Reviewerly/exHarmony huggingface dataset:
MAV-UAV Collaborative Situation Assessment Based on Dynamic Intuitionistic Fuzzy Cognitive Maps

In the collaborative situation assessment of manned aircraft vehicle (MAV) and unmanned aircraft vehicle (UAV), because of the uncertainty, dynamic and fuzzification of the battlefield environment information and associated information, how to obtain relatively accurate situation assessment results is a problem to be solved. In this paper, a dynamic intuitionistic fuzzy cognitive map (DIFCM) is formed by combining intuitionistic fuzzy sets with fuzzy cognitive map. A MAV-UAV cooperative situation assessment model is established based on DIFCM. Considering the human factor, the workload of pilot is introduced into the evaluation model as a concept node. Each node in the model is composed of membership degree and non membership degree, and the node states are compared by the score and accuracy. Through the simulation experiment, the rationality and feasibility of the evaluation model are verified. Different initial battlefield information, environment variables and node hesitation are set up to analyze the situation assessment output. The experimental results show that the situation assessment model can objectively evaluate the battlefield situation and provide more decision-making information for commanders.
An Efficient Feature Fusion Technique for Text-Independent Speaker Identification and Verification

Speaker identification and verification is an important research area that finds applications in forensics voice verification, mobile banking and security authentication for access control. Various techniques for feature extraction are available in the literature. In this work, a speech feature fusion extraction technique based on fusion of time domain, frequency domain and cepstral domain features has been proposed. Supervised machine learning classification algorithms are used for speaker feature classification. Performance of proposed technique has been evaluated on two open-source speech datasets. Performance metrics of training time and accuracy (validation and test) are measured with the help of confusion matrix. The results indicate that even with smaller training datasets, the average accuracy achieved is 2.97 and 8.97% better and training time 1.95 and 2.03 s less as compared to MFCC and (MFCC + delta + delta delta) MFCC + Δ + Δ2, respectively.
Impact Of Work Load On Nursing Patient Interaction In Tertiary Care Hospital Punjab

This study was done to assess the nursing knowing among nurse-patient workload by stratified random sampling. A total of 100 samples was selected and given the self-designed and pre-tested questionnaire. The data was collected and analyzed by using percentages and frequency. It is found that there is positive communication among Nurse- Patient. There is no significant (p&lt;0.005) relationship between communication with age, sex, and education. But there is a significant relationship of quality of communication with the number of times patient was admitted in tertiary care Hospital
ANALISIS REVETMENT SEBAGAI PERLINDUNGAN TEBING SUNGAI BATANG LEMBANG DALAM UPAYA PENGENDALIAN BANJIR KOTA SOLOK

Solok City as a developing city where there is a natural river that passes through the middle which does not escape from flooding every year. The river located in Solok City is the Batang Lembang river. Rainfall used in hydrological planning are Sumani station, Sukarami station and Danau di Bawah station. The calculation of the maximum discharge used in this plan is the Rational method, the Hasper method and the Nakayasu HSS method. The discharge used is the HSS Nakayasu Q25 method with consideration for the calculation of flood routing using the time parameter, which is every hour. The average width of the existing river is 40 m. The planning river width in this analysis is 45 m and the slope of the revetment is 1:1. The revetment structure is divided into each segment of 20 meters. The cliff protection is made of stone masonry 45 cm thick and the embankment height is 2.5 m. The revetment foundation is planned with a concrete pile foundation with a diameter of 400 mm and a depth of 8 meters. After calculating the revetment analysis, the protection of the Batang Lembang riverbank is safe against landslides and is stable against various forces acting on the building.Keywords: flood discharge, river cross section design, cliff protection.
The interface design of Chinese input keyboard applied to eye movement control system

The typing system of eye movement control mainly adopts the layout of QWERTY keyboard, the design of which increases the bimanual alternate operation based on the frequency of letters to relieve fatigue and improve efficiency. Nevertheless, with regard to the eye control operation, such design significantly increases the movement path of the eyes, which in turn hinders the typing efficiency. In view of this, this research firstly makes statistics on the selected Chinese word set and the transition frequency between letters according to Pinyin input method. In an attempt to ensure that users can minimize eye movement while typing, a greedy algorithm is used to generate an irregular letter layout. On the basis of this layout, a new eye-controlled keyboard layout is designed in this research in combination with the principles of interface interaction design and the physiological characteristics of eye movement control. Finally, by virtue of setting the contrasted eye-controlled typing experiment, this research demonstrates that the new eye-controlled keyboard layout presents a certain degree of advantages in input efficiency and relieving user fatigue.
Research on Calibration Method of Binocular Camera-Lidar

Aiming at the problem of obtaining internal parameters of binocular camera-lidar, a joint calibration method is proposed. Firstly, the corner points in the left and right images are detected and sub-pixel precision is performed, and the corner points in the left and right images are matched through epipolar constraints. Then, the internal parameters of the binocular camera are obtained according to the three-dimensional space coordinate relationship and the corner point matching relationship. Through the coordinate relationship between the feature points of the lidar and the feature points of the binocular camera, the binocular camera and the lidar are jointly calibrated to obtain the external parameters of the binocular camera-lidar system. Finally, an experiment was designed and carried out for the method proposed in this paper. The experimental results show that the calibration method proposed in this paper is effective.
Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization

Domain generalization (DG) methods aim to develop models that generalize to settings where the test distribution is different from the training data. In this paper, we focus on the challenging problem of multi-source zero shot DG (MDG), where labeled training data from multiple source domains is available but with no access to data from the target domain. A wide range of solutions have been proposed for this problem, including the state-of-the-art multi-domain ensembling approaches. Despite these advances, the na\"ive ERM solution of pooling all source data together and training a single classifier is surprisingly effective on standard benchmarks. In this paper, we hypothesize that, it is important to elucidate the link between pre-specified domain labels and MDG performance, in order to explain this behavior. More specifically, we consider two popular classes of MDG algorithms -- distributional robust optimization (DRO) and multi-domain ensembles, in order to demonstrate how inferring custom domain groups can lead to consistent improvements over the original domain labels that come with the dataset. To this end, we propose (i) Group-DRO++, which incorporates an explicit clustering step to identify custom domains in an existing DRO technique; and (ii) DReaME, which produces effective multi-domain ensembles through implicit domain re-labeling with a novel meta-optimization algorithm. Using empirical studies on multiple standard benchmarks, we show that our variants consistently outperform ERM by significant margins (1.5% - 9%), and produce state-of-the-art MDG performance. Our code can be found at https://github.com/kowshikthopalli/DREAME
Distributed energy management study based on blockchain technology

This article proposes a power network management system based on blockchain technology to address the difficulties in distributed energy management in smart grids and power resource dispatching. Ethereum is used as the development platform to build a blockchain for power interaction management which mainly involves distributed energy transactions. In order to guarantee the rationality of energy transactions in the grid, this article presents a distributed power dispatching strategy using K-means clustering algorithm and particle swarm optimisation (PSO) algorithm. Finally, the distributed power matching transactions are completed in the power interaction management blockchain with the presence of power operation smart contracts. The experimental results show that the consensus mechanism and power dispatching strategy designed in this paper effectively solve the matching problem in distributed power trading. The application of the power operation smart contract further promotes the success rate of the transaction and effectively reduces its time consumption.
Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation

Open-domain conversational systems are assumed to generate equally good responses on multiple domains.Previous work achieved good performance on the single corpus, but training and evaluating on multiple corpora from different domains are less studied.This paper explores methods of generating relevant responses for each of multiple multi-domain corpora.We first examine interleaved learning which intermingles multiple corpora as the baseline.We then investigate two multidomain learning methods, labeled learning and multi-task labeled learning, which encode each corpus through a unique corpus embedding.Furthermore, we propose Domainspecific Frequency (DF), a novel word-level importance weight that measures the relative importance of a word for a specific corpus compared to other corpora.Based on DF, we propose weighted learning, a method that integrates DF to the loss function.We also adopt DF as a new evaluation metric.Extensive experiments show that our methods gain significant improvements on both automatic and human evaluation.We share our code and data for reproducibility. 1
Streaming Inference for Infinite Non-Stationary Clustering

Learning from a continuous stream of non-stationary data in an unsupervised manner is arguably one of the most common and most challenging settings facing intelligent agents. Here, we attack learning under all three conditions (unsupervised, streaming, non-stationary) in the context of clustering, also known as mixture modeling. We introduce a novel clustering algorithm that endows mixture models with the ability to create new clusters online, as demanded by the data, in a probabilistic, time-varying, and principled manner. To achieve this, we first define a novel stochastic process called the Dynamical Chinese Restaurant Process (Dynamical CRP), which is a non-exchangeable distribution over partitions of a set; next, we show that the Dynamical CRP provides a non-stationary prior over cluster assignments and yields an efficient streaming variational inference algorithm. We conclude with experiments showing that the Dynamical CRP can be applied on diverse synthetic and real data with Gaussian and non-Gaussian likelihoods.
Personal credit risk identification based on boosting-C5.0 algorithm

Currently, for the problem of personal credit risk identification, the most commonly used method is to optimize the parameters of the model through bionic algorithms to obtain higher accuracy, but it may face the risk of lower precision. Some scholars also discussed the identification of personal credit risk from the perspective of combination models. From the perspective of integrated learning, based on C5.0 algorithm and using boosting technology, this paper constructs the boosting-c5.0 personal credit risk identification model, and uses UCI German personal credit data set to verify the performance of the model. The study found that the accuracy, recall, precision and AUC value of boosting-C5.0 model are better than SVM, logistic and C5.0 models.
Knowledge graph for different platforms with radar: construction and application

At present, the analysis and identification of unknown targets mostly rely on manual labor, which can not make fast and accurate decisions for the current situation. As a knowledge representation method in the era of big data, knowledge graph has the capabilities of semantic processing, open interconnection, and logical combing. In this paper, we construct a knowledge graph of different platforms with radar through three main steps: data acquisition, knowledge extraction, and knowledge storage, and then propose a knowledge reasoning framework. Based on it, when faced with massive data, combined with the obtained related intelligence information, we can effectively analyze and identify the type of target platform, and then estimate the opponent’s actions and intentions, and assist us to make command decisions.
Using Zero-Knowledge Proof for Secure Data Transmission on Distributed Network

Data security plays a major role in computer network. Because it helps to transmit data in secure way over the Internet. So we need to use strong security method for secure data transaction. Cryptography is a security tool which helps to transmit information from one place to another place over computer network. Cryptography follows encryption and decryption methods for data transmission. Cryptographic technique is completely based on key generation because it needs keys to transmit data between users. However cryptography works well in secure data transmission but it needs keys to provide security for data. In cryptography generation of keys taking more time than transmission of data. So in this paper we discuss about Zero-Knowledge Proof (ZKP) which is also based on cryptographic technique. ZKP is also useful in secure data transmission without sharing key values between users. This paper tells about overview of ZKP and how it is useful in data transmission.
Innovative Technologies for Involving Higher School Students in Reading

The aim of the study is to theoretically and methodologically substantiate the need of using innovative technologies to involve higher school students in reading. The paper reveals the essence of the technologies of conceptual and figurative reading, collective immersion in a literary text; algorithms of actions while applying the innovative technologies that intensify the cognitive process are described; the effectiveness of using the proposed reading technologies in the educational process is substantiated. The scientific novelty of the work lies in the fact that algorithms for applying the technologies of conceptual and figurative reading and collective immersion in a literary text aimed at involving higher school students in reading have been developed. As a result, it has been proved that the technologies of conceptual and figurative reading and collective immersion in a literary text are applicable to institutions of higher education of any profile and provide the required level of quality in training specialists.
IDENTIFICATION OF GEOCHEMICAL MARKERS OF VOLCANISM BY THE CLUSTER ANALYSIS FOR THE BOTTOM SEDIMENTS OF THE THERMAL LAKE FUMAROLNОЕ ACCORDING TO SR-XRF

We carried out layer-by-layer scanning (with a step of 1 mm) of the bottom sediments of the thermal lake Fumarolnое with SR-XRF (X-ray fluorescence analysis using synchrotron radiation). The lake is located in the caldera of the Uzon volcano (Kamchatka). The section of the bottom sediments of lake IV Fumarolnое covering the time interval from 260 AD to 2012 is diverse in chemical and mineral composition. Two pyroclastic horizons are observed. The chemical composition of the bottom sediments showed the presence of different layers in which such chemical elements as: Ca, Sr, As, Sb, Mo. Cluster analysis performed for chemical elements revealed the boundaries of layers with different geochemical characteristics. The boundaries of these layers coincide with the horizons identified by mineralogical analysis. At the same time, statistical methods of geochemical data processing allowed unambiguously identifying pyroclastic horizons by elemental composition. We show that the data of SR-XRF analysis in conjunction with cluster analysis can be used to separate the gravity core into layers. The data are in good agreement with the separation data into layers using mineralogical methods.
Assessment of functional literacy of students in computer science based on the criteria-based approach

Everyone uses a traditional five-point grading system to assess students' activities, however, it is not always possible to objectively evaluate students’ work using this method. Therefore, the authors use the criteria-based student assessment system in our lessons. The purpose of this article was to compile a model of a system for assessing students' functional literacy based on a criteria-based approach. An experiment was conducted on 32 people and data was collected using observation and interviews. As a result of the study, it was revealed that the assessment system makes it possible to determine how successfully one or another educational material is mastered, or a certain practical skill is formed. Based on the conducted practical experiment, the effectiveness of the content-structural model of the system for assessing the functional literacy of students based on the criteria-based approach in computer science has been proved. Keywords: Criteria-based assessment; computer literacy; integrated information; self-assessment procedures.
Application of color image encryption algorithm based on logistic sine cosine mapping in image processing

The rapid development of science and technology has brought convenience for photographing and sharing images. However, with the rapid increase of image data, security problems such as leakage and tampering also occur frequently. The application of image encryption technology is imminent, especially the encryption of color images is in urgent need of improvement and development. The traditional encryption technology is mainly aimed at data stream encryption, which has low efficiency, large amount of calculation and certain limitations. Based on transform domain encryption, the image is transformed from spatial domain to frequency domain for encryption, and then transformed to spatial domain, which belongs to a kind of lossy encryption. Encryption based on chaos has large key space, simple implementation and fast encryption speed. Generally, multiple chaotic systems need to be used to enhance the security of encryption. Therefore, a simple and secure spatial encryption algorithm for RGB three channels of color image is designed in this paper. Firstly, a safer chaotic sequence is generated by logistic sine cosine mapping, and then four rounds of scrambling and diffusion pixels are designed by chaotic sequence. Finally, In a series of security analysis experiments, the security and effectiveness of color image encryption algorithm based on logistic sine cosine mapping are verified.
Research on fuzzy clustering based on improved sparrow algorithm

Focusing the problem that the traditional fuzzy c-means clustering (FCM), which is a local search algorithm, it uses iterative hill climbing technology, which is sensitive to initial values and easy to fall into local minimums. An FCM method based on improved sparrow algorithm is proposed. Improve some problems that appear in Fuzzy c-means clustering (FCM) at this stage, and then combine them with association rules. First, through Logistic chaotic mapping, the initialization of the population can traverse the entire solution space and obtain the initial optimal position of the sparrow, so that it has a richer population and solves the problem of the randomness of the initialization population. Secondly, the adaptive t-distribution mutation operator is introduced into the sparrow algorithm, and the number of iterations of the algorithm is used as the degree of freedom parameter of the t-distribution to enhance the diversity of the population and greatly avoid the probability of the algorithm falling into local optimum. Then combine with the FCM algorithm to cluster the data. The innovation of this algorithm is that the sparrow algorithm and FCM are combined for the first time, combining the advantages of the two algorithms, the effect of data clustering is better, and the time of clustering is also accelerated. Compared with the proposed algorithm FCM algorithm, SSA-FCM algorithm and GA-FCM algorithm, it has stronger optimization ability and better clustering effect.
Chinese text semantic matching method oriented to information interaction

Text semantic matching is an important issue in natural language processing, and it is widely used in question answering systems, dialogue systems, and information retrieval. Text matching models can be divided into representation-based text matching models and interaction-based text matching models. Aiming at the problem that the existing text matching models tend to ignore the global information, a Chinese text semantic matching model oriented to information interaction is proposed. This model uses interactive attention and self-attention to make the text's own structure for information interaction, and at the same time increases the deep semantic interaction of the two texts, and obtains abundant semantic information vectors. Experiments show that the information interaction-oriented semantic matching model proposed in this paper is superior to the traditional model on the verified Chinese text data set.
Moment-based Invariants for Probabilistic Loops with Non-polynomial Assignments

We present a method to automatically approximate moment-based invariants of probabilistic programs with non-polynomial updates of continuous state variables to accommodate more complex dynamics. Our approach leverages polynomial chaos expansion to approximate non-linear functional updates as sums of orthogonal polynomials. We exploit this result to automatically estimate state-variable moments of all orders in Prob-solvable loops with non-polynomial updates. We showcase the accuracy of our estimation approach in several examples, such as the turning vehicle model and the Taylor rule in monetary policy.
A trusted remote attestation protocol based on attributes for containerized virtual network functions

The virtual network function carried by the container has the advantages of strong orchestration ability and high flexibility. However, due to the poor natural isolation of the container, the security of the containerized virtual network function has attracted much attention. After trusted measurement of containerized virtual network functions based on trusted computing, how to reduce the privacy exposure of containerized virtual network functions under the premise of effective response to challengers has become an urgent problem to be solved. This paper proposes a trusted remote attestation protocol based on attribute certificates and ciphertext policy attribute-based encryption for containerized virtual network function, and uses Ban logic to perform inference analysis on this protocol. The results prove that the proposed is correct and can meet the expected goals.