Reviewerly/exHarmony huggingface dataset:
Rolling window Algorithm for Robots Path Planning Using AWS

Choosing a good algorithm for the path planning for the mobile robots will save the precious amount of time and also will save the investment. Path planning for the robots are necessary as it decides the trajactory in which robot will movewithout collision. In this paper we chose rolling window algorithm for the path planning. Here, the rolling window algorithm ends up being an effective way to address the problem of how to set up the path for mobile robots in the environment that is dynamic in nature. The sensors are used by the robot to analyse the environment locally and the grid map is updated again and again based on the analysis. This will reduce the covering path on the Rolling window. Then, according to the planned path the robot will move further. Updating of the grid and planning of the path will be carried out repeatedly till the target is reached. This paper proposes a rolling window algorithm to resolve the problem and reach to the target in optimal time without collision. Simulation result shows that the method we have used helps the robot in covering the entire space with shorter trajectory length in dynamic environment.
I + PD Temperature Controller Tuned for Ant Colony Optimization (ACO) on an ARM Platform

In recent years, the development of new industrial processes in engineering areas has allowed the implementation of new control strategies to ensure the effective operation of the control process within the industry. This paper presents the development of an I + PD controller for a temperature process applied to a control plant trainer (CPT); using the bio-heuristic method of ant colony optimization (ACO) to obtain the appropriate parameters for the tuning of the controller in the plant. On the STM32F board with advanced RISC machine (ARM) technology, the controller is implemented, allowing the sending and receiving of data in real time; for the design of the I + PD control, MATLAB/SIMULINK software is used, working together with the Wainjung library, which allows data collection. To verify the efficiency of the I + PD control, the Wilcoxon test is used, which analyzes the Integral of the time-weighted absolute error (ITAE) of the I + PD control versus the ITAE of a conventional PID control.
Institutional Quality, Regulatory Environment and Microeconomic Performance: Evidence from Transition and Non-transition Developing Countries

Institutional Quality, Regulatory Environment and Microeconomic Performance: Evidence from Transition and Non-transition Developing Countries-Institutional Quality, Labor Market Flexibility, Financial Market Development, Transition Economies, Non-transition Economies
Card3DFace—An Application to Enhance 3D Visual Validation in ID Cards and Travel Documents

The identification of a person is a natural way to gain access to information or places. A face image is an essential element of visual validation. In this paper, we present the Card3DFace application, which captures a single-shot image of a person’s face. After reconstructing the 3D model of the head, the application generates several images from different perspectives, which, when printed on a card with a layer of lenticular lenses, produce a 3D visualization effect of the face. The image acquisition is achieved with a regular consumer 3D camera, either using plenoptic, stereo or time-of-flight technologies. This procedure aims to assist and improve the human visual recognition of ID cards and travel documents through an affordable and fast process while simultaneously increasing their security level. The whole system pipeline is analyzed and detailed in this paper. The results of the experiments performed with polycarbonate ID cards show that this end-to-end system is able to produce cards with realistic 3D visualization effects for humans.
Design of Automatic Test System for Magnetic Head Based on Machine Vision

Hard Disk head testing needs to be done in a dust-free environment and an ESD-free environment. Especially with the rapid development of magnetic heads from MR, TMR to PMR, the flying height of the magnetic head is reduced from 18 nm to less than 2 nm, which makes the production and test conditions of magnetic heads increasingly harsh. As a result, the traditional low- tech labor-intensive production does not meet the new demand. In terms of the particularity of the dynamic testing of hard disk heads, an automatic testing system for the dynamic performance of hard disk heads was developed. In the system, an Epson industrial robot and a CCD camera were used as the actuator, and to build the robot hand-eye Vision System. Thus, the difficulty in the development process from all-man to robot automation in the dynamic performance test of the magnetic head is solved.
Ensemble Online Sequential Extreme Learning Machine for Air Quality Prediction

Online Sequential Extreme Learning Machine (OS-ELM) has been confirmed by numerous studies to be an effective algorithm for online learning scenarios. However, we found that some parameters of OS-ELM are randomly assigned and remain unchanged in the subsequent learning process, which leads to great instability in the model performance in practice. To alleviate this problem, we propose a novel ensemble OS-ELM algorithm (EOS-ELM-R) for solving air quality prediction problems. EOS-ELM-R uses multiple distribution functions to initialize the random parameters of the base OS-ELM models and its final output is the average of the predictions of these base models. Extensive experimental results on two real-world air quality prediction problems show that EOS-ELM-R is effective, and it can achieve better generalization capabilities than similar algorithms.
On multiple base discrete logarithm problem in $\mathbb {G}_m^{n}$ and $E_d^{n}$

In this paper, we consider local to global principle in $n$th power of $\mathbb {G}_m$ and $n$th power of CM elliptic curve $E_d: y^2=x^3-d^2x$. In both cases, for $n \gt 1$, we give examples where local to global multiple base discrete logarithm problem
Application Analysis of Cognitive Internet of Things Technology on Permeability in Mining

Computer simulation technology is a comprehensive technology, which realizes the simulation of the research object through the effective combination of computer software and control system and display system. In the process of coal mine production, the efficiency and safety of mining has always been a problem that coal mining enterprises pay more attention to. Through the application of computer simulation system in the process of coal mining, this paper mainly analyzes the simulation efficiency of different mining methods, and simulates the underground operation, so as to find the potential safety hazards and avoid the safety problems of mining. This paper focuses on the application of computer simulation system in the process of coal mining.
An Approach to Improve Searching Textual Data in Email Dataset

In current times it is very hard to imagine a day without the use of a computer. Communication among people has increased drastically with the help of the internet using social media platforms. Among these various modes of communication, email plays a vital role in connecting people globally using a unique ID called email ID which is used to identify different users, there’s also instant messaging and other modes of communication over the internet via a social media service such as Facebook, WhatsApp, etc. Using these services users can communicate by sending messages and data in the form of attachments to one another. In this paper, we try to implement feature selection techniques such that we can find the strongest relationships between users which are low cost and can be easily implemented to recognize users writing patterns and also help in searching and providing human-like search results and responses. In this case, we have used the Enron email corpus which is made public for research by the CALO project.
Spread Spectrum Based Speech Steganography Using RDWT

Major issue nowadays is keeping control of the data and ensuring security due to its exponential growth in speech communications. This occurs because of people's growing passion in new technologies, communication network explosion. Thus, this article focusing on development of the speech steganography system with the secret message sharing along with speech and the secrete message is also encrypted with the hyper electric curve cryptography (HECC) for more security. Thus, to reduce the errors, the better way is to remove the unwanted noises in the human speech, the framing and windowing method implemented for speech enhancement. Then, the Redundant Discrete wavelet Transform (RDWT), and Matrix formulation methods are used for the embedding the message into the speech. The RDWT also used for the extraction of speech signal and message data from the Stego signal. The proposed method implemented and tested against the various attacks. From the simulation results, it is observed that the proposed method gives the better Statistical analysis and gives the better noise free reconstructed speech and message data as compared to the state of art approaches.
A Coherent Cooperative Learning Framework Based on Transfer Learning for Unsupervised Cross-Domain Classification

In the practical application of medical image analysis, due to the different data distributions of source domain and target domain and the lack of the labels of target domain, domain adaptation for unsupervised cross-domain classification attracts widespread attention. However, current methods take knowledge transfer model and classification model as two separate training stages, which inadequately considers and utilizes the intrinsic information interaction between modules. In this paper, we propose a coherent cooperative learning framework based on transfer learning for unsupervised cross-domain classification. The proposed framework is constructed by two classifiers trained by transfer learning, which can respectively classify images of source domain and target domain, and a Wasserstein CycleGAN for image translation and data augmentation. In the coherent process, all modules are updated in turn, and the data is transferred between different modules to realize the knowledge transfer and collaborative training. The final prediction is obtained by a voting result of two classifiers. Experimental results on three pneumonia databases demonstrate the effectiveness of our framework with diverse backbones.
Research on the Evaluation Words Recognition in Scholarly Papers’ Peer Review Texts

Peer review is an important means of academic evaluation. The evaluation words in peer review texts reflect the important viewpoints of reviewers. In this paper, An evaluation words recognition method for peer review texts (TransPeerBCC) is proposed based on transfer learning method and BiLSTM-CNN-CRF framework. TransPeerBCC first classifies direct evaluation word and indirect evaluation word, and then uses BiLSTM-CNN-CRF framework to identify these words. At the same time, in order to improve the recognition accuracy, the model parameters of public domain data are transferred to the peer review texts’ using the transfer learning method. The effectiveness of the method is verified by the experimental dataset, and the identified evaluation words are quantitatively analyzed.
Blockchain-based node data detection scheme for the Internet of Things system

Aiming at the risks of node security and data protection of the Internet of Things, this paper proposes a node detection scheme of the IoT based on blockchain technology to protect the information of the IoT system. This scheme makes consensus on the system nodes through the combination of the IoT and the blockchain, records the information data of the nodes, and stores the information of the nodes of the IoT using the security characteristics of the blockchain, which increases the security of the IoT system and effectively prevents the tampering of the node data. Based on the characteristics of the IoT node, a new block structure is simplified and designed to make the block smaller in capacity and easier to store in the IoT node. The detection of nodes in the IoT and a traceability process to determine whether there is a malicious node based on the detection results becomes more reliable. It can effectively solve the problem of node security and data modification in the development of the IoT.
Implementation of Pipelined Adder Tree for Long Short-Term Memory Cells

The characteristics of recurrent and functional reduction in RNNs (Recurrent Neural Networks) may cause an inefficient execution in conventional hardware such as CPU (Central Processing Unit) and GPU (Graphics Processing Unit). Their large number of recurrent data prevents the CPU from exploiting their locality, while the reduction feature limits the GPU parallelism. As a result, several dedicated hardware for efficient RNN execution has been proposed recently. In this paper, we propose a processing element that performs primary operations (i.e., multiplication and accumulation operation and activation function operation) of LSTM (Long Short-Term Memory), the most dominant model of RNN We implemented the processing element on an FPGA (Field-Programmable Gate Array) and verified the accuracy by measuring the BLEU score (Bilingual Evaluation Understudy score) of the PyTorch-based LSTM application. It was decreased by 2.3% compared to the CPU result.
A Compressed Hidden Naive Bayesian Classifier

This paper proposes a compressed hidden naive Bayesian (C-HNB) classifier which is an improved version of hidden naive Bayesian (HNB) by compressing the Bayesian network structure and calculating the attribute correlation with maximal information coefficient (MIC). In C-HNB, we remodel the Bayesian network structure based on the chain rule of joint probability distribution so that the number of hidden parent nodes in C-HNB is smaller than the number of hidden parent nodes in HNB, which reduces the training complexity of Bayesian network. In addition, the attribute correlation in C-HNB is calculated with MIC rather than the mutual information, which makes the Bayesian network more stable because the calculation of mutual information is severely influenced by the discretization of continuous attribute. On the selected KEEL benchmark data sets, we compare the classification performances of C-HNB with HNB. The comparative results show that C-HNB can obtain the better prediction accuracy with the less training time in comparison with HNB and thus the experimental results demonstrate the effectiveness of C-HNB.
Instructional design: How to design the expected learning outcomes of students?

The article is devoted to current issues of lesson design based on student expected learning outcomes. One of the distinctive features of recently approved new Federal State Educational Standards for primary and basic general education is refined and detailed requirements for the expected educational outcomes. In this regard, tools for the teacher to develop those outcomes in order to plan a lesson or a study course in a logical way taking into account the educational interests of students are in dire need. The authors of the article consider the Understanding by Design model as such a tool, since this framework makes it possible to design learning outcomes (distinguishing between understanding, acquisition and transfer goals) and direct the learning process towards desired results. The article provides theoretical foundations for the development of an instructional design model, examines the stages of the design of learning outcomes, the selection of study activities and the identification of teaching strategies. A description of the first stage of design — the design of the expected learning outcomes according to different facets of understanding and of the acquisition and transfer goals — is given in detail.
Psychological and Pedagogical Features of Training Future Psychologists

The relevance of the topic submitted for consideration in the framework of this scientific research is determined by the need for a deep study of various aspects of training future specialists in the field of psychology in higher pedagogical educational institutions, in the context of the wide demand for the specialty of a psychologist in society and the need to improve the quality of training specialists in this field. The main purpose of this research work is to study the key features of psychological and pedagogical training of future psychology specialists at the stage of training in an educational institution, from the point of view of forming their competencies necessary for conducting further professional activities. The leading approach in this research work is a combination of methods of systematic analysis of the currently available methods of training specialists in the field of psychology with an analytical study of the features of teaching in modern educational institutions, from a pedagogical and psychological point of view, in order to form the most complete and objective picture of scientific research. The results obtained in the course of this research reflect the existing problems of the modern education system in the context of the training system of future psychologists, both professional and from a purely pedagogical point of view, and serve as a qualitative basis for formulating final conclusions based on them. The results and conclusions of this research work are of significant practical importance for improving the training systems of future psychologists in modern educational institutions in terms of forming their competencies necessary for high-quality further professional activity
Paradoxes of truth-in-context-X

Abstract We may suppose that the truth predicate that we utilize in our semantic metalanguage is a two-place predicate relating sentences to contexts, the truth-in-context-X predicate. Seeming paradoxes pertaining to the truth-in-context-X predicate can be blocked by placing restrictions on the structure of contexts. While contexts must specify a domain of contexts, and what a context constant denotes relative to a context must be a context in the context domain of that context, no context may belong to its own context domain. A generalization of that restriction appears to block all of the paradoxes of truth-in-context-X. This restriction entails that, in a certain sense, we cannot talk about the context we are in. This result will be defended, up to a point, on broadly ontological grounds. It will also be conjectured that our semantic metalanguage can be regarded as semantically closed.
Constructing bounded degree graphs with prescribed degree and neighbor degree sequences

Let D=(d1,d2,…,dn) and F=(f1,f2,…,fn) be two sequences of positive integers. We consider the following decision problems: is there a (i) multigraph, (ii) loopless multigraph, (iii) simple graph, (iv) cycle-free graph (forest or tree), (v) caterpillar G=(V,E) such that for all k, d(vk)=dk and ∑w∈N(vk)d(w)=fk (d(v) is the degree of v and N(v) is the set of neighbors of v). Here we show that all these decision problems can be solved in polynomial time if Δ≔maxkdk is bounded. The problems are converted into an integer programming feasibility problem in which both the number of variables and the number of inequalities depend only on Δ but not on n. The problem is motivated by NMR spectroscopy of hydrocarbons. The algorithm has been implemented in the ZIMPL language, and its applicability is demonstrated on trees up to n=1000 vertices. The average reconstruction time for trees with 1000 vertices is still less than 40 ms.
Speech-To-Story: Gerando Histórias de Usuário

In agile environments, requirement engineers conduct interviews with clients to create user stories manually. However, a manual approach is subject to human failures such as ambiguity, communication problems, and incomplete requirements. These failures are detected during the validation process of the stories that can be performed days after the interview, causing on wasted time and rework. This paper presents Speech-To-Story: a mobile solution for requirements gathering, generating automatically user stories for their immediate validation. Questions are offered for the engineer to conduct the structured interview, capturing the responses of the clients. The user stories can be validated and corrected during interview time.
Learning to Detect Adversarial Examples Based on Class Scores

Given the increasing threat of adversarial attacks on deep neural networks (DNNs), research on efficient detection methods is more important than ever. In this work, we take a closer look at adversarial attack detection based on the class scores of an already trained classification model. We propose to train a support vector machine (SVM) on the class scores to detect adversarial examples. Our method is able to detect adversarial examples generated by various attacks, and can be easily adopted to a plethora of deep classification models. We show that our approach yields an improved detection rate compared to an existing method, whilst being easy to implement. We perform an extensive empirical analysis on different deep classification models, investigating various state-of-the-art adversarial attacks. Moreover, we observe that our proposed method is better at detecting a combination of adversarial attacks. This work indicates the potential of detecting various adversarial attacks simply by using the class scores of an already trained classification model.
Higher-Order Conditional Random Field established with CNNs for Video Object Segmentation

We perform the task of video object segmentation by incorporating a conditional random field (CRF) and convolutional neural networks (CNNs).Most methods employ a CRF to refine a coarse output from fully convolutional networks.Others treat the inference process of the CRF as a recurrent neural network and then combine CNNs and the CRF into an end-to-end model for video object segmentation.In contrast to these methods, we propose a novel higher-order CRF model to solve the problem of video object segmentation.Specifically, we use CNNs to establish a higher-order dependence among pixels, and this dependence can provide critical global information for a segmentation model to enhance the global consistency of segmentation.In general, the optimization of the higher-order energy is extremely difficult.To make the problem tractable, we decompose the higher-order energy into two parts by utilizing auxiliary variables and then solve it by using an iterative process.We conduct quantitative and qualitative analyses on multiple datasets, and the proposed method achieves competitive results.
Mining Emergency Event Logs to Support Resource Allocation

Effective emergency resource allocation is essential to guarantee a successful emergency disposal, and it has become a research focus in the area of emergency management. Emergency event logs are accumulated in modern emergency management systems and can be analyzed to support effective resource allocation. This paper proposes a novel approach for efficient emergency resource allocation by mining emergency event logs. More specifically, an emergency event log with various attributes, e.g., emergency task name, emergency resource type (reusable and consumable ones), required resource amount, and timestamps, is first formalized. Then, a novel algorithm is presented to discover emergency response process models, represented as an extension of Petri net with resource and time elements, from emergency event logs. Next, based on the discovered emergency response process models, the minimum resource requirements for both reusable and consumable resources are obtained, and two resource allocation strategies, i.e., the Shortest Execution Time (SET) strategy and the Least Resource Consumption (LRC) strategy, are proposed to support efficient emergency resource allocation decision-making. Finally, a chlorine tank explosion emergency case study is used to demonstrate the applicability and effectiveness of the proposed resource allocation approach.