Reviewerly/exHarmony huggingface dataset:
Modeling the Software Prototyping Process in a Research Context

The paper examines the Third Mission of universities from the point of view of company collaboration in the prototype development process. The paper presents an implementation of university-enterprise collaboration in prototype development described by means of process modeling notation. In this article, the focus is on modeling the software prototyping process in a research context. This research paper introduces prototype development in a university environment. The prototypes are made in collaboration with companies, which offered real-world use cases. The prototype development process is introduced by a modeling procedure with four example prototype cases. The research method used is an eight-step process modeling approach. The goal was to find instances of activity, artifact, resource, and role. The results of modeling are presented using textual and graphical notation. This paper describes the data elicitation, where the process knowledge is collected using stickers-on-the-wall technique, and the creation of the model is described. Finally, the shortcomings found in our existing practices and possibilities for improving our prototype development processes and practices are discussed.
Efficient Path Planning and Real-Time Obstacle Avoidance for Mobile Robots

With the development of modern technologies, intelligent mobile robots are widely needed. However, the challenges in path planning and obstacles avoiding for mobile robots still remain. When the obstacles dynamically appear and the robot does not know the exact location of the obstacles as well as the shape of the obstacles, how to conduct efficient path finding and obstacle avoiding is still open. In this paper, we investigate how a self-walking robot can observe and adopt efficient strategies to avoid the dynamic obstacles that appear in the way to the destination. We propose a practical robot navigation model while considering the dimension of the robot and the obstacles observed. Based on this model, efficient navigation algorithms are developed using the proposed techniques of surpassing, bypassing, and virtual blocks. Our experiment and implementation with V-REP shows that the proposed schemes are effective in path planning and obstacle avoiding.
E-Health Bot to change the Face of Medicare

Chatbot or conversational bot is a tool to ease the communication between human beings and computers. When someone talks to a chatbot, it seems as if you are talking to an actual human being. Chatbots carry out conversation in auditory and textual manner. Chatbots work on the principles of Artificial Intelligence and are automated devices. Their response is based upon the input of the user. They analyze the request, identify intent and entities and then compose their reply which may be data from the database or an API (application programming interface) call. These devices are trained with the help of actual data. The device matches the questions asked by the user to the best suitable answer using tools and models of machine learning. Chatbots seem to be self-sufficient after they go live but human intervention is very crucial in its configuration, training and implementation. Since the database for outputs is limited, it is possible that chatbot encounters an unknown query which is to be solved by a human being. However, chatbots do hold an advantage over human beings as they are available 24/7 and have access to huge amount of information. These chatbots use API for providing response to different requests of the user. This paper proposes the idea of a new chatbot which will assist the users with their medical needs, the Medicbot. This chatbot will be able to perform multiple functions with the combination of various Artificial Intelligence forms such as semantic understanding and natural language processing. Medicbot will be operated with the help of a virtual assistance (serves the purpose of a personal assistant) known as Medico.
Comparing Generalisation Using Crowd-Sourced vs Expert Labels for Galaxies Classification

Increasingly large amounts of data are available to astronomers. The Sloan Digital Sky Survey notes that manually classifying all captured galaxies would be an intractable task taking in the order of a hundred years to complete. It follows that scientists have turned to crowd-sourced labelling and machine learning, such as galaxy Zoo 2, to automate this task. If galaxy classification is to be automated in this way, then understanding how these results hold up to expert Hubble tuning classification schemes such as the academically accepted Revised Shapley-Ames catalogue of bright galaxies is timely. We use the Galaxy Zoo 2 data to train a base ResNet-50 model for transfer learning which we use to predict the Hubble types of Galaxies in the Revised Shapley-Ames catalogue. This result demonstrates how well the Galaxy Zoo 2 responses generalise to an expertly labelled catalogue of galaxies. We also train a ResNet-50 model on the EFIGI catalogue, which contains expertly labelled galaxies, and test on the Revised Shapley-Ames catalogue to draw a comparison to the crowd-sourced Galaxy Zoo 2's performance. Our results show the effectiveness of using transfer learning for galaxy classification. Further, we reinforce that more recent architectures such as ResNet-50 improve on state of the art in this task. We also demonstrated that an expertly labelled dataset of galaxies is marginally better for training a model to predict Hubble types of galaxies than a non-expert, citizen-science labelled dataset of galaxies. However, the generalisation to the Revised Shapley-Ames is not sufficient for real-world application and further investigation is required.
Evolutionary Algorithm Driven Explainable Adversarial Artificial Intelligence

It is well-known that machine learning algorithms can be susceptible to undesirable effects when exposed to conditions that are not expressed adequately in the training dataset. This leads to a growing interest throughout many communities; where do algorithms and trained models break? Recently, methods such as generative adversarial neural networks and variational autoencoders were proposed to create adversarial examples that challenge algorithms. This results in artificial intelligence having higher false detections or completely losing recognition. The problem is that existing solutions, are for the most part, black boxes. Current gaps include how do we better control and understand adversarial algorithms. Herein, we propose the concept of an adversarial modifier set as an understandable and controlled way to generate adversarial examples. This is achieved by exploiting the improved evolution-constructed algorithm to identify ideal features that a victim algorithm values in imagery. These features are combined to realize a tuple library that preserves spatial relations. Last, a set of algorithmically controlled modifiers that generate the imagery are found by examining the content of the false imagery. Preliminary results are encouraging and demonstrate that this approach has benefits in both generating explainable adversarial examples, as well as shedding some insight into victim algorithm decision making.
A Simple Feature Extraction Method for Analysis of Hand Written Characters

Abstract The printed and handwritten alphabets as character recognition by feature extraction are described during this proposed work. A predefined set of 108 samples each containing twenty-six alphabets taken by paper handwritten are taken as set for training. The system portrayed is a good alternative solution for HCR &amp; plays well with detection of handwritten characters. Here, the method experiences binarization and pre-processing for the segmentation stage. Individual characters after segmentation undergo feature extraction part. The artificial neural network is trained using the extracted features. A simple ANN is used as a classifier utilizing the average epochs and MSE for detecting the HCR recognition without a false alarm.
RETRACTED: Strategy of library information resource construction based on FPGA and embedded system

With the persistent improvement of Internet innovation, the period of large information has shown up. Customary library asset sharing mode is still there and won't have the option to exploit the data island mode's assets. To tackle this issue, planned a converse intermediary innovation dependent on the joint development and sharing arrangement of the squid library's data assets in this investigation. After the course finished, the log work, the client opening capacity, and asset sharing were tried. However, a piece of the framework is normal; some little subtleties will influence the client experience. In this manner, before placing it into utilization, have to streamline the framework's code. Additionally, as far as sharing assets, the plan, to accomplish a login with similar client data in the various information bases; clients can look through similar assets in a different data set. With the coming of large information, in the library of expert secondary school, are confronted with new difficulties and new chances. Library managers, mindful of the desperation and the requirement for change, give more consideration to the library's development, change the library's capacity and area, change the administration idea, and break the customary reasoning mode. Simultaneously, advancing the higher professional school library's feasible and solid improvement is progressive development of the executives' model.
RSAN: A Retinex based Self Adaptive Stereo Matching Network for Day and Night Scenes

It is essential in many robot tasks to retrieve depth information, while it still remains a challenging problem to get robust depth in unfavorable conditions such as night or rainy environments. With the development of convolutional neural networks (CNNs), a large number of algorithms have emerged to tackle the problem of dark image enhancement and depth estimation, but there are few works focus on recovering depth map in dark environments and normal light condition. To meet this demand, we proposed a neural network which takes the paired stereo images in all light conditions as input and estimates the fully scaled depth map. The network contains a novel feature extractor and a stereo matching module which follows a light-weight manner to guarantee this work practical for real robotic applications. We introduced the Retinex Theory into depth estimation and trained the decomposition module with LOL dataset. Then it is adapted into depth estimation by fusing the decompose module into stereo matching algorithm. The whole network is then trained in an end-to-end manner. To demonstrate the robustness and effectiveness of our proposed method, we perform various studies and compare our results to the state-of-the-art algorithms in depth estimation as well as direct combination of image enhancement and stereo matching algorithm. We also collect stereo images in real night environments and present the improved performance of our network.
Cognito - Intuitive Auto Data Exploratory Toolkit

In any machine learning model, the eminence of discovered knowledge is directly associated with the quality of data used for analysis. In the technological world, massive growth is observed in the scale of data generation. Big data resolve many challenges of the organization by predicting the future or finding valuable insights from the data. Despite this, Big data itself is a major challenge in front of researchers because the data doesn't come in cleaned format. Data preprocessing becomes a crucial phase that handles anomalies and noise in the data. Though various data pre-processing algorithms are available to clean the dataset, data analysts spend a large amount of time to achieve it. Cognito being an open-source python library offers various features to reduce the time and efforts of the data scientist. Cognito facilitates the automatic pre-processing and fundamental data analysis by providing the cleaned dataset, summarized report about the dataset, features of each column of the dataset, and possible questions that can be asked to the dataset in an attractive format. It provides output in CSV format and a summary of the dataset in PDF format. Developers do not need to wrap their heads around or waste their time analyzing the data. It also reduces the user's effort by a considerable margin. Moreover, the cleaned data generated by Cognito is crunched, thus reducing the space required. Cognito automatically manages various data pre-processing operations such as feature selection, missing value imputation, normalization, outlier treatment, data reduction with additional features of auto-generated description insights.
BIS: A New Swarm-Based Optimisation Algorithm

This paper presents a novel swarm-based search algorithm: the bio-breeding intelligent swarm (BIS) algorithm. BIS agents imitate the offspring and maturity phases of the typical lifecycle of an animal. As in nature, the BIS algorithm makes gender distinction among agents and the main search strategy exploits competition between male agents in an attempt to provide a better location for females. BIS agents embark on various nature-inspired mating strategies and the inspiration for the reproduction model is derived from temperature-dependent sex determination (TSD), a reptilian reproduction system. The BIS algorithm's TSD inspired reproduction model enables female agents to control the gender of offsprings based on guidance provided by their male mates, subsequently resulting in regulation of the male-female ratio in the swarm which in turn auto-controls the balance of exploration and exploitation within the population of agents. The efficiency of the BIS algorithm was tested over a wide range of benchmarks including unconstrained high dimensional and real-world problems. The BIS algorithm performed very well in comparison with a number of leading population-based stochastic search methods, finding the highest number of global optimums.
Linguistic Economy Applied to Programming Language Identifiers

Though many different readability metrics have been created, there still is no universal agreement defining readability of software source code.The lack of a clear agreement of source code readability has ramifications in many areas of the software development life-cycle, not least of which being software maintainability.We propose a measurement based on Linguistic Economy to bridge the gap between mathematical and behavioral aspects.Linguistic Economy describes efficiencies of speech and is generally applied to natural languages.In our study, we create a large corpus of words that are likely to be found in a programmer's vocabulary, and a corpus of existing identifiers found in a collection of open-source projects.We perform a usage analysis to create a database from both of these corpora.Linguistic Economy suggests that words requiring less effort to speak are used more often than words requiring more effort.This concept is applied to measure how difficult program identifiers are to understand by extracting them from the program source and comparing their usage to the database.Through this process, we can identify source code that programmers find difficult to review.We validate our work using data from a survey where programmers identified unpleasant to review source files.The results indicate that source files identified as unpleasant to review source code have more linguistically complicated identifiers than pleasant programs.
Distributed State Estimation for Stochastic Linear Hybrid Systems

This paper studies the problem of distributed state estimation for stochastic linear hybrid systems. Building on the centralized interacting multiple model algorithm, a novel distributed state estimation technique is proposed. In our distributed setting, a network of sensors is employed and each sensor measures only a portion of the system outputs. It should be noted that the system might not be observable for each individual sensor. In this paper, we aim to develop an effective scheme that enables the sensor network to collectively estimate the hybrid system states (both the continuous states and discrete modes), by leveraging the cooperation among multiple sensors. Consequently, each sensor only needs to process a relatively small set of data and will be able to locally and identically observe the states of stochastic linear hybrid systems. The validation and performance of the proposed scheme are demonstrated by numerical simulations on an aircraft tracking problem finally.
WITHDRAWN: Recognition and prevention of cyberharassment in social media using classification algorithms

Cyberbullying is the aggressive pressure or harassment produce through electronic devices like laptops, desktop or computer, smart phones, and tablets. The harassment can occur in the environment of social networking sites, chat rooms, and gaming platforms where people can view and take part in the exchanging of their thoughts. The different kind of cyberbullying or harassment creates the humiliation through hateful comments on digital platforms/apps, or through SMS or messaging. It involves the sending posting, or sharing negative, disgusting, nasty or wrong information about another individual for causing embarrassment and the malicious and unjustified harming of a person's good reputation. Machine learning algorithms give a chance to find successfully predicting, detecting and preventing undesirable, harmful and unwanted opinions of human behavior, such as cyberharassment. It gives the pivotal information on new ways in the way of detecting and preventing antagonistic or violent human behavior, including cyber harassment detection and prevention in online social environmental sites. ML algorithms gives an prospect to completely expect, detect and prevent hurtful attitude of human behavior, such as cyber bullying. A model is proposed to detect and prevent the negative opinion in social media using Classification model algorithms. Profanity detection library function and Natural Language Processing are used to detect and prevent the negative opinions in the social media. This proposed model is helpful for the Government to identify the negative opinions given by the users in the social media and they can take actions. The development of such an application has the ability to stop and reduce the rate of cyberbullying that has been happening social media.
Improvement of Ant Colony Method Track Planning Based on Artificial Potential Field Method

In this paper, Ant Colony Method is combined with Artificial Potential Field Method, to propose a new optimization method to enhance the robustness of t original algorithm. The core of new algorithm is aim to improve original path planning of ant colony method by using artificial potential field method, and to complete the allocation of primary data of ant colony algorithm. And the optimization method is applied to 3D track planning of UAV, and it is proved to be effective by simulation.
A Mining Policy based Malicious Encrypted Traffic Detection Scheme

Malicious encrypted traffic poses a great threat to cyber space owing to its ability to bypass traditional traffic detection schemes. Malicious encrypted traffic detection is a challenging task and has attracted researchers' attention nowadays. Specifically, the detection task is subject to difficult feature mining and unsatisfactory results. Therefore, a mining policy based detection scheme is proposed, which mines more efficient features based on a rule based mining strategy and achieves well learning effect with machine learning algorithm-LightGBM. In this scheme, raw traffic is parsed to log files with Bro and features are extracted based on connection-tetrad. Accordingly, the rule-based feature mining strategy is proposed based on several rules. Then features are fed to LightGBM to train a detection model. A set of experiments show that the feature mining strategy is effective and our work improves malicious encrypted traffic detection effect.
A Tour of Type-1 and Interval Type-2 Fuzzy Sets Theory

A succinct review of type-1 basic theory is described in this chapter and should be enough for the reader, who is not familiar with fuzzy set theory, to be able to understand these concepts (Jafelice et al. Teoria dos Conjuntos Fuzzy. Springer Briefs in Mathematics SBMAC (in Portuguese), vol 17, 2nd edn. Springer, Berlin, 2012). The next topic is the description of a fuzzy technique that expands the relationship between the function’s variations and the respective state variables, to obtain through a FRBS the trajectory of the state variables as a function of time. In the sequence, an introduction to a new fuzzy system identification with multiple inputs and single output is reviewed. These two aforesaid methodologies are used in the applications of the type-1 and interval type-2 fuzzy sets in Chap. 3. Type-2 fuzzy set concept and its components are defined as being: the primary membership, the secondary membership function, the domain of uncertainty, and the upper and lower membership functions. Examples are shown and exercises are proposed for illustration and to reinforce the subject matter. Next, a more comprehensive and didactic explanation of interval type-2 fuzzy sets, describing recent theoretical adjustments made by Mendel, are developed (Mendel et al. Inf Sci 340–341:337–345, 2016; Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, 2nd edn. Prentice-Hall, Upper Saddle River, 2017). Three methods of inference for the interval type-2 FRBS are presented: Wu, Takagi–Sugeno–Kang, and Mamdani.
Design and Analysis of A Nonlinear Guidance Law for Small UAV

The work presented in this paper focuses on the design of a generic robust nonlinear flight guidance and control system for a small fixed-wing UAV uncertainty and external disturbances. Integrated UAV waypoints guidance scheme based on Nonlinear Guidance Law (NLGL) is presented. To follow a desired path, a Virtual Track Point (VTP) is introduced on the path and makes the UAV chase it. The UAV updates its heading directly toward the VTP. As time progresses, the UAV will move toward the path and asymptotically follow the path. The Adaptive Modified Incremental Backstepping (AMIBKS) flight control law is a robust adaptive controller as it is completely independent of the flight simulation model and mainly depends on the accurate measurement of the utilized sensors. The AMIBKS controller is designed for tracking commands in both roll and pitch channels. The PID control law is used to control the desired guidance law by introducing the desired roll angle which is controlled by the AMIBKS controller. The side-slip angle is regulated using a PI controller to avoid the coupling with the desired roll command by generating a normal side force that opposes the applied control force. The feed-forward controller is employed with the elevator control surface command to avoid the loss of altitude while turning, with the rudder control surface command to eliminate the coupling before existing. The stability analysis is proved using the Lyapunov theory. Also, several simulations taking into account high wind disturbances and model uncertainties are performed to analyze the behavior and the robustness of the control the designed nonlinear flight guidance and control system.