Miracle-dz/UniFAR-FTUs huggingface dataset:
Blockchain as an Enabler for Cybersecurity Use Case: Electronic Health Records in Switzerland

In the application of Electronic Health Records (EHR), cybersecurity is an essential control and needs to be strongly considered to fulfil data protection requirements. Regarding cybersecurity needs in Healthcare, blockchain-based technologies seem promising due to the inherent security features. Therefore, this study investigates in cybersecurity requirements for EHR and whether a block-chain-based solution can cover these. There are already approaches which apply Blockchain for EHR, but these do not explicitly consider cybersecurity, which forms the research gap. As a unit of analysis, 'Hyperledger Sawtooth' as an enterprise blockchain platform was used. The results showed that Hyperledger Saw-tooth performs quite well regarding the coverage of cybersecurity-relevant requirements for EHR. However, there are 'natural' divergences concerning specific cybersecurity attributes between blockchain-based and non-blockchain-based systems. The outcome of this study is a generic assessment tool which can be used to assess the coverage of cybersecurity requirements for both blockchain-based and non-blockchain-based EHR systems.
Surface structure refinement including anomalous crystal truncation rods

A surface structure refinement procedure is introduced, which uses both anomalous and non-anomalous crystal truncation rod data simultaneously. It is shown how a single structural model can be refined against data sets measured at different wavelengths and how this can greatly reduce correlations between fit parameters. The structure factors are computed taking into account the anomalous dispersion corrections and are scaled to the data, whereby each data set is assigned its own scale factor. The procedure is implemented in the widely used surface diffraction program ROD. The structure of a one unit cell thin LaAlO3 film on an SrTiO3(001) substrate is refined by making use of a non-anomalous data set and one taken at the La L1-edge.
Water Transport, Retention, and Survival of Escherichia coli in Unsaturated Porous Media: A Comprehensive Review of Processes, Models, and Factors

The vadose zone can function as both a filter and a passage for bacteria. This review evaluates when and why either effect will apply based on available literature. It summarizes theories and experimental research that address the related, underlying bacterial attenuation processes, the applied macro-scale modeling approaches, and the influencing factors - including the cell, soil, solution and system characteristics. Results point to that the relative importance of each removal mechanism depends on the moisture content and the solution ionic strength. The limitations of available modeling approaches are discussed. It remains unclear in which contexts these are reliable for predictions. The temporal first-order kinetic Escherichia coli (E. coli) removal coefficient ranges three orders of magnitude, from 10−4 to 10−1/min. Results suggest that this rate depends on the pore-water velocity. Spatial filtration of E. coli increases with slower flow and higher collector surface heterogeneity. It could be insignificant in the case of heavy and sudden infiltration and subsequent transport in preferential flow paths, induced, for example, by plant roots or cracks in clayey soils. Future research thus needs to address transport as an effect of extreme weather events such as droughts and subsequent floods.
Hydrogen bond analysis of Type 1 antifreeze protein in water and the ice/water interface

Antifreeze proteins (AFPs) are a group of structurally very diverse proteins with the unique capability of inhibiting ice crystal growth. Although significant progress has been made in the identification of different families of these proteins, the molecular mechanism of their action is unclear. The previously postulated mechanism of hydrogen bonding between the threonine residues of AFP and the water molecules in the ice surface has been disproved by mutation studies with non-polar residues. Currently, the mechanism of antifreeze activity cannot be fully understood from experimental or computational studies. Computational modeling studies have examined protein–ice interactions, mostly in vacuo. These studies have neglected the effects of the water phase. It has been shown that the vacuum is a very poor approximation for the water properties. Thus, to gain an insight into the molecular mechanism of these proteins we have computationally modeled a more realistic system comprising of AFP Type I from winter flounder (HPLC6), water and ice without any constraints. The results from this study show that the protein forms hydrogen bonds with the water molecules in the ice/water interfacial region. However, a comparison of the results with the protein in water simulations shows that there is no significant gain of hydrogen bonds for protein in the interfacial region compared to in the solvent. These results support the hypothesis that hydrogen bonding is not the primary reason for interaction of HPLC6 with the ice/water interfacial region.
Ayurvedic plant identification using Transfer Learning

– Our healthy ancestors mostly depended on plants around them for their medical reasons. There are hundreds of species of plants around us in our environment which will serve us as home remedies for all most all the diseases and keeps us healthy when taken as daily consumption. Especially in Sri Lanka we have our own set of rare Ayurvedic herbs. But most of us unable to identify these plants due to lack of knowledge. There are existing applications which can identify plants with low prediction accuracies. Also, those applications are based on foreign plant data sets that do not include the valuable herbs and shrubs with medicinal qualities. Hence this research work proposes a mobile application which can identify Ayurvedic plants using Convolution Neural Network models (CNN) with Transfer learning. Three CNN architectures have been applied in this study experimentally to obtain the best accurate model. Ayurvedic plant dataset is trained on CNN from scratch and obtained about 70% of accuracy. Anyhow training a model from scratch is too costly. To overcome this challenge transfer learning has been applied in the Ayurvedic dataset. The pre-trained deep learning models used in this study are Google-Net and Mobile-Net and the accuracies obtained are 80% and 93% respectively. Finally, the Mobile-Net, a small efficient convolution neural network which produces the highest accuracy is used to produce the final prediction model. This model is then used to build a mobile application in the Android platform with TensorFlow-Lite which can identify the Ayurvedic plants using the built-in camera module.
Early pleural effusion detection from respiratory diseases including COVID-19 via deep learning

Pleural effusion is the build-up of excess fluid between the pleura layers around the lung. This fluid may be transudative or exudative. Pneumonia and cancer are common exudative causes of pleural effusion. Other causes include tuberculosis and recently discovered COVID-19. Physicians are able to diagnose pleural effusion through the use of chest radiographs. In this work, we propose, instead, the early detection of pleural effusion from tuberculosis, pneumonia, and COVID-19 diseases on chest radiographs using deep learning. The performance results show that the early detection of pleural effusion from pneumonia and tuberculosis have the highest accuracy. They further show that the deep learning architecture can distinguish bacterial pneumonia and COVID-19 diseases from pleural effusion the best.
Application of Containerized Microservice Approach to Airline Sentiment Analysis

Containers are getting more popularity than the virtual machines by offering the benefits of virtualization along with the performance nearby bare metal. Standardizing support of Docker containers among various cloud providers has made them a trendy solution for developers. In this paper, we elaborate on containerized microservice, leveraging the lightweight Docker container technology. The evolution of microservice architecture allows applications to be structured into independent modular components making them easier to manage and scale. As a special case, the containerized sentiment analysis microservice is deployed using popular classification approaches. We implement and compare eight machine learning algorithms: Multinomial Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbour, AdaBoost, Support Vector Machine, Multilayer Perceptron, and Stochastic Gradient Descent to analyze and classify the tweets into positive, negative, and neutral sentiments. Experimental results procured for the Twitter US Airline Sentiment dataset show that Support Vector Machine, Multinomial Naive Bayes, Stochastic Gradient Descent, and Random Forest outperform the other algorithms. We believe that this research study will assist companies and organizations to improve their services by precisely analyzing Twitter data.
Sensitivity Analysis for the Lossless Dielectric Material in Wireless Power Transmission

In this paper, we propose the analysis of dielectric sensitivity of transmitting and receiving coils in wireless power transmission system. The diameter of the dielectric disk under test is defined using the diameter of the coil, the transmission distance and the distribution of the electromagnetic field along the virtual vertical axis. The proposed method for acquiring the dielectric sensitivity is applied to two representative coil models and the validation of this analysis is proved by the simulated results.
Anomalous State Recognition of Lane-changing Behavior using a Hybrid Autoencoder Architecture

This paper presents a hybrid unsupervised architecture for anomalous lane-changing behavior recognition. Anomaly detection aims to identify unusual driving behavior caused by either environmental or phycological stimuli, and is of great important in road safety. First, a Recurrent Convolutional Autoencoder (RC-AE) is built to explore the spatial-temporal features derived from the high-dimensional behavior data. Second, Reconstruct Error analysis of the autoencoder and one-class support vector machine method are both applied to identify anomalous lane-changing behavior in the learned feature space by autoencoder. Last, we employ T-Distributed Stochastic Neighbor Embedding (T-SNE) for data visualization in the anomaly detection. Based on the kernel density estimation analysis, anomalous and normal lane-changing sample groups display distinct difference over probability distributions. The findings contribute to a better understanding on drivers’ natural lane-changing behavior, and can provide important insight into real-time personalized unusual lane-changing behavior monitoring system development.
A Pragmatic Cybersecurity Strategies for Combating IoT-Cyberattacks

Massive Cyberattacks, especially Distributed Denial of Service (DDoS), remain an overwhelming threat to new emerging computer paradigms such as Cloud Computing and Internet of Things, deployed within critical infrastructures (healthcare, aviation, energy, industrial control and military). Furthermore, conventional security approaches and strategies are no longer appropriate to deal with this kind of cyberattacks, for the reason that they have major limitations. Consequently, identification and efficient detection of anomalies in this context requires not only suitable mechanisms integrating correct detection, but also early detection with an autonomous operating mode. This paper focuses on DDoS cyberattack detection and mitigation method based on artificial intelligence model affecting IoT network infrastructure.
Vehicle speed trajectory estimation using road traffic and infrastructure information

This paper describes a novel method to estimate vehicle speed trajectory at a road-link scale. Road network infrastructure and topology have a significant impact on the vehicle’s driver behavior. We propose a dynamic speed estimation based on road macroscopic features, available anywhere through digital maps webservices. The method combines machine learning techniques and stochastic approaches to construct dynamic speed trajectories. This allows the estimation of several speed trajectories per road-link to take into account various possible driving behaviors. The model has been trained and validated on a database of 50 million kilometers of 1 Hz driving recordings coming from a crowdsensing project. Estimated speed trajectories are compared to these driving recordings and promising results are obtained in terms of reproducing real driving behavior. A map of traffic pollutant emission is presented as a first application.
OPSCAN: Density-based Spatial Clustering in Opportunistic Networks

In modern opportunistic networks, network operations can be improved through knowledge of spatial information of low and high density areas, predictions of the mobility of nodes in the space, as well as the spatial distribution of nodes. Such information can be used to adapt forwarding decisions. In this paper, we introduce an efficient opportunistic spatial clustering algorithm, OPSCAN (Opportunistic Spatial Clustering of Applications with Noise). Based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise), a density-based clustering algorithm that discovers arbitrary-shaped clusters in a dataset and distinguishes noise points. OPSCAN is capable of clustering network nodes into high density clusters, while maintaining sparse areas of nodes between clusters. Clusters share spatial information of the network such as area density, mobility statistics and information about other clusters and their nodes. Knowledge of edge nodes in the clusters is also made available for utilization in more efficient forwarding decisions. Simulations show that our algorithm is capable of producing dense, homogeneous clusters and accurately outlining cluster edges. We have used the Silhouette Coefficient to measure cluster homogeneity against density-based clustering algorithms DBSCAN and ST-DBSCAN (Spatial-Temporal DBSCAN), a DBSCAN-based spatial-temporal variant on "GeoLife" dataset. We have found OPSCAN outperforms DBSCAN by a coefficient of 0.81 to 0.73 for the same minimum distance, under-performing ST-DBSCAN by 0.87 to 0.81 for that distance. OPSCAN requires only two inputs as compared to four for ST-DBSCAN. As the distance parameter is increased, OPSCAN produces homogeneous clusters more closely to ST-DBSCAN.
Novel Arithmetics in Deep Neural Networks Signal Processing for Autonomous Driving: Challenges and Opportunities

This article focuses on the trends, opportunities, and challenges of novel arithmetic for deep neural network (DNN) signal processing, with particular reference to assisted- and autonomous driving applications. Due to strict constraints in terms of the latency, dependability, and security of autonomous driving, machine perception (i.e., detection and decision tasks) based on DNNs cannot be implemented by relying on remote cloud access. These tasks must be performed in real time in embedded systems on board the vehicle, particularly for the inference phase (considering the use of DNNs pretrained during an offline step). When developing a DNN computing platform, the choice of the computing arithmetic matters. Moreover, functional safe applications, such as autonomous driving, impose severe constraints on the effect that signal processing accuracy has on the final rate of wrong detection/decisions. Hence, after reviewing the different choices and tradeoffs concerning arithmetic, both in academia and industry, we highlight the issues in implementing DNN accelerators to achieve accurate and lowcomplexity processing of automotive sensor signals (the latter coming from diverse sources, such as cameras, radar, lidar, and ultrasonics). The focus is on both general-purpose operations massively used in DNNs, such as multiplying, accumulating, and comparing, and on specific functions, including, for example, sigmoid or hyperbolic tangents used for neuron activation.
Plug the Database & Play With Automatic Testing: Improving System Testing by Exploiting Persistent Data

A key challenge in automatic Web testing is the generation of syntactically and semantically valid input values that can exercise the many functionalities that impose constraints on the validity of the inputs. Existing test case generation techniques either rely on manually curated catalogs of values, or extract values from external data sources, such as the Web or publicly available knowledge bases. Unfortunately, relying on manual effort is generally too expensive for most practical applications, while domain-specific and application-specific data can be hardly found either on the Web or in general purpose knowledge bases.This paper proposes DBINPuTs, a novel approach that reuses the data from the database of the target Web application, to automatically identify domain-specific and application-specific inputs, and effectively fulfill the validity constraints present in the tested Web pages. DBINPUTS can properly cope with system testing and maintenance testing efforts, since databases are naturally and inexpensively available in those phases. To extract valid inputs from the application databases, DBINPUTS exploits the syntactic and semantic similarity between the identifiers of the input fields and the ones in the tables of the database, automatically resolving the mismatch between the user interface and the schema of the database. Our experiments provide initial evidence that DBINPUTS can outperform both random input selection and Link, a competing approach for searching inputs from knowledge bases.
Presenting the Multi-view Traffic Intersection Dataset (MTID): A Detailed Traffic-Surveillance Dataset

This work presents a novel and unique traffic surveillance dataset, the MTID. When capturing data for traffic surveillance, the positioning of the camera is crucial, and depending on the task different approaches provide different advantages. Multiple viewpoints, however, are rarely compared. Our dataset gives the ability to analyze the difference between two viewpoints in great detail. A complex traffic scene has been captured simultaneously from two different viewpoints: that of a camera mounted on existing infrastructure, and an that of a drone. The frames from each video capture have been synchronized in time and all road users have been carefully annotated down to pixel-level accuracy. The dataset consists of 3100 frames from each viewpoint, containing 18883 individual annotations on the pole viewpoint, and 50274 individual annotations on the drone viewpoint. The dataset is freely available online*. Apart from the dataset, which is our main contribution, we also provide benchmark detection results for four different groups of road users for other researchers to compare their results with. We show that the detection problem is challenging, as we achieve mAPs of only 22.62% and 27.75% using a pre-trained state-of-the-art detector on the two viewpoints.
Retrieve and Refine: Exemplar-based Neural Comment Generation

Code comment generation which aims to automatically generate natural language descriptions for source code, is a crucial task in the field of automatic software development. Traditional comment generation methods use manually-crafted templates or information retrieval (IR) techniques to generate summaries for source code. In recent years, neural network-based methods which leveraged acclaimed encoder-decoder deep learning framework to learn comment generation patterns from a large-scale parallel code corpus, have achieved impressive results. However, these emerging methods only take code-related information as input. Software reuse is common in the process of software development, meaning that comments of similar code snippets are helpful for comment generation. Inspired by the IR-based and template-based approaches, in this paper, we propose a neural comment generation approach where we use the existing comments of similar code snippets as exemplars to guide comment generation. Specifically, given a piece of code, we first use an IR technique to retrieve a similar code snippet and treat its comment as an exemplar. Then we design a novel seq2seq neural network that takes the given code, its AST, its similar code, and its exemplar as input, and leverages the information from the exemplar to assist in the target comment generation based on the semantic similarity between the source code and the similar code. We evaluate our approach on a large-scale Java corpus, which contains about 2M samples, and experimental results demonstrate that our model outperforms the state-of-the-art methods by a substantial margin.
Joint Placement Latency Optimization of the Control Plane

Software Defined Networking (SDN) is a programmable network architecture that decouples the control plane (controller logic) from the data plane (forwarding plane) and provides flexible network management. A logically centralized control plane supports SDN feasibility. A single controller may not be capable to control the whole network. To tackle this issue, deploying multiple controllers are a necessity to handle a large network. Controller placement problem (CPP) is one of the most important issues in SDN to improve the scalability and influences the latency and performance of the whole network. Most of the previous methods only focused on latency between controllers and their associated switches but ignoring the latency between controllers and the accumulated latency of the control plane.In this paper, we define an accumulated latency to solve CPP, which takes into consideration both the latency between the controller and their associated switches and inter-controller latency. We formulate an optimization problem as a mixed-integer linear programming (MILP) under the constraint of latency. The objective of our study is to minimize the accumulated latency and minimizing the number of network controllers while optimizing their placement for achieving an optimal balance at the same time. The performance of our method is evaluated on the Internet2 OS3E real network topology. Results demonstrate that the proposed method is promising.
Controller-Aware Path optimization for Enhancing Path Tracking Performance

In a typical hierarchical autonomous driving framework, the motion planner and controller are developed separately for convenience. In most cases, the planner does not have knowledge of the controller tracking behavior, which leads to prediction error of the planning system when computing a reference path. In this paper, we introduce a controlleraware path optimization method for improving path tracking performance. The contributions of this paper are twofold. First, we present a policy gradient method for controller behavior learning which can learn and predict a range of typical path tracking controllers’ performance precisely. Second, we propose a controller-aware path optimization method which optimizes the reference path respecting the learned controller behavior and vehicle dynamics constraints. Furthermore, we verify the effectiveness of the proposed method in reducing path tracking errors on two sets of typical sampling reference path and on a challenging path in the V-REP simulator, which indicates that the proposed method can significantly improve path tracking performance without changing the controller configuration.