Reviewerly/exHarmony huggingface dataset:
Side-channel Leakage Assessment Metrics: A Case Study of GIFT Block Ciphers

Determination of an adequate level of security and providing subsequent mechanisms to achieve it, is one of the most pressing problems regarding embedded computing devices. While there are some solutions available for resource-rich computer systems, direct application of these solutions to resource-constrained environments are often unfeasible. The fundamental problem for such resource-constrained systems is the fact that current cryptographic algorithms utilize significant energy consumption and storage overhead. Both the cryptographic algorithm and its physical implementation affect the resilience of a cryptosystem against side-channel attacks. A side-channel attack represents a process that exploits leakages in order to extract sensitive information such as the key. This paper focuses on Correlation Power Analysis (CPA) which is side-channel attack based on the power consumption leakage. In 2016 the U.S. Commerce Department’s National Institute of Standards and Technology (NIST) initiated the call for proposals of new cryptographic algorithms to strengthen the cryptographic defense of networked devices against cyberattacks and to protect the data created by those innumerable device. This work evaluates S-boxes used by NIST candidates PICCOLO, GIFT, and PRESENT, as well as several S-box variants that demonstrated sufficient weaknesses against classical cryptanalysis, for a quantitative comparison in terms of resiliency to CPA attack. Three well-known theoretical metrics are evaluated: transparency order (TO and RTO), non-linearity, and signal-to-noise (SNR) ratio, aiming to characterize the resistance of these S-boxes against adversaries exploiting physical leakages. Experimental results from attacks on an 8-bit XMEGA were obtained via the ChipWhisperer platform and of all the S-boxes evaluated, GIFT64 with a PICCOLO S-box was found to be the most susceptible to CPA. Results showed that variations in TO and RTO were not sufficient to ensure practical CPA resistance and that among S-boxes with equal non-linearity there were no significant differences in the TO and SNR variants.
A Deep Convolutional Neural Network for Detection of Malaria Parasite in Thin Blood Smear Images

The Malaria disease parasite detection in laboratories has evolved drastically with advent of Medical Imaging and Artificial Intelligence. This new technologically advance method has proved to be great for medical practitioners because of its independence from external factors such as human intervention and manufacturing defects which may have adverse effects over the results. This paper describes a Deep Learning based image classification approach for the detection of malarial parasite existing in thin blood smear images, using Convolutional Neural Network (CNN) for effective feature extraction and accurate classification. The proposed CNN model can automatically extract intrinsic and discriminative features from images provided. The CNN performs perfectly with image data. This paper annotates a comparison of the observed accuracy of proposed model using three different optimizers on training and validation data. Our custom Deep CNN model can classify the parasitized an uninfected with 93.47 percent accuracy.
Object Detection and Movement Tracking Using Tubelets and Faster RCNN Algorithm with Anchor Generation

Object detection in images and videos has become an important task in computer vision. It has been a challenging task due to misclassification and localization errors. The proposed approach explored the feasibility of automated detection and tracking of elephant intrusion along forest border areas. Due to an alarming increase in crop damages resulted from movements of elephant herds, combined with high risk of elephant extinction due to human activities, this paper looked into an efficient solution through elephant’s tracking. The convolutional neural network with transfer learning is used as the model for object classification and feature extraction. A new tracking system using automated tubelet generation and anchor generation methods in combination with faster RCNN was developed and tested on 5,482 video sequences. Real-time video taken for analysis consisted of heavily occluded objects such as trees and animals. Tubelet generated from each video sequence with intersection over union (IoU) thresholds have been effective in tracking the elephant object movement in the forest areas. The proposed work has been compared with other state-of-the-art techniques, namely, faster RCNN, YOLO v3, and HyperNet. Experimental results on the real-time dataset show that the proposed work achieves an improved performance of 73.9% in detecting and tracking of objects, which outperformed the existing approaches.
Lightweight and Secure Face-based Active Authentication for Mobile Users

Active Authentication (AA) systems continuously authenticate users on smartphones. With high quality front-facing cameras available on recent smartphones, face-based active authentication emerges as a good candidate for AA systems. On the other hand, secure authentication of mobile users is a big concern in biometric systems. Mobile match-on-card (MMOC) technique takes advantage of SIM/eSIM card as a secure element (SE) to protect biometric templates and verify users isolated from the smartphone's environment. However, resource limitations of smart cards make MMOC authentication hard to implement. In this paper, we propose two system architectures for MMOC face-based AA systems. In Cloud-assisted MMOC architecture (CA-MMOC), we use cloud resources for model selection and training. Full MMOC architecture (F-MMOC) relies only on SIM/eSIM card's resources for enrollment and verification. A quantization scheme is proposed to make the authentication system implementable on SIM cards, plus a speed-up technique to reduce on-card execution time. Using a public mobile video dataset, we evaluate the proposed system. Our evaluation results show that the proposed MMOC authentication achieves high accuracy in real-time with a small memory footprint on SIM, and is suitable for cross-platform authentication. We also implement the CA-MMOC system on a real smartphone and evaluate the system's performance overhead in terms of power consumption, CPU and memory usage.
The optimal correlation detector?

SUMMARY Correlation detectors are now used routinely in seismology to detect occurrences of signals bearing close resemblance to a reference waveform. They facilitate the detection of low-amplitude signals in significant background noise that may elude detection using energy detectors, and they associate a detected signal with a source location. Many seismologists use the fully normalized correlation coefficient C between the template and incoming data to determine a detection. This is in contrast to other fields with a longer tradition for matched filter detection where the theoretically optimal statistic C2 is typical. We perform a systematic comparison between the detection statistics C and C|C|, the latter having the same dynamic range as C2 but differentiating between correlation and anticorrelation. Using a database of short waveform segments, each containing the signal on a 3-component seismometer from one of 51 closely spaced explosions, we attempt to detect P- and S-phase arrivals for all events using short waveform templates from each explosion as reference signals. We present empirical statistics of both C and C|C| traces and demonstrate that C|C| detects confidently a higher proportion of the signals than C without evidently increasing the likelihood of triggering erroneously. We recall from elementary statistics that C2, also called the coefficient of determination, represents the fraction of the variance of one variable which can be explained by another variable. This means that the fraction of a segment of our incoming data that could be explained by our signal template decreases almost linearly with C|C| but diminishes more rapidly as C decreases. In most situations, replacing C with C|C| in operational correlation detectors may improve the detection sensitivity without hurting the performance-gain obtained through network stacking. It may also allow a better comparison between single-template correlation detectors and higher order multiple-template subspace detectors which, by definition, already apply an optimal detection statistic.
Automated translation for Yemeni's Sign Language to Text UsingTransfer Learning-based Convolutional Neural Networks

Self-expression and understanding people language are among the most important things to be considered. Deaf and dumb is a group of that they facing difficulty to express themselves and communicate with others. This group of people is trying to communicate with others using “sign language”. This study is design to enhance the communication channel between these people with their society using technology. A prototype system was design to translate the Yemeni sign language into text. Deep learning algorithms included in the system using convolutional neural network (CNN) with various transfer Learning models. System evaluation is used torch and tensorflow libraries as training and testing dataset of Yemeni sign language. Accuracy comparison results obtained among various models included in this study such as Visual Geometry Group (VGG16), Residential Energy Services Network (ResNet), Google Network (GoogleNet), and Densely Connected Convolutional Network (DenseNet). We found that the accuracy results obtained for each model were (ConveNet = 98.66%), (Sequential CNN= 98.34%), (GoogleNet = 98.36%), (Vgg16 = 90,46%), (DenseNet = 99.65%), and the best result was (ResNet152 = 99.78%). This study showed the ability of technology to enhance the communication methods between deaf and their society with a suitable translation accuracy.
Early Detection of Lung Carcinoma Using Machine Learning

Lung cancer is a poorly understood disease. Smokers may develop lung cancer due to the inhalation of carcinogenic substances while smoking, but non-smokers may develop this disease as well. Lung cancer can spread to other parts of the body and this process is called metastasis. Because the lung cancer is difficult to identify in the initial stages. The objective of this work is to reduce the mortality rate of the disease by identifying it at an earlier stage based on the existing symptoms. Artificial intelligence plays active roles in tasks such as entropy extraction through preprocessing strategies, ordinal to cardinal value conversions, table normalizations for easy meta computations, and preparation of machine learning tools for iterative processes to achieve rational convergence. The machine learning methodologies incorporated in this work are the cross-validation classification tree, random forest cross-validation classification, and random tree, all of which are included in an ensemble algorithm. The ensemble algorithm classifies lung cancer with maximum precision rates. The outcome of the classification provides 94.3% accuracy, which is the highest precision rate in comparison with the conventional methodologies. Semantics preprocessing of a lung cancer training set is performed with least entropy, and then translation, aggregation, and navigation based methodologies are applied for identifying the disease at its initial stage.
An N-Gram Feature-Based Sentiment Classification Model for Drug User Reviews

Online social media websites produce a large number of reviews, comments, posts, and tweets about drugs and medications. Drug users and caregivers are expressing their opinions, which exactly reveals a patient's mood, activity, satisfaction, and side effects of the drug such as positive and negative. Moreover, it gives meaningful information to drug users, doctors, and drug developers. In this paper, we study an n-gram feature-based sentiment classification model for Abilify oral user reviews using three machine-learning algorithms such as Naïve Bayes (NB), Maximum Entropy (MaxEnt), and Support Vector Machine (SVM). The drug user reviews are extracted for different age groups with the duration of the treatment. The performance of the system is studied for the presence, frequency, and TF-IDF (Term Frequency-Inverse Document Frequency) of n-gram features using precision, recall, f-measure, and accuracy.
Automatic Recognition of Handwritten Urdu Characters

Various Optical Character Recognition (OCR) methods, especially, machine learning models, work towards the solution of recognizing patterns in intelligent ways from data that is originally not available in digital format. These patterns are converted into data that a machine can recognize (by the proper algorithm) and can further manipulate for various manipulations. The basic characteristics of the implementation presented here are based on a balance between the complexity of the algorithm applied and the highest precision that can be obtained. In this paper, we attempt to recognize precise patterns from a set of handwritten characters without making the implementation intractably complex. Specifically, in the context of the Urdu language spoken and written by more than a hundred million people around the world, very little exploration has been carried out. This paper proposes the application of an established machine learning model, namely, using the Support Vector Machine (SVM) algorithm; and investigates the efficiency of its application for recognizing handwritten characters of the Urdu language, a subject that has never been investigated before.
Memory-aware Efficient Deep Learning Mechanism for IoT Devices

Deep learning neural networks are of critical importance to enable next-generation IoT devices. However, due to the limited computation power, memory space, and energy, it remains a grand challenge to deploy those algorithms on IoT devices efficiently as they demand high computation, energy, and memory footprint. Numerous pruning methods of deep learning algorithms have been proposed to minimize the latency, energy, and weights. However, few consider the running time memory footprint and the overhead caused by data movement between the volatile memory and non-volatile memory. This paper proposes four novel memory-ware mechanisms for implementing CNN models on self-restrained embedded systems. The proposed techniques maximize the use of high-speed volatile memory and provide three implementation choices to achieve the minimum energy cost, SRAM space usage, and inference latency, as well as a hybrid trade-off choice of the three features. The experimental evaluation compares their energy cost, time latency, and required run-time memory footprint and demonstrates high implementation efficiency.
ABEBox: A data driven access control for securing public cloud storage with efficient key revocation

Besides providing data sharing, commercial cloud-based storage services (e.g., Dropbox) also enforce access control, i.e. permit users to decide who can access which data. In this paper we advocate the separation between the sharing of data and the access control function. We specifically promote an overlay approach which provides end-to-end encryption and empowers the end users with the possibility to enforce access control policies without involving the cloud provider itself. To this end, our proposal, named ABEBox, relies on the careful combination of i) attribute-based encryption for custom policy definition and management, with ii) proxy re-encryption to provide scalable re-keying and protection to key-scraping attacks, with a novel revocation procedure. Moreover, iii) we concretely embed our protection mechanisms inside a public domain virtual file system module to provide an overlay and trivial-to-use transparent service which can be deployed on top of any arbitrary cloud storage provider.
Monthly calibration and optimization of Ångström-Prescott equation coefficients for comprehensive agricultural divisions in China

Ångström-Prescott equation (AP) is the algorithm recommended by the Food and Agriculture Organization (FAO) of the United Nations for calculating the surface solar radiation (Rs) to support the estimation of crop evapotranspiration. Thus, the as and bs coefficients in the AP are vital. This study aims to obtain coefficients as and bs in the AP, which are optimized for China’s comprehensive agricultural divisions. The average monthly solar radiation and relative sunshine duration data at 121 stations from 1957–2016 were collected. Using data from 1957 to 2010, we calculated the monthly as and bs coefficients for each subregion by least-squares regression. Then, taking the observation values of Rs from 2011 to 2016 as the true values, we estimated and compared the relative accuracy of Rs calculated using the regression values of coefficients as and bs and that calculated with the FAO recommended coefficients. The monthly coefficients, as and bs, of each subregion are significantly different, both temporally and spatially, from the FAO recommended coefficients. The relative error range (0–54%) of Rs calculated via the regression values of the as and bs coefficients is better than the relative error range (0–77%) of Rs calculated using the FAO suggested coefficients. The station-mean relative error was reduced by 1% to 6%. However, the regression values of the as and bs coefficients performed worse in certain months and agricultural subregions during verification. Therefore, we selected the as and bs coefficients with the minimum Rs estimation error as the final coefficients and constructed a coefficient recommendation table for 36 agricultural production and management subregions in China. These coefficient recommendations enrich the case study of coefficient calibration for the AP in China and can improve the accuracy of calculating Rs and crop evapotranspiration based on existing data.
An Efficient method for Aspect Based Sentiment Analysis Using SpaCy and Vader

Many types of research have been carried out since today regarding Sentiment Analysis. But, very few amongst them have been able to move forward from sentiment analysis to a very useful concept of Aspect-Based Sentiment Analysis. Sentiment Analysis is typically done on three major levels, namely, Document-level, Sentence-level, and Aspect-level. The need for Aspect-Based Sentiment Analysis was raised when people understood that a whole text may have different types of sentiments related to different entities. Although, good results were derived from the Sentiment Analysis but digging more into the sentiment part was becoming difficult by following the same old methods. Aspects are the entities on which the reviews are given and different sentiments can be associated with different entities. Therefore, this research work is carried out to ease the things related to aspect-based sentiment analysis. In this research, an NLP (Natural Language Processing) model is trained on the text/reviews, and then the sentiment and aspects are mines using the vader, textblob, and spaCy library respectively. This research work will help highly in speeding-up the study of Aspect-Based Sentiment Analysis and will ease the struggle in mining the aspects in the text in accordance to the sentiment.
Explaining Semi-Supervised Text Alignment Through Visualization

The analysis of variance in complex text traditions is an arduous task when carried out manually. Text alignment algorithms provide domain experts with a robust alternative to such repetitive tasks. Existing white-box approaches allow the digital humanities to establish syntax-based metrics taking into account the spelling, morphology and order of words. However, they produce limited results, as semantic meanings are typically not taken into account. Our interdisciplinary collaboration between visualization and digital humanities combined a semi-supervised text alignment approach based on word embeddings that take not only syntactic but also semantic text features into account, thereby improving the overall quality of the alignment. In our collaboration, we developed different visual interfaces that communicate the word distribution in high-dimensional vector space generated by the underlying neural network for increased transparency, assessment of the tool's reliability and overall improved hypothesis generation. We further offer visual means to enable the expert reader to feed domain knowledge into the system at multiple levels with the aim of improving both the product and the process of text alignment. This ultimately illustrates how visualization can engage with and augment complex modes of reading in the humanities.
Factorized Modulation For Singleshot Lightfield Acquisition

A light field (LF), which is represented as a set of dense multiview images, has been utilized in various 3-D applications. To make LF acquisition more efficient, researchers have investigated compressive sensing methods by incorporating modulation or coding functions into the camera. In this work, we investigate a challenging case of compressive LF acquisition in which an entire LF should be reconstructed from only a single coded image. To achieve this goal, we propose a new modulation scheme called factorized modulation that can approximate arbitrary 4-D modulation patterns in a factorized manner. Our method can be hardware-implemented by combining the architectures for coded aperture and pixel-wise coded exposure imaging. The modulation pattern is jointly optimized with a CNN-based reconstruction algorithm. Our method is validated through extensive evaluations against other modulation schemes.
Robust model adaption for colour-based particle filter tracking with contextual information

Color-based particle filters have emerged as an appealing method for targets tracking. As the target may undergo rapid and significant appearance changes, the template (i.e. scale of the target, color distribution histogram) also needs to be updated. Traditional updates without learning contextual information may imply a high risk of distorting the model and losing the target. In this paper, a new algorithm utilizing the environmental information to update both the scale of the tracker and the reference appearance model for the purpose of object tracking in video sequences has been put forward. The proposal makes use of the well-established color-based particle filter tracking while differentiating the foreground and background particles according to their matching score. A roaming phenomenon that yields the estimation to shrink and diverge is investigated. The proposed solution is tested using both simulated and publicly available benchmark datasets where a comparison with six state-of-the-art trackers has been carried out. The results demonstrate the feasibility of the proposal and lie down foundations for further research on tackling complex visual tracking problems.