Miracle-dz/UniFAR-FTUs huggingface dataset:
Fountain Code Enabled ADS-B for Aviation Security and Safety Enhancement

Automatic Dependence Surveillance-Broadcast (ADS-B) is transforming all aspects of aviation, including commercial aircraft and unmanned aerial systems (UAS). ADS-B service broadcasts traffic information and flight information to improve aviation safety and efficiency in the air and on runways, reduce costs, and lessen harmful effects on the environment. However, due to its broadcast nature, on one hand, ADS-B is vulnerable to malicious cybersecurity attacks relevant to broadcasting; on the other hand, broadcasting could be leveraged to further improve the safety of aviation. The objective of this paper is to investigate how to enhance aviation security and safety by leveraging the inherent broadcast property of ADS-B. To be specific, we propose two novel schemes for enhancing aviation cybersecurity and safety, respectively. The first scheme, motivated by the fact that fountain codes have been successfully applied in various broadcasting scenarios for ensuring the reliability, we propose to leverage Fountain Code to enhance the security of ADS-B. The second scheme, which is based on information integration, to detect collaboratively the comprehensive weather information to enhance the safety of aviation. The performance evaluation results demonstrate the feasibility of integrating fountain codes with ADS-B for encryption and the feasibility of collaborative detection to bypass turbulent weather conditions. To our knowledge, our work is the first attempt to apply fountain code to enhance aviation security and safety.
Detecting Bilateral Symmetry in Single Object Image Through Slope Matching

The computational treatment of symmetry is a complex problem in computer vision and computational geometry as well. In this paper detection of global bilateral symmetry in gray level images has been considered without prior segmentation. The proposed methodology detects symmetry based on slopes of the symmetric points which are filtered from the feature points extracted by Scale Invariant Feature Transform (SIFT) algorithm. This approach depends on all the points inherent to line of bilateral symmetry exhibits same inclination in 2D plane. This concept helps us further to find out bilateral symmetry in the image. For demonstration, the algorithm is tested on random images The main contribution of this paper is that the approach is computationally very simple and can be extended very easily to be applied to images having multiple symmetries.
Impact of Adversarial Examples on the Efficiency of Interpretation and Use of Information from High-Tech Medical Images

In this paper we discuss the possibility of adversarial examples appearance in high-tech medical images (Computer tomography and Magnetic resonance imaging), due to the noise inherent in the technology of their formation, and therefore we suggest ways to counteract this effect. As the idea of the paper we put two questions: 1. Can individual instances of real high-tech medical images work as AE when being analyzed with the use of neural networks? 2. Is it possible to defend oneself against such «natural» adversarial attacks with the simplest possible means? In our research, we tried the following defence methods: adversarial training, Gaussian data augmentation and bounded RELU (see section 3 for a detailed description). We conducted the experiment with the use of the neural network - a variant of convolutional network structure combining U-Net with the region proposal networks. As the source data two datasets were chosen - the Lung Image Database Consortium image collection containing 1018 lung cancer screening thoracic CT scans and Brain MRI DataSet containing clinical imaging data of glioma patients (a total of 274 cases). The experiments showed that the degree of manifestation of AE varies depending on the type of training model. When training a model not using techniques of defences on adversarial examples, the number of incorrectly recognized images is quite large (200 per 10,000 for CT and 285 per 10,000 for MRI). By proper selecting of the activation function of CNN, it can be reduced to 60 and 68, respectively. With augmentation of training dataset by Gaussian noised images, this number drops to 21 and 26. An even greater reduction in the number of incorrectly recognized images is achieved using the Adversarial Training method −12 and 15. Thus, it is shown that the adversarial effect is possible after the application of adversarial training techniques, but the degree of noise in such an image will be much higher than before using these techniques, and it will be easy enough for the doctor to recognize them visually and exclude them from further consideration.
Construction of a Liar Corpus and Detection of Lying Situations

Recognizing lies in human interaction is one of the most important and challenging tasks in artificial intelligence. For example, it leads to the development of a human-computer interaction system that detects a liar in a conversation. In another example, some systems in the future might occasionally need to tell a lie for not hurting user's feelings, namely a white lie for trouble-free conversations. In this paper, we construct a video corpus for developing a system that detects lies in conversations. The corpus consists of 540 question-answer pairs by 9 persons; 270 true answers and 270 lies. We manually analyze the images in the corpus for recognizing lies. In addition, we apply machine learning techniques, support vector machines with local binary pattern features and geometric features, to our corpus. We discuss the results of the analysis of the corpus and the machine learning approaches to our corpus.
Blockchain and IoT for Security and Privacy: A Platform for Diabetes Self-management

Diabetes is one of the most common disease over the world which requires a daily self-care in order to be controlled. Nowadays, diabetes self-management can benefit from the recent advanced technologies such as Internet of things (wearables and medical sensors) to take measurements and track health data. In this paper, we present a platform architecture based on the IoT and Blockchain to facilitate the follow-up of diabetes and to help patients to self-manage it properly. Our architecture combines the IoT with the Blockchain technology in order to collect patients' data, share it with their healthcare teams in a near real-time and in a secure manner while preserving patient's privacy.
Attentional Pattern Classification for Automatic Dementia Detection

This paper proposes a novel technique for the automatic detection of dementia based on the attentional matrices test (AMT) for selective attention assessment. The original test provides three matrices, of increasing difficulty, and the test taker is asked to mark target digits assigned. In our proposal, AMT was developed on a digitizing tablet, equipped with an electronic pen. Tablet technology enables the acquisition of additional measures to those that can be obtained by observing the execution of the traditional paper-based test. These measures reflect the dynamics of the handwriting process, particularly the pauses and hesitations while the pen is not in contact with the pad surface. Handwriting measures can then serve as an input to machine learning algorithms to automatize disease detection. In contrast to the traditional approach, dynamic handwriting analysis can provide a means to better evaluate the visual search of the patient, as well as motor planning. To evaluate the effectiveness of the proposal, a classification study was carried out involving 29 healthy control subjects and 36 demented patients. We employed different machine learning algorithms and an ensemble scheme. We observed the first matrix to be the most discriminating, while the ensemble of the best classification models over the three matrices provided the best classification performance [i.e., an area under the ROC curve (AUC) of 87.30% and a sensitivity of 86.11%]. Our proposal has the potential to provide a cost-effective and easy-to-use diagnostic tool, which may also support mass screening of the population.
Diabetic Retinopathy Detection Based on Deep Convolutional Neural Networks for Localization of Discriminative Regions

Diabetic Retinopathy (DR) is the leading cause of avoidable vision impairment. Currently, manual DR detection is a time consuming task, which relies on well-trained clinicians with skills. In this paper, we propose a novel and automatic diabetic retinopathy (DR) detection method using deep convolutional neural networks (DCNNs). To identify the region of interests (ROIs), we design an attention mechanism for scoring the specific regions, refered as regions scoring map (RSM). The RSM is based on deep convolutional neural networks, which are trained only with image-level labels on a large scale DR dataset. Specifically, the RSM is mainly inserted into deep residual networks between intermediate stages. With RSM, the proposed model can score the different regions of an retina image to highlight the discriminative ROIs in terms of image severity level. In experiments, around 30000 colour retinal images are used to train the proposed model and around 5000 images are collected to evaluate its classification performance. The results show that our DCNN model can obtain comparable performance while achieving the merits of providing the RSM to locate the discriminative regions of the input image.
Light Field Image Compression with Random Access

In light field compression, besides coding efficiency, providing random access to individual views is also a very significant factor. Highly efficient compression methods usually lack random access. Similarly, random access methods usually reduce the compression efficiency. To address this trade-off, a light field image encoding method is proposed in this paper which favors random access. In the proposed scheme 15x15 view images are divided into 25 independent 3x3 view images which are called Macro View Image (MVI). To encode MVIs, the central view image is used to compress its immediate neighboring view images using a hierarchical reference structure. To encode the central view of each MVI, the most central view image, along with the center of at most three MVIs, are used as the reference images for the disparity estimation. In addition, the proposed method enables the use of parallel computation to improve encoding/decoding time complexity. To reduce memory footprint in case a Region of Interest (ROI) is required, HEVC tile partitioning is used.
Parasitic-Aware Automatic Analog CMOS Circuit Design Environment

In this work, the parasitic-aware design automation of the two-stage op-amp and bulk-driven OTA in 0.13um are presented using two well-known swarm-optimization algorithms namely, ABC and PSO algorithms. To achieve the parasitic-aware design, we utilized configurable layouts. We also consider process and temperature variations in the automatic layout-level design of the op-amp. The average design time for layout-level design for op-amp using ABC algorithm is only 108 minutes while that for bulk-driven OTA is only 9 minutes. The obtained results reveal that the concept of parasitic-aware design using configurable layouts is an effective tool for designing high-performance analog CMOS circuits.
Incremental Clustering Approach for Evolving Trajectory Data Stream

Trajectory data stream contain an enormous amount of data about spatial and temporal information of moving objects. Clustering the trajectory may bring benefits to several applications such as traffic monitoring system, behavior analysis of animal movement pattern, weather forecasting. In many real applications, trajectory data keep coming into the database or server for immediate analysis. Most existing approaches analyze the whole object trajectory from the static database rather than the current movement dynamic data. These methods cannot get a concise result because the objects always move then their position has changed over time. In this paper, we address the problem of monitoring the evolution of moving objects over time and propose incremental Sub-Trajectory Clustering based on Micro-group (iSTCM) framework to reduce computational time complexity. As an experiment, the performance of our proposed algorithm will conduct on real taxicab datasets and compare the efficiency and cluster quality as effectiveness with another state of the art methods.
Combination of Gabor Filter and Convolutional Neural Network for Suspicious Mass Classification

In this paper, we present an innovative method for classifying suspicious regions in mammography images into massive or normal ones. We consider Gabor filter as a processing step to make the feature of masses in mammography images clearly. Then we use convolutional neural network to train and test the detected masses from real data sources in Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). The result is quite sanguine when compared with other approaches. We achieve the sensitivity of 96% in 2700 regions detected from MIAS and 1000 regions extracted from DDSM.
FE-aided analytical method to predict the capabilities of line-start synchronous motors

This paper deals with the analysis and design of a line-start synchronous machine. Such an investigation is carried out by means of an analytical model based on equivalent circuit parameters estimated by finite element (FE) analysis. Different approaches are considered for such a model, comparing their features. The main purpose is to obtain a rapid and precise procedure that allows to properly design the machine. The synchronization capability and the steady state performance of line-start synchronous machines are investigated. Considerations on the characteristics of these machines are given, together with comments about their experimental realization. The results of the proposed procedure are validated by time-stepping FE analysis and experimental measurements on a prototype of line start reluctance machine.
Consistency Assessment between Diploma Policy and Curriculum Policy using Character-Level CNN

It is necessary to handle an enormous amount of electronic text data in public institutions in this Big Data Era. Therefore, a support system using information technology is desired. In this paper, we will consider the preliminary system using character-level convolutional neural networks to judge the consistency between two official documents; diploma policy and curriculum policy for education program of university.
Software Energy Measurement at Different Levels of Granularity

Power usage is mainly attributed to hardware. However, hardware resources are controlled by software instructions, which determine how it should behave. This paper presents an overview of the different methods for measuring the power and energy consumption of software programs. We propose a taxonomy in which we classify software measurement methods into different categories from different perspectives. We take into consideration software granularity levels as well as hardware facets. Software granularity concerns the structural facets of software. Hardware granularity concerns the levels of hardware resources. Energy measurements of lower software/hardware levels can be more challenging. We study and evaluate software energy measurement methods for battery-powered devices (e.g., laptops, smartphones, and embedded systems). Our results suggest that some software measurement tools can be capable of generating power readings of lower levels of hardware while some other tools can support estimating power for lower levels of software.
Machine Foveation: An Application-Aware Compressive Sensing Framework

Embedded vision applications generally face tight resource constraints. Biological vision systems are optimized to operate under similar conditions; they use highly heterogeneous sensing patterns to capture only the most valuable information within scenes. Our exploration of similar approaches in embedded systems has led to the design of Machine Foveation–a general-purpose, application-aware compressive sensing related framework that uses a cascaded network architecture integrating an autoencoder and application network to determine the importance of each pixel. The cascaded structure results in inherent regulation of the autoencoder network, forcing it to learn a representation that retains a given feature only if it is crucial to the overall application. The framework further uses scene awareness for reducing the number of bits necessary to represent the image data. This reduces sensed data at minimal or no decrease in task accuracy and reduces signal communication latency and corresponding energy consumption in embedded systems. For example, when evaluated on the Fashion-MNIST data set, channel bandwidth requirements are reduced by 77.37% and signal communication latency is reduced by 64.6%, with an accuracy loss of only 0.32%.
Dynamic Model of a Proton-Exchange Membrane Fuel Cell using Equivalent Electrical Circuit

This work aims to establish a new simplified dynamic model for a proton-exchange membrane fuel cell (PEMFC) which is an electrochemical device that converts the fuel (hydrogen) into electricity. This model captures the voltage-current (V-I) runtime dynamics using an equivalent electrical circuit. These electrical characteristics are critical when integrating the fuel cell with power conditioning unit (PCU). All the equivalent electrical circuit parameters are assumed to be a function of the normalized hydrogen pressure from the external tank to effectively capture the non-linear behavior. The results show reasonably accurate model prediction with a minimal error in the order of millivolts.
Gait Recognition using Double-Window and CNN Classification on Freestyle Walks

In this paper, we introduce a new technique for human identification using the gait of freestyle walks. Gait represents locomotions in animals and human beings. For a human, it is called a bipedal walk or a bipedal run. Each person has his or her own unique character of gait movements. We propose a gait recognition technique based on the concept of double-windows. A double-window is a series of consecutive usable frames in one walking sequence. One double-window is divided into two equal groups of frames. Gait features are extracted from double-windows using sliding window techniques. The data size of gait features of a walking sequence based on the proposed technique is smaller than those extracted using existing methods. The proposed double-window feature extraction technique also solves a viewpoint issue. Convolutional Neural Networks and k-Nearest Neighbor are used in the classification process. The accuracy of our proposed double-window technique is 99.33% which outperforms other techniques.
Forward and Reciprocal Noisy Coded Networks: Precoding, Topology, and Error Analysis

In this paper, a new class of problems are highlighted capitalizing on recent proofs of fundamental relations between information theory and estimation theory in noisy network-coded flows [1]. In particular, when the network is represented by a directed graph $\boldsymbol{\mathcal{G}} = (\boldsymbol{V} ,\boldsymbol{\mathcal{E}})$ and under the assumption of uncorrelated noise over information flows between the directed links connecting transmitters, switches (relays), and receivers. We show that there exist closed-form relations for the gradient of the mutual information with respect to different components of - forward and reciprocal- networks’ system matrix M and the MMSE, what is called the network I-MMSE. On the one hand, this result allows for studying effects of the error propagation and backward traceability with respect to effects of the network topology, topological changes when nodes are mobile or ‘ad-hoc’ along the transmission paths into the network capacity. On the other hand, we shed light into compensation methods where a change in the precoding process can be used as an alternative change to the network topology or vice versa when optimizing coded flows or when optimizing the topology or the decoding complexity. We provide optimal precoding designs that are adapted to the network level, where a network can be harmonized and information flows optimized and adapted to the awareness of the network topology. This paper is the first to present the failure of RLNC to be optimal with topology awareness. In particular, we highlight that DLNC is optimal for a network topology with deterministic matrix.
Predicting Elections: Social Media Data and Techniques

During the last decade, the use social media has provided a virtual community where the users express their intention, opinion and communicate with others. Organizations and researchers are very much interested to investigate the polarity of the user's opinions for making predictions and planning of future plans. The use and influence of social media in politics have been recognized by many researchers and political parties in making electoral predictions and devising future campaign strategies. As the literature on making electoral predictions is increasing day by day, this study aims to give an overview of the current state of approaches being utilized, social media used for data collection and the outcomes of the proposed approaches, either succeeded in making accurate electoral predictions using social media data and approaches used or not. This study gives a quick overview and guides researchers in understanding the approaches previously adopted and data sources utilized in predicting electoral results.
Toward the Evaluation of Handwashing Skills Based on Image Processing

Various infectious diseases have been known today. In hospitals, according to international guidelines, the hand hygiene is educated and practiced through handwashing for the purpose of preventing infectious diseases. Actually, several studies have been conducted to evaluate the skills of handwashing. However, in the previous works, the quantitative and objective evaluations have not been performed. In this paper, we propose a new method to evaluate the skill of handwashing. The method is based on the image processing, and the evaluation is realized in a quantitative and qualitative manner. Eighty-four students in department of nursing participated in our experiment, and our proposed method was validated. As the result, the area of pseudo-dirty spots on hand after washing could be extracted quantitatively and objectively. Our method will be applicable to education of nursing toward the prevention of infection spread.
Impact of Weibull Distribution on Prediction Based Sensing in Energy Harvesting Cooperative CRN

The spectral utilization and energy efficiency are two major factors that are predominantly driving the future of wireless communication. Cognitive Radio Network (CRN) along with energy harvesting is an encouraging solution to overcome spectrum under-utilization and energy paucity. In this paper, performance of prediction based sensing in a cooperative CRN is investigated under OR fusion rule in a binary hypothesis model. The role of distribution function on performance of such model is enormous. In this paper, we introduced Weibull distribution function in characterizing the primary user activity and analyze the performance of various parameters. Spectrum efficiency and quality of service of PUs are improved by using prediction based sensing CRN. Cognitive radio (CR) node have ability to harvest energy either from radio frequency (RF) sources or from non-RF sources. CR node starts transmission and harvests energy based on modify decision of fusion center. Impact of number of cooperative CR nodes, number of frames, prediction error, sensing time, collision constraint and splitting parameter on throughput and energy harvesting are investigated. Analytical expression for detection probability, false alarm probability, betterment in spectrum reuse, normalized throughput, energy harvesting together with energy penalty are derived, and the results are presented in the paper.