for determining how similar two sentences are. This paper introduces a hybrid method which considers not only semantic information including lexical databases, word embeddings, and corpus statistics, but also implied word order information. With lexical databases, our method models human common sense knowledge, and that knowledge can then be adapted to be used in different domains with the incorporation of corpus statistics. Therefore, the methodology is applicable across several domains. Both benchmark standards and mean human similarity datasets have been used to test the methodology. This model outperforms other similar models when tested on these two datasets, giving the highest correlation value for both word and sentence similarity.
Convexity and Contrast Guided Gate Mechanism for Salient Object Detection

Visual attention has a primary role in salient object detection. This paper presents a visual saliency detection method that extracts the salient region from an image based on Human Visual Attention System. The core idea of the proposed method contemplates the laws of the Gestalt principle for object-based visual attention. According to the law of Gestalt psychology, the convexity of an object is the most important cue to attract human attention. The convexity of an object plays a vital role to segregate the figureure from its background. This proposed method aims to unify the contrast information from the various colour channels and generate an intermediate saliency map of the desired object. Then Convex Hull-based object priors have been evaluated to estimate the final saliency map. Our approach has been validated using publicly available datasets, i.e. ECSSD and MSRA. Experimental results show that the proposed method is outperforming the existing state-of-art method.
Analysis of Information Security Problems in Electronic Management with Possible Solutions

The rapid development of the use of the Internet is laying the foundation for the development of digital communities. Their development and expansion involves individuals across the country (and around the world), but it also presents a number of challenges and opportunities for national governments. Take the e-government project as an example, where the flow of large amounts of documents, such as land documents, police records and other types of documents, electronically creates a security problem. In such organizations, each department is very important, so access to the network only by authorized people and access to information is a topical issue. The need to understand and implement information security ensures the safe operation of e-government organizations.
Local Binary Pattern Based on Magnitude Ranking for Texture Classification

Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. There are many LBP variants that can achieve great texture classification results. For most LBP variants, we found two shortcomings. The first is that only the sign information of local difference is considered and the magnitude information of the local difference is discarded. The second is that LBP operator adopts a fixed weight in the encoding process. Based on these two points, this paper proposes a new operator, local binary pattern based on magnitude ranking (MRLBP), and proposes a feature dimension reduction method. Combining the operator and the method, we have excellent texture classification accuracy across six common datasets, with an average of around 2 percent less than the best LBP variants. More importantly, the computational complexity of MRLBP is several times lower than that of these LBP variants.
High Utility Co-location Patterns with Instance Utility

In this chapter, we present a new interestingness measure for mining high utility co-location patterns from spatial data sets with instance-specific utilities. In the new interestingness measure, we take the intra-utility and inter-utility into consideration to capture the global influence of each feature in co-locations. We present a basic algorithm for mining high utility co-locations. In order to reduce high computational cost, some pruning strategies are given to improve the efficiency.
English and Chinese Corpus Collection and Intelligent Semantic Recognition System Based on Big Data

With the help of the part-of-speech tagging in the “Modern Chinese Dictionary” published in 2005 and the “Concise Oxford English Dictionary” published in 2011, the conversion words in English and Chinese vocabulary are compared and found that there are 30 conversion words in Chinese and English respectively. Species and 66 types. This article attempts to introduce the semantic analysis of the English-Chinese parallel corpus into the semantic recognition system. By labeling different levels of the corpus, including grammatical labeling, shallow semantic labeling and deep semantic labeling, the corresponding English-Chinese translation teaching system is designed, and the guiding system is improved by using comparatively systematic data modelling. Compared with the methods without the integration of data mining, the proposed model is more accurate.
Survey on Initialization Methods of Cuckoo Search Algorithm

We know that initialization has a key point on the performance of evolutionary algorithms. However, there are relatively few studies on the initialization of the cuckoo search algorithm. Therefore, this paper considers a systematic study of the initialization method of the cuckoo search algorithm. Several classic initial methods are used, including chaotic map, uniform design, opposition-based learning, quadratic interpolation, and some superior probability distribution methods. Through comparison on 20 test functions, the experiment result shows that opposition-based learning can perform well in both low and high dimensions among these initial methods. In addition, the initialization method has little effect on low-dimensional functions, but a suitable initialization method can greatly affect the performance of high-dimensional functions. Furthermore, increase population size appropriately can lead better performance while dimension raised.
Vehicle Image Generation Going Well with the Surroundings

In spite of the advancement of generative models, there have been few studies generating objects in uncontrolled real-world environments. In this paper, we propose an approach for vehicle image generation in real-world scenes. Using a subnetwork based on a precedent work of image completion, our model makes the shape of an object. Details of objects are trained by additional colorization and refinement subnetworks, resulting in a better quality of generated objects. Unlike many other works, our method does not require any segmentation layout but still makes a plausible vehicle in an image. We evaluate our method by using images from Berkeley Deep Drive (BDD) and Cityscape datasets, which are widely used for object detection and image segmentation problems. The adequacy of the generated images by the proposed method has also been evaluated using a widely utilized object detection algorithm and the FID score.
Detecting Fraudulent Transactions using Hybrid Fusion Techniques

Fraud is one of the most extensive ethical issues in the Financial (Banking) industry. The research aims to create a robust model for predicting fraudulent transactions based on the transactions made by the consumer in the past and present, compare as well as analyse different algorithms that best suit our needs. This paper also focuses on handling the imbalance in the datasets as well as creating a Machine Learning model with high Accuracy, F1-score, AUC, Precision as well as Recall which is achieved using a fusion method in which models are selected from the tested classifiers like Logistic Regression, XGBoost, Random Forest Classifier, Fusion Model, Gaussian NB, and SGDClassifier. Only the models with values of every metric above a certain threshold are selected to churn out maximum performance from the model. The model proposed in this paper uses a probability-based weighted average function for the prediction of fraudulent transactions which yielded a 99% score over all the considered metrics.
An Improved Maxwell’s Equations Derived Optimization Algorithm with Low Time Complexity

The Maxwell’s equations derived optimization (ME-DO) is a novel optimization algorithm, which can obtain the accurate and robust solutions for challenging electromagnetic optimization problems. However, the original MEDO suffers from the high time complexity issue for complex electromagnetic problems. To alleviate the issue, we analytically find the key factors of its high time complexity in MEDO, and propose an improved method to reduce the time complexity, which is named as low-time-complexity Maxwell’s equations derived optimization (LTC-MEDO). Some typical numerical benchmarks are used to test the performance of the proposed LTC-MEDO, and the results show that the optimization time can be reduced significantly, while the accuracy and stability of the algorithm can be basically maintained. LTC-MEDO is also applied to a classic case of electromagnetic interference (EMI) analysis, with a satisfying result.
Approximation Algorithm for the Capacitated Correlation Clustering Problem with Penalties

Correlation clustering problem is an elegant clustering problem and has many applications in protein interaction networks, cross-lingual link detection, etc. In this paper, we introduce the capacitated correlation clustering problem with penalties by combining the capacitated correlation clustering problem and the correlation problem with penalties. There are two main contributions in this paper. The first one is that we give an integer programming for the capacitated correlation clustering problem with penalties. The second one is that we provide an LP-based \((4/(4-5\alpha ), 8/\alpha )\)-bi-criteria approximation algorithm for this problem, where parameter \(\alpha \in (0,4/9]\).
Glenn Shafer — A short biography

This editorial article is a biography of Glenn Shafer, briefly covering his early years, his education, and his contributions as an academic to research, teaching, and administration.
Federated Learning Application on Telecommunication-Joint Healthcare Recommendation

Federated Learning (FL) is proposed to overcome data silos in model training of Machine Learning (ML) through joint modeling with privacy-preserving. Mobile Network Operators (MNOs) and Healthcare Providers (HPs) share common users with different features. Data silo exists due to the inaccessibility from MNOs to HPs for privacy-preserving. Therefore, the data of MNOs and HPs can be utilized together in FL settings to empower each other&#x0027;s service. In this paper, we established a Federated Gradient Boosting Decision Tree (FGBDT) system based on SecureBoost, a lossless federated ensemble model found on GBDT, to improve user classification for HPs in Healthcare Recommendation with MNOs&#x0027; data. Our experiment shows that FGBDT has a 9.71&#x0025; of precision increase and a 4&#x0025; of F1 score improvement, and a 10.45&#x0025; of cumulative precision improvement in a real operational practice than GBDT.
Disentangled Representations for Arabic Dialect Identification based on Supervised Clustering with Triplet Loss

In this paper, we propose a novel supervised clustering with triplet (SCT) loss that effectively learns disentangled representations for Arabic dialect identification (ADI). To improve the performance of ADI using latent representation based approaches, we need to extract embeddings that include only dialect related information by dissociating all the irrelevant information such as gender, channel, and speaker. In consideration of the embedding-level distribution, our proposed SCT loss minimizes intra-class variations and maximizes inter-class variations. Specifically, it uses the centroid of each dialect as a triplet component, thereby avoiding the issue of choosing an undesirable triplet component due to random sampling. Experimental results on the ADI-17 dataset show that our proposed method significantly outperforms conventional state-of-the-art methods in terms of the identification accuracy.
Countering a Single Small Unmanned Aerial System: A Kinetic Approach

Drones are becoming more popular and more accessible to the average person. With this increase in availability, the threat to our national security must be addressed. The goal of this research is to develop and prototype a kinetic means to take down a single drone, approaching head-on at high velocity and constant altitude. Background research was conducted to thoroughly understand the need, which included interviewing various potential end-users of the product. Through this process, it was determined that novel concepts would be highly valued, so numerous rounds of ideation were performed to develop a large initial set of solution concepts, to increase the probability of producing novel solutions. The selected concept was a dual-prop fixed wing plane with a first person view (FPV) camera feature, attached net gun that shoots a net at the hostile drone, and a triggering system. The system was analyzed subsystem-by-subsystem to identify potential failure modes and to verify that the final system would meet the quantified design requirements. Ultimately, a prototype was built and tested with a total cost under $3500. To prove initial feasibility, a constrained experiment was performed, which involved securing the blue-force aircraft to a cable and “flying” it along a fixed path. Under these conditions, the net gun was able to deploy successfully and entangle a surrogate target under conditions that are similar to those anticipated for future operations.
Innovation Mode of Architectural Interior Design Based on Big Data

With the big data of social development, the development of my country’s construction industry has gradually transitioned from a development model that simply pursues scale and speed to a new stage that focuses on urban development and people’s needs. The traditional way of expressing buildings in two-dimensional planes can no longer meet people’s needs for integrated interior design of buildings. In this regard, the purpose of this article is to conduct innovative research on architectural interior design models in the context of big data. This article first starts from reality, combs the representative domestic and international architectural interior design planning theories, and explores architectural interior design models suitable for domestic project construction. This paper proposes an integrated model of building interior design based on big data technology. This paper aims at the expression of the three-dimensional model of the building interior, combined with BIM technology, designing the architectural spatial information model from the perspective of concept and logic. For the designed building space information model, this paper starts from the traditional plane CAD data, and conducts big data research on the simplified algorithm of CAD data. Secondly, it builds the model of the special building components of the research object, and then realizes the interior design model Construct. Experimental results show that the architectural interior design model proposed in this paper can effectively manage and analyze interior design, and people’s satisfaction with design has increased by about 25%, meeting people’s needs, and providing an important reference for architectural interior design.
Evaluation and Validation of Subfertility Ontology and Decision Support System

Evaluation and Validation of ontology verify the quality and perfection of the ontology. Decision Support System ’s Evaluation and Validation provides an error-free and accurate system for users. This paper describes the validation of ontology procedures which verifies the content of the ontology and examines the application of developed subfertility treatment method ontology. We gathered ontology expert suggestions and assessment methods for developed ontology also from the users’ feedback from the doctors and medical students. Ontology accuracy and quality validated by Corpus-based Approach, Delphi Method, Modified Delphi Method, and OntOlogy Pitfall Scanner (web-based pitfall scanner tool) are some approaches employed for ontology evaluation. Certain concepts such as DL Query and SPARQL Query have been utilized to assess ontology, which is included in the Protégé OWL Ontology editor 5.5. The technique of verification adheres to the coherence and structure of ontology while the methods of validation focus to assess their application in the real world. Validity and assessment determine the ontology model’s consistency and user satisfaction. Decision Support System (DSS) evaluated and validated by the field test and user’s feedback. This Evaluation and Validation provide the perfectness of decision-making in a specified domain.