Reviewerly/exHarmony huggingface dataset:
Detecting Drones Status via Encrypted Traffic Analysis

We propose a methodology to detect the current status of a powered-on drone (flying or at rest), leveraging just the communication traffic exchanged between the drone and its Remote Controller (RC). Our solution, other than being the first of its kind, does not require either any special hardware or to transmit any signal; it is built applying standard classification algorithms to the eavesdropped traffic, analyzing features such as packets inter-arrival time and size. Moreover, it is fully passive and it resorts to cheap and general purpose hardware. To evaluate the effectiveness of our solution, we collected real communication measurements from a drone running the widespread ArduCopter open-source firmware, mounted onboard on a wide range of commercial amateur drones. The results prove that our methodology can efficiently and effectively identify the current state of a powered-on drone, i.e., if it is flying or lying on the ground. In addition, we estimate a lower bound on the time required to identify the status of a drone with the requested level of assurance. The quality and viability of our solution do prove that network traffic analysis can be successfully adopted for drone status identification, and pave the way for future research in the area.
A visual cognizance based multi-resolution descriptor for human action recognition using key pose

Human activity recognition using videos sequences is a well-known phenomenon which has many real-life applications such as daily assistive living, security and surveillance, patient monitoring, robotics, and sports analysis. Recently, single or still images based action recognition is becoming very popular due to spatial cues present in an image and required less computation. Hence, a robust framework is constructed by computation of textural and spatial cues of still images at multi-resolution. A fuzzy inference model is used to select the single key image from action video sequences using maximum histogram distance between stacks of frames. To represent, these key pose images the textural traits at various orientations and scales are extracted using Gabor wavelet while shape traits are computed through a multilevel approach called Spatial Edge Distribution of Gradients (SEDGs). Finally, a hybrid model of action descriptor is developed using shape and textural evidence, which is known as Extended Multi-Resolution Features (EMRFs) model. The highest classification accuracy is achieved through SVM classifier on various human action datasets: Weizmann Action (100%), KTH (95.35%), Ballet (92.75%), and UCF YouTube (96.36%). The highest accuracy achieved on these datasets are compared with similar state-of-the-art approaches and EMRFs shows superior performance.
A new approach for mobile robot localization based on an online IoT system

In Mobile Robotics, localization is a primordial task, as it makes possible the navigation of the robot, thus enabling it to carry out its activities. From the emergence of the Internet of Things (IoT), there was a different approach of interacting objects with each other, as well as between objects and humans. Based on the context presented, this article evidences the utilization of IoT for the development of a system aimed for localization mobile robots employing Convolutional Neural Networks (CNN) in the process of feature extraction of the images, according to the concept of Transfer Learning. The mechanism uses the topological mapping method to orient themselves in the exploration environment considered. The effectiveness of the approach is demonstrated by parameters such as Accuracy, F1-score and time of processing. The IoT system confers the centralization of processing, reducing costs and allowing reuse of the robot’s idle computing power. Combined with this benefit, CNN still achieves 100% Accuracy and F1-Score, proving to be an effective technique for the required activity. With this, the proposed approach demonstrates to be efficient for the use in the task of locating mobile robots.
Fast face detection on mobile devices by leveraging global and local facial characteristics

Face detection is an extensively studied problem in the last few decades. Recently, significant improvements have been achieved by the deep neural network (DNN) solution. It is however challenging to apply the DNN technique to mobile devices directly due to its limited computational power and memory. In this work, we present a proposal generation acceleration framework for real-time face detection. Specifically, we adopt the convolutional neural network cascade as the baseline and develop an acceleration scheme to speed up its inference time. The acceleration scheme is motivated by the observation that the computational bottleneck of the baseline arises in the proposal generation stage, where each level of the dense image pyramid has to go through the network. To address this issue, we reduce the number of image pyramid levels by utilizing both global and local facial characteristics (i.e., global face and facial parts). We conduct experiments on public WIDER-face and FDDB datasets and show that the proposed scheme offers the state-of-the-art face detection performance at a much faster speed.
An enhanced approach for detecting double JPEG compression with the same quantization matrix

The number of different JPEG coefficients between two successive JPEG compressed images with the same quantization matrix will monotonically decrease in general. This intrinsic property can be used to detect double JPEG compression with the same quantization matrix. However, such a decreasing tendency is not significant for some JPEG images compressed with low quality factors (QFs). It is especially difficult to detect double JPEG compression in this scenario. By analyzing the statistical properties of JPEG coefficients with various QFs, an enhanced random perturbation based double JPEG compression detection method is proposed in this paper. Unlike existing random perturbation strategy that just indiscriminately selects JPEG coefficients for modification, in the proposed method, only JPEG coefficients with values ±1 are modified by a novel modification strategy. Experimental results demonstrate that the proposed method outperforms some state-of-the-art detection methods, including the original random perturbation based one, especially for the case of low QFs.
AAEE – Automated evaluation of students’ essays in Arabic language

Assessing student’s essay writing and providing thoughtful feedback is a truly labor-intensive and time-consuming task. With human instructors already overwhelmed, the alternate is to consider a computer-based grading. Recent advances have generated renewed interest in automatic evaluation of essays (AEE). The AEEs instantaneous feedback and more consistent grading helps students draft better essays. This work presents a system to automatically grade the school children essays in Arabic, calling it AAEE for “automatic Arabic essays evaluator”. The system is modeled upon the scoring scheme followed by the school instructors in Saudi Arabia. The instructors had specific criteria upon which an essay is assessed. Putting these criteria together we developed a system that relies on Latent Semantic Analysis, and Rhetorical Structure Theory. With this design we are able to assess individual components of the essay such as language proficiency, structure of the essay etc. To test the system, we collected essays by local school children covering grades 7–12. A total of 350 different handwritten essays—spanning eight different topics—each transcribed into computer readable format. The AAEE shows that 90% of the test essays were correctly scored, and a correlation of 0.756 between automatic and teachers’ scoring. This exceeds the human-human correlation of 0.709 for the Arabic essays.
Extracting features with medical sentiment lexicon and position encoding for drug reviews

Medical sentiment analysis refers to the extraction of sentiments or emotions from documents retrieved from healthcare sources, such as public forums and drug review websites. Previous studies prove that sentiment analysis for clinical documents has the potential for assisting patients with information for self assessing treatments, providing health professionals with more insights into patients' health conditions, or even managing relations between patients and doctors. Nevertheless, the lack of data used for empirical experiments in previous research indicates that there are strong needs for a systematic framework in order to identify medical field specific sentiments. We propose a new feature extraction approach utilising position embeddings to generate a medical domain enhanced sentiment lexicon with position encoding representation for drug review sentiment analysis. Experiments on different feature extraction methods using two types of sentiment lexicons with various machine learning classifiers, support the superior performance of sentiment classification with position encoding incorporated medical sentiment lexicon for drug review datasets.
Factors influencing the information security behaviour of IT employees

Although non-IT employees have received considerable attention in security research, IT employees have received scant attention, even though they might be very different to non-IT employees. To address this gap, we developed a model based on protection motivation theory, deterrence theory and the theory of reasoned action. We conducted a quantitative survey of IT employees to determine which factors influenced their information security behavioural intentions. Although sometimes contrary to findings regarding non-IT employees, the influential factors were: self-efficacy; and perceived impact of a potential event; with cues to action exerting a significant influence on that perceived impact. These results have significant academic and practical implications.
Small Aircraft Detection in Remote Sensing Images Based on YOLOv3

Abstract Accurate and rapid detection in remote sensing images is of great significance in military navigation, environmental monitoring, and civil applications. Due to the small object detection problem of remote sensing images, higher requirements and challenges are put forward for object detection technology. In recent years, the one-stage object detector and the two-stage object detector based on convolutional neural network have made great achievements in the field of image classification and detection. The one-stage object detector is generally superior to the two-stage object detector in detection speed, but the detection accuracy is inferior to the two-stage target detector. In this paper, YOLOv3 is used as a one- stage target detector for small aircraft detection of remote sensing images. We chose appropriate anchors to cover the size distribution in our experimental data by using dimension clusters. The experimental results show that YOLOv3 not only exceeds the conventional one-stage object detector in speed, but also well match the accuracy of the two-stage object detector. The YOLOv3 achieved excellent detection accuracy and low processing time at the same time for small aircraft detection.
Identification of geochemical anomaly and gold potential mapping in the Sonakhan Greenstone belt, Central India: An integrated concentration-area fractal and fuzzy AHP approach

The present work combines Concentration-Area (C-A) fractal model with the Fuzzy Analytical Hierarchy Process (FAHP) on a GIS platform for mapping gold potential in the Sonakhan Greenstone Belt, India. A set of stream sediment geochemical data obtained over a part of the study area was used to delineate potential regions of gold mineralisation. In order to develop a suitable predictive model, the gold exploration target was taken as target (dependent variable) and concentration of pathfinder elements of gold such as Au, As, Ag, Hg, Sb and Se in the stream sediments were used as predictors (independent variables). The C-A fractal model was applied to the geochemical data of each element to decompose anomaly and background components of the spatial dispersion of element concentration in the study area. AHP combined with fuzzy set theory (FAHP) was used to determine the priority or, the weight of each evidential geochemical anomaly map. The Gold exploration targets are delineated by multiplying the weights with the respective fuzzy normalised geochemical anomaly map of the gold pathfinder elements and integrating the weighted geochemical evidential layers using fuzzy Gamma operator. The reliability of the outcome was assured by the coincidence of known gold mineralisation with the potential regions delineated in the final gold potential map. The areas with high potential are good targets for detailed exploration of gold in the region. The results highlight that (a) application C-A fractal model can effectively separate geochemical anomalies as it considers the spatial variation of the data unlike the conventional statistical methods which account only the frequency of the data, (b) the FAHP allows more flexibility in judgements of multiple decision makers in a group and reduces the inconsistency of the result which is essentially required in the knowledge-driven predictive mapping of mineral prospectivity.
Whale Optimisation Algorithm for high-dimensional small-instance feature selection

In this paper, eight variants of the Whale Optimisation Algorithm (WOA), that are based on eight different transfer functions, are introduced and used as search strategies in a wrapper feature selection model. Feature selection is a challenging task in machine learning process. It aims to minimise the size of a dataset by removing redundant and/or irrelevant features, with no information loss, to improve the efficiency of the learning algorithms. The used transfer functions belong to two different families; S-shaped and V-shaped. The proposed approaches have been tested on nine different high-dimensional medical datasets, with a low number of samples and multiple classes. The results revealed a superior performance for the V-shaped based approaches over the the S-shaped approaches. Moreover, the results of the V-shaped approach is compared with well-known feature selection approaches, and the superiority of the proposed approach is proven.
A Novel Improved Bat Algorithm Based on Hybrid Parallel and Compact for Balancing an Energy Consumption Problem

This paper proposes an improved Bat algorithm based on hybridizing a parallel and compact method (namely pcBA) for a class of saving variables in optimization problems. The parallel enhances diversity solutions for exploring in space search and sharing computation load. Nevertheless, the compact saves stored variables for computation in the optimization approaches. In the experimental section, the selected benchmark functions, and the energy balance problem in Wireless sensor networks (WSN) are used to evaluate the performance of the proposed method. Results compared with the other methods in the literature demonstrate that the proposed algorithm achieves a practical method of reducing the number of stored memory variables, and the running time consumption.
Enacting Identities

Participatory design is an essential design strategy for creating artifacts and experiences that reflect the voices of the population being designed for and with. The participatory design process can serve not only to research resulting artifacts but also as an empowering activity for those who participate. This paper explores how participatory design can serve as a context for young participants to enact and voice their emerging identities and reveals how different participatory design activities have unique affordances for supporting this identity enactment. Focusing on a group of 12 and 13-year-old African American girls, this paper presents a case study showing how participatory design activities served as venues for the girls to reflect characteristics of their current identities, project future identities, and apply aspects of their identities to shape materials for others. In doing so, we contribute a case study showing how participatory design allows participants to enact their identities, helping researchers gain insight into characteristics of those they are designing with and for. This paper advances our understanding of participatory design as a design approach for youth, especially as it relates to issues of broadening participation, identity, and equity.
Few-Shot Visual Classification Using Image Pairs With Binary Transformation

Accurately classifying images using few-shot samples have been widely explored by researchers. However, these methods have two drawbacks. First, images are often used independently. Second, class imbalance is ignored and hinders the classification accuracy with the increment of classes. To tackle these two drawbacks, in this paper, we propose a novel visual classification method using image pairs with binary transformation (IPBT). For one image, we bundle it with each training image into an image pair by concatenating the representations of the two images along with their similarity. The class consistency of two images is used to split the image pairs into binary groups. One group contains image pairs of the same class, while the other group consists of images pairs belonging to different classes. We train classifiers to separate the binary groups apart. To classify a testing image, we first bundle it with all the training images that are then predicted using the learned binary classifier. The image pair with the largest response is selected, and the testing image is assigned to the same class of the paired image. We conduct few-shot visual classification experiments on three public image datasets. The experimental results and analysis show the effectiveness of the proposed IPBT method.
A comparative study of 3D reconstruction methods in stereo digital image correlation

The measurement accuracy of stereo digital image correlation (DIC) depends greatly on the accuracy of the reconstructed 3D point. There are four 3D reconstruction methods used in stereo DIC, such as least square method (LSM), optimal method (OM), mid-point method (MM), and geometrical optimal method (GOM). The 3D point from the OM is reconstructed by minimizing the re-projection error, while in the GOM the 3D point with the minimal re-projection error on the shortest line segment between the lines of sight is regarded as the desired one. However, no research has been conducted to compare the performances of these four 3D reconstruction methods in stereo DIC. In addition, it is generally believed that the accuracy of the 3D point having the minimal re-projection error is the highest, whereas, it is not validated in stereo DIC. Therefore, the objective of this study is to compare the performances of the four 3D reconstruction methods in stereo DIC in terms of measurement accuracy and computation efficiency. Simulation and experiment results demonstrate that the accuracies of the four methods are nearly the same and the lesser re-projection error may not imply the higher accuracy of the reconstructed 3D point if the matching accuracy of the corresponding points (CP) has already been determined. Finally, the LSM is recommended to be used in stereo DIC for 3D reconstruction due to its highest computation efficiency without compromising the measurement accuracy.
Encrypted traffic classification of decentralized applications on ethereum using feature fusion

With the prevalence of blockchain, more and more Decentralized Applications (DApps) are deployed on Ethereum to achieve the goal of communicating without supervision. Users habits may be leaked while these applications adopt SSL/TLS to encrypt their transmission data. Encrypted protocol and the same blockchain platform bring challenges to the traffic classification of DApps. Existing encrypted traffic classification methods suffer from low accuracy in the situation of DApps.
Smart substitutional reality: Integrating the smart home into virtual reality

Recent advances in VR technology allow users to consume immersive content within their own living room. Substitutional Reality (SR) promises to enhance this experience by integrating the physical environment into the simulation. We propose a novel approach, called Smart Substitutional Reality (SSR), that extends the passive haptics of SR with the interactive functionality of a smart home environment. SSR is meant to serve as a foundation for serious games. In this paper, we describe the concept of SSR alongside the implementation of a prototype in our smart lab. We designed multiple virtual environments with a varying degree of mismatch compared to the real world, and added selected objects to induce additional haptic and thermal stimuli to increase immersion. In two user studies we investigate their impact on spatial presence and intrinsic motivation which are especially important for serious games. Results suggest that spatial presence is maintained with higher mismatch, and increased by inducing additional stimuli. Intrinsic motivation is increased in both cases.
Performance analysis of synchronous and asynchronous distributed genetic algorithms on multiprocessors

Because of their effectiveness and flexibility in finding useful solutions, Genetic Algorithms (GAs) are very popular search techniques for solving complex optimization problems in scientific and industrial fields. Parallel GAs (PGAs), and especially distributed ones have been usually presented as the way to overcome the time-consuming shortcoming of sequential GAs. In the case of applying PGAs, we can expect better performance, the reason being the exchange of knowledge during the parallel search process. The resulting distributed search is different compared to what sequential panmictic GAs do, then deserving additional studies. This article presents a performance study of three different PGAs. Moreover, we investigate the effect of synchronizing communications over modern shared-memory multiprocessors. We consider the master-slave model along with synchronous and asynchronous distributed GAs (dGAs), presenting their different designs and expected similarities when running in a number of cores ranging from one to 32 cores. The master-slave model showed a competitive numerical effort versus the other dGAs and demonstrated to be able to scale-up well over multiprocessors. We describe how the speed-up and parallel performance of the dGAs is changing as the number of cores enlarges. Results of the island model show that synchronous and asynchronous dGAs have different numerical performances on a multiprocessor, the asynchronous algorithm having a faster execution, thus more attractive for time demanding applications. Our results and statistical analyses help in developing a novel body of knowledge on PGAs running in shared memory multiprocessors (versus overwhelming literature oriented to distributed memory clusters), something useful for researchers, beginners, and final users of these techniques.