Reviewerly/exHarmony huggingface dataset:
Detection and Performance Evaluation of Online-Fraud Using Deep Learning Algorithms

Online News Portals are currently one of the primary sources used by people, though its credibility is under serious question. Because the problem associated with this is Click-bait. Click-baiting being the growing phenomenon on internet has the potential to intentionally mislead and attract online viewership thereby earning considerable revenue for the agencies providing such false information. There is need for accurately detecting such events on online-platforms before the user becomes a victim. The solution incorporates a Novel Neural Network Approach based on FastText Word2Vec Embeddings provided by Facebook and Natural Language Processing where Headlines are specifically taken into consideration. The proposed system consists of Hybrid Bi- Directional LSTM-CNN model and MLP model. Promising Results have been achieved when tested on a dataset of 32,000 columns equally distributed as Click-bait and Non-Click-bait, in terms of Accuracy, Precision and Recall. The graphs achieved are also self-explanatory in terms of reliability of the system. A comparative analysis is also been done to show the effectiveness of our design with respect to detecting Click-bait which is heavily present on-line.
An Overview of Detecting Fake Accounts on Twitter Networks

A social networking phenomenon grew extremely through the last twenty years. The number of people on social media platforms are incrementing at a greater level of malicious use. This Social network attracts millions of users across the world and their interaction with social networking has affected their life. This kind of population has led to different problems, including the possibility of exposing incorrect information to their users through fake accounts which results in the spread of malicious content. This situation can result in a huge damage in the real world to society. In our research, an overview of the methods of Twitter fake accounts detection presented with discussing their effectiveness. The datasets which are commonly used by Twitter fake accounts detection approaches are highlighted and the most used attributes are categorized and identified. Also, in the end of this study we will present the related work and give the description of the proposed method with presenting the results’ analysis.
A Novel Mismatching Elimination Algorithm Based on Catadioptric Panoramic Images

Catadioptric panoramic images are gaining increasingly attention in the field of computer vision. However, these images&#x2019; characteristics of complex projection imaging and distortion make most mismatiching elimination algorithms difficult to be applied to the ring shaped catadiptric panoramic images directly. In order to solve the above problems, a mismatching elimination algorithm based on azimuthal angles of features is proposed in this paper. The novel algorithm is inspired by the warping visual homing algorithm. It firstly makes use of azimuthal angles of features in one image to estimate the azimuthal angles of their corresponding ones in its matching image. Then it eliminates the matching feature pairs whose azimuthal angle deviation is too large by comparing the estimated value and the actual value. The novel algorithm is proved to be effective by extensive experiments based on panoramic image databases.
Monitoring state of mind using Natural Language Processing

State of mind is essential in every person&#x2019;s life to keep their life stable, calm and peaceful. The key factors that contribute towards them are mood, emotion and thoughts. Monitoring them is key to a person's overall development and allows family and friends to support them to a greater extent. One aspect of getting a report on this is using the words being typed by the person on their computer or mobile, browser history, songs and movies played. This paper proposes a sentiment analysis system where information about mood and thoughts are conveyed by means of natural language processing. These logs captured during each time session are classified to gain insights about the person. This reduces the likelihood of any drastic decisions taken by the person and provides a better means of conveying the information and maintaining communication between them and their relations.
Improved Similarity Based Fuzzy C-Means Clustering Algorithm

To solve the shortcoming of the fuzzy c-means clustering algorithm(FCM) that fails to cluster the non-convex datasets effectively, we propose an improved fuzzy c-means clustering algorithm(IFCM). IFCM first constructs the nearest neighbor network graph by finding the r neighbors of each data point through the 3N algorithm, if a connected relationship exists on two data points in the graph, the Euclidean Distance is employed to measure the distance between them; Otherwise, it performs a more reasonable similarity calculation strategy, which merges the distance value mentioned above with the Euclidean Distance between the other two data points which have the furthest distance in the dataset. Meanwhile, it updates the membership degree of each data point in each cluster and the center of each cluster. IFCM will repeat the process mentioned above until all the data points in the dataset have been classified properly. Further analysis on artificial and UCI datasets verifies the importance of similarity strategy improvement for clustering, justifying the rationality and effectiveness of IFCM.
Tree Isomorphism

Tree identification is the problem of determining whether two trees are isomorphic, and it is a fundamental problem with a variety of interesting applications in diverse scientific and engineering disciplines. The related problems of subtree isomorphism and maximum common subtree isomorphism generalize pattern matching in strings to trees, and find application whenever hierarchical structures, structures described by trees, need to be compared. Combinatorial algorithms are discussed in this chapter for testing isomorphism of ordered and unordered trees, finding one or all isomorphisms of a tree as a subtree of another tree, and finding one or all maximum common subtree isomorphisms between two trees. These algorithms use the techniques given in Chap. 2, and some of them also build upon the algorithms discussed in Chap. 3 for traversing trees.
Power Law Transformation based Weighted Clipping Tri-Histogram Equalisation

A new tri-histogram equalisation algorithm based on weighted clipping is suggested for digital image enhancement using histogram equalisation. The power law transformation function is used to perform the clipping operation which avoids the over-enhancement and under-enhancement of the image. The clipped histogram is further divided into three sub-histograms on the basis of the mean and standard deviation of the input image. Sub-histograms are independently equalised and the combined to produce the final enhanced image. Experimental results clearly shows the weighted clipping tri-histogram equalisation method enhances the image quality. Comparisons made with other methods also demonstrate the superiority of the proposed algorithm. Subjective and objective quality assessment of proposed method outperformed the other state-of-the-art methods. Objective metrics like Entropy, Feature Similarity Index Matrix (FSIM), Visual Saliency Induced Matrix (VSI) and Gradient Magnitude Similarity Matrix(GMSD) have been evaluated for the comparison purpose. Proposed method work very well both for gray and color images.
Ticket Dispensation Using Face Detection and Classification

Face detection in recent years attracted more developers and researchers in various domains in creating an intelligent system for a purpose. This paper introduces an approach to automatically dispense tickets to customers by face detection and classification. Dispensation is done by detecting the face of the people at the ticket counter and classifying them into three different categories to issue tickets. Face detection and classification methods depend on certain features within the face. The machine learning algorithms used are a pre-trained model namely Haar feature-based cascade classifier for face detection, MobileNetV2, and Support Vector Machine (SVM) for classification. The dataset has been manually collected, which has the images of people residing in different parts of Tamil Nadu, India under various age groups. The performance of MobileNetV2 and SVM models are analyzed. Hence MobileNetV2 performs better than SVM.
An Adaptive Exploratory Q-Learning Algorithm for Multiple Target Path Planning

Inspired by the adaptive parameter adjustment of meta-heuristic algorithms, we propose here an adaptive exploratory mechanism with dynamic ε selection probability for the original Q-learning algorithm to enhance its convergence ability and alleviate falling into local optimum. In the original Q-learning algorithm, the ε value with greedy selection method is fixed in the whole exploratory process, and it seriously affects the search efficiency the agents. Firstly, this paper proposes a new exploratory which defines a particular value for each state-action pair and creates a dynamic state transition Table to control the exploratory probability of each state transition. Secondly, to better regulate its exploratory ability, this paper introduces an adaptive exploratory mechanism to dynamically control state transition value according to the overall distribution of agents. Finally, the proposed adaptive exploratory Q-learning (AE-Q-learning) algorithm is simulated in well-known grid map for multiple target path planning problem. The experimental results demonstrate that the AE-Q-learning algorithm is effective and feasible, and it also exhibits better convergence accuracy and exploratory ability compared the original Q-learning algorithm and other state-of-the-art improved Q-learning algorithms.
An Information System Design of Web-Based Student Learning Outcomes Assessment at Pekayon Jaya VI Elementary School

The elementary school of Pekayon Jaya VI Bekasi is one of the educational institutions which certainly has learning and teaching activities that is not able to be separated from the management of student learning outcomes. The institution manages 2013 curriculum-based assessments and implements them every six months. Due to the unavailability of an effective system that accommodates the assessment process of student learning outcomes, the solutions are required in the form of an advanced system. The purpose of this research is to create an advanced information system's design for web-based learning outcomes assesment of students in elementary school of Pekayon Jaya VI Bekasi. The programming language for this system uses the PHP language, database storage with MySQL, and the development of the system's design waterfall method. The stages of waterfall methods include analysis, design, programs, testing, and maintenance. The result of this study is the availability of an information system that can facilitate the admin, homeroom teacher, and other teachers related to the management of the student learning outcomes, as well as the availability of an information system to facilitate the student's guardian in viewing reports of the student's learning grades.
Publishing and Marketing

This chapter thinks through the implications of authorship for publishing in terms of marketing over the last 150 years, mainly in the UK fiction industry. The UK Chartered Institute of Marketing does not define what marketing is, but the American Marketing Association describes it as “the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large.”1 This does not imply that marketing lies entirely in the hands of marketers – or indeed of any one person or organization (such as a publisher): while marketers may devise strategies and objectives, the role of chance in the achievement of results is commonly recognized. Marketing, furthermore, goes well beyond the generation of advertising copy: it embraces product design, pricing, and targeted communications at specific times in particular places so as to manage relationships key for sales.
Holonomic Mobile Robot Planners: Performance Analysis

Many algorithms have been proposed to tackle the path planning problem in mobile robots. Among the well-known and established algorithms are the Probabilistic Road Map (PRM) algorithm, A* algorithm, Genetic algorithm (GA), Rapidly-exploring random tree (RRT), and dual Rapidly-exploring random trees (RRT-connect). Hence, this paper will focus on the performance comparison between the aforementioned algorithms concerning computation time, path length, and fail and success rate for producing a path. For the sake of fair and conclusive results, simulation is conducted in two phases with four different environments, namely, free space environment, low cluttered environment, medium cluttered environment, and high cluttered environment. The results show that RRT-connect has a high success rate in producing a feasible path with the least computation time. Hence, RRTs-based sampling algorithms, in general, and RRT-connect, in specific, will be explored in-depth for possible optimization.
NoteTogether: A Novel Distributed Note Taking Platform with Blockchain

NoteTogether [1] seeks to bring interactivity to a very crucial aspect of online learning: consuming video media. We developed a hybrid distributed platform that provides a shared space for watching and annotating video media. Analytics are provided to all users to highlight how users are interacting with a video. NoteTogether utilizes Ethereum Blockchain to ensure data security and scalability by offloading data storage and processing requirements to the distributed Ethereum network.
Intelligent-IoT Based Digital Racking System For Retail Shops

Nowadays, Intelligent Internet of things (IoT) have essential influence on human life. Generally, the normal issue among retail stores is that the supervisor doesn&#x0027;t have data of which rack is going to get unfilled and should be refilled. This issue leads to substantial loss. The objective of this paper is to utilize computer vision procedures and IoT in racking system to perform object detection. In the proposed IoT based smart shelf Intelligent IoT the changes about rack and products are distinguished in real-time utilizing OpenCV. This task is complex, because of the high number of possible variations in which similar scene of images can happen into the scene. The simple solution proposed here is both best in class locators dependent on deep learning and traditional methods for object detection. The IoT empowers connectivity of physical object with the real-time communication by ESP8266 and data logged into cloud. The smart shelf rack and products details are logged into Database Management system are some desirable characteristics that result.
Open Source Software Development Challenges

GitHub is the most common code hosting and repository service for open-source software (OSS) projects. Thanks to the great variety of features, researchers benefit from GitHub to solve a wide range of OSS development challenges. In this context, the authors thought that was important to conduct a literature review on studies that used GitHub data. To reach these studies, they conducted this literature review based on a GitHub dataset source study instead of a keyword-based search in digital libraries. Since GHTorrent is the most widely known GitHub dataset according to the literature, they considered the studies that cite this dataset for the systematic literature review. In this study, they reviewed the selected 172 studies according to some criteria that used the dataset as a data source. They classified them within the scope of OSS development challenges thanks to the information they extract from the metadata of studies. They put forward some issues about the dataset and they offered the focused and attention-grabbing fields and open challenges that we encourage the researchers to study on them.
Human Classification in Aerial Images Using Convolutional Neural Networks

Akshatha, K. R. Karunakar, A. K. Satish Shenoy, B.Automatic detection of people in aerial images has potential applications in traffic monitoring, surveillance, human behavior analysis, etc. However, developing an algorithm for detection of human locations in aerial images is challenging because of the small target size, cluttered background, and varying appearance of humans. Deep learning-based object detections frameworks internally use the standard convolutional neural network (CNN) based classifiers for feature extraction and classification. Though these pre-trained classifiers perform image classification tasks with very good accuracy, they are computationally complex and hence require huge computation time. In this work, we custom-designed CNN-based classifiers to perform the human classification in aerial images and compared the performance with the standard VGG-16 based human classifier. Custom-designed classifier with fewer number of layers achieved a reduced computation time while maintaining good accuracy.
Gender Classification Models and Feature Impact for Social Media Author Profiling

Automatic profiling models infer demographic characteristics of social network users from their generated content or interactions. Due to its use in business (targeted advertising, market studies...), automatic user profiling from social networks has become a popular task. Users’ demographic data is also crucial information for more socially concerning tasks, such as automatic early detection of mental disorders. For this type of users’ analysis task, it has been demonstrated that the way users employ language is an essential indicator that contributes to the effectiveness of the models. For this reason, we also believe that considering the usage of the language from both psycho-linguistic and semantic characteristics it is useful for detecting variables such as gender, age, and user’s origin. A proper selection of features will be critical for the performance of retrieval, classification, and decision-making software systems, a proper selection of features will be critical. In this work, we shall discuss gender classification as a part of the automated profiling task. We present an experimental analysis of the performance of existing gender classification models for automated profiling based on external corpus and baselines. We also investigate the role of linguistic characteristics in the model’s classification accuracy and their impact on each gender. Following that analysis, we have developed a feature set for gender classification models in social networks that outperforms existing benchmarks in terms of accuracy.
The Out-of-Vocabulary Spelling Correction Model in a New Domain for End-to-End Automatic Speech Recognition

Although Automatic Speech Recognition (ASR) has reached the production level, the recognition of out-of-vocabulary (OOV) in a new domain is still a challenge. OOVs in a new domain refers to those words that only occur in certain conditions, such as proper nouns in medical domain or geographic names, which are rarely used in daily life. In this work, we propose an approach to correct the recognition results for the OOVs in a new domain on Mandarin. We incorporate a Transformer-based spelling correction module into the end-to-end ASR system to correct recognition errors of OOVs in certain domains. Specifically, we utilize a text-to-speech (TTS) system and unpaired text-only data containing the OOVs of certain domains to generate audios. The baseline ASR uses the TTS audio to generate a set of text-text pairs, which represent the ground truth and the hypothesis of ASR. After that, we use the text-text pairs to train our spelling correction model to correct recognition errors of OOVs in certain domains. The approach proposed reduces CER from 19.5% to 10.2% on the domain dataset on average while remaining stable on the convention dataset. This result shows that our approach works effectively on OOVs recognition in new domains.
Sentiment Analysis of Social Media Twitter with Case of Large Scale Social Restriction in Jakarta using Support Vector Machine Algorithm

When the Large-Scale Social Restrictions (LSSR or PSBB in Indonesian) policy was implemented it the policy was not entirely obeyed by the community which then reaped various opinions and responses on various social media, especially on Twitter. This study aims to conduct a sentiment analysis to find out the cause or phenomena that occur based on the opinions or views of Twitter. The Tweet data about the implementation of LSSR both part 1 and part 2 in Jakarta were obtained as many as 1080 opinions using the crawling method then the data is manually labelled with two labels, which are positive and negative after labelled the data is cleaned after and the data is processed by being weighted using the Bag of Words and TF-IDF extraction feature. The classification process is carried out with four splitting data scenarios, with 60:40, 70:30, 80:20, 90:10 then classified using the Support Vector Machines algorithm. The final result of this study shows that the classification accuracy results using the Support Vector Machine algorithm with 90:10 data splitting ratio using the TFIDF extraction feature is superior with an accuracy value of 85.185% and F1-Score 72.413%, which is better when compared to the Bag of words extraction feature which produces an accuracy value of 83.333% and F1-Score 66.666%. As for this study, Twitter users tend to give opinions with negative sentiments, which contain complaints and discomfort regarding the implementation of the LSSR policies, both the first LSSR and the second LSSR. Finally, the results of this research are also expected to be input for the government when making better policies in the future.
Securing Energy Networks: Blockchain and Accounting Systems

The energy sector is facing increasing risks, mainly concerning fraudulent activities and cyberattacks. This paradigm shift in risks would require innovative solutions. This paper proposes an innovative architecture based on Distributed Ledger Technologies (Blockchain) and Triple Entry Accounting (X-Accounting). The proposed architecture focusing on new applications of payment and billing would improve accountability and compliance as well as security and reliability. Future research can extend this architecture to other energy technologies and systems like EMS/SCADA and associated applications.