Reviewerly/exHarmony huggingface dataset:
Attack Graph Generation and Visualization for Industrial Control Network

Attack graph is an effective way to analyze the vulnerabilities for industrial control networks. We develop a vulnerability correlation method and a practical visualization technology for industrial control network. First of all, we give a complete attack graph analysis for industrial control network, which focuses on network model and vulnerability context. Particularly, a practical attack graph algorithm is proposed, including preparing environments and vulnerability classification and correlation. Finally, we implement a three-dimensional interactive attack graph visualization tool. The experimental results show validation and verification of the proposed method.
Teachers’ Perceptions Towards the Use of Mobile Augmented Reality

This paper examines the perceptions of Greek Primary School teachers about the educational use of mobile augmented reality (MAR). The research of this study includes the design and development of an educational application which involves augmented reality and mobile learning. Additional information and digital features, following the school curriculum, were superimposed to the user’s real world through the utilization of mobile devices. Primary school teachers were familiarized with the application and formed their opinions regarding mobile augmented reality in the teaching process after using it. Then, they filled out a questionnaire based on the technology acceptance model (TAM). The analysis of the data that were collected showed significant correlations between the variables. In particular, it was found that teachers’ intention to use the mobile AR application that was designed is related both to perceived usefulness and to perceived ease of use. Teachers enjoyed the mobile augmented reality experience and considered the app as a useful and easy to use tool, which has a lot to offer to their teaching.
COUNTATA

Information regarding the counts of attributes combination is central to the profiling of a data set. It may reveal bias; it can help determine fitness for use. While counts of individual attribute values may be stored in some data set profiles, there are too many combinations of attributes for it to be practical to store counts for each combination. To this end, we present the notion of storing a "label" of limited size that can be used to obtain good estimates for these counts. A label contains information regarding the count of selected patterns-attributes values combinations-in the data. We define an estimation function, that uses this label to estimate the count of every pattern. Intuitively, there is a trade-off between the label size and its estimation error. We propose a demonstration of Countata, a system that allows the user to examine this trade-off as well as the label's count information. We will demonstrate the usefulness of Countata using real-life data, and illustrate the effectiveness of our estimation paradigm.
Towards Poisoning the Neural Collaborative Filtering-Based Recommender Systems

In this paper, we conduct a systematic study for the very first time on the poisoning attack to neural collaborative filtering-based recommender systems, exploring both availability and target attacks with their respective goals of distorting recommended results and promoting specific targets. The key challenge arises on how to perform effective poisoning attacks by an attacker with limited manipulations to reduce expense, while achieving the maximum attack objectives. With an extensive study for exploring the characteristics of neural collaborative filterings, we develop a rigorous model for specifying the constraints of attacks, and then define different objective functions to capture the essential goals for availability attack and target attack. Formulated into optimization problems which are in the complex forms of non-convex programming, these attack models are effectively solved by our delicately designed algorithms. Our proposed poisoning attack solutions are evaluated on datasets from different web platforms, e.g., Amazon, Twitter, and MovieLens. Experimental results have demonstrated that both of them are effective, soundly outperforming the baseline methods.
Problems and Prospects of Mixed Education at the University During the Digitalization Period

The author addresses the urgent problem of the synthesis of traditional and e-learning and considers one of the promising formats of study at a university - blended learning. The terminological analysis revealed analogues to this training: blend training, mix training, blended learning, hybrid learning, integrated or web-based advanced learning. Its essence is a reasonable combination of offline and online technologies, methods, interaction techniques and tools. The methodological basis of the study was a comprehensive, integrative-competency and professional approaches. Turning to numerous domestic and foreign studies, the author proves the inevitability and popularity of this form of education. One of the varieties of this approach is described - inverted learning. In blended learning in the digital age, along with traditional technologies, web services and platforms, social networks, instant messengers, distance learning systems, collaboration environments, smart contracts, etc. are actively used. The article outlines the advantages, problems and prospects of the blended learning concept. One of the acute problems is the online training of teachers. Minister of Science and Higher Education of the Russian Federation V. Falkov urges urgent training of all teachers by 100%.
ARM cortex M4-based extensible multimodal wearable platform for sensor research and context sensing from motion &amp; sound

We present an extensible sensor research platform suitable for motion- and sound-based activity and context recognition in wearable and ubiquitous computing applications. The 30x30mm platform is extensible through plug-in boards, which makes it well suited to explore novel sensor technologies. Its firmware can acquire 9-axis inertial measurement unit (IMU) data and device orientation in quaternions at up to 565Hz, sound at 16KHz and external analog inputs, without any programming, allowing for use by non-experts.
Evaluation of Face Recognition Schemes for Low-computation IoT System Design

The authenticated door-lock security system for smart-home is a critical Internet-of-Things (IoT) application. The most pressing bottleneck of running algorithms in IoT devices is the availability of low computation resources. The state-of-the-art algorithms which work significantly well in high-end computing unit more often fail to operate in IoT devices like Raspberry pi. This work targets design of a face authenticated door-lock security system in Raspberry pi and, therefore, evaluates the performance of conventional face detection and recognition models. The local binary pattern histogram (LBPH), eigenface and fisherface algorithms are considered for evaluation. The evaluation analyzes the accuracy of a model in detecting human faces, its computation time and vulnerability to photo hacking while implemented in Raspberry pi platform. It covers a large number of stored faces and the cases of variation in angle and illumination in the face image. The python and c <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sup> bindings to the opencv framework are considered to create a fully functional face recognition system hardware.
Enhancing Particle Filtering using Gaussian Processes

This contribution presents a novel resampling scheme that leverages Gaussian Processes (GPs) to more accurately approximate the posterior distribution from a set of random measures and, ultimately, enhance resampling by sampling from such approximation. Resampling is a critical step in particle filtering, impacting its estimation performance and parallelization capabilities. The approach can be seen as a kernel-based density approximation. As a byproduct, we are able to i) derive an explicit formula for minimum mean squared error (MMSE) state estimation, and ii) provide a well defined optimization problem for determining the maximum a posteriori (MAP) state estimation. The results on a target tracking problem show the performance improvements of the so-called Gaussian Process Particle Filter (GPPF) when compared to standard particle filtering.
Usage of Learning Management System Web Analytics in Blended Learning Self-Study Evaluation

Learning Management System (LMS) analytics data is proposed to be used in developing algorithms for evaluating students’ self-studies. Development of such algorithms is relevant considering annual growth of disciplines that apply blended learning. In blended learning model selfstudy can be done online in LMS which makes it possible to analyze patterns how students interact with learning materials and perform exercises of various complexity. Different criteria and indicators are aggregated into numeric metrics that following designed methodology evaluates self-study performance of each student. Designed methodology uses algorithms that evaluate self-study results by using empirical LMS analytics data. Developed algorithms allow us on one hand to interpret empirical data for self-studies evaluation, and on the other hand to correct and improve students’ learning path. This paper presents results of using developed methodology deployed in LMS BlackBoard on the example of Information Technology blended learning course in Far Eastern Federal University.
Elephant Herding Optimization for Multi-Level Image Thresholding

Multilevel thresholding plays a significant role in the arena of image segmentation. The main issue of multilevel image thresholding is to select the optimal combination of threshold value at different level. However, this problem has become challenging with the higher number of levels, because computational complexity is increased exponentially as the increase of number of threshold. To address this problem, this paper has proposed elephant herding optimization (EHO) based multilevel image thresholding technique for image segmentation. The EHO method has been inspired by the herding behaviour of elephant group in nature. Two well-known objective functions such as ‘Kapur's entropy' and ‘between-class variance method' have been used to determine the optimized threshold values for segmentation of different objects from an image. The performance of the proposed algorithm has been verified using a set of different test images taken from a well-known benchmark dataset named Berkeley Segmentation Dataset (BSDS). For comparative analysis, the results have been compared with three popular algorithms, e.g. cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization (PSO). It has been observed that the performance of the proposed EHO based image segmentation technique is efficient and promising with respect to the others in terms of the values of optimized thresholds, objective functions, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The algorithm also shows better convergence profile than the other methods discussed.
Pose and Velocity Estimation Algorithm for UAV in Visual Landing

Visual landing of Unmanned Aerial Vehicles (UAVs) is an application scenario of robot visual navigation. State estimation is an important part in the landing mission. With the development of computer hardware and algorithms, the speed and accuracy of visual processing techniques have been greatly improved. Aiming at the requirements of intelligent multi-rotor UAVs to improve their autonomous capabilities, we propose an algorithm to estimate the states (attitude, position and velocity) of UAVs based on an onboard camera during the landing phase. Firstly, the control points of a visual target are extracted by detecting an ArUco marker in vision as landing target. The attitude and position, named pose of UAVs are then calculated. According to the requirements to mission quickness and accuracy, a method based on corner point interpolation of Lucas-Kanade algorithm is proposed to calculate the dense optical flow. Then the integral of spherical optical flow is obtained by optical flow field and some constraints on the attitude of the UAV. Finally, the velocity of UAV is calculated based on the combination of the optical flow integral with the estimated pose of the UAV. This estimation algorithm has obvious advantages in that it only uses the visual sensor and can reduce the interference of noise points in the optical flow field by integral of spherical optical flow.
Restriction on Cut in Cyclic Proof System for Symbolic Heaps

It has been shown that some variants of cyclic proof systems for symbolic heap entailments in separation logic do not enjoy the cut elimination property. To construct complete system, we have to consider the cut rule, which requires some heuristics to find cut formulas in bottom-up proof search. Hence, we hope to achieve some restricted variant of cut rule which does not change provability and does not interfere with automatic proof search without heuristics. This paper gives a limit on this challenge. We propose a restricted cut rule, called the presumable cut, in which cut formula is restricted to those which can occur below the cut. This paper shows that there is an entailment which is provable with full cuts in cyclic proof system for symbolic heaps, but not with only presumable cuts.
Speaker recognition based on dynamic time warping and Gaussian mixture model

At present, most of the speaker recognition models are based on the MFCC cepstrum feature of the mixture Gaussian model, because MFCC represents the speaker's voice characteristics, but the voice between different speakers is easy to be imitated. In view of this, this paper adds the pitch period to this model, and uses the combination of the two for speaker recognition. The pitch period contains the voice frequency structure information. Although it will be affected by the speaker's health, it is not easy to be imitated. At the same time, in view of the slow recognition speed caused by the conventional direct de mixing of Gaussian mixture model, this paper first uses DTW to calculate the shortest distance of pitch period between speech samples, and then uses GMM to calculate the maximum likelihood probability of distribution of test samples in the first few training samples with small scores. Through the experimental analysis, it can be seen that the speaker recognition model combined with DTW and GMM can improve the recognition speed. The accuracy and the reduction of recognition time are significant.
Prediction of MSWI furnace temperature based on TS fuzzy neural network

Furnace temperature is an important indicator to control and optimize the Municipal Solid Wastes Incineration (MSWI) process. However, limited by the environment and instruments, it is difficult to measure the furnace temperature online and accurately. In this paper, a TS fuzzy neural network is utilized to design the prediction model in MSWI process, trying to obtain the real-time and accurate measurement of the furnace temperature. First, the mechanism of the MSWI process is introduced in brief. Then, the structure and training method of the TS-fuzzy-neural-network-based prediction model is introduced in details, which helps to build the nonlinear relationship between the furnace temperature and other process variables. Finally, the designed prediction model is applied to a real MSWI plant, and simulation results demonstrate the effectiveness and outperformance of the proposed methodology.
Attribute Selection Effect on Tree-Based Classifiers for Letter Recognition

This study presents evaluation measures for attribute selection effect on classification performance in classifying the 26 uppercase letters in the English alphabet. Attribute selection is an essential method in the classification phase to measure the attribute significance related to the class label since not all attributes are significant for letter recognition. Therefore, insignificant attributes should be reduced by applying dimensionality reduction. The filter-based attribute selection methods using Information Gain, Gain Ratio, Correlation, and Chi-square are proposed. The performances of attribute selection are evaluated by tree-based classifiers using J48, CART, and Random Forest algorithms with the measures of accuracy, precision, recall, F-measure, and processing time. The results indicate that the use of attribute selection methods provides the increase of classification performances for letter recognition. The reduction of insignificant attributes is discussed in terms of the effect on classification accuracy and the processing time. The optimal number of selected attributes is determined for each attribute selection, it provides better classification accuracy with more time-efficient.
A Reference Path Guided RRT* Method for the Local Path Planning of UGVs

This paper proposes a novel local path planning algorithm for Unmanned Ground Vehicles (UGVs). Rapidly-exploring Random Tree Star (RRT*) has recently gained immense popularity in the path planning as it provides a probabilistically complete and asymptotically optimal solution. But the uniform random sampling strategy of the traditional RRT* results in blind searching and massive computation. Our algorithm introduces a reference path guided non-uniform sampling approach to improve the planning performance. When the reference path is available, the RRT* based planner samples along the reference path. When there are obstacles on the reference path, guide points are generated to guide the sampling. Simulation results show that the algorithm can reduce the blindness of search, decrease the number of extended nodes, and generate smoother paths.
Feature based random forest nurse care activity recognition using accelerometer data

The The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data addresses the important issue about care and the need for assistance systems in the nursing profession like automatic documentation systems. Data of 12 different care activities were recorded with an accelerometer attached to the right arm of the nurses. Both, laboratory and field data were taken into account. The task was to classify each activity based on the accelerometer data. We participated as team Gudetama in the challenge. We trained a Random Forest classifier and achieved an accuracy of 61.11% on our internal test set.
The Best Laid Plans or Lack Thereof: Security Decision-Making of Different Stakeholder Groups

Cyber security requirements are influenced by the priorities and decisions of a range of stakeholders. Board members and CISOs determine strategic priorities. Managers have responsibility for resource allocation and project management. Legal professionals concern themselves with regulatory compliance. Little is understood about how the security decision-making approaches of these different stakeholders contrast, and if particular groups of stakeholders have a better appreciation of security requirements during decision-making. Are risk analysts better decision makers than CISOs? Do security experts exhibit more effective strategies than board members? This paper explores the effect that different experience and diversity of expertise has on the quality of a team's cyber security decision-making and whether teams with members from more varied backgrounds perform better than those with more focused, homogeneous skill sets. Using data from 208 sessions and 948 players of a tabletop game run in the wild by a major national organization over 16 months, we explore how choices are affected by player background (e.g.,~cyber security experts versus risk analysts, board-level decision makers versus technical experts) and different team make-ups (homogeneous teams of security experts versus various mixes). We find that no group of experts makes significantly better game decisions than anyone else, and that their biases lead them to not fully comprehend what they are defending or how the defenses work.
A machine learning approach for efficient multi-dimensional integration

Abstract Many physics problems involve integration in multi-dimensional space whose analytic solution is not available. The integrals can be evaluated using numerical integration methods, but it requires a large computational cost in some cases, so an efficient algorithm plays an important role in solving the physics problems. We propose a novel numerical multi-dimensional integration algorithm using machine learning (ML). After training a ML regression model to mimic a target integrand, the regression model is used to evaluate an approximation of the integral. Then, the difference between the approximation and the true answer is calculated to correct the bias in the approximation of the integral induced by ML prediction errors. Because of the bias correction, the final estimate of the integral is unbiased and has a statistically correct error estimation. Three ML models of multi-layer perceptron, gradient boosting decision tree, and Gaussian process regression algorithms are investigated. The performance of the proposed algorithm is demonstrated on six different families of integrands that typically appear in physics problems at various dimensions and integrand difficulties. The results show that, for the same total number of integrand evaluations, the new algorithm provides integral estimates with more than an order of magnitude smaller uncertainties than those of the VEGAS algorithm in most of the test cases.
A Quadruplet Loss for Enforcing Semantically Coherent Embeddings in Multi-Output Classification Problems

This article describes one objective function for learning semantically coherent feature embeddings in multi-output classification problems, i.e., when the response variables have dimension higher than one. Such coherent embeddings can be used simultaneously for different tasks, such as identity retrieval and soft biometrics labelling. We propose a generalization of the triplet loss that: 1) defines a metric that considers the number of agreeing labels between pairs of elements; 2) introduces the concept of similar classes, according to the values provided by the metric; and 3) disregards the notion of anchor, sampling four arbitrary elements at each time, from where two pairs are defined. The distances between elements in each pair are imposed according to their semantic similarity (i.e., the number of agreeing labels). Likewise the triplet loss, our proposal also privileges small distances between positive pairs. However, the key novelty is to additionally enforce that the distance between elements of any other pair corresponds inversely to their semantic similarity. The proposed loss yields embeddings with a strong correspondence between the classes centroids and their semantic descriptions. In practice, it is a natural choice to jointly infer coarse (soft biometrics) + fine (ID) labels, using simple rules such as k-neighbours. Also, in opposition to its triplet counterpart, the proposed loss appears to be agnostic with regard to demanding criteria for mining learning instances (such as the semi-hard pairs). Our experiments were carried out in five different datasets (BIODI, LFW, IJB-A, Megaface and PETA) and validate our assumptions, showing results that are comparable to the state-of-the-art in both the identity retrieval and soft biometrics labelling tasks.