Reviewerly/exHarmony huggingface dataset:
UNDERSTANDING HOW CHATBOTS WORK: AN EXPLORATORY STUDY OF MENTAL MODELS IN CUSTOMER SERVICE CHATBOTS

Chatbots are changing customer service interactions, enabling higher reliance on self-serving behavior.There seems to be an emphasis on designing these chatbots to be as humanlike as possible.One drawback is that such humanlike design might lead users to apply the same mental models when interacting with chatbots as they do when interacting with human customer service personnel.Arguably, this may cause issues in the chatbot interaction because the technology may not be capable of handling interactions at the same level of sophistication as human personnel.Thus, the mental models that users apply are important for successful system interactions, but little research have been dedicated towards understanding these mental models.To close this research gap, we asked 16 users to interact with two customer service chatbots to explore the mental models guiding their interactions.Based on qualitative interviews and screen-captured videos of the participants' dialogues, an exploratory analysis indicated that the participants drew on two types of mental models to understand, predict, and interact with chatbots: a human-oriented model and a technology-oriented model.We discuss our findings and their theoretical and practical implications.
Processing and Understanding Text

In the previous chapters, we saw a glimpse of the entire natural language processing and text analytics landscape with essential terminology and concepts. Besides this, we were also introduced to the Python programming language, essential constructs, syntax, and learned how to work with strings to manage textual data. To perform complex operations on text with machine learning or deep learning algorithms, you need to process and parse textual data into more easy-to-interpret formats. All machine learning algorithms, be they supervised or unsupervised techniques, work with input features, which are numeric in nature. While this is a separate topic under feature engineering, which we shall explore in detail in the next chapter, to get to that step, you will need to clean, normalize, and preprocess the initial textual data.
Advanced Approaches of Handwritten Digit Recognition Using Hybrid Algorithm 

Image processing is an interesting and widely used field in the research area. Handwritten digit recognition is a sub module of the image processing. Although lots of research has been done so for in this field and still continues. This paper suggested a novel approach of off-line handwritten digit recognition. The paper classified the digits into four regions: the left part, right part, upper part and lower part. In these four parts the images are identified on the basis of curve. The curve of a left part image is converted into pixels and as well as rest of all parts. Then these pixels are compared through decision tree and the result of comparison describes the format of digit. This method is used to test the various handwritten digit form modified NIST and MNIST databases, which shows the great success rate
Analysis the Structures of Some Symmetric Cipher Algorithms Suitable for the Security of IoT Devices

In the past years, the Internet of Things (IoT) used in different applications and very wide range of fields (e.g. cloud services, smart environments, logistics, social and personal domains, and healthcare services). The IoT included a variety of components and devices such as RFID devices, wireless sensors, actuators, and wireless networks. Also, the IoT with smart devices adopted in various companies, organizations and public services systems. For instance, some devices like Notebooks and smartphones have been used to perform different management activities and duties. These smart devices relied on data exchange and data storage resources in clouds computing services. In this context, the saved data and exchanged data required protection against hacking operations, transferred with more secure communications channels, and safe storage environment in the clouds and local storage systems. In this paper, we proposed some encryption algorithms to tackle the issue of data confidentiality in the IoT applications. This research provided analysis and investigation of these encryption algorithms in light of components of the designs, versions of these algorithms, encryption keys, block size, round functions and the techniques used in the designs.
Data Mining Based Outlier Cluster Detection Algorithm

Outlier detection has been engaged and considered field as of late. Exception recognition is a significant information mining movement with various applications, including MasterCard misrepresentation location, disclosure of crimes in electronic trade, video reconnaissance, climate forecast, and pharmaceutical re-search. The assignment of anomaly recognition is to distinguish the information objects from heaps of all objects. Outlier mining normally utilized in different fields. The vast majority of the strategies gives more regard for distinguish information objects from worldwide view which is in suitable for multidimensional information sets. According to technology, outlier recognition will be troublesome by utilizing conventional arrangements. In this, we are utilizing two calculations, for example, ODA and LOF. ODA used to locate the mean or exceptions from multi-dimensional informational indexes. LOF used to discover the likelihood of that anomalies utilizing thickness based calculations. Exception location is a significant undertaking in information mining. Nearby anomalies contrasting with their nearby neighborhood rather than worldwide information. Anomaly discovery is an essential assignment in information mining. Nearby exceptions contrasting with their neighborhood close articles rather than worldwide information dissemination. The procedure segmented into two phases, online and disconnected. Small scale groups are made in online stage and last bunches are produced in disconnected stage. Exception location is a significant assignment in information mining. Nearby exceptions contrasting with their neighborhood rather than worldwide information. Relative thickness of an item against its neighbors as the marker of the level of the article being anomaly which is allotted to nearby exception factor.
EFP-tree: an efficient FP-tree for incremental mining of frequent patterns

Frequent pattern mining from dynamic databases where there are many incremental updates is a significant research issue in data mining. After incremental updates, the validity of the frequent patterns is changed. A simple way to handle this state is rerunning mining algorithms from scratch which is very costly. To solve this problem, researchers have introduced incremental mining approach. In this article, an efficient FP-tree named EFP-tree is proposed for incremental mining of frequent patterns. For original database, it is constructed like FP-tree by using an auxiliary list without any reconstruction. Consistently, for incremental updates, EFP-tree is reconstructed once and therefore reduces the number of tree reconstructions, reconstructed branches and the search space. The experimental results show that using EFP-tree can reduce reconstructed branches and the runtime in both static and incremental mining and enhance the scalability compared to well-known tree structures CanTree, CP-tree, SPO-tree and GM-tree in both dense and sparse datasets.
LSTM with compensation method for text classification

As a foundational task, text classification is widely used in the field of Natural Language Processing (NLP). In the recent research on text classification, neural network-based methods have produced promising results. Nevertheless, most previous works ignore the fact that information may be lost or misinterpreted after the calculation of the neural network. In the research of this paper, we avoid such problems by using historical information, such as the original information of a text and the output information of the hidden layers, then performing text classification. This proposed method is called Long Short-Term Memory with compensation, or simply, LSTM-Com, which dynamically selects the important historical information as compensation for the neural network. In the classification experiment, the improved algorithm showed excellent performance compared to the baseline.
Network-Simulated Generation of Human Faces with Expressions and Orientations for Deep Learning Classification

Human face recognition is a complex task, and it is important as it can be applied to assist people worldwide, such as those in the medical field or security. For example, human faces can be used for detecting pain or emotion. Nevertheless, a drawback of deep learning methods that need a lot of data to process is key. In this study, a deep-learning-based technique, which is used to classify, that generates a synthetic image of the facial expression and orientation by utilizing the Wasserstein generative adversarial network (WGAN) is presented. The WGAN can improve the performance of the deep learning method. The proposed system certainly generates images with a small number of datasets compared to the large datasets. This research aims to solve the problem of deep learning by increasing the accuracy of the system. The generated output coincides with the real image dataset. The application using ResNet-50 and RetinaNet as a pre-model for the prediction and detection of the human faces revealed a rapid prediction time and accuracy during the assessment test.
Practicality of an Android Based Chemistry Adventure Game as a Chemical Bond Learning Media

The purpose of this study is to test the development of a proper Chemistry Adventure Game as an android-based game media on Chemistry Bond material at MA Negeri 2 Mojokerto. Practicality. Practicality is obtained from students' observation and questionnaire responses. The research method used is a research and development body carried out until practicality of android-based game media for students. Practicality test at X MIPA 1 class MA Negeri 2 Mojokerto. The data obtained from the practicality of the media Chemistry Adventure Game (1) 86,3% students, activity with a very practical category, (2) 87,88% questionnaire responses with a very practical category. Based on the results of the study, it can be concluded that the developed Chemistry Adventure game is suitable for use as a learning media.
Introduction to MATLAB Programming

The chapter introduces the idea of algorithms and scripts. Programming style, such as the top-down design approach and use of commenting for documentation, are explained. Input and output statements are introduced, including the formatting of output. Simple plot functions and functions that annotate plots are introduced in this chapter as it is easiest to produce plots using scripts. Reading from a file using load and writing to a file using save is demonstrated. The concept of a user-defined function is introduced with the type of function that calculates and returns a single value. The use of stand-alone code files for functions and the use of local functions within script files are both demonstrated.
High F-score Model for Recognizing Object Visibility in Images with Occluded Objects of Interest

Article investigates recognition of partially occluded objects of interest.Images from retail store self checkout area often contain products that are covered by a customer's body parts, are placed inside semi-transparent plastic bags, include intensive glare, or some combination of these.In order to categorize objects of interest in images with partially occluded objects, the first step is to decide if an image contains enough information about the object of interest in order to be categorized.The most famous computer vision data sets -such as Imagenet, Canadian Institute for Advanced Research (CIFAR), Digits by National Institute of Standards and Technology (MNIST) -are made of images that contain clearly visible, distinctive objects of interest and are only labelled with binary information about object existence; reduced visibility objects are absent in the mentioned datasets.Such binary visibility labels are not suitable for solving the recognition task of object occlusion level.In this study authors categorize images into [not] containing enough information about objects of interest in order to be categorized.Authors analyze a dataset collected in a real retail store self checkout area where objects of interest are various products.The proposed method uses 6 categories of occlusion variously grouped.Authors received >0.9 F-score in best model separating images into object visible/invisible categories.
Machine Learning Paradigm towards Content Based Image Retrieval on High Resolution Satellite Images

In the current era, content based image retrieval based on pattern recognition and classification using machine learning paradigm is an innovative way. In order to retrieve high resolution satellite images Support Vector Machine (SVM) a machine learning paradigm is helpful for learning process and for pattern recognition and classification; ensemble methods give better machine learning results. In this paper, SVM based on random subspace and boosting ensemble learning is proposed for very high resolution satellite image retrieval. The learned SVM ensemble model is used to identify the images that most similar informative for active learning. A bias-weighting system is developed to direct the ensemble model to pay more attention on the positive examples than the negative ones. The UCMerced land use satellite image dataset is used for experimental work. Accuracy and error rate are found to be precise. The tentative effects illustrate that the proposed model derived enhanced retrieval accurateness at the optimum level as well as significantly more effective than existing approaches. The proposed method can diminish the gap dimensionality and conquer the difficulty. The comparisons are evaluated by using precision and recall measurements. Comparative analysis observed that the retrieval time for a particular image have been reduced and the precision is increased. The primary aim of this paper is to represent the significance of ensemble learning with support vector machine in efficient retrieval of image.
Social Media Data using Various Classification Algorithms in Datamaning

Data Mining is one of the most successful domains in research. It describes the past and speculates the future for analysis. There are several techniques used in data mining. Among them classification is one of the main data mining techniques based on machine learning. In classification technique data set is classified into predefined set of groups or classes. Mathematical techniques such as decision tree, linear regression, neural networks and statistics are used for classification methods. Classification is a problem to identify which set of categories the new observation belongs to using training data set. This paper analyses the data taken from social media and uses the classification algorithm for making a comparative study on social advertisement using python.
Evaluating Self-Efficacy Pertaining to Cybersecurity for Small Businesses

Small businesses are easy victims of cyberattacks due to their limited resources and insufficient training. Furthermore, many small business owners' attitudes diminish their need for safeguards because they think that they are not likely to be attacked. Yet, small businesses experience Denial of Service (DoS) attacks, Distributed Denial of Service (DDos) attacks, phishing, vishing, and tail gating as well as theft of confidential information and hardware. Consequently, numerous small businesses close or experience detrimental results -- loss of consumer trust, lawsuits, credit monitoring fees, tarnished reputations, and lost operational costs. Since past research demonstrated that training positively impacts self-efficacy, this paper explores the effects of cybersecurity training on participants' self-efficacy towards small business cybersecurity practices. Survey participants were face-to-face and virtual attendees at a public university's Cybersecurity for Small Businesses Conference. To evaluate the attendees' perceived self- efficacy, a pre-and post-survey included cybersecurity questions with demographic questions. The results show a significant difference in scores for overall cybersecurity self-efficacy before and after such training.
A Novel CBR-Decision Tree Based Intelligent Car Fault Diagnosis System (CFDS)

Fault diagnostic systems find numerous applications in almost all service domains. Now a days, interest lies on intelligent fault diagnosis. For this, a huge database of cases called Case Base (CB) comprising of fault descriptions and their solutions needs to be maintained. Case Based Reasoning (CBR) is a popular Artificial Intelligence technique that supports huge databases that find popular applications in fault diagnosis systems. CBR is a very useful process for solving problems, detecting and diagnosing faults, learning, reasoning and supporting decisions. As the size of CB increases, the accuracy of the system too increases leading to an increase in computational time complexity. So CBR techniques are coupled with machine learning approaches to reduce the same. This paper proposes a CBR methodology based Intelligent Car Fault Diagnosis System (CFDS) that integrates decision tree as a machine learning technique and jaccard similarity method to diagnose faults of cars accurately in a minimum time. A car fault diagnosis and detection system requires individual expertise gathered from personal experience and technical skills. Many times, not only the fault, but also the car part from where the fault originates or the cause of the fault needs to be known to handle or repair the problem. So, to help car mechanics as an assistant tool, CFDS is proposed; so that they can deal with various types of car faults very easily. Here, the proposed methodology integrates decision trees and jaccard similarity method to diagnose faults where the usage of decision trees is to store cases and jaccard similarity method is used to calculate the similarity percentages between user new query case and stored cases in the CB. User can post a new query about his car problem to the user interface of the CFDS. The CFDS uses the proposed methodology to find the solutions of that problem, and finally then at last these solutions are displayed to the user. To obtain better performance of the CFD system, this paper introduces a novel model of CBR cycle called CR4 model that is slightly modified version of traditional CBR cycle of R4 model, proposed by Aamodt and Plaza in 1994.
Mammogram Classification using Curvelet Coefficients and Gray Level Co-Occurrence Matrix for Detection of Breast Cancer

Computer-aided diagnosis system plays an important role in diagnosis and detection of breast cancer. In computer-aided diagnosis, feature extraction is one of the important steps. In this paper, we have proposed a method based on curvelet transform to classify mammogram images as normal -abnormal, benign and malignant. The feature vector is computed from the approximation coefficients. Directional energy is also calculated for all sub-bands. To select the efficient feature we used t-test and f-test methods. The selected feature is applied to Artificial Neural Network (ANN) classifier for classification. The effectiveness of the proposed method has been tested on MIAS database. The performance measures are computed with respect to normal vs. abnormal and benign vs. malignant for using approximation subband and energy feature of all curvelet coefficients. The highest classification accuracy of 95.34% is achieved for normal vs. abnormal and 80.86% is achieved for benign vs malignant class using energy feature of all curvelet coefficients.
A real-time matching algorithm using sparse matrix

Aiming at the shortcomings of the traditional image feature matching algorithm, which is computationally expensive and time-consuming, this paper presents a real-time feature matching algorithm. Firstly, the algorithm constructs sparse matrices by Laplace operator and the Laplace weighted is carried out. Then, the feature points are detected by the FAST feature point detection algorithm. The SURF algorithm is used to assign the direction and descriptor to the feature for rotation invariance, and then the Gaussian pyramid is used to make it scalable invariant. Secondly, the match pair is extracted by the violent matching method, and the matching pair is purified by Hamming distance and symmetry method. Finally, the RANSAC algorithm is used to get the optimal matrix, and the affine invariance check is used to match the result. The algorithm is compared with the classical feature point matching algorithm, which proves that the method has high real-time performance under the premise of guaranteeing the matching precision.
Identification and Classification of Cyberbully Incidents using Bystander Intervention Model

Cyberharassment is bullying and degrading the adults by means of posting the comments like hurtful and derogatory humor over the internet in an online community. Though few bystanders ever try to reduce the conflicting effects of cyberbullying, and bystanders ever endeavor to interrupt. This will analyze the chattels of articulatory study on bystander intervention using the caricatured procedural made online Social Networking Sites. The proposed works mainly focus on the analysis of direct intervention by bystanders. The direct intervention allows bystanders to do reporting and blocking of cyberbully activities as additional features here. It will generate a report which contains the details of bully by means of alert message and block that bully by the bystander with the victim’s permission in the Facebook. This proposed framework will detect cyberbully words from the short hand text and emoticons on the comment sections using Latent semantic analysis (LSA). The Cyberbully words will be classified using a Random Decision Forest algorithm.
A Methodology for Unsupervised Feature Learning in Hyperspectral Imagery Using Deep Belief Network

Deep learning approaches have received major interest in the field of remote sensing.Hyperspectral imaging has rich data that are distributed in multidimensions.It is challenging to apply deep learning algorithms due to the limited amount of labelled data.So, unsupervised feature extraction approaches are used to overcome this limitation.In this study, we propose an unsupervised feature learning approach using deep belief network (DBN).In the proposed framework, the input hyperspectral image is segmented using entropy rate superpixel segmentation and filtered by domain transform recursive filter which extracts spatial and spectral information effectively.Then the features are learned by improved DBN.In the traditional methods, DBN is stacked with restricted Boltzmann machine (RBM) which is suitable for only binary value data.In the proposed framework, we used Gaussian-Bernoulli RBM which was constructed for real value data such as images.The experiments were carried out using Pavia University dataset.The results show that the proposed network has good performance in terms of classification accuracy and computation time.