Reviewerly/exHarmony huggingface dataset:
Assessing Risk Estimations for Cyber-Security Using Expert Judgment

In this paper, we show a use-case of structured expert judgment to assess the risk of a cyber-security attack. We showcase the process of elicitating unknown and uncertain values using multiple experts and combining these judgments by weighing the experts based on their performance. The performance of an expert is assessed using the information and calibration score calculated from the judgments of calibration questions. The judgments are stated with three-points estimates of minimum, most likely, and maximum value, which serve as input for the PERT probability distribution. For the use-case, the input values frequency, vulnerability, and impact were asked. The combined results are propagated along an attack path to calculate the risk of a cyber-security attack. This was done using RISKEE, a tool for assessing risk in cyber-security and implementing the combination of expert judgments and propagation of the values in an attack-tree. It uses an attack graph to model the attack paths and applies probability distributions for the input values to consider the uncertainty of predictions and expert judgments. We also describe experiences and lessons-learned for conducting an expert elicitation to acquire input values for estimating risks in cyber-security.
Budget-constrained dynamic Bag-of-Tasks scheduling algorithm for heterogeneous multi-cloud environment

Cloud computing has reached huge popularity for delivering on-demand services on a pay-per-use basis over the internet. However, since the number of cloud users evolves, multi-cloud environment has been introduced where clouds are interconnected in order to satisfy customers' requirements. Task scheduling in such environments is very challenging mainly due to the heterogeneity of resources. In this paper, a budget-constrained dynamic Bag-of-Tasks scheduling algorithm for heterogeneous multi-cloud environment is proposed. By performing experiments on synthetic data sets that we propose, we demonstrate the effectiveness of the algorithm in terms of makespan.
Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation

Magnetic resonance imaging (MRI) stays one of the most essential noninvasive methods for brain diagnostics. It allows obtaining the detailed 3D image of the brain, including various types of soft tissues. In this paper, we compare the influence of the multichannel data composition approach on the model’s performance. We consider the binary brain tumor segmentation problem evaluating the Dice, Recall and Precision metrics. One common way to process the medical images with the use of neural networks is to use 2D slices as the input. In contrast to the RGB images, there are plenty of methods of how to combine the multi-channel MRI data structure into the common format for ML-based algorithms. After evaluating several possible combinations we demonstrate the most performance improvement by 6–7% in Dice & Recall metrics using the pseudo-RGB approach.
Face Occlusion Detection Based on SSD Algorithm

In recent years, as an important technology in deep learning, target detection has been widely used in all aspects of life. Aiming at the problem of occlusions in face recognition, this paper adopts SSD (Single Shot MultiBox Detector) deep learning target detection algorithm to classify and locate face occlusions. The average precision of all categories (mAP) reached 95.46% through the self-built data set of 7 types of common face occlusion. Experiments show that this method can effectively detect the face occlusion, which provides a new idea for automatic intelligent face recognition and has a broad application prospect.
Keyword Template Based Semi-supervised Topic Modelling in Tweets

The performance of supervised and semi-supervised approaches for topic modelling is highly depended on the priorGopal, Greeshma N. information used for its tagging. Tweets are short-length texts and hence demand supplementaryKovoor, Binsu C. information to infer their topic. The correlation of a word with a topic changes with time in social media. Therefore it isMini, U. not appropriate to fix a tag for a keyword for indefinite time. Here we have proposed a framework for the adaptive selection of the keywords for tagging with the help of external knowledge. The keyword template will be updated appropriately with the time slice in consideration. The evaluation matrices have shown that this model is giving consistent and accurate latent topic identification in short text.
Semantic Restful Service Composition Using Task Specification

Existing Web API search engines allow only category-based browsing and keyword- or tag-based searches for RESTful services. In other words, they do not enable the discovery or composition of real-world RESTful services by application developers. This paper outlines a novel scheme, called Transformation–Annotation–Discovery (TAD), which transforms OpenAPI (Swagger) documents related to RESTful services into a graph structure and then automatically annotates the semantic concepts on graph nodes using Latent Dirichlet Allocation (LDA) and WordNet. TAD can then be used for service composition based on the user requirements specified in two modules: a service discovery chain and logical-operation-based composition. The service discovery chain uses the Hungarian algorithm to assess service interface compatibility in order to facilitate the retrieval of services capable of bridging the gap between specified user requirements and the discovered services. The logical-operation-based composition module identifies services that semantically fit the user requirements, based on the structure of the service flow. Those candidate services are then sent to service discovery chains to enable the simultaneous search for potential composition solutions. System prototype and experiment results demonstrate the feasibility and efficacy of the proposed scheme.
Deep Learning for Sensor-Based Activity Recognition: Recent Trends

The field of human activity recognition (HAR) using different sensor modalities poses numerous challenges to the researchers working in this domain. Though traditional pattern recognition approaches performed well in this regard earlier, the cost of poor generalization and the cost of shallow learning due to the handcrafted features have opened a new door for deep learning in this field. This chapter discusses the importance of deep learning in sensor-based activity recognition explaining the deep models and their use in previous research works. This chapter also represents the importance of transfer learning and active learning in this field, that are new research topics. Finally, this chapter shows the challenges of using deep models along with feasible solutions.
High-throughput architecture for post-quantum DME cryptosystem

Quantum computers have the potential to solve difficult mathematical problems efficiently, therefore meaning an important threat to Public-Key Cryptography (PKC) if large-scale quantum computers are ever built. The goal of Post-Quantum Cryptography (PQC) is to develop cryptosystems that are secure against both classical and quantum computers. DME is a new proposal of quantum-resistant PKC algorithm that was presented for NIST PQC Standardization competition in order to set the next-generation of cryptography standards. DME is a multivariate public key, signature and Key Encapsulation Mechanism (KEM) system based on a new construction of the central maps, that allows the polynomials of the public key to be of an arbitrary degree. In this paper, a high-throughput pipelined architecture of DME is presented and hardware implementations over Xilinx FPGAs have been performed. Experimental results show that the architecture here presented exhibits the lowest execution time and highest throughput when it is compared with other PQC multivariate implementations given in the literature.
Visual identification of persons: Facial image comparison and morphological comparative analysis

The visual identification of persons is an indispensable evaluation element for answering forensic questions. For a forensic analysis the terminology and methods of visual identification need to be defined accurately. The methodological basis for the identification of a person can be found in the scientific field of anthropology. In-depth knowledge of anthropological research methods and extensive experience in the implementation of these methods and knowledge about human morphology and variability are decisive factors that make identity evaluation less error prone and scientifically sound.
A Minimum Defense Cost Calculation Method for Attack Defense Trees

The cyberphysical system (CPS) is becoming the infrastructure of society. Unfortunately, the CPS is vulnerable to cyberattacks, which may cause environmental pollution, property losses, and even casualties. Furthermore, in contrast to the conventional Internet, the devices in CPSs are more specific, and the device systems may not be upgraded or installed with new programs during their life spans. The selection of the best defense nodes for defeating cyberattacks is quite challenging in CPSs. To overcome this issue, several attack-defense modeled methods have been proposed. However, few existing studies have considered the defense cost, which is usually a determinant in practice. In this paper, we propose a method for choosing optimal defense nodes that (1) can defeat specific attacks and (2) are inexpensive. First, the atom attack defense tree (A2DTree) is proposed by adding constraints to the conventional attack defense tree (ADTree). Second, the algebraic method is used to efficiently calculate the minimum defense cost. On this basis, a minimum defense cost calculation tool is designed and implemented. Finally, the effectiveness of the proposed method is verified with two typical case studies, and a comparative experiment of related work is carried out. The results show that the method can correctly and efficiently identify the optimal defense nodes and calculate the minimum defense cost of a CPS.
Enabling Robust Horizon Picking From Small Training Sets

Seismic interpretation is a complex procedure that depends on many and interdependent data analyses. One of the essential steps in this process is picking horizons in seismic images, which is time-consuming and prone to errors when performed manually. In this context, having a reliable horizon picking tool is fundamental for accurate seismic interpretation. Although several methods for horizon picking have been proposed in the literature and many tools made available in the industry, most require numerous iterations and manual corrections for delivering satisfactory results. In this article, we present a three-step approach that improves the robustness of horizon picking using state-of-art semantic segmentation neural networks followed by geometry-based processing of horizon points to identify and remove outliers. For the well-known Netherlands F3 Block data set, this approach allows accurate results from a few training annotated inlines and delivers geometry-consistent horizons through all the cubes. We further present results for an additional set comprising four cubes with different seismic structures to provide some robustness evidence of our approach. The results reported in this study indicate that the proposed methodology can provide a good trade-off between accuracy and time spent on manually picking horizons.
Shield Construction Safety Risk Assessment of Metro Tunnels Based on Cloud Model

To reduce the safety accidents during the shield construction of metro tunnel and improve the safety level of metro construction, a shield construction risk assessment model of metro tunnel was established based on cloud model. Firstly, the safety evaluation index system of metro tunnel shield construction was established from person, machine, environment and management dimensions. Then, the cloud model, which could effectively solve the random uncertainty and fuzzy uncertainty of index factors, was used to construct the evaluation method. Finally, the evaluation model was applied to the Chengdu Metro Line 11. The results show that the safety risk of shield construction in Chengdu Metro Line 11 is between the secondary risk and the third level risk. It is necessary to pay attention to the daily management, and pay more attention to the environmental risks.
Balanced Map Coverage using Reinforcement Learning in Repeated Obstacle Environments

This paper demonstrates novel Complete Coverage Path Planning using reinforcement learning to enable a robot to complete map coverage at high speeds in complicated environments, like factories and airline cabins, with many repeated obstacles, such as furniture and walls. Our framework trains the robot in a simulated environment to move to uncovered areas and to avoid frequent collisions using rewards. Additionally, it encourages the robot to complete map coverage missions efficiently and quickly. We select the Machine-Learning Agent provided by Unity3D to build a fragment (sample cell) of an airline cabin environment in which to train the robot. We implement Proximal Policy Optimization as the main training network, and added curiosity functions (i.e., intrinsic rewards) to encourage the robot to explore uncovered areas during training. We use Generative Adversarial Imitation Learning to guide the training policy's convergence close to the expert data. Experimental results show that our optimal policy enables complete map coverage in complicated environments. We provide demonstrations comparing random motion methods to reinforcement learning networks to show differences in map coverage, trajectory length, and time-cost.
Exploring the Relation Between Co-changes and Architectural Smells

Abstract The interplay between Maintainability and Reliability can be particularly complex and different kinds of trade-offs may arise when developers try to optimise for either one of these two qualities. To further understand how Maintainability and Reliability influence each other, we perform an empirical study using architectural smells and source code file co-changes as proxies for these two qualities, respectively. The study is designed using an exploratory multiple-case case study following well-know guidelines and using fourteen open source Java projects. Three different research questions are identified and investigated through statistical analysis. Co-changes are detected by using both a state-of-the-art algorithm and a novel approach. The three architectural smells selected are among the most important from the literature and are detected using open source tools. The results show that 50% of co-changes eventually end up taking part in an architectural smell. Moreover, statistical tests indicate that in 50% of the projects, files and packages taking part in smells are more likely to co-change than non-smelly files. Finally, co-changes were also found to appear before smells 90% of the times a smell and a co-change appear in the same file pair. Our findings show that Reliability is indirectly affected by low levels of Maintainability even at the architectural level. This is because low-quality components require more frequent changes by the developers, increasing chances to eventually introduce faults.
A rule-based assistive control algorithm for safe navigation for a powered wheelchair

This paper addresses the problem of safe navigation in an environment with randomly placed static obstacles. We convert a commercial powered wheelchair into a semi-autonomous vehicle with limited sight (environment awareness), by instrumenting it using off-the-shelf ultrasonic sensors and associated electronic boards (Arduino). In the continuity of our previous work where we had used stochastic dynamic programming to formulate optimization problems which led to relatively large size look-up tables that can be used as supervisory control, here we propose to extract rules using those results (that data). The advantage of this approach is a low-computational cost for future online implementation, and the drawback is a suboptimal policy. The feasibility is assessed by running simulations in a fairly realistic environment (Unity3D).
Fraudulent e-Commerce Website Detection Model Using HTML, Text and Image Features

Many of Internet users have been the victims of fraudulent e-commerce websites and the number grows. This paper presents an investigation on three types of features namely HTML tags, textual content and image of the website that could possibly contain some patterns that indicate it is fraudulent. Four machine learning algorithms were used to measure the accuracy of the fraudulent e-commerce websites detection. These techniques are Linear Regression, Decision Tree, Random Forest and XGBoost. 497 e-commerce websites were used as training and testing dataset. Testing was done in two phases. In phase one, each features was tested to see its discriminative capability. Meanwhile in phase two, these features were combined. The result shows that textual content has consistently outperformed the other two features especially when XGBoost was used as a classifier. With combined features, overall accuracy has improved and best result of accuracy recorded was 98.7% achieved when Linear Regression was used as a classifier.
Development of mobile learning on acid and base to improve student performance

Abstract The aim of this research is to (1) make a Mobile Media Learning on Acid and Base and to determine the quality of the media produced, (2) determine the feasibility of the media used in learning process. The subjects of this research were students of 54 Senior High School Jakarta, 42 Senior High School Jakarta, and 7 chemistry teachers. The research was conducted from January to May 2018. The method used in this research is Design-Based Research. The steps performed in this research are: (1) needs analysis, (2) developing mobile learning, (3) validation test of mobile learning media, (4) media testing by students and small scale of teachers, (5) media trials by students and teachers on a large scale. The results obtained from expert validation tests, small class trials, large class trials, and effectiveness of the media, it can be concluded that the mobile media learning developed in this research is good enough to use in the learning process. The n-gain value obtained from the effectiveness test is 0.76 which can be categorized as high effectiveness, based on the Paired t-test the Tcount value is 28.048. The paired t-test value is Tcount&gt;Ttable (2.03), so it can be concluded that there is a significant difference between pre-test score and post-test score. That difference means that there is improvement in students average score after using the mobile media learning.
Estimating Solar Power Plant Data Using Time Series Analysis Methods

When meteorological data such as temperature, precipitation, weather events and economic data such as stock prices and exchange rates reach large levels, it may be necessary to analyze them with time series analysis methods. The aim of this research is to analyze the data of solar power plants with time series and make predictions for the future. To achieve this goal, solar panel data with historical depth will be collected, the collected data will be trained and predicted by various time series analysis methods and comparison will be made according to the prediction success among the related models. Methodology: With this study, using Python 3.6 and R 3.6.1, the time series estimation models were modeled with AR, ARMA, SARIMA, DES and TES, the difference between the real value and the predicted value of the data was found by the RMSE (Square Root of the Mean Square Error) method and it was seen which model has the best ability to estimate the dataset. In addition, with the trend and seasonality of the data, detailed information about the dataset was obtained with descriptive analysis and graphics. As a result, it was seen that using SARIMA or TES models in the datasets that show seasonal change in the light of the studies and estimations performed gives better results.
Human Languages in Source Code

Computer science education has promised open access around the world, but access is largely determined by what human language you speak. As younger students learn computer science it is less appropriate to assume that they should learn English beforehand. To that end, we present CodeInternational, the first tool to translate code between human languages. To develop a theory of non-English code, and inform our translation decisions, we conduct a study of public code repositories on GitHub. The study is to the best of our knowledge the first on human-language in code and covers 2.9 million Java repositories. To demonstrate CodeInternational's educational utility, we build an interactive version of the popular English-language Karel reader and translate it into 100 spoken languages. Our translations have already been used in classrooms around the world, and represent a first step in an important open CS-education problem.
Detection of Animal Behind Cages Using Convolutional Neural Network

There are many attempts on detecting animal using Convolutional Neural Network. However, many of them failed to detect animals behind cage bars as the mesh patterns of such bars usually affected a detectability of a detection model. A main hypothesis is that most of existing models trained for detecting animals does not have enough pictures of animals behind cage bars as in a training set. In this paper, panda and deer are used as case examples. The training data is gathered specifically for this research work. The M2Det is used as the main network together with the transfer learning approach and its pretrained weights. In our experiments, it is found that a number of training images of animals behind cage bars greatly affects the detection performance. Also, adding more training images of animals without cages could also improve the performance of the detection model on the same task.
Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

In this paper, an improved interactive multiple model adaptive strong tracking random weighted cubature Kalman filter (IIMM-ASTRWCKF) algorithm is developed to overcome the low tracking accuracy and easy divergence when dealing with complex maneuvering situations.The algorithm is improved in two aspects: On the one hand, ASTRWCKF is used as the sub filter of IMM algorithm to filter different motion models.By introducing the random weight factor to replace the original weight factor, the accuracy of the algorithm is improved.At the same time, the adaptive strong tracking filter is added to update the prediction covariance matrix and noise covariance matrix for the stability of the algorithm.On the other hand, this algorithm proposes a new method to improve the probability conversion accuracy of IMM by adding time-varying factor to adjust Markov probability transfer matrix.Compared with the performance of IMM-CKF and in dealing with maneuvering problems, IIMM-ASTRWCKF algorithm has better tracking accuracy in solving maneuvering problem.