Reviewerly/exHarmony huggingface dataset:
Bi-LSTM and Ensemble based Bilingual Sentiment Analysis for a Code-mixed Hindi-English Social Media Text

India is a multilingual and multi-script country and a large part of its population speaks more than one language. It has been noted that such multilingual speakers switch between languages while communicating informally. The code-mixed language is very common in informal communication and social media, and extracting sentiments from these code-mixed sentences is a challenging task. In this work, we have worked on sentiment classification for one of the most common code-mixed language pairs in India i.e. Hindi-English. The conventional sentiment analysis techniques designed for a single language don't provide satisfactory results for such texts. We have proposed two approaches for better sentiment classification. We have proposed an Ensembling based approach which is based on hybridization of Naive Bayes, SVM, Linear Regression, and SGD classifiers. We have also developed a bidirectional LSTM based novel approach. The approaches provide quite satisfactory results for the code-mixed Hindi-English text.
Incremental classifier in crime prediction using bi-objective Particle Swarm Optimization

Nowadays, the increase in criminal activities has resulted in a massive generation of crime reports describing the details of the crime incidents. Analyzing these reports for crime type prediction helps the law enforcement agencies deal with crime prevention strategies. But it is quite a demanding and difficult task to consider these reports individually and determine their crime types. In the proposed work, an efficient classifier has been designed to analyze the crime reports which not only predict the crime types of the reports but at the same time upgrades itself with the help of new crime reports. Therefore, this task demands an incremental supervised learning technique that continuously learns the existing classifier based on the new set of reports and information already extracted from the old set of reports. Developing an incremental classifier infuses the knowledge that keep coming from the newly generated reports and help in increasing the report-discriminating power of the classifier. In this work, we have applied a Bi-objective Particle Swarm Optimization technique for generating an efficient incremental classifier for classifying and predicting the crime reports dynamically. Crime reports of different countries, such as India, the United States of America, and the United Arab Emirates, have been collected from online classified newspapers to measure the performance of the proposed as well as some state-of-the-art classifiers. Also, the method has been evaluated based on an unbiased police witness narrative crime reports and finally, a statistical test has been performed using all four considered datasets to measure the statistical significance of the proposed methodology.
Convolution Neural Networks: A Comparative Study for Image Classification

Wide range of convolution neural network architectures are available for image classification, segmentation and object detection. Most of the architecture focus on accuracy as primary factor for implementation. However, when it comes to real time application deployment, there are other primary factors like memory and performance which is equally important. Also, each CNN architecture showcases its advantages and limitations but comparison over their peers are not equally considered. The goal of this paper is to provide a comparative study of various CNN architecture for image classification and serve as a guide for selection based on applications requirement and hardware capabilities. In this paper, we discuss about 18 different CNN state of art architectures that are widely used. In order to access model suitability for a given problem, CIFAR-10 image dataset is trained on different architectures with a specified set of hyper-parameters to measure the accuracy, performance and memory consumption. The experiment findings are presented to suggest suitable CNN architecture based on application/hardware attributes.
Operators Training Using Simulation And VR Technology

This paper introduces a method for training operators using digital technologies. More specifically the training is focused on the teleoperation and remote control of resources and sensors using a simulated environment with virtual elements, before entering the real one. The objective is to improve the training of the operators and bring them closer to Industry 4.0 technologies where robotic and other automation solutions are tackled to support production activities. In this training activity the benefits of using a simulated environment as well as VR technology are presented. More specifically, a simulation application has been created and can be executed on a PC showing a virtual environment where a mobile platform can navigate freely around, calculating its path, and use its camera for obstacle or part detection purposes. On the other side, the VR application can be executed on a smartphone device with Android OS and can be used to see the field of view of the camera attached on the robot. The aforementioned applications can be connected through TCP/IP connection and enable the operator in real time to see and navigate in the virtual world using either the VR or simulation application. Furthermore, a specific scenario has been created and elaborated in the paper, where the robot needs to navigate inside a virtual infrastructure and perform inspection activities using its camera. Lastly, the benefits of each application are further detailed in the paper, focusing mainly on the validation of the inspection automated solution and its remote control in an intuitive and immersive way.
CAST: Authoring Data-Driven Chart Animations

We present CAST, an authoring tool that enables the interactive creation of chart animations. It introduces the visual specification of chart animations consisting of keyframes that can be played sequentially or simultaneously, and animation parameters (e.g., duration, delay). Building on Canis [19], a declarative chart animation grammar that leverages data-enriched SVG charts, CAST supports auto-completion for constructing both keyframes and keyframe sequences. It also enables users to refine the animation specification (e.g., aligning keyframes across tracks to play them together, adjusting delay) with direct manipulation and other parameters for animation effects (e.g., animation type, easing function) using a control panel. In addition to describing how CAST infers recommendations for auto-completion, we present a gallery of examples to demonstrate the expressiveness of CAST and a user study to verify its learnability and usability. Finally, we discuss the limitations and potentials of CAST as well as directions for future research.
A Survey on Blockchain and Edge Computing applied to the Internet of Vehicles

With the advent of Intelligent Transportation Systems (ITS), data from various sensors embedded into vehicles or smart cities infrastructure are of utmost importance. This ecosystem will require processing power and efficient trust mechanisms for information exchange in vehicle-to-everything (V2X) communications. To accomplish these requirements, both edge computing and blockchain have been recently adopted towards a secure, distributed, and computation empowered Internet of Vehicles (IoV). This paper surveys prominent solutions for blockchain-based vehicular edge computing (VEC), provides a taxonomy, highlights their main features, advantages, and limitations to provide subsidies for further proposals.
Police Complaint Management System using Blockchain Technology

The criminal activities in India are increasing at a rapid rate. Many of these activities go unreported. Even after having an online portal for the police for storing FIRs and NCRs, most of the FIRs are handwritten as a traditional practice. In most of the cases, the complainant has to be present in the police station to file a cognizable offense. An effective system for e-governance was started in 2009 named Crime and Criminal Tracking Network and Systems (CCTNS) for the entire country. However, it is a centralized system for a particular state. Thus there is a need for a completely decentralized system for assuring that there is no central point of failure in the system and complaints are managed securely protected from unauthorized access. Our aim is to propose a blockchain-based solution to manage complaints against both cognizable and non-cognizable offenses. The FIR filed by the police will be encrypted, stored in the IPFS and hash is added to the blockchain network. If the police decide not to file the FIR under pressure or deny receiving any complaint, then the complainant will have strong proof against him/her as the complaint along with its timestamp was stored on the blockchain network. Having all the records stored in an immutable database would remove any chances of the FIR/NCR being tampered and going unnoticed.
Human Action Recognition by Discriminative Feature Pooling and Video Segment Attention Model

We Introduce a simple yet effective network that embeds a novel Discriminative Feature Pooling (DFP) mechanism and a novel Video Segment Attention Model (VSAM), for video-based human action recognition from both trimmed and untrimmed videos. Our DFP module introduces an attentional pooling mechanism for 3D Convolutional Neural Networks that attentionally pools 3D convolutional feature maps to emphasize the most critical spatial, temporal, and channel-wise features related to the actions within a video segment, while our VSAM ensembles these most critical features from all video segments and learns (1) class-specific attention weights to classify the video segments into the corresponding action categories, and (2) class-agnostic attention weights to rank the video segments based on their relevance to the action class. Our action recognition network can be trained from both trimmed videos in a fully-supervised way and untrimmed videos in a weakly-supervised way. For untrimmed videos with weak labels, our network learns attention weights without the requirement of precise temporal annotations of action occurrences in videos. Evaluated on the untrimmed video datasets of THUMOS14 and ActivityNet1.2, and trimmed video datasets of HMDB51, UCF101, and HOLLYWOOD2, our network achieves promising performance, compared to the latest state-of-the-art method. The implementation code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MoniruzzamanMd/DFP-VSAM-Networks</uri> .
A Machine Learning Model for Data Sanitization

Discovering important knowledge that may be available from databases while preserving the privacy of sensitive information can be considered a hot research subject of data mining in recent times. With the establishment of strong Internet of Things (IoT) networks globally, several data-intensive applications will be developed. Privacy of information over the network is increasingly relevant, and as edge computing has grown more important, applications running over networks require protection. The privacy of information while utilizing data is a trade-off that needs to be addressed. In the past, many heuristics and meta heuristics-based approaches were revealed to sensitize sensitive information in privacy-preserving data mining (PPDM). They perturb the original database to hide sensitive information using addition or deletion operations. This is a known NP-hard problem. In this paper, we propose data privacy of IoT connected devices over heterogeneous networks. A deep re-enforcement learning-based technique is applied to sensitize sensitive information from a given database while keeping the balance between privacy protection and knowledge discovery during the sanitization process. Furthermore, minimizing known side effects that can be caused in the sanitization process is also be considered. Substantial experiments are conducted on both synthetic and real-world datasets. Results are evaluated based on sanitization side effects which include failing to hide sensitive items as well as choosing not to hide sensitive items. The proposed approach shows significant performance improvement compared to meta-heuristics (Genetic Algorithm, Particle Swarm Optimization) and heuristics (Greedy) approaches by our evaluation.
Fall Detection Based on RetinaNet and MobileNet Convolutional Neural Networks

The problem of falling is a major health problem resulting in serious injuries and sometimes lead to death especially for elderly. Elderly people aged over than 75 are exposed to accidental deaths due to falls. Approaches based on computer vision give a promising and an effective solution for detection human falls. This paper presented a method for fall detection which based on combining convolutional neural networks RetinaNet and Mobilenet in addition to handcrafted features. Traditional human detection methods may result in human shape deformation which affect the performance of fall detection frameworks. Therefore, the proposed framework depends on RetinaNet for detecting humans with shorter computing time and higher accuracy compared with the traditional human detection methods. Then, the proposed framework relies on handcrafted features to represent shape and motion properties of the detected human. The proposed framework extracts aspect ratio and head position as shape features and motion history image as a motion feature of the detected human to create the feature map. This feature map is used in training MobileNet network to classify the human motion into fall or not-fall. The proposed framework is evaluated using UR and FDD datasets and the experimental results proved the efficiency of the proposed framework achieving up to 98% accuracy compared with the state-of-the-art methods.
Why Shouldn’t All Charts Be Scatter Plots? Beyond Precision-Driven Visualizations

A central tenet of information visualization research and practice is the notion of visual variable effectiveness, or the perceptual precision at which values are decoded given visual channels of encoding. Formative work from Cleveland & McGill has shown that position along a common axis is the most effective visual variable for comparing individual values. One natural conclusion is that any chart that is not a dot plot or scatterplot is deficient and should be avoided. In this paper we refute a caricature of this "scatterplots only" argument as a way to call for new perspectives on how information visualization is researched, taught, and evaluated.
Social Network Analytics for Supervised Fraud Detection in Insurance

Abstract Insurance fraud occurs when policyholders file claims that are exaggerated or based on intentional damages. This contribution develops a fraud detection strategy by extracting insightful information from the social network of a claim. First, we construct a network by linking claims with all their involved parties, including the policyholders, brokers, experts, and garages. Next, we establish fraud as a social phenomenon in the network and use the BiRank algorithm with a fraud‐specific query vector to compute a fraud score for each claim. From the network, we extract features related to the fraud scores as well as the claims' neighborhood structure. Finally, we combine these network features with the claim‐specific features and build a supervised model with fraud in motor insurance as the target variable. Although we build a model for only motor insurance, the network includes claims from all available lines of business. Our results show that models with features derived from the network perform well when detecting fraud and even outperform the models using only the classical claim‐specific features. Combining network and claim‐specific features further improves the performance of supervised learning models to detect fraud. The resulting model flags highly suspicions claims that need to be further investigated. Our approach provides a guided and intelligent selection of claims and contributes to a more effective fraud investigation process.
On greedy randomized block Kaczmarz method for consistent linear systems

The randomized block Kaczmarz method aims to solve linear system Ax=b by iteratively projecting the current estimate to the solution space of a subset of the constraints. Recent works analyzed the method for the overdetermined least-squares problem, showing expected linear rate of convergence to the ordinary least squares solution with the use of a randomized control scheme to choose the subset at each step. This paper considers the natural follow-up to the randomized control scheme—greedy strategies like the greedy probability criterion and the almost-maximal residual control, and show convergence to a least-squares least-norm solution. Numerical results show that our proposed methods are feasible and have faster convergence rate than the randomized block Kaczmarz method.
An automatic sampling ratio detection method based on genetic algorithm for imbalanced data classification

Imbalanced data are a common phenomenon in both theoretical research and real-world applications. At a data level, standard classification algorithms cannot effectively learn and make predictions from imbalanced data, and this problem is generally solved by using oversampling, undersampling, or hybrid sampling methods. However, most of the current sampling methods use random sampling ratios, and the resulting classification performance can be undesirable and unstable. To obtain satisfactory and stable classification performance, we proposed three algorithms to automatically determine the sampling ratios for oversampling, undersampling, and hybrid sampling methods, based on a genetic algorithm. Experiments were performed to test the algorithms’ effectiveness by utilizing five widely used standard classification algorithms on 14 different imbalanced datasets using two oversampling, two undersampling, and four hybrid sampling methods. The statistical test results showed that for all five standard classification algorithms, sampling methods that used our proposed algorithms achieved the best classification results. Using area under the receiver operating characteristic curve (AUC) as the evaluation metric, it was demonstrated that the proposed algorithms for automatically determining the sampling ratio outperformed the random sampling ratio.
Learn class hierarchy using convolutional neural networks

Abstract A large amount of research on Convolutional Neural Networks (CNN) has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed as hierarchical classification problems, in which the classes to be predicted are organized in a hierarchy of classes. In this paper, we propose a new architecture for hierarchical classification, introducing a stack of deep linear layers using cross-entropy loss functions combined to a center loss function. The proposed architecture can extend any neural network model and simultaneously optimizes loss functions to discover local hierarchical class relationships and a loss function to discover global information from the whole class hierarchy while penalizing class hierarchy violations. We experimentally show that our hierarchical classifier presents advantages to the traditional classification approaches finding application in computer vision tasks. The same approach can also be applied to some CNN for text classification.
A Secure and Privacy-Preserving Machine Learning Model Sharing Scheme for Edge-Enabled IoT

With the popular use of IoT devices, edge computing has been widely applied in the Internet of things (IoT) and regarded as a promising solution for its wide distribution, decentralization, low latency.At the same time, in response to the massive computing data and intelligent requirements of various applications in the IoT, artificial intelligence (AI) technology has also achieved rapid development.As a result, edge intelligence (EI) for the Internet of Things has attracted widespread attention.Driven by the requirement that making full use of data, machine learning (ML) models trained in EI are usually shared.However, there may be some security and privacy issues due to the openness and heterogeneity of edge intelligence.How to ensure flexible data access and data security as well as the accountability for edge nodes and users in EI model sharing have become important issues.In this article, we propose a Ciphertext Policy Attribute Based Proxy Re-encryption (CP-ABPRE) scheme with accountability to address the security and privacy issues in EI model sharing.In our scheme, a user can delegate the access right to others to make model access more flexible.Furthermore, each entity that may need to be held accountable is embedded a unique ID to achieve traceability.Finally, security analysis and performance evaluation are given to prove that our scheme is CPA secure and does not lose much efficiency with more features.
A lightweight machine learning based security framework for detecting phishing attacks

A successful phishing attack is prelude to various other severe attacks such as login credentials theft, unauthorized access to user's confidential data, malware and ransomware infestation of victim's machine etc. This paper proposes a real time lightweight machine learning based security framework for detection of phishing attacks through analysis of Uniform Resource Locators (URLs). The proposed framework initially extracts a set of highly discriminating and uncorrelated features from the URL string corpus. These extracted features are then used to transform the URL strings into their corresponding numeric feature vectors, which are eventually used to train various machine learning based classifier models for identification of malicious phishing URLs. Performance analysis of the proposed security framework on two well known datasets: Kaggle dataset and UNB dataset shows that it is capable of detecting malicious phishing URLs with high precision, while at the same time maintain a very low level of false positive rate. The proposed framework is also shown to outperform other similar security frameworks proposed in the literature.
A new non-kernel quadratic surface approach for imbalanced data classification in online credit scoring

Imbalanced data sets are very common in online credit scoring. Their imbalanced structures may cause statistical bias and poor performance of those traditional models. Hence, how to properly deal with them and dig useful information from them are very crucial for the risk management. This paper proposes a new approach to address the imbalanced data classification in this area by directly generating two quadratic surfaces in the original space. In this way, it avoids the time-consuming task for searching a proper kernel function and its corresponding parameters in the traditional support vector machine (SVM) models, hence significantly improve the total efficiency of the approach. Moreover, the homocentric structure and maximization margin principle are applied to obtain a good performance of our model on the issue. Besides, fuzzy weight is also incorporated to further increase the classification accuracy and robustness. It is worth noting that the linear programming structure of our model not only guarantees the global optimality of its solution, but also leads to a much higher efficiency than those benchmark models. In addition, the fewer parameters in our model further saves more time in the tuning process. Hence it is quite suitable for handling those huge-sized problems in this big data era. Finally, we conduct a comprehensive experiment to compare the performances and efficiencies of different methods. The numerical results on various data sets strongly verify the superiority of our method to some benchmark methods in handling the imbalanced data classification in online credit scoring.