Reviewerly/exHarmony huggingface dataset:
Classification of Cervical Biopsy Images Based on LASSO and EL-SVM

Cervical biopsy (biopsy) is an important part of the diagnosis of cervical cancer.The artificial classification of biopsy images in diagnosis is difficult and depends on the clinical experience of pathologists.However, the classification accuracy of computerized biopsy tissue images with similar lesions is low, and the problem of incomplete experimental objects needs to be addressed.This paper proposes a method of cervical biopsy tissue image classification based on least absolute shrinkage and selection operator (LASSO) and ensemble learning-support vector machine (EL-SVM).Using the LASSO algorithm for feature selection, the average optimization time was reduced by 35.87 seconds while ensuring the accuracy of the classification, and then serial fusion was performed.The EL-SVM classifier was used to identify and classify 468 biopsy tissue images, and the receiver operating characteristic (ROC) curve and error curve were used to evaluate the generalization ability of the classifier.Experiments show that the normalcervical cancer classification accuracy reached 99.64%, the normal-low-grade squamous intraepithelial lesion (LSIL) classification accuracy was 84.25%, the normal-high-grade squamous intraepithelial lesion (HSIL) classification accuracy was 87.40%, the LSIL-HSIL classification accuracy was 76.34%, the LSILcervical cancer classification accuracy was 91.88%, and the HSIL-cervical cancer classification accuracy was 81.54%.
A Superior Arabic Text Categorization Deep Model (SATCDM)

Categorizing Arabic text documents is considered an important research topic in the field of Natural Language Processing (NLP) and Machine Learning (ML).The number of Arabic documents is tremendously increasing daily as new web pages, news articles, social media contents are added.Hence, classifying such documents in specific classes is of high importance to many people and applications.Convolutional Neural Network (CNN) is a class of deep learning that has been shown to be useful for many NLP tasks, including text translation and text categorization for the English language.Word embedding is a text representation currently used to represent text terms as real-valued vectors in vector space that represent both syntactic and semantic traits of text.Current research studies in classifying Arabic text documents use traditional text representation such as bag-of-words and TF-IDF weighting, but few use word embedding.Traditional ML algorithms have already been used in Arabic text categorization, and good results are achieved.In this study, we present a Multi-Kernel CNN model for classifying Arabic news documents enriched with n-gram word embedding, which we call A Superior Arabic Text Categorization Deep Model (SATCDM).The proposed solution achieves very high accuracy compared to current research in Arabic text categorization using 15 of freely available datasets.The model achieves an accuracy ranging from 97.58% to 99.90%, which is superior to similar studies on the Arabic document classification task.
Communication without Interception: Defense against Modulation Detection

We consider a communication scenario, in which an intruder tries to determine the modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the intruder, while guaranteeing that the intended receiver can still recover the underlying message with the highest reliability. This is achieved by constellation perturbation at the encoder, similarly to adversarial attacks against classifiers in machine learning. In image classification, the perturbation is limited to be imperceptible to a human observer, while in our case the perturbation is constrained so that the message can still be reliably decoded by a legitimate receiver that is oblivious to the perturbation. Simulation results demonstrate the viability of our approach to make wireless communication secure against both state-of-the-art deep-learning- and decision-tree-based intruders with minimal sacrifice in the communication performance.
Sentiment Analysis of US Airlines Tweets Using LSTM/RNN

Nowadays, a million users use social networking services such as Twitter to tweet their products and services by placing the reviews based on their opinions. Sentiment analysis has emerged to analyze the twitter data automatically. Sentiment classification techniques used to classify US airline tweets based on sentiment polarity due to flight services as positive, negative and neutral connotations done on six different US airlines. To detect sentiment polarity, we explored word embedding models (Word2Vec, Glove) in tweets using deep learning methods. Here, we investigated sentiment analysis using the Recurrent Neural Network (RNN) model along with Long-Short Term Memory networks (LSTMs) units can deal with long term dependencies by introducing memory in a network model for prediction and visualization. The results showed better significant classification accuracy trained 80% for training set and 20% for testing set which shows that our models are reliable for future prediction. To improve this performance, the Bidirectional LSTM Model (Bi-LSTM) is used for further investigation studies.
Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks

Targeted sentiment classification aims to predict the emotional trend of a specific goal. Currently, most methods (e.g., recurrent neural networks and convolutional neural networks combined with an attention mechanism) are not able to fully capture the semantic information of the context and they also lack a mechanism to explain the relevant syntactical constraints and long-range word dependencies. Therefore, syntactically irrelevant context words may mistakenly be recognized as clues to predict the target sentiment. To tackle these problems, this paper considers that the semantic information, syntactic information, and their interaction information are very crucial to targeted sentiment analysis, and propose an attentional-encoding-based graph convolutional network (AEGCN) model. Our proposed model is mainly composed of multi-head attention and an improved graph convolutional network built over the dependency tree of a sentence. Pre-trained BERT is applied to this task, and new state-of-art performance is achieved. Experiments on five datasets show the effectiveness of the model proposed in this paper compared with a series of the latest models.
Mining API usage scenarios from stack overflow

APIs play a central role in software development. The seminal research of Carroll et al. [15] on minimal manual and subsequent studies by Shull et al. [79] showed that developers prefer task-based API documentation instead of traditional hierarchical official documentation (e.g., Javadoc). The Q&A format in Stack Overflow offers developers an interface to ask and answer questions related to their development tasks. With a view to produce API documentation, we study automated techniques to mine API usage scenarios from Stack Overflow. We propose a framework to mine API usage scenarios from Stack Overflow. Each task consists of a code example, the task description, and the reactions of developers towards the code example. First, we present an algorithm to automatically link a code example in a forum post to an API mentioned in the textual contents of the forum post. Second, we generate a natural language description of the task by summarizing the discussions around the code example. Third, we automatically associate developers reactions (i.e., positive and negative opinions) towards the code example to offer information about code quality. We evaluate the algorithms using three benchmarks. We compared the algorithms against seven baselines. Our algorithms outperformed each baseline. We developed an online tool by automatically mining API usage scenarios from Stack Overflow. A user study of 31 software developers shows that the participants preferred the mined usage scenarios in Opiner over API official documentation. The tool is available online at: http://opiner.polymtl.ca/. With a view to produce API documentation, we propose a framework to automatically mine API usage scenarios from Stack Overflow, supported by three novel algorithms. We evaluated the algorithms against a total of eight state of the art baselines. We implement and deploy the framework in our proof-of-concept online tool, Opiner.
Personal knowledge base designer: Software for expert systems prototyping

In most cases, the complexity of expert systems engineering depends on the complexity of knowledge base engineering. This process includes formalization and programming tasks. In this connection, the use of visual programming, model transformation and code generation principles are relevant. We present a new software with similar properties. Our software provides the use of a domain-specific notation for rule modeling, namely, Rule Visual Modeling Language (RVML); wizards for creating and editing knowledge base elements; conceptual models and canonical spreadsheet tables as main sources of domain knowledge. The core of the new software is a unified model for representing and editing knowledge in the form of logical rules, as well as its interpretation using the built-in rule engine. This enables the use of conceptual models in the form of UML class diagrams, concept maps, mind maps, Ishikawa diagrams and others as a source of information, and also helps involve non-programming users in the process of knowledge base engineering and to minimize coding errors. Our empirical results demonstrate the ability to use the proposed software for prototyping rule-based knowledge bases by transforming different conceptual models. Two case studies are also presented.
Agile Scrum Issues at Large-Scale Distributed Projects

The agile model is a very vast and popular model in use in the software industry currently. It changes the way software is developed. It was introduced in 2001 to overcome deficiencies of software development in a workshop arranged by researchers and practitioners who were involved with the agile concept. They introduced the complete agile manifesto. The agile model has main components that make it more viable for use in well-organized software development. One of these is scrum methodology. The reason for the agile-scrum popularity is its use for small-scale projects, making small teams and allows change requests at any stage of a project from the client. It works for client satisfaction. Instead of so much popularity and distinctive features, agile-scrum also has some limitations when used for large scale projects development that makes it less efficient for development. This article discusses the agile-scrum methodology and its limitations when using for large-scale project organization.
An ECDSA Approach to Access Control in Knowledge Management Systems Using Blockchain

Access control has become problematic in several organizations because of the difficulty in establishing security and preventing malicious users from mimicking roles. Moreover, there is no flexibility among users in the participation in their roles, and even controlling them. Several role-based access control (RBAC) mechanisms have been proposed to alleviate these problems, but the security has not been fully realized. In this work, however, we present an RBAC model based on blockchain technology to enhance user authentication before knowledge is accessed and utilized in a knowledge management system (KMS). Our blockchain-based system model and the smart contract ensure that transparency and knowledge resource immutability are achieved. We also present smart contract algorithms and discussions about the model. As an essential part of RBAC model applied to KMS environment, trust is ensured in the network. Evaluation results show that our system is efficient.
A new automatic thresholding algorithm for unimodal gray-level distribution images by using the gray gradient information

Automatic thresholding is an important image-processing technique that extracts target regions of interest from background based on the gray-level information. There are many methods that have been proposed to provide an appropriate threshold value for an image with an evident bimodal gray-level histogram. Nevertheless, most of these methods can not accurately and efficiently segment an image with a unimodal gray-level distribution. In this study, a new approach of determining the optimal threshold is proposed to address the above issues by introducing the gray gradient of pixels. The gray gradient value of each pixel is calculated by performing an element multiplication with the image patch and the Sobel operator masks. Then, the gray gradient distribution is obtained on the basis of the results of statistical averaging the gray gradient values of the pixels with different gray-levels. By analysing the morphology of the gray gradient distribution histogram, the optimal threshold for image segmentation is obtained. Furthermore, several test images with unimodal gray-level distributions are segmented by other algorithms, such as the Otsu algorithm, MaxEntropy algorithm and Valley-Emphasis algorithm, to assess the performance of our proposed algorithm. In addition, conventional physical mercury intrusion porosimetry (MIP) and nuclear magnetic resonance (NMR) are used to test the porosity of the imaged samples, to further verify the accuracy of the new algorithm. The comparison results indicate that the proposed algorithm demonstrates a better segmentation performance over those of the other methods for an image with a unimodal gray-level distribution and results in a porosity that is in accordance with that of the MIP and NMR test.
Knowledge-driven Biometric Authentication in Virtual Reality

With the increasing adoption of virtual reality (VR) in public spaces, protecting users from observation attacks is becoming essential to prevent attackers from accessing context-sensitive data or performing malicious payment transactions in VR. In this work, we propose RubikBiom, a knowledge-driven behavioural biometric authentication scheme for authentication in VR. We show that hand movement patterns performed during interactions with a knowledge-based authentication scheme (e.g., when entering a PIN) can be leveraged to establish an additional security layer. Based on a dataset gathered in a lab study with 23 participants, we show that knowledge-driven behavioural biometric authentication increases security in an unobtrusive way. We achieve an accuracy of up to 98.91% by applying a Fully Convolutional Network (FCN) on 32 authentications per subject. Our results pave the way for further investigations towards knowledge-driven behavioural biometric authentication in VR.
Towards Real Time Vehicle Counting using YOLO-Tiny and Fast Motion Estimation

Real-time vehicle detection, tracking and counting from surveillance cameras is a main part for many applications in smart cities. Usually, this task encounters some problems in practice, like the lack of real-time processing of the videos or the errors in detection and/or tracking. In this paper we propose an approach for real time vehicle counting by using Tiny YOLO for detection and fast motion estimation for tracking. Our application is running in Ubuntu with GPU processing, and the next step is to test it on low-budget devices, as Jetson Nano. Experimental results show that our approach achieves high accuracy at real time speed (33.5 FPS) on real traffic videos.
Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks

The high energy cost of processing deep convolutional neural networks impedes their ubiquitous deployment in energy-constrained platforms such as embedded systems and IoT devices. This article introduces convolutional layers with pre-defined sparse 2D kernels that have support sets that repeat periodically within and across filters. Due to the efficient storage of our periodic sparse kernels, the parameter savings can translate into considerable improvements in energy efficiency due to reduced DRAM accesses, thus promising significant improvements in the trade-off between energy consumption and accuracy for both training and inference. To evaluate this approach, we performed experiments with two widely accepted datasets, CIFAR-10 and Tiny ImageNet in sparse variants of the ResNet18 and VGG16 architectures. Compared to baseline models, our proposed sparse variants require up to ~82% fewer model parameters with 5.6× fewer FLOPs with negligible loss in accuracy for ResNet18 on CIFAR-10. For VGG16 trained on Tiny ImageNet, our approach requires 5.8× fewer FLOPs and up to ~83.3% fewer model parameters with a drop in top-5 (top-1) accuracy of only 1.2% (~2.1%). We also compared the performance of our proposed architectures with that of ShuffleNet and MobileNetV2. Using similar hyperparameters and FLOPs, our ResNet18 variants yield an average accuracy improvement of ~2.8%.
Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective

Hadoop has become a promising platform to reliably process and store big data. It provides flexible and low cost services to huge data through Hadoop Distributed File System (HDFS) storage. Unfortunately, absence of any inherent security mechanism in Hadoop increases the possibility of malicious attacks on the data processed or stored through Hadoop. In this scenario, securing the data stored in HDFS becomes a challenging task. Hence, researchers and practitioners have intensified their efforts in working on mechanisms that would protect user’s information collated in HDFS. This has led to the development of numerous encryption-decryption algorithms but their performance decreases as the file size increases. In the present study, the authors have enlisted a methodology to solve the issue of data security in Hadoop storage. The authors have integrated Attribute Based Encryption with the honey encryption on Hadoop, i.e., Attribute Based Honey Encryption (ABHE). This approach works on files that are encoded inside the HDFS and decoded inside the Mapper. In addition, the authors have evaluated the proposed ABHE algorithm by performing encryption-decryption on different sizes of files and have compared the same with existing ones including AES and AES with OTP algorithms. The ABHE algorithm shows considerable improvement in performance during the encryption-decryption of files.
Model checking smart contracts for Ethereum

One important promise of the blockchain technology is the concept of smart contracts. They offer means for the secure execution of procedures that no entity can manipulate. This enables applications like the automation of business processes, i.e. entire business relationships in peer-to-peer collaborations can be automated securely. While the blockchain guarantees proper execution it assures the correctness of business collaboration. Since a smart contract cannot be easily changed or updated once instantiated, one has to be absolutely sure that the program code works as expected. This paper presents our tool chain that comprises the formalization of the semantics of smart contracts, via a functional specification of blockchain operations towards a formal representation of the smart contract at question, that can be formally analyzed for correct implementation via model checking.
Exploring Global and Local Linguistic Representations for Text-to-Image Synthesis

The task of text-to-image synthesis is to generate photographic images conditioned on given textual descriptions. This challenging task has recently attracted considerable attention from the multimedia community due to its potential applications. Most of the up-to-date approaches are built based on generative adversarial network (GAN) models, and they synthesize images conditioned on the global linguistic representation. However, the sparsity of the global representation results in training difficulties on GANs and a shortage of fine-grained information in the generated images. To address this problem, we propose cross-modal global and local linguistic representations-based generative adversarial networks (CGL-GAN) by incorporating the local linguistic representation into the GAN. In our CGL-GAN, we construct a generator to synthesize the target images and a discriminator to judge whether the generated images conform with the text description. In the discriminator, we construct the cross-modal correlation by projecting the image representations at high and low levels onto the global and local linguistic representations, respectively. We design the hinge loss function to train our CGL-GAN model. We evaluate the proposed CGL-GAN on two publicly available datasets, the CUB and the MS-COCO. The extensive experiments demonstrate that incorporating fine-grained local linguistic information with cross-modal correlation can greatly improve the performance of text-to-image synthesis, even when generating high-resolution images.
LocJury: An IBN-Based Location Privacy Preserving Scheme for IoCV

Stemming from the recent evolutionary progress of the 5G wireless communication and Internet of Things (IoT) relevant technologies, the vision of the Internet of Connected Vehicles (IoCV) has become more apparent. On the basis of the state-of-the-art IoCV conceptual implementations, the location of the vehicles is one of the essential driven data of IoCV, and the location privacy issue needs to be taken into account. However, when scrutinizing into IoCV, noticeable challenges of location-aware scenario has raised. For IoCV, location is more than just query criteria like in Location-based Services (LBSs) of mobile Internet. It is also the underpinning data of various types of IoCV underlying mechanisms and functions. This difference makes preserving location privacy in IoCV quite different from the traditional privacy scenarios. In this paper, an overall analysis of end-user location privacy in IoCV was performed. To solve the location privacy dilemma, we proposed an intent prediction-based approach named LocJury, which benefits from the emerging concept of Intent-based Networking (IBN). LocJury provides location privacy by learning and estimate the intent of location access and will penalize those malicious location accesses. By simulating the conceptual IBN-based IoCV application scenario, which relies on the location accesses, the performance of LocJury is evaluated under various circumstances. The simulation result verified the effectiveness of our proposed method.
Semisupervised Approach to Surrogate-Assisted Multiobjective Kernel Intuitionistic Fuzzy Clustering Algorithm for Color Image Segmentation

Multiobjective evolutionary algorithms (MOEAs) are effective optimization methods. To improve the segmentation performance and time efficiency of MOEAs-based fuzzy clustering algorithms for color images, a semisupervised surrogate-assisted multiobjective kernel intuitionistic fuzzy clustering (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> MKIFC) algorithm is proposed in this article. The main contributions of S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> MKIFC can be summarized as follows: 1) semisupervised kernel intuitionistic fuzzy objective functions are constructed for optimization to search satisfactory segmentation results; 2) to reduce the computational cost, the Kriging model is used to predict the values of objective functions instead of directly calculating the expensive objective functions; 3) a semisupervised selection strategy and a semisupervised model management mechanism are proposed to balance the convergence and diversity and improve the predicted accuracy of the Kriging model, respectively; and 4) a novel semisupervised kernel intuitionistic fuzzy cluster validity index is defined to select the optimal solution from the final nondominated solution set. Experimental results on two color image libraries demonstrate that S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> MKIFC outperforms state-of-the-art methods in segmentation performance and meanwhile possesses a low time cost.