Reviewerly/exHarmony huggingface dataset:
A Joint Approach to Detect Malicious URL Based on Attention Mechanism

To improve the accuracy and automation of malware Uniform Resource Locator (URL) recognition, a joint approach of Convolutional neural network (CNN) and Long-short term memory (LSTM) based on the Attention mechanism (JCLA) is proposed to identify and detect malicious URL. Firstly, the URL features including texture information, lexical information and host information are extracted and filtered, and pre-processed with encode. Then, the feature matrix more relevant to the output are chose according to the weight of the attention mechanism and input to the constructed parallel processing model called CNN_LSTM, combinating CNN and LSTM to get local features. Next, the extracted local features are merged to calculate the global features of the URLs to be detected. Finally, the URLs are classified by the SoftMax classifier using global features, the accuracy of the model in malicious URL recgonition is 98.26%. The experimental results show that the JCLA model proposed in this paper is better than the traditional deep learning model or CNN_LSTM combined model for detecting malicious URLs.
Delimiting geochemical anomalies in the exploration of covered deposits with multifractal methods and using stream sediment data from the Iberian Pyrite Belt, Southwest Iberia

The Iberian Pyrite Belt (IPB) is a world-class province of Upper Paleozoic age for volcanic- and shale-hosted massive sulfides that includes 5 of the 10 supergiant (>150 Mt) deposits of their kind in the World. The volcano-sedimentary sequences hosting the ores are locally exposed, commonly covered by flysch-type successions of variable thickness (the Baixo Alentejo Flysch Group, BAFG) as a result of primary (sedimentary) features and superimposed effects due to Variscan folding and tectonic stacking. In addition and when affected by late-developed geomorphic depressions, the exhumed Paleozoic basement is also capped by Meso-Cenozoic sediments. The total thickness of cover sequences on top of the litho-stratigraphic sequence of interest can reach 100′s of meters, creating problems for mineral exploration. A large stream sediment (multi-element) geochemical database for the IPB was done in the 90′s during a wide-ranging regional survey. This database was reassessed by means of multifractal methods in view of their faculty to enhance the detection of faint geochemical anomalies and provide geochemical indicators to deep buried ore systems. Several anomalies were detected, namely for Cu, Zn, and Co along NW-SE to WNW-ESE trends. The main anomalous geochemical trends identified include zones that: (i) extend along the Mira Formation of BAFG, opening for the first time two non-traditional areas for further exploration studies; (ii) run along the boundary between the Mira and Mértola Formations of BAFG, probably related to different geochemical signatures and late-developed, fault-controlled, epigenetic ore systems; and (iii) overlap windows of volcanic-sedimentary sequences within the Cenozoic detrital cover and the site below which the deep buried deposit of Lagoa Salgada is located.
A Novel Multi-Camera Global Calibration Method for Gaze Tracking System

In a 3-D gaze tracking system, the system calibration is used to determine the positions of the light sources and the screen in the camera coordinate system, which plays an important role in the estimation of the 3-D gaze and the point of regard (PoR). The traditional way for the system calibration is the mirror-based method, in which a mirror is used to make the light sources and the screen can be captured by the camera through specular reflection. However, the mirror-based method is imprecise and complex. In this paper, a novel system-calibration method for the 3-D gaze tracking system is proposed. On the basis of the system camera, a calibration camera is placed on the opposite of the screen to constitute a multi-camera system. The images are captured by the two cameras to estimate the light sources and the screen of the system camera coordinate frame. Simulations and experiments were carried out to illustrate the validation of the method. This method solves the problem that the light sources and the screen are not in view of the system camera. In addition, it is easy to implement while ensuring high accuracy and providing a new method for other similar calibration systems.
GM-VPC: An Algorithm for Multi-robot Coverage of Known Spaces Using Generalized Voronoi Partition

SUMMARY In this paper we address the problem of coverage path planning (CPP) for multiple cooperating mobile robots. We use a ‘partition and cover’ approach using Voronoi partition to achieve natural passive cooperation between robots to avoid task duplicity. We combine two generalizations of Voronoi partition, namely geodesic-distance-based Voronoi partition and Manhattan-distance-based Voronoi partition, to address contiguity of partition in the presence of obstacles and to avoid partition-boundary-induced coverage gap. The region is divided into 2 D ×2 D grids, where D is the size of the robot footprint. Individual robots can use any of the single-robot CPP algorithms. We show that with the proposed Geodesic-Manhattan Voronoi-partition-based coverage (GM-VPC), a complete and non-overlapping coverage can be achieved at grid level provided that the underlying single-robot CPP algorithm has similar property.We demonstrated using two representative single-robot coverage strategies, namely Boustrophedon-decomposition-based coverage and Spanning Tree coverage, first based on so-called exact cellular decomposition and second based on approximate cellular decomposition, that the proposed partitioning scheme completely eliminates coverage gaps and coverage overlaps. Simulation experiments using Matlab and V-rep robot simulator and experiments with Fire Bird V mobile robot are carried out to validate the proposed coverage strategy.
Design of information system architecture for the recommendation of tourist sites in the city of Manta, Ecuador through a Chatbot

Nowadays, the development of computer systems for service recommendations is becoming increasingly common. On Internet, users can find from recommended web sites that appear as advertising, to virtual assistants that interact with the people through -per example- a chat. The current paper will present a solution for the recommendation of tourist places of the city of Manta, Ecuador. These recommendations will be performed through the decision trees algorithm and will have a chatbot as user interface in Facebook Messenger. The presented solution integrates different components, and this paper will describe the proposal by means of diagrams of software architecture. Within these components, the integration of Web services such as IBM Watson Assistant and Google Dialogflow will be presented.
A Geometric Optimization Approach for the Detection and Segmentation of Multiple Aneurysms

Abstract We present a method for detecting and segmenting aneurysms in blood vessels that facilitates the assessment of risks associated with the aneurysms. The detection and analysis of aneurysms is important for medical diagnosis as aneurysms bear the risk of rupture with fatal consequences for the patient. For risk assessment and treatment planning, morphological descriptors, such as the height and width of the aneurysm, are used. Our system enables the fast detection, segmentation and analysis of single and multiple aneurysms. The method proceeds in two stages plus an optional third stage in which the user interacts with the system. First, a set of aneurysm candidate regions is created by segmenting regions of the vessels. Second, the aneurysms are detected by a classification of the candidates. The third stage allows users to adjust and correct the result of the previous stages using a brushing interface. When the segmentation of the aneurysm is complete, the corresponding ostium curves and morphological descriptors are computed and a report including the results of the analysis and renderings of the aneurysms is generated. The novelty of our approach lies in combining an analytic characterization of aneurysms and vessels to generate a list of candidate regions with a classifier trained on data to identify the aneurysms in the candidate list. The candidate generation is modeled as a global combinatorial optimization problem that is based on a local geometric characterization of aneurysms and vessels and can be efficiently solved using a graph cut algorithm. For the aneurysm classification scheme, we identified four suitable features and modeled appropriate training data. An important aspect of our approach is that the resulting system is fast enough to allow for user interaction with the global optimization by specifying additional constraints via a brushing interface.
Emotion Recognition from Micro-Expressions: Search for the Face and Eyes

In the paper the problem of the emotion recognition from the micro-expressions is considered. The psychological background of this problem from the modern viewpoint is described in short. There are seven universal emotions which are usually could be recognized. The application fields of the emotion recognition are indicated. The most known databases for the facial micro-expression research are highlighted. The state-of-the-art of the micro-expression spotting and recognition methods is given. One of the main preliminary steps before the emotion recognition is investigated more thoroughly. It is the task of searching for the areas of a face and eyes in the image. Two algorithms implemented to solve this task are compared: the algorithm based on the color intensity estimation and the Viola - Jones algorithm. The results for of these algorithms for images of the people with the different skin colors are represented. The advantages and the drawbacks of the considered algorithms are discussed. The survey and experimental research results are proposed to further use in the emotion recognition pipeline.
Identification of Cybersecurity Specific Content Using the Doc2Vec Language Model

It has become more challenging for the security analysts to identify cyber threat related content on the Internet because of the vast amount of publicly available digital texts. In this research, we proposed building an autonomous system for extracting cyber threat information from publicly available information sources. We tested a neural embedding method called doc2vec as a natural language filter for the proposed system. With cybersecurity-specific training data and custom preprocessing, we were able to train a doc2vec model and evaluate its performance. According to our evaluation, the natural language filter was able to identify cybersecurity specific natural language text with 83% accuracy.
Political advertising revisited: digital campaigning and protecting democratic discourse

Abstract This paper is concerned with the legal and regulatory control of electoral campaigning online, in particular ‘microtargeting’. There has been a longstanding consensus in the UK on how to control political advertising, yet the shift of expenditure to the online environment, together with innovations in digital campaigning tools, are exposing tensions and gaps in the current regime. One central harm associated with microtargeting is its potential to undermine meaningful democratic deliberation. The paper interrogates the issues through the lens of electoral law and regulation, and questions the extent to which a recalibration is necessary to deal with the challenges of digital campaigning.
Modeling stack overflow tags and topics as a hierarchy of concepts

Developers rely on online Q&A forums to look up technical solutions, to pose questions on implementation problems, and to enhance their community profile by contributing answers. Many popular developer communication platforms, such as the Stack Overflow Q&A forum, require threads of discussion to be tagged by their contributors for easier lookup in both asking and answering questions. In this paper, we propose to leverage Stack Overflow’s tags to create a hierarchical organization of concepts discussed on this platform. The resulting concept hierarchy couples tags with a model of their relevancy to prospective questions and answers. For this purpose, we configure and apply a supervised multi-label hierarchical topic model to Stack Overflow questions and demonstrate the quality of the model in several ways: by identifying tag synonyms, by tagging previously unseen Stack Overflow posts, and by exploring how the hierarchy could aid exploratory searches of the corpus. The results suggest that when traversing the inferred hierarchical concept model of Stack Overflow the questions become more specific as one explores down the hierarchy and more diverse as one jumps to different branches. The results also indicate that the model is an improvement over the baseline for the detection of tag synonyms and that the model could enhance existing ensemble methods for suggesting tags for new questions. The paper indicates that the concept hierarchy as a modeling imperative can create a useful representation of the Stack Overflow corpus. This hierarchy can be in turn integrated into development tools which rely on information retrieval and natural language processing, and thereby help developers more efficiently navigate crowd-sourced online documentation.
On the use of Blockchain technologies in WiFi networks

Recently, Blockchain technology has been the focus of large research efforts spanning several application domains. Use of Blockchain has been proposed in wireless networks as well. In this paper an overview will be presented of the existing literature about the application of Blockchain in wireless access networks. Then, focus will be put on two distinct uses of Blockchain: i) application of Blockchain for providing anonymous access control to wireless access networks; ii) exploitation of the proof-of-work (a key component of Blockchain) as a mechanism for congestion avoidance replacing exponential backoff when network traffic load is high. Application of Blockchanin mechanisms in the two above cases will be investigated critically. The analysis carried out in the paper shows that, even if there are several technical issues that must be solved yet, the application of Blockchain mechanisms in wireless access networks is very promising.
Steganalysis of VoIP Streams with CNN-LSTM Network

Steganalysis of the Quantization Index Modulation (QIM) steganography in VoIP (Voice-over IP) stream is conducted in this research. VoIP is a popular media streaming and communication service on the Internet. QIM steganography makes it possible to hide secret information in VoIP streams. Detecting short and low embedding rates of QIM steganography samples remains an unsolved challenge. Recently, neural network models have been demonstrated to be capable of achieving remarkable performances and be successfully applied to many different tasks. The mainstream architectures of neural network include Convolution Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which adopt totally different ways to understand various signals. In this paper, we first indicate a proper way to combine the strengths of these two architectures and then construct a novel and unified model called CNN-LSTM network to detect QIM-based steganography. In our model, Bidirectional Long Short-Term Memory Recurrent Neural Network (Bi-LSTM) is utilized to capture long time contextual information in carriers and CNN was used subsequently to capture both local features and global ones as well as temporal carrier features. Experiments showed that our model can achieve the state-of-art result in detecting QIM-based steganography in VoIP streams.
An Immersive Virtual Reality Experience for Learning Spanish

While increases in the development of virtual reality (VR) have expanded the possibilities of learning foreign languages, research for computer assisted language learning in VR is lacking [1]. Virtual reality headsets continue to become more accessible to end users, and there is a current lack of educational language learning applications for such platforms. There are many tools and applications for language learning, however most lack the aspect of immersion – a proven method of learning a foreign language as seen in experiments involving overseas language experiences [2]. Therefore, this paper presents an interactive VR experience for learning Spanish through an immersive interaction and game-play, followed by the results from a focus group regarding user experience feedback. Additionally, this paper will detail the design and development of the current prototype. Results indicate that participants found this method of language learning to be more enjoyable than traditional methods because it did not feel like they were studying.
Validating Virtual Reality as an Effective Training Medium in the Security Domain

Virtual Reality (VR) training simulations are an idea which is being explored in numerous industries and professions. However, evidence purporting to the effectiveness of VR technology in relation to standard real-world exercises is still relatively thin. In this paper, we discuss our approach for validating the effectiveness of a VR training for law enforcement professionals in the context of the AUGGMED project, and present results of the validation study. Our study indicates that realistic VR-based trainings, either by themselves or in combination with the traditional hands-on training, can be as effective as highly resource-intensive practical training sessions.
Mineralization-alteration footprints in the Olympic Dam IOCG district, South Australia: The Acropolis prospect

The Acropolis prospect, 20 km southwest from the Olympic Dam Cu-U-Au-Ag deposit, South Australia, is a vein-style magnetite (±apatite ±hematite) system. A whole-rock dataset comprising 4864 core samples from 14 drillholes was analysed using multivariate statistical analyses to understand and identify geochemical signatures of mineralization, as well as the expressions and extents of hydrothermal alteration. Statistical analysis included unsupervised (principal component analysis, hierarchical and k-means clustering) and supervised (random forests) machine learning algorithms. The geology of the Acropolis prospect is presented as a 3D geological model complemented by cross-sections. The results of statistical analyses are overlaid and interpreted relative to the geological model, and encompass a projection of sodic and propylitic alteration as PC3, and mineralization signature as PC1. Although the mineralization footprint of the Acropolis prospect partially overlaps with a Hiltaba Suite granite, it is not centred on the granite body. A distinct ‘magnetite’ signature of Fe-V-Ni-Co is developed in the southwestern part of Acropolis and represents samples containing >60 wt% Fe. In contrast, the ‘hematite’ signature displays an association of REE, W, Sn, Sb, U, Th, Ca and P, and is present throughout the Acropolis prospect with the exception of drillhole ACD5, which is non-mineralized. Interpolated values of Cu (> 200 ppm) indicate an offset from Fe-rich veins, thus supporting a genetic model in which Cu-bearing mineralization overprints Cu-Au-deficient magnetite-dominant veins. The results obtained provide insights into the evolution from magnetite to hematite-dominant IOCG systems and may provide a proxy for exploration of shallow and economically significant IOCG deposits in the eastern Gawler Craton.
Selective Poisoning Attack on Deep Neural Networks †

Studies related to pattern recognition and visualization using computer technology have been introduced. In particular, deep neural networks (DNNs) provide good performance for image, speech, and pattern recognition. However, a poisoning attack is a serious threat to a DNN’s security. A poisoning attack reduces the accuracy of a DNN by adding malicious training data during the training process. In some situations, it may be necessary to drop a specifically chosen class of accuracy from the model. For example, if an attacker specifically disallows nuclear facilities to be selectively recognized, it may be necessary to intentionally prevent unmanned aerial vehicles from correctly recognizing nuclear-related facilities. In this paper, we propose a selective poisoning attack that reduces the accuracy of only the chosen class in the model. The proposed method achieves this by training malicious data corresponding to only the chosen class while maintaining the accuracy of the remaining classes. For the experiment, we used tensorflow as the machine-learning library as well as MNIST, Fashion-MNIST, and CIFAR10 as the datasets. Experimental results show that the proposed method can reduce the accuracy of the chosen class by 43.2%, 41.7%, and 55.3% in MNIST, Fashion-MNIST, and CIFAR10, respectively, while maintaining the accuracy of the remaining classes.
Contrast Stretching-Based Unwanted Artifacts Removal from CT Images

This paper presents a contrast stretching-based image enhancement technique to remove unwanted artifacts, such as flesh and to enhance bony regions from Computed Tomographic (CT) images. Our technique is based on enhancing the dynamic range of the image by linear contrast stretching through histogram modeling and intensity transformation function. The intensity range: low and high-intensity values are heuristically computed, and squared shape mask is moved to clean the image further. Experiments are carried out on several patient-specific CT images (source: Prism Medical Diagnostics lab, Chhatrapati Shivaji Maharaj Sarvopachar Ruganalay and Ashwini Hospital, India). Our results show that the technique provides the reliable promising results. Besides, the tool is simple, faster and easy to implement.
Nonlocal Means Filtering Based Speckle Removal Utilizing the Maximum <i>a Posteriori</i> Estimation and the Total Variation Image Prior

In this paper, the problem of image speckle removal is addressed.To alleviate the pepper-salt remainder in the speckled image, we propose to utilize the nonlocal means filtering, where the weighting coefficients are derived based on the maximum a posteriori estimation with the total variation image prior.As a result, the objective function of the pixel fitting term plus the total variation regularizer is formulated, and it is solved with the majorization-minimization approach.To avoid the computationally intractable step size selection in the huge-scale gradient-based optimization, we split and solve the variables in the pixel fitting term and regularizer by means of the alternating direction method of multipliers.Performance analysis is performed for the Rayleigh and Gamma distributed signal models.The simulation and experimental results show the superior performance compared with other image despeckling methods in terms of various metrics and visual perception. INDEX TERMSNonlocal means filtering, speckle, maximum a posteriori estimation, total variation image prior, majorization-minimization approach, alternating direction method of multipliers.