Reviewerly/exHarmony huggingface dataset:
Comparison of simple feedforward neural network, recurrent neural network and ensemble neural networks in phishing detection

Abstract The internet has been one of the greatest advancements in technologies. It has brought many advantages to today’s society in many domains such as e-commerce, entertainment and supply chain, amongst others. However, it is also a double-edged sword which has brought many threats to the computer systems and devices known as cyber-attack, and one of these threats would be phishing attack. A phishing attack is where the scammer tries to impose or clone the legitimate email or website in order to deceive the victim to key in their personal information such as username and password. Phishing attack has been one of the most common attacks that happens every day on the Internet especially through email. Many methods have been devised to encounter phishing attack, and one of approaches is through training and monitoring team. These manual approaches, however, are user’s experience-dependent and cost-inefficient. Therefore, many have adopted AI approach instead to detect phishing attack. This paper is one of the many efforts to detect the phishing attack through email by adopting AI method. The objective of this paper is to investigate the performance of feedforward neural network, recurrent neural network and ensemble neural network in phishing email detection. The result of this comparison is empirically evaluated.
Transitioning into states of immersion: transition design of mixed reality performances and cinematic virtual reality

Cinematic virtual reality (CVR) experiences are increasingly expanding their use of additional media outside the VR headset, including scenography, spatial sound, and live performance, to successfully transition their audience into the experience. As such, they promote a heightened interest in transition design to secure experience continuity. In this paper, the authors argue that returning to the Human–Computer Interaction (HCI) concept of trajectories provides a fruitful starting point for considering transition design in CVR installations. However, explicit explorations of transitions and their design in HCI literature are limited. This contrasts with a strong design focus ‘in the wild’ across immersive genres including performance art, theme parks, video games, and live-action roleplay. Therefore, this paper advances the trajectory framework by returning to existing case studies with a transition design focus in order to discuss two existing design strategies and to mark future directions for research in the context of CVR.
Spatio-Temporal Fish-Eye Image Processing Based on Neural Network

In this paper, we describe an approach to correct the distortion in fish-eye image. Due to pros of large FOV (field of view), fish-eye camera is widely used in computer vision discipline. However, the serious distortion in fish-eye image set up barriers to image processing. This paper focus on adapting neural network for correcting distortions in fish-eye image. Up to now, traditional correction models such as latitude-longitude, sphere and grid template establish a certain model that is ideal and may not fit the reality situation. The method used in our study is known as neural network. Instead of approximately regarding the fish-eye distortion model as high-order polynomial function, we use neural network to learn the relationship between the distorted and corrected image. The mean square error for the difference between the corrected and ideal points is 4.1345 pixel per point, which is much smaller than the result of polynomial model (32.0809 pixel per point), and the run time for this algorithm is moderate. The results of the experiment indicate that correction model based on neural network can solve the fish-eye distortion in an acceptable error and high efficiency.
An Adaptive Space-Filling Curve Trajectory for Ordering 3D Datasets to 1D: Application to Brain Magnetic Resonance Imaging Data for Classification

In this work, we develop an adaptive, near-optimal, 3-Dimensional (3D) to 1D ordering methodology for brain magnetic resonance imaging (MRI) data, using a space-filling curve (SFC) trajectory, which is adaptive to brain’s shape as captured by MRI. We present the pseudocode of the heuristics for developing the SFC trajectory. We apply this trajectory to functional MRI brain activation maps from a schizophrenia study, compress the data, obtain features, and perform classification of schizophrenia patients vs. normal controls. We compare the classification results with those of a linear ordering trajectory, which has been the traditional method for ordering 3D MRI data to 1D. We report that the adaptive SFC trajectory-based classification performance is superior than the linear ordering trajectory-based classification.
Towards Dendrite Spherical Neurons for Pattern Classification

This paper introduces the Dendrite Spherical Neuron (DSN) as an alternative to the Dendrite Ellipsoidal Neuron (DEN), in which hyperspheres group the patterns from different classes instead of hyperellipses. The reasoning behind DSN is simplifying the computation of DEN architecture, where a centroid and covariance matrix are two dendritic parameters, whereas, in DSN, the covariance matrix is replaced by a radius. This modification is useful to avoid singular covariance matrices since DEN requires measuring the Mahalanobis distance to classify patterns. The DSN training consists of determining the centroids of dendrites with the k-means algorithm, followed by calculating the radius of dendrites as the mean distance to the two nearest centroids, and finally determining the weights of a softmax function, with Stochastic Gradient Descent, at the output of the neuron. Besides, the Simulated Annealing automatically determines the number of dendrites that maximizes the classification accuracy. The DSN is applied to synthetic and real-world datasets. The experimental results reveal that DSN is competitive with Multilayer Perceptron (MLP) networks, with less complex architectures. Also, DSN tends to outperform the Dendrite Morphological Neuron (DMN), which uses hyperboxes. These findings suggest that the DSN is a potential alternative to MLP and DMN for pattern classification tasks.
Improved Two-Step Binarization of Degraded Document Images Based on Gaussian Mixture Model

Image binarization is one of the most relevant preprocessing operations influencing the results of further image analysis conducted for many purposes. During this step a significant loss of information occurs and the use of inappropriate thresholding methods may cause difficulties in further shape analysis or even make it impossible to recognize different shapes of objects or characters. Some of the most typical applications utilizing the analysis of binary images are Optical Character Recognition (OCR) and Optical Mark Recognition (OMR), which may also be applied for unevenly illuminated natural images, as well as for challenging degraded historical document images, considered as typical benchmarking tools for image binarization algorithms. To face the still valid challenge of relatively fast and simple, but robust binarization of degraded document images, a novel two-step algorithm utilizing initial thresholding, based on the modelling of the simplified image histogram using Gaussian Mixture Model (GMM) and the Monte Carlo method, is proposed in the paper. This approach can be considered as the extension of recently developed image preprocessing method utilizing Generalized Gaussian Distribution (GGD), based on the assumption of its similarity to the histograms of ground truth binary images distorted by Gaussian noise. The processing time of the first step, producing the intermediate images with partially removed background information, may be significantly reduced due to the use of the Monte Carlo method. The proposed improved approach leads to even better results, not only for well-known DIBCO benchmarking databases, but also for more demanding Bickley Diary dataset, allowing the use of some well-known classical binarization methods, including the global ones, in the second step of the algorithm.
Towards Privacy-Preserving Local Monitoring and Evaluation of Network Traffic from IoT Devices and Corresponding Mobile Phone Applications

This paper describes ways for users to gain an insight into the actual communication flow of their Internet of Things (IoT) devices. The paper’s main objective is to enable a comparison of the flow with the devices’ intended purpose understandable to the device user. On the basis of what the device sends, the user should be enabled to decide whether the traffic is legitimate or not. With our framework no additional data will leave the user’s premises at any time. Only when a user decides that the traffic is unwanted communication flows the user can voluntarily transfer selected excerpts to a third party for further analysis. This limits data leakage compared to existing security incident event management (SIEM) solutions, where the monitoring third party seeks to constantly collect all information about the user’s traffic and thus constantly gets sensitive information. In this paper we propose a first set of tools for purely local analysis and user-friendly local visualizations. By this we educate the local user/operator of the IoT deployment and allow for more informed and more transparent decisions. Thus, we show that a privacy-preserving and thus more data-protection (GDPR) compliant monitoring of IoT-related network traffic is possible – and showcase how it will look.
Incremental inference of collective graphical models

We consider incremental inference problems from aggregate data for collective dynamics. In particular, we address the problem of estimating the aggregate marginals of a Markov chain from noisy aggregate observations in an incremental (online) fashion. We propose a sliding window Sinkhorn belief propagation (SW-SBP) algorithm that utilizes a sliding window filter of the most recent noisy aggregate observations along with encoded information from discarded observations. Our algorithm is built upon the recently proposed multi-marginal optimal transport based SBP algorithm that leverages standard belief propagation and Sinkhorn algorithm to solve inference problems from aggregate data. We demonstrate the performance of our algorithm on applications such as inferring population flow from aggregate observations.
WiFi Data Leakage Detection

Abstract The applications that we install on smart phones generates multitude of network traffic patterns, background activities, periodic updates etc. This application data leak side channel information like packet size, transfer timing, volume etc. This information can be used and exploited by an intruder to steal confidential user information. This paper focuses on various threats to the user’s private activities with the increase in commercial interest in tracing publicly broadcasted wireless data. The paper demonstrates mechanisms for inferring the user behavior from encrypted wireless network activity. It also shows how an absolutely inert eavesdropper can detect the information that is being transmitted over the network.
An experimental study of vehicle detection on aerial imagery using deep learning-based detection approaches

Abstract Deep convolutional neural network technology is widely used to deal with general object detection in computer vision, and it achieved remarkable progress. Unmanned aerial vehicles provide large numbers of aerial imagery that significantly facilitate several applications including traffic monitoring, surveillance, tracking, rescue, and safe military tasks. This paper presents an experimental study to evaluate the performances of several state-of-the-art deep learning-based detection approaches on vehicle detection from aerial imagery. The pre-trained models, including Faster R-CNN, R-FCN, and SSD, are adopted from the TensorFlow model zoo, and the VEDAI dataset is used as the benchmark. The results show that Faster R-CNN combined with Resnet101 backbone achieved the highest mAP, which is 39.73% on the COCO metric. This experimental study expects to be a guideline to choose suitable approaches for particular applications.
Understanding the Impact of Feedback on Knowledge Sharing in Modern Code Review

Modern Code Review (MCR) is a key practice to improve code quality and share knowledge among authors and reviewers. Feedback from the reviewer is an important aspect of the review process that initiates the process of knowledge sharing. In MCR the knowledge sharing though reported as an important benefit, however also reported as a challenge for author and reviewer. It is argued that the reviewers do not share knowledge in the form of insightful, constructive and formative feedback. Developers are facing poor knowledge sharing challenge in addition to other MCR challenges. It is understood that the feedback from the reviewer is an important facet to share knowledge. There are various factors associated with the feedback provided by reviewers that hamper the knowledge sharing aspect in MCR process. There is a gap in the MCR literature concerning the exploration of feedback conceptualization, importance and its impacts on knowledge sharing. Therefore, this study explores the factors associated with feedback that impact knowledge sharing in MCR. Systematic Literature Review (SLR), grounded theory and expert opinion strategies are followed to recognize the unique, categorized and validated list of feedback concerning factors that impact knowledge sharing in MCR. The examination discovers 42 factors such as feedback usefulness, harsh comments, criticism on the author, broadcast feedback, etc. The identified 42 factors are grouped into 14 unique categories, for instance, feedback communication, feedback language, feedback content, feedback temporal aspects, etc. The performed study has suggestions for members involved in engaging in MCR activities and investigators attracted research this zone to widen the investigation and share knowledge adequately among team members by bearing in mind and coping with the negative effect of recognized variables and thus minimizing the production of software engineering wastes.
Man-on-Man Brutality Identification on Video data using Haar Cascade Algorithm

Fight detection is still an open quandary for video concept detection. There has been little prosperity in engendering an algorithm that can detect the fight in surveillance videos with high-performance values. The proposed methodology is called Haar Cascade. Firstly, motion vector images are trained by using a machine learning algorithm for the consecutive frames. Secondly, motion vector features are extracted to their angles and magnitudes. Afterwards, velocity and angle-based motion co-occurrence features are calculated by considering both current and past motion vectors. The result obtained using the proposed algorithm in different videos got a high rate of success. It discriminates fighting scenes with high true positive rates. By using this methodology, can eradicate violence, crime, fights, etc. This is helpful to reduce violence and make the way to detect fights happen also. While comparing to the existing project, this gives a high rate of detection.
ProbInfer: Probability-based AS path inference from multigraph perspective

Knowing the paths between autonomous systems (ASes) is essential to understanding the behavior of the Internet routing system and contributes to many Internet services. Despite Traceroute being a useful tool that can conveniently reveal AS paths, the accuracy is affected by intermediate techniques, and it is impossible to implement Traceroute between arbitrary hosts. There are also methods that infer the AS path using BGP data, but the accuracy cannot always meet our expectations. The most difficult part of AS path inference is how to find the best path among the candidate paths. In this paper, we proposed ProbInfer: a probability-based AS path inference method that from the perspective of multigraph. The probabilistic method can efficiently solve the problem of finding the best path by scoring each candidate path. The multigraph can conveniently stitch AS paths and provide characters used to improve the accuracy of the method. First, ProbInfer separates the candidate paths into two types: SingleShortest (easy to infer) and MultiShortest (hard to infer and the majority). Second, we design the features used to make decisions from three aspects; especially, the features that come from stitching points and the multigraph show great value. Last, we scored the two types of candidate paths using a machine learning algorithm that trains different models for each situation. The results show that compared with state-of-the-art methods, ProbInfer performs much better.
An efficient IP core of Consultative Committee for Space Data Systems (CCSDS) Recommended Authenticated Cryptographic Algorithm

Mission planners are continuously striving to ensure that spacecraft, associated ground systems, communication equipment and all transmitted data are adequately protected against attacks. In this paper, we present an Intellectual Property (IP) Core of Advanced Encryption Standard/ Galois Counter Mode (AES/GCM) with 256-bit key in FPGA suited for space data systems. This is the algorithm recommended by Consultative Committee for Space Data Systems (CCSDS) in order to ensure confidentiality, authenticity and integrity of data. Since space missions are vulnerable to anomalies caused by Single Event Effects (SEE), our implementation does not use memory blocks in FPGA. Sub-field arithmetic is used in the implementation of Substitution Box (S-box) module. Efficient architectures are used in the design of the MixColumn with embedded ShiftRows and Key generation modules using XOR operations and avoiding conditional shifts. Shift and Add method is used to implement the Galois Field (GF) (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">128</sup> ) Multiplier in the GCM authentication function. The design is ported to the Xilinx Space Grade Virtex-4 device XQR4VLX200. The functionality of AES/GCM implementation is verified using standard test vectors from National Institute of Standards and Technology (NIST). An implementation of AES/GCM using one each of AES-256 and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">128</sup> ) multiplication cores achieves a high resource efficiency of 18% slices and a maximum frequency of 139.725 MHz
Electron cloud spin generated by microring <scp>space‐time</scp> control circuit for <scp>3D</scp> quantum printing

Abstract A three‐dimensional (3D) quantum printing technique is proposed that uses electron spin cloud projections generated by microring‐embedded gold grating. A dark soliton pulse with a center wavelength of 1.50 μm excites the gold grating, leading to electron cloud oscillations. By using suitable parameters, the whispering gallery mode is obtained, which is the result of trapping light (electrons) inside the silicon microring. The plasma wave frequency is generated at the Bragg wavelength at which the electron density is obtained. The electron spin‐down and spin‐up forms the x‐axis and y‐axis along the propagation z‐axis. In this proposal, the propagation wave axis is applied to increase the printing resolution. The printing points are formed by the spin‐down, spin‐up, and no spin associated with [x, y, z] as [1, 0, −]. The electron spins are distinguished by the time sequence and by modulating with the Gaussian pulse of 1.30, 1.10, and 0.80 μm, from which the spin states of the electron can be detected and characterized. The shortest space‐time paradox gap is around 50 fs, which is the limitation of all measurements (observations). The electron transport spin projection is achieved with respect to the time function in which the 3D image is printed with a resolution of 50 fs ( 10 −15 s ). In application, the trapped electron densities within the circuit are made possible via wireless connection for long‐distance transmission.
Prediction of Direct Normal Irradiance Based on Ensemble Deep Learning Models

Due to the intermittency and instability of solar power plant output, the key components are often in danger of being scrapped. Therefore, it is necessary to accurately predict the output of solar power plants. At present, the solar radiation intensity is considered to be one of the most important factors affecting the output of solar power plants. In essentially, the solar radiation intensity is a general concept, which is composed of several parameters. Among these parameters, direct normal Irradiance (DNI) prediction has become a hot and difficult research topic. In recent years, with the continuous improvement of hardware performance, a deep learning model has become one of the best methods to solve most time series prediction problems. In this paper, an ensemble deep learning model is proposed. This model integrates the convolutional neural network (CNN) and long short-term memory (LSTM) neural network. The former is responsible for extracting spatial features from data, while the latter is responsible for extracting temporal features. As a case study, the proposed method will predict the DNI of four typical days (spring equinox, summer solstice, autumn equinox and winter solstice) and its seven days before and after which is based on the meteorological data of Zhangbei. The results show that the deep learning model can be effectively applied to DNI prediction. However, the method proposed in this paper has a higher prediction accuracy compared with other models.
The SpaceStation App: Design and Evaluation of an AR Application for Educational Television

Due to the rising popularity of streaming services, television networks are experiencing pressure to keep the attention of the younger audience. Especially in the field of Edutainment, platforms like YouTube or TED are serious competitors and require broadcasters to come up with novel ideas to engage viewers in their program. In this work, we present the augmented reality (AR) SpaceStation application, designed to supplement the viewing of educational videos about the ISS. We evaluated users’ experience during the interaction with the app in a within-subject user study (N = 31) and assessed their workload. During the interaction with the SpaceStation App, participants experienced a higher workload compared to a video-only condition; nonetheless, they considered AR a valuable and enjoyable addition. This paper concludes with a discussion from the perspectives of viewers, content creators, and hosts, and states initial ideas on how to design television programs with AR content, without creating information overload.
CONQUEST: A Framework for Building Template-Based IQA Chatbots for Enterprise Knowledge Graphs

The popularization of Enterprise Knowledge Graphs (EKGs) brings an opportunity to use Question Answering Systems to consult these sources using natural language. We present CONQUEST, a framework that automates much of the process of building chatbots for the Template-Based Interactive Question Answering task on EKGs. The framework automatically handles the processes of construction of the Natural Language Processing engine, construction of the question classification mechanism, definition of the system interaction flow, construction of the EKG query mechanism, and finally, the construction of the user interaction interface. CONQUEST uses a machine learning-based mechanism to classify input questions to known templates extracted from EKGs, utilizing the clarification dialog to resolve inconclusive classifications and request mandatory missing parameters. CONQUEST also evolves with question clarification: these cases define question patterns used as new examples for training.