Reviewerly/exHarmony huggingface dataset:
Web-AM: An Efficient Boilerplate Removal Algorithm for Web Articles

In general, web-pages contain extra information in the form of noise such as navigational elements, sidebars, and advertisements in addition to the main content. This noise is primarily not related to the main content and it impacts the data mining and information retrieval tasks. Detecting the relevant information and noise is challenging due to the diversity in the structure of web-pages. Researchers have built algorithms such as Boilerpipe and JustText to detect the noise present in web articles. In this paper, we present an algorithm "Web-AM" to remove noise from web articles using the HTML tree structure by extending the Boilerpipe Article Extractor algorithm. Although, Boilerpipe has very good performance in extracting the main content but it fails to filter the noise present inside the main article. We make the initial selection of main content using Boilerpipe and remove noise using that structure. The filtration of the main content and noise is performed on the basis of the text length and formatting. For the evaluation of Web-AM, we build our own corpus of Urdu language web articles (CURWEB). In addition, we use L3S-GN1 and CleanPortalEval for the evaluation. Our results show 3-21% improvement in Precision by Web-AM as compare to Boilerpipe and JustText algorithms. Web-AM can be effectively used for information retrieval, content summarization, and web-page classification tasks.
ADoCW: An Automated method for Detection of Concealed Weapon

In technologically advanced era, surveillance is a proven method for the monitoring of the individual's activity in the crowd. Security of infrastructure, as well as individual, is one of the major concerns because of the influential growth of radical elements or suspicious persons in the society. Continuous manual monitoring of the CCTV surveillance is difficult and monotonous task, so there is an urgent requirement to develop an automated surveillance systems. The security surveillance system has potential to detect any kind of concealed object (like firearms or any weapon including knife, scissors etc.) which may pose a threat to the security. In this paper, we propose a novel framework for the detection and classification of concealed weapons through analysis of CCTV stream data. The classification framework is developed with the categorization of various concealed weapons through deep learning based object detection and classification techniques. For the detection of concealed weapon, multi-sensor stream data capturing framework is designed using sensor fusion techniques and also embedded with the feature extraction and segmentation of imsegmentation of images module. Faster R-CNN (Region-based Convolutional Neural Network) model is trained for classification ofages module. Faster R-CNN (Region-based Convolutional Neural Network) model is trained for classification of weapons over collected dataset. Finally, several directions of work and tasks are provided as future work for the various research communities.
Opportunities of using artificial intelligence in hardware verification

Over the last decade, usage of integrated circuits met an unprecedently spread in most domains of human activity. Processors are embedded in more and more products, consequently, request for their manufacturing increased exponentially. Since hardware verification is a very time-consuming step in electronics development, engineers make huge efforts to reduce its time by increasing automation of this process, and artificial intelligence is an ideal candidate for that job. Aligning to this global effort, advanced methods for improving verification performance are analyzed in this paper. Current research represents an important step before proceeding to implementation of artificial intelligence concepts inside integrated circuit verification process. Machine learning in verification can be used for multiple directions: generating code for driving full range of stimuli needed to bring verified circuit into all its functional states; analyzing test results; filling coverage gaps and list of examples can highly continue. However, a big challenge is to decide which parts of verification environment can best address needs of automation and which verification techniques are most suitable for supplying data to automatic code generators. In this article, some features of e language are highlighted as welcome bridges between hardware verification and artificial intelligence.
Medical patient appointments management using smart software system in UAE

Taking an appointment means go to the medical center, asking about the suitable doctor for your case, spend a lot of time, or make a phone call, or take an appointment with general doctor, after that let he/she decide to which specialists you have to go; it is very long, and boring process. The main aim of this research is supporting Smart Cities Approach in UAE by designing and implementing system and mobile application “Mwa3edk” to add new concepts for the process of taking appointments with doctors in hospitals and medical clinics by transferring this process into the online world technology. This system will be able to connect a huge number of hospitals and clinics with users over UAE; and enable people to look for doctors in different locations and take appointments that suite them. In addition, users can describe their symptoms then the application will give them recommendations according to what they described using the embedded artificial intelligent method, this will help users to avoid one step, where they can take appointment directly with a specialized doctor instead of meeting the general doctor first.
Can structured EHR data support clinical coding? A data mining approach

Structured data formats are gaining momentum in electronic health records and can be leveraged for decision support and research. Nevertheless, such structured data formats have not been explored for clinical coding, which is an essential process requiring significant manual workload in health organisations. This article explores the extent to which fully structured clinical data can support assignment of clinical codes to inpatient episodes, through a methodology that tackles high dimensionality issues, addresses the multi-label nature of coding and optimises model parameters. The methodology encompasses transformation of raw data to define a feature set, build a data matrix representation, and testing combinations of feature selection methods with machine learning models to predict code assignment. The methodology was tested with a real hospital dataset and showed varying predictive power across codes, while demonstrating the potential of leveraging structuring data to reduce workload and increase efficiency in clinical coding.
Text-to-image via mask anchor points

Text-to-image is a process of generating an image from the input text. It has a variety of applications in art generation, computer-aided design, and data synthesis. In this paper, we propose a new framework which leverages mask anchor points to incorporate two major steps in the image synthesis. In the first step, the mask image is generated from the input text and the mask dataset. In the second step, the mask image is fed into the state-of-the-art mask-to-image generator. Note that the mask image captures the semantic information and the location relationship via the anchor points. We also developed a user-friendly interface which helps parse the input text into the meaningful semantic objects. As a result, our framework is able to produce clear, reasonable, and more realistic images. The experiments on the most challenging COCO-stuff dataset illustrate the superiority of our proposed approach over the previous state of the arts.
AES-RC4 Encryption Technique to Improve File Security

In communicating in public networks, data security becomes very important. There are many risks of data theft by other parties. Cryptography is one technique for securing data. In cryptography, there are two main processes, namely encryption, and decryption. Encryption is the process of encoding the original or plaintext file into an encrypted or ciphertext file. While decryption is the process of returning encrypted data or ciphertext into original or plaintext data. There are various encryption algorithms nowadays, where the more complex encryption logically encryption will be stronger and harder to solve. This study proposed a combination of the AES and RC4 algorithms to provide protection against confidential data. The combination of this method aims to make the algorithm more complex and strong. To find out how safe an algorithm is, test the avalanche effect (AE). AE measurement is done by changing the value of one bit in the key, then the two results are ciphertext compared to the difference. The greater the difference, the better the encryption results. The ideal AE is around 50% and the greater the AE value means the better the quality of data encryption. Based on the combination test of the AES-RC4 method, the best AE results are 58.2%, where this value is far better than the AES algorithm, RC4 only or RC4-AES.
Beyond algorithmic noise or how to shuffle parallel implementations?

Summary Noise is an important ingredient for side‐channel‐analysis countermeasures security. However, physical noise is in most cases not sufficient to achieve high‐security levels. As an outcome, designers traditionally aim to emulate noise by harnessing shuffling in the time domain and algorithmic noise in the amplitude domain. On one hand, harnessing algorithmic noise is limited in architectures/devices which have a limited data‐path width. On the other hand, the performance degradation due to shuffling is considerable. A natural complement to operation shuffling is the hardware‐based intra‐cycle shuffling (ICS), which typically shuffles the sample time of bits within a clock cycle (instead of micro‐processor operations). Such architecture eliminates the performance overhead due to shuffling within a single cycle, it is algorithm‐independent, i.e., no need in partitioning of operations, and as it is hardware‐based, the data‐path width can be tailored to better exploit algorithmic‐noise. In this manuscript, we first analyze the noise components in physical designs to better model the algorithmic noise. We then perform an information‐theoretic (IT) analysis of both shuffling countermeasures . The last part of the manuscript deals with real‐world architectures analysis: IT analysis of an Advanced Encryption Standard (AES) core implemented over a 32‐ and 128‐bit wide data‐path embedded with intra‐cycle shuffling and two flavors of shuffling generation (memory‐based and on‐line permutation generation). The manuscript is concluded by underling the benefits which can be achieved with the ICS architecture.
Interpretable Run-Time Monitoring and Replanning for Safe Autonomous Systems Operations

Autonomous robots, especially aerial vehicles, when subject to disturbances, uncertainties, and noises may experience variations from their desired states and deviations from the planned trajectory which may lead them into an unsafe state (e.g., a collision). It is thus necessary to monitor their states at run-time when operating in uncertain and cluttered environments and intervene to guarantee their and the surrounding's safety. While Reachability Analysis (RA) has been successfully used to provide safety guarantees, it doesn't provide explanations on why a system is predicted to be unsafe and what type of corrective actions to perform to change the decision. In this work we propose a novel approach for run-time monitoring that leverages a library of previously observed trajectories together with decision tree theory to predict if the system will be safe/unsafe and provide an explanation to understand the causes of the prediction. We design an interpretable monitor that checks at run-time if the vehicle may become unsafe and plan safe corrective actions if found unsafe. For each prediction, we provide a logical explanation - a decision rule - that includes information about the causes that lead to the predicted safety decision. The explanation also includes a set of counterfactual rules that shows what system variables may bring the system to the opposite safety decision, if changed. We leverage such an explanation to plan corrective actions that always keep the vehicle safe. Our technique is validated both with simulations and experiments on a quadrotor UAV in cluttered environments under the effect of previously untrained disturbances.
Color Image Authentication using dual watermarks

In this paper, we have presented a spatial domain watermarking technique using dual watermarks embedded in two planes of color image. The test color image (medical/ general) of size 512×512 are firstly sliced up into three planes. The two planes (red and blue) are divided into 4×4 non-overlapping blocks. Each plane of the image is embedded with 128×128 sized watermark along with the mean of each block, once its 2 LSB's are set to zero. The authentication of the data is done using fragile watermark which easily detects tampering in images. The proposed scheme reports a Peak-Signal-to-Noise (PSNR) value of 49.07 dB while as Structural Similarity index (SSIM) of 0.9843 is obtained which is better than already existing schemes for the same payload.
A Chebyshev polynomial feedforward neural network trained by differential evolution and its application in environmental case studies

This paper introduces a polynomial feedforward neural network based on Chebyshev polynomials able to effectively model non-linear and highly complex environmental data. The data sets were cautiously selected from the fields of biology, ecology, climate, and environmental management, and economics as to represent a scientifically meaningful and consistent corpus of disparate domains of intensive focus and interest in current ecological/environmental research, covering issues related to body growth/age, biomass production, energy efficiency/consumption, and ecology/geographic extension. The proposed network uses a number of layers to estimate the output in terms of a weighted sum of truncated Chebyshev series expansions applied to linear combinations of the input variables, and it is trained by the differential evolution algorithm. Its performance was compared to three neural networks. First, a polynomial feedforward network that uses Hermite polynomials as activation function in the hidden nodes; second, a radial basis function neural network; third, a Takagi-Sugeno-Kang neuro-fuzzy network. All the above networks were trained by evolutionary optimization algorithms. The comparison was carried out by standard criteria such as the root mean square error and the mean absolute error. Moreover, a non-parametric Kruskal-Wallis statistical test used to compare the median values of the root mean square errors between methods. The main experimental outcomes are: (a) the network's efficiency improves for higher polynomial orders, (b) the statistical analysis suggests that the proposed network appears to be very competitive to the other three networks.
Flexible NTT Accelerators for RLWE Lattice-Based Cryptography

In this work, we propose methods to design flexible and energy-efficient hardware accelerators for ring learning with error (RLWE) lattice-based cryptographic protocols, such as key agreement and digital signature. We apply the proposed methods to design the first programmable DMA-based family of accelerators for the Number Theoretic Transform (NTT), a commonly used kernel inside variants of RLWE protocols NewHope and Kyber. We validate our methods by integrating the accelerators into an HLS-based System on Chip (SoC) simulator. Experiments confirm the suitability of the flexible DMA-based accelerators for their use as part of lattice-based schemes. Our proposed designs are capable of executing new variants of lattice-based schemes with superior energy efficiency compared to executing the scheme entirely on the main processor, but without modifying the hardware acceleration platform. Performance improvements are up to 2x, energy consumption improves up to 1.9x, and energy-delay product (EDP) improves up to 3.9x. Together with such improved energy efficiency and performance, the flexibility inherent in our accelerators provides insights for, while reducing the risk of early adoption of lattice-based PQC cryptographic protocols in hardware products.
Choosing The Most Optimum Text Preprocessing Method for Sentiment Analysis: Case:iPhone Tweets

Preprocessing is the initial stage in several text processing tasks, including sentiment analysis. Preprocessing is an important step in sentiment analysis because it could affect the result accuracy significantly. However, previous studies on preprocessing that discussed the selection of preprocessing methods were rarely conducted. In this study, we analyze the effect of preprocessing methods on sentiment analysis task. We performed the sentiment analysis as a classification on product opinion, whether the sentiment is positive or negative. We conducted an experiment using Tweets that talk about iPhone. We observed seven different preprocessing methods and the combination of it. The preprocessing methods are: casefolding, expressive lengthening, emoticons handling, removing URLs, slang handling, punctuations handling, stopwords removal and stemming. The results show that a combination of five methods: URL removal, emoticon handling, case folding, expressive lengthening and stemming is the most optimum method with an accuracy of 70.88% on sentiment analysis.
Attribute-Guided Deep Polarimetric Thermal-to-visible Face Recognition

In this paper, we present an attribute-guided deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces. The coupled framework contains two sub-networks, one dedicated to the visible spectrum and the second sub-network dedicated to the polarimetric thermal spectrum. Each sub-network is made of a generative adversarial network (GAN) architecture. We propose a novel Attribute-Guided Coupled Generative Adversarial Network (AGC-GAN) architecture which utilizes facial attributes to improve the thermal-to-visible face recognition performance. The proposed AGC-GAN exploits the facial attributes and leverages multiple loss functions in order to learn rich discriminative features in a common embedding subspace. To achieve a realistic photo reconstruction while preserving the discriminative information, we also add a perceptual loss term to the coupling loss function. An ablation study is performed to show the effectiveness of different loss functions for optimizing the proposed method. Moreover, the superiority of the model compared to the state-ofthe-art models is demonstrated using polarimetric dataset.
Social- and Content-Aware Prediction for Video Content Delivery

An ever-increasing number of videos in mobile social networks increase the waiting time of video downloading. Video prefetching is a viable way to save time during video downloading, while prefetching all the videos is resource wasteful if the videos are not watched. Therefore, careful prediction of whether a user will watch a video is critical for efficient video content delivery. Most existing work on social media recommendation focuses on the top-N problem to recommend multiple videos. In this paper, we deal with the problem of predicting whether a video will be watched by a user for efficient video content delivery in mobile social networks. We propose a Social- and Content-aware Video content delivery Prediction method (SCVP) for the problem by capturing the intrinsic relationship among users and videos. We design five metrics to estimate the factors of active degree of users, social tier between users, similarity between videos, similarity between user interest and video content, and video popularity. We then use combined prediction for the video content delivery prediction to incorporate the impacts of the five factors on the prediction. Finally, we conduct experiments through simulations. Experimental results demonstrate that the proposed method SCVP can predict whether a video is watched by users with high accuracy.
Overview of Deep Convolutional Neural Network Approaches for Satellite Remote Sensing Ship Monitoring Technology

Abstract Remote sensing (RS) monitoring of ships has important significance in both military and civilian fields. The RS ship detection aims to locate the position of the ship in the remote sensing image and extract its characteristics. Traditional ship target detection algorithms cannot meet the demands for speed and precision of SAR remote sensing and optical remote sensing data. With the development of artificial intelligence technology, the target detection technology such as deep learning algorithms has made significant progress in RS ship detection. Deep learning has become a heated topic in research. This paper has analyzed and summarized previous researches on the application of deep learning algorithms in ship detection technologies based on SAR and optical remote sensing images in recent years and has provided suggestions for future studies. In the future, deep learning-based technologies for RS ship detection will use more data, such as data from multiple sensors in multiple channels. Deep neural networks will also include more rules and specialized knowledge. Its structure will become more complicated and eventually develop into a neural network like the human brain.
Process Variation Mitigation on Convolutional Neural Network Accelerator Architecture

Convolutional Neural Network (CNN) accelerators are popular specialized platforms for efficient CNN processing. As semiconductor manufacturing technology scales down to nano scale, process variation dramatically affects the chip's quality. Process variation causes delay variation within the chip due to transistor parameter differences. CNN accelerators adopt a large number of Processing Elements (PEs) for parallel computing, which are highly susceptible to process variation effects. Fast CNN processing desires consistent performance among PEs, otherwise the processing speed is limited by the slowest PE within the chip. In this work, we first quantitatively model and analyze the impact of process variation on CNN accelerator's operating frequency. We further analyze the utilization of CNN accelerator and the characteristics of CNN models. We then leverage the PE underutilization to propose a sub-matrix reformation mechanism and leverage the pixel similarity of images to propose a weight transfer technique. Both techniques are able to tolerate the low-frequency PEs, and achieve performance improvement at chip level. Evaluation results show our techniques are able to achieve significant processing speed improvement with negligible accuracy loss.
A novel scheme to address the fusion uncertainty in multi-modal continuous authentication schemes on mobile devices

Interest in continuous mobile authentication schemes has increased in recent years. These schemes use sensors on mobile devices to collect the biometric data about a user. The use of multiple sensors in a multi-modal scheme has been shown to improve the accuracy. However, sensor scores are often combined using simplistic techniques such as averaging. To date, the effect of uncertainty in score fusion has not been explored. In this paper, we present a novel Dempster-Shafer based score fusion approach for continuous authentication schemes. Our approach combines the sensor scores factoring in the uncertainty of the sensor. We propose and evaluate five techniques for computing uncertainty. Our proof-of-concept system is tested on three state-of-the-art datasets and compared with common fusion techniques. We find that our proposed approach yields the highest accuracies compared to the other fusion techniques and achieves equal error rates as low as 8.05%.