Reviewerly/exHarmony huggingface dataset:
Near real-time twitter spam detection with machine learning techniques

The popularity of social media networks, such as Twitter, leads to an increasing number of spamming activities. Researchers employed various machine learning methods to detect Twitter spam. However, majorities of existing researches are limited to theoretically study, few of them can apply detection techniques to real-time scenario. In this paper, we bridge the gap by proposing a near real-time Twitter spam detection system, which provides near real-time tweets data acquisition, light-weight features extraction from a specific Twitter account, training detection model, and online visualizing detection results. In this system, account-based and content-based features are extracted to facilitate spam detection. The models that are applied to our Twitter spam detection system are trained based on 1.5 million public tweets and nine mainstream algorithms. In addition, in order to efficiently reduce training time spent on massive data and save the cost of model updating, a parallel computing technique is introduced to train and update the models in this system. Empirical results verify that the model can achieve satisfactory performance based on our datasets. Furthermore, we implement a near real-time Twitter spam detection system which can better protect users from combating spams. This system also acts as a tweets collection tool, allowing researchers to test the performance of trained classifiers in realistic scenarios.
Learning Crisp Boundaries Using Deep Refinement Network and Adaptive Weighting Loss

Significant progress has been made in boundary detection with the help of convolutional neural networks. Recent boundary detection models not only focus on real object boundary detection but also “crisp” boundaries (precisely localized along the object's contour). There are two methods to evaluate crisp boundary performance. One uses more strict tolerance to measure the distance between the ground truth and the detected contour. The other focuses on evaluating the contour map without any postprocessing. In this study, we analyze both methods and conclude that both methods are two aspects of crisp contour evaluation. Accordingly, we propose a novel network named deep refinement network (DRNet) that stacks multiple refinement modules to achieve richer feature representation and a novel loss function, which combines cross-entropy and dice loss through effective adaptive fusion. Experimental results demonstrated that we achieve state-of-the-art performance for several available datasets.
Formal Verification of Secure Evidence Collection Protocol using BAN Logic and AVISPA

The effect of digitization has led to an increased dependency on the internet. At the same time, cyber-attacks are on the rise due to this increased digitization. In cybercrime cases, digital evidence is of utmost importance. The forensic investigation process always begins after the incident occurred, by that time intelligent attackers got enough time to destroy the traces. This paper proposes a prior evidence capture protocol, that will help in the simultaneous collection of evidence when the crime has occurred. This collected evidence is in the form of device fingerprint which will uniquely identify the fingeprintee client device. In the future, if the dispute arises these prior captured device fingerprints can be used as legal evidence and help in the process of forensic investigation. The proposed protocol uses the concept of a trusted time stamping server (TTSS) to prove the integrity and non-repudiation of the collected evidence. The timestamps are attached by the trusted third party TTSS with all collected evidence, these timestamps cannot be changed by local client devices. The paper also provides security validation of the proposed protocol by using Burrows–Abadi–Needham (BAN) logic. The formal verification is also done by using the AVISPA tool. The results of AVISPA shows that the proposed protocol is safe under OFMC and Cl-AtSe model.
Efficient online learning with improved LSTM neural networks

We introduce efficient online learning algorithms based on the Long Short Term Memory (LSTM) networks that employ the covariance information. In particular, we introduce the covariance of the present and one-time step past input vectors into the gating structure of the LSTM networks. Additionally, we include the covariance of the output vector, and we learn their weight matrices to improve the learning performance of the LSTM networks where we also provide their updates. We reduce the number of system parameters through the weight matrix factorization where we convert the LSTM weight matrices into two smaller matrices in order to achieve high learning performance with low computational complexity. Moreover, we apply the introduced approach to the Gated Recurrent Unit (GRU) architecture. In our experiments, we illustrate significant performance improvements achieved by our methods on real-life datasets with respect to the vanilla LSTM and vanilla GRU networks.
Bangla Word Prediction and Sentence Completion Using GRU: An Extended Version of RNN on N-gram Language Model

Textual information exchange, by typing the information and send it to the other end, is one of the most prominent mediums of communication throughout the world. People occupy a lot of time sending emails or additional information on social networking sites where typing the whole information is redundant and time-consuming in this advanced era. To make textual information exchange more speedy and easier, word predictive systems are launched which can predict the next most likely word so that people do not have to type the next word but select it from the suggested words. In this study, we have proposed a method that can predict the next most appropriate and suitable word in Bangla language, and also it can suggest the corresponding sentence to contribute to this technology of word prediction systems. This proposed approach is, using GRU (Gated Recurrent Unit) based RNN (Recurrent Neural Network) on n-gram dataset to create such language models that can predict the word(s) from the input sequence provided. We have used a corpus dataset, collected from different sources in Bangla language to run the experiments. Compared to the other methods that have been used such as LSTM (Long Short Term Memory) based RNN on n-gram dataset and Naïve Bayes with Latent Semantic Analysis, our proposed approach gives better performance. It gives an average accuracy of 99.70% for 5-gram model, 99.24% for 4-gram model, 95.84% for Tri-gram model, 78.15%, and 32.17% respectively for Bi-gram and Uni-gram models on average.
A decision support system for urban infrastructure inter-asset management employing domain ontologies and qualitative uncertainty-based reasoning

Urban infrastructure assets (e.g. roads, water pipes) perform critical functions to the health and well-being of society. Although it has been widely recognised that different infrastructure assets are highly interconnected, infrastructure management in practice such as planning, installation and maintenance are often undertaken by different stakeholders without considering these dependencies due to the lack of relevant data and cross-domain knowledge, which may cause unexpected cascading social, economic and environmental effects. In this paper, we present a knowledge based decision support system for urban infrastructure inter-asset management. By considering various infrastructure assets (e.g. road, ground, cable), triggers (e.g. pipe leaking) and potential consequences (e.g. traffic disruption) as a holistic system, we model each sub-domain using a modular ontology and encapsulate the interdependence between them using a set of rules. Moreover, qualitative likelihood is assigned to each rule by domain experts (e.g. civil engineers) to encode the uncertainty of knowledge, and an inference engine is applied to predict the potential consequences of a given trigger with location specific data and the encoded rules. A web-based prototype system has been developed based on the above concept and demonstrated to a wide range of stakeholders. The system can assist in the process of decision making by aiding data collation and integration, as well as presenting potential consequences of possible triggers, advising on whether additional information is needed or suggesting ways of obtaining such information. The work shows an intelligent approach to integrate and process multi-source data to pioneer a novel way to aid a complex decision process with a high social impact.
Wearable Computing With Distributed Deep Learning Hierarchy: A Study of Fall Detection

With the development of technologies, an increasing number of wearable devices that are currently at the heart of the development of the Internet of Things are used around the world. The concerns about privacy, in particular healthcare wearable devices, are exacerbated as the requirement for self-health monitoring increases. In addition, many sources of data are geographically separated and might not be allowed to release due to patients or regulatory constraints. Accordingly, in this paper, a distributed hierarchical deep learning system is proposed. The proposed system applying a distributed hierarchical neural network over a cloud server and smartphones. The system enables multiple smartphones to train a shared consensus model collaboratively while keeping the private data locally to protect data privacy, and the system takes advantage of the abundant computational resources on the cloud server to lower the computational overhead on smartphones. The proposed system is demonstrated by a fall detection study which is the common healthcare issue among human beings. The patients’ data are collected from multiple wearable devices including the smartphone, the smartwatch, and the smart insoles. The experimental results show that the distributed hierarchical deep learning system can reproduce the accuracy, specificity, precision, and sensitivity of centralized machine learning while preserving privacy.
PEGNet: Progressive Edge Guidance Network for Semantic Segmentation of Remote Sensing Images

Owing to the rapid development of deep neural networks, prominent advances have been recently achieved in the semantic segmentation of remote sensing images. As the vital components of computer vision, semantic segmentation, and edge detection have strong correlation whether in the extracted features or task objective. Prior studies treated edge detection as a postprocessing operation to semantic segmentation, or they implicitly combined the two tasks. We consider that pixels around the edges are easy to be misdivided because of the prevalence of intraclass inconsistencies and interclass indistinctions, which reflect the discriminative ability of models to distinguish different classes. In this letter, we propose a multipath atrous module to first enrich the deep semantic information. Then, we combine the enhanced deep semantic information and dilated edge information generated by canny and morphological operations to obtain edge-region maps via edge-region detection module, which identifies pixels around the edges. Then, we relearn these error-prone pixels using a guidance module for the segmentation branch in a progressive guided manner. Combined with edge and segmentation branches, our progressive edge guidance network achieves an overall accuracy of 91.0% on the ISPRS Vaihingen test set, which is the new state-of-the-art result.
Hypothesis Testing Based Tracking With Spatio-Temporal Joint Interaction Modeling

Data association is one of the key research in tracking-by-detection framework. Due to frequent interactions among targets, there are various relationships among trajectories in crowded scenes which leads to problems in data association, such as association ambiguity, association omission, etc. To handle these problems, we propose hypothesis-testing based tracking (HTBT) framework to build potential associations between target by constructing and testing hypotheses. In addition, a spatio-temporal interaction graph (STIG) model is introduced to describe the basic interaction patterns of trajectories and test the potential hypotheses. Based on network flow optimization, we formulate offline tracking as a MAP problem. Experimental results show that our tracking framework improves the robustness of tracklet association when detection failure occurs during tracking. On the public MOT16, MOT17 and MOT20 benchmark, our method achieves competitive results compared with other state-of-the-art methods.
Efficacy of Virtual Reality in Painting Art Exhibitions Appreciation

Virtual reality (VR) technology has been employed in a wide range of fields, from entertainment to medicine and engineering. Advances in VR also provide new opportunities in art exhibitions. This study discusses the experience of art appreciation through desktop virtual reality (Desktop VR) or head-mounted display virtual reality (HMD VR) and compares it with appreciating a physical painting. Seventy-eight university students participated in the study. According to the findings of this study, painting evaluation and the emotions expressed during the appreciation show no significant difference under these three conditions, indicating that the participants believe that paintings, regardless of whether they are viewed through VR, are similar. Owing to the limitation of the operation, the participants considered HMD VR to be a tool that hinders free appreciation of paintings. In addition, attention should be paid to the proper projected size of words and paintings for better reading and viewing. The above indicates that through digital technology, we can shorten the gap between a virtual painting and a physical one; however, we must still improve the design of object size and the interaction in the VR context so that a virtual exhibition can be as impressive as a physical one.
Efficient Image Hashing with Geometric Invariant Vector Distance for Copy Detection

Hashing method is an efficient technique of multimedia security for content protection. It maps an image into a content-based compact code for denoting the image itself. While most existing algorithms focus on improving the classification between robustness and discrimination, little attention has been paid to geometric invariance under normal digital operations, and therefore results in quite fragile to geometric distortion when applied in image copy detection. In this article, a novel effective image hashing method is proposed based on geometric invariant vector distance in both spatial domain and frequency domain. First, the image is preprocessed by some joint operations to extract robust features. Then, the preprocessed image is randomly divided into several overlapping blocks under a secret key, and two different feature matrices are separately obtained in the spatial domain and frequency domain through invariant moment and low frequency discrete cosine transform coefficients. Furthermore, the invariant distances between vectors in feature matrices are calculated and quantified to form a compact hash code. We conduct various experiments to demonstrate that the proposed hashing not only reaches good classification between robustness and discrimination, but also resists most geometric distortion in image copy detection. In addition, both receiver operating characteristics curve comparisons and mean average precision in copy detection clearly illustrate that the proposed hashing method outperforms state-of-the-art algorithms.
Multi-Source Transfer Learning via Ensemble Approach for Initial Diagnosis of Alzheimer’s Disease

Alzheimer's disease (AD) is one of the most common progressive neurodegenerative diseases, and the number of AD patients has increased year after year with the global aging trend. The onset of AD has a long preclinical stage. If doctors can make an initial diagnosis in the mild cognitive impairment (MCI) stage, it is possible to identify and screen those at a high-risk of developing full-blown AD, and thus the number of new AD patients can be reduced. However, there are problems with the medical datasets including AD data, such as insufficient number of samples and different data distributions. Transfer learning, which can effectively solve the problem of distribution discrepancy between training and test data and an insufficient number of target samples, has attracted increasing attention over recent years. In this paper, we propose a multi-source ensemble transfer learning (METL) approach by introducing ensemble learning and our tri-transfer model that uses Tri-Training, which ensures the transferability of source data by the tri-transfer model and high performance through ensemble learning. The experimental results on the benchmark and AD datasets demonstrate that our proposed approach has effective transferability, robustness, and feasibility, and is superior to existing algorithms. Based on METL, we propose an auxiliary diagnosis system for the initial diagnosis of AD, which helps doctors identify patients in the MCI stage as quickly as possible and with high accuracy so that measures can be taken to prevent or delay the occurrence of AD.
Hand Gesture Recognition system for physically challenged people using IoT

One of the rising fields of research are hand gesture recognition. Being an important part of non-verbal interaction is a critical part of our everyday life. Hand management recognition systems allow us to communicate with the machine, which is more familiar to the human being, creative, normal, user-friendly. Hand gesture recognition covers a wide range of applications including contact with the human machine, sign language, immersive game technology etc. To perform this we have implemented a method of hand recognition and extraction of features by means of a web camera in real time. The picture is captured in this method through the system's webcam. The initial processing of the input image and thresholding are used to remove image noise and smooth the image. Following this, apply region filling in the gesture or object of interest to fill holes. The “HU moments” are used for feature extraction and classification using KNN algorithm. This helps in improving the classification though machine learning and recognition step. We have tested 43 gestures which include 26 gestures of English alphabets and achieved a confidence of 99.9%.
Electronic Voting based on Virtual ID of Aadhar using Blockchain Technology

The e-voting system is one of the frameworks which decreases the percentage of abstention and to ensure the security against tampering of the votes, Blockchain is a distributed and decentralized ledger that is used to record the transactions in an efficient and verifiable manner. Blockchain plays an important role in the e-voting system which ensures the security of votes by preventing modification of data stored in blocks using the cryptographic technique. Aadhar integration to the e-voting system overcomes the duplication or tampering of votes. The proposed scheme provides the secured e-voting system by using biometric details and VID(Virtual ID) of voters obtained from the Aadhar database to cast the Vote and also using the digital signature as the key for the encryption of the votes inside the block.
Signature Image Hiding in Color Image using Steganography and Cryptography based on Digital Signature Concepts

Data Transmission in network security is one of the most vital issues in today's communication world. The outcome of the suggested method is outlined over here. Enhanced security can be achieved by this method. The vigorous growth in the field of information communication has made information transmission much easier. But this type of advancement has opened up many possibilities of information being snooped. So, day-by-day maintaining of information security is becoming an inseparable part of computing and communication. In this paper, the authors have explored techniques that blend cryptography & steganography together. In steganography, information is kept hidden behind a cover image. In this paper, approaches for information hiding using both cryptography & steganography is proposed keeping in mind two considerations - size of the encrypted object and degree of security. Here, signature image information is kept hidden into cover image using private key of sender & receiver, which extracts the information from stego image using a public key. This approach can be used for message authentication, message integrity & non-repudiation purpose.
Multicriteria decision‐making taxonomy for DevOps challenging factors using analytical hierarchy process

Abstract Development and operations (DevOps) practices significantly accelerate and automate the continuous delivery and deployment of software systems. However, adopting DevOps concepts is not a straightforward job. Most organizations are not able to keep pace with the rhythm of continuous delivery and deployment, which are key DevOps attributes. Despite the significance of DevOps programs, it is still unknown why software development firms are demotivated or unable to adopt them. We tried to fill this gap by investigating, prioritizing, and developing the taxonomy of the key factors that could impact the adaptation and implementation of DevOps practices. We extracted a total of 16 factors from the available literature and empirically assessed them using the survey approach. The identified factors are further classified into three core categories of the software process improvement (SPI) manifesto. The analytical hierarchy process (AHP) approach was used to calculate the prioritization weight for each factor and present it as a taxonomy. The developed taxonomy provides a roadmap to tackle the key challenges to implementing DevOps and offers suggestions for streamlining DevOps practices.
A Blockchain-Assisted Intelligent Transportation System Promoting Data Services with Privacy Protection

Being able to obtain various environmental and driving data from vehicles is becoming more and more important for current and future intelligent transportation systems (ITSs) to operate efficiently and economically. However, the limitations of privacy protection and security of the current ITSs are hindering users and vehicles from providing data. In this paper, we propose a new ITS architecture by using blockchain technology solving the privacy protection and security problems, and promoting users and vehicles to provide data to ITSs. The proposed architecture uses blockchain as a trust infrastructure to protect users' privacy and provide trustworthy services to users. It is also compatible with the legacy ITS infrastructure and services. In addition, the hierarchical organization of chains enables the scalability of the system, and the use of smart contracts provides a flexible way for introducing new services in the ITS. The proposed architecture is demonstrated by a proof of concept implementation based on Ethereum. The test results show that the proposed architecture is feasible.