Reviewerly/exHarmony huggingface dataset:
Modelling Security Risk Scenarios Using Subjective Attack Trees

We propose a novel attack tree model, called a subjective attack tree, aiming to address the limitations of traditional attack trees, which use precise values for likelihoods of security events. In many situations, it is often difficult to elicit accurate probabilities due to lack of knowledge, or insufficient historical data, making the evaluation of risk in existing approaches unreliable. In this paper, we consider the modelling of uncertainty about probabilities, via subjective opinions, resulting in a model taking second-order uncertainty into account. We propose an approach to derive subjective opinions about security events based on two main criteria, namely a vulnerability level and technical difficulty to conduct an attack, using subjective logic. These subjective opinions are then used as input parameters in the proposed model. The propagation method of subjective opinions is also discussed. Our approach is evaluated against traditional attack trees using the Stuxnet self-installation scenario. Our results show that taking uncertainty about probabilities into account during security risk analysis can lead to different outcomes, and therefore different security decisions.
Enabling Security Analysis of IoT Device-to-Cloud Traffic

End-to-end encryption is now ubiquitous on the internet. By securing network communications with TLS, parties can insure that in-transit data remains inaccessible to collection and analysis. In the IoT domain however, end-to-end encryption can paradoxically decrease user privacy, as many IoT devices establish encrypted communications with the manufacturer's cloud backend. The content of these communications remains opaque to the user and in several occasions IoT devices have been discovered to exfiltrate private information (e.g., voice recordings) without user authorization. In this paper, we propose Inspection-Friendly TLS (IF-TLS), an IoT-oriented, TLS-based middleware protocol that preserves the encryption offered by TLS while allowing traffic analysis by middleboxes under the user's control. Differently from related efforts, IF-TLS is designed from the ground up for the IoT world, adding limited complexity on top of TLS and being fully controllable by the residential gateway. At the same time it provides flexibility, enabling the user to offload traffic analysis to either the gateway itself, or cloud-based middleboxes. We implemented a stable, Python-based prototype IF-TLS library; preliminary results show that performance overhead is limited and unlikely to affect quality-of-experience.
CI2FA: Continuous Indoor Two-factor Authentication Based On Trilateration System

Beacon frame is a type of management frame that is transmitted periodically by Wi-Fi Access Points (APs) for the purpose of establishing connections; also, it announces the existence of a network, allowing devices to find and identify the network and subsequently to request access. It contains information about the network needed to make the request. Within a range of different radio frequency (RF) technologies, Wi-Fi-capable devices can sense beacon frames. The primary advantage of a broadcast message is that devices have the ability to read and process the enclosed data with no need to be connected to the corresponding network. Our research utilizes data within these transmitted messages to perform two-factor authentication (2FA) scheme that, unlike existing methods, with minimal user involvement. We can ensure that both devices belong to the original user by determining if both of a user's devices are in the same physical area. The proposed method relies on an object that a user knows, an object that a user owns, and - a significant presented contribution of this scheme - an object in the user's environment. The newly designed method presented in this study is empirically evaluated based on real login attempts.
Measuring the effect of monitoring on a cloud computing system by estimating the delay time of requests

Leveraging cloud computing services to improve the flexibility and scalability of the IT services delivered to end-users is becoming increasingly important. However, organizations using cloud computing services face many challenges: decreased visibility into the performance of services being delivered to their end-users. Organizations using cloud computing services need to have visibility not only into the performance of applications that have moved to the cloud, but also into the different computing resources on which these applications depend. The monitoring system monitors the running state of virtual machines, response time and throughput. By analysing this data, you can identify the causes of service delays. During the operation of the monitoring system, virtual machines are suspended, a service delay occurs in order to avoid this delay. Therefore in this paper, a cloud computing model is proposed that takes into account the time spent for monitoring, calculations and probabilities of delay and waiting time are made, blocking probabilities are also put to account and results are shown in diagrams and the effects are discussed.
Developing Theory With Theoretical Concept Reviews

In this article, we intersect the research streams on conducting better literature reviews and on building better theory to present detailed guidelines for developing theory using theoretical concept synthesis, where a literature review presents the elements of theory from primary studies, an approach that has been demonstrated to increase citations in both information systems and general management literature reviews. We distinguish reviews of three different scopes of topic treatment: topical for scoping out a theoretical landscape, propositional for developing a new theoretical model, and confirmatory for testing a theory. We provide specific guidance on how to search for, extract and synthesize the elements of theory using theoretical concept synthesis for reviews of all three scopes, as well as other appropriate techniques. We discuss how to craft a theoretical contribution and then present applications of these guidelines for literature reviews of various scales of breadth.
Effects of Parallel Structure and Serial Structure on Convolutional Neural Networks

Abstract Based on KERAS’ FASHION_MNIST data set, a convolutional neural network with serial structure and parallel structure was set up for training. The serial structure can be used for deep feature mining and the parallel structure can be used to describe the first impression. The results show that the parallel structure has better effect and faster speed for simple images.
Towards Attacker Attribution for Risk Analysis

An attacker model is one of the key models in risk analysis and other security related tasks. The goal of the research is the attacker model specification in the form of attacker profile, i.e. as a set of attributes. In the paper we introduce the formal attacker model and its attributes, we describe the input data for the experiments and the process of their handling, and, finally, we describe the conducted experiments demonstrating an appropriateness of the selected attributes. In the future research we plan to extend the experiments and to introduce the risk analysis technique using the proposed attacker model.
WITHDRAWN: Research on Encrypting Accounting Data Using Des Algorithm under the Background of Microprocessor System

E-banking data through Internet banking and e-banking is known for its various authorities to get to the source. The essence of the banking sector is the management of current accounts. Data discovery is a significant change in the atmosphere of working in the banking sector purchased. The bank's actualized innovation strategy is data security on Automated Teller Machines (ATMs)-ATMs, online banking, telephone banking, mobile banking, etc. Security is an important concern of all online banking systems. The last ten years have seen a tumultuous development of data discovery, which has led to significant encryption advances to ensure reliability and data classification worldwide. The most influential secure information or business is the currency industry on the Internet and online business in need of security during any transaction. Cryptography is the center of the mystery. A common-sense method of encoding data complete mystery. DES (Data Encryption Standard) - symmetric key encryption Triple-DES and its variants have emerged in economic and other structures and are considered an important component in ensuring that personal information about the security office in the past three decades part of the area. And the investigation of speculation in the implementation of DES in the future to ensure that information is properly are described. Symmetric key encryption options and other transactions are also considered legitimate.
Entropy-based goal-oriented emergence management in self-organizing systems through feedback control loop: A case study in NASA ANTS mission

The vast number of agents in artificial systems and maintenance issues have persuaded researchers to study self-organization. As a result of self-organization, some behaviors and structures emerge at a global level of the system. These emergents are created spontaneously and without a preexisting blueprint. Therefore, in some cases, they conflict with system goals (such as availability) in artificial self-organizing systems. So, many researchers' attention has been attracted to designing guided self-organizing systems and managing emergence. In this paper, a feedback control loop (inspired by IBM's control loop and relying on our proposed definition of emergence) is designed to manage emergence to achieve system goals. The emergence detection and management process is performed periodically by evaluating some agents' entropy in the system. Experiments on the NASA ANTS case study show the effectiveness of the proposed emergence management unit (called EMU). The results indicate that at all radio ranges considered, the entropy increment percentage when the EMU is enabled is much less than when the EMU is disabled. Besides, the availability of ruler agents using EMU is equal to or higher than the availability without using EMU.
Lithium (LI) Pegmatite Mapping using Artificial Neural Networks (ANNS): Preliminary Results

Satellite-based mineral exploration will be increasingly relevant in the future to achieve more conscious exploration models. Therefore, new applications to high-demand mineral commodities such as lithium (Li) are emerging. Previous applications to the study area of Fregeneda (Spain) - Almendra (Portugal) showed a high number of false positives despite their ability to identify Li pegmatites. So, the objective of the present work is to improve the classification results using Artificial Neural Networks (ANNs). For that, the same Sentinel-2A images were used and a three-layer feedforward network was computed using the backpropagation method. The ANN created was able to identify all the open-pit mines exploiting Li pegmatites in the area. However, the high number of Li false positives persisted. Future applications may include Convolutional Neural Networks (CNNs) to improve these results.
An Efficient Approach for Ranking of Semantic Web Documents by Computing Semantic Similarity and Using HCS Clustering

In today's era, with the availability of a huge amount of dynamic information available in world wide web (WWW), it is complex for the user to retrieve or search the relevant information. One of the techniques used in information retrieval is clustering, and then the ranking of the web documents is done to provide user the information as per their query. In this paper, semantic similarity score of Semantic Web documents is computed by using the semantic-based similarity feature combining the latent semantic analysis (LSA) and latent relational analysis (LRA). The LSA and LRA help to determine the relevant concepts and relationships between the concepts which further correspond to the words and relationships between these words. The extracted interrelated concepts are represented by the graph further representing the semantic content of the web document. From this graph representation for each document, the HCS algorithm of clustering is used to extract the most connected subgraph for constructing the different number of clusters which is according to the information-theoretic approach. The web documents present in clusters in graphical form are ranked by using the text-rank method in combination with the proposed method. The experimental analysis is done by using the benchmark datasets OpinRank. The performance of the approach on ranking of web documents using semantic-based clustering has shown promising results.
Quantitative Approximation Results for Complex-Valued Neural Networks

Until recently, applications of neural networks in machine learning have almost exclusively relied on real-valued networks. It was recently observed, however, that complex-valued neural networks (CVNNs) exhibit superior performance in applications in which the input is naturally complex-valued, such as MRI fingerprinting. While the mathematical theory of real-valued networks has, by now, reached some level of maturity, this is far from true for complex-valued networks. In this paper, we analyze the expressivity of complex-valued networks by providing explicit quantitative error bounds for approximating $C^n$ functions on compact subsets of $\mathbb{C}^d$ by CVNNs that employ the modReLU activation function, given by $\sigma(z) = {ReLU}(|z| - 1), {sgn} (z)$, which is one of the most popular complex activation functions used in practice. We show that the derived approximation rates are optimal (up to log factors) in the class of modReLU networks with weights of moderate growth.
Dropout Deep Belief Network Based Chinese Ancient Ceramic Non-Destructive Identification

A non-destructive identification method was developed here based on dropout deep belief network in multi-spectral data of ancient ceramic. A fractional differential algorithm was proposed to enhance the spectral details by making use of the difference between the first and second-order differential pre-process spectral data. An unsupervised multi-layer restricted Boltzmann machine (RBM) was employed to extract some high-level features during pre-training. Some weight and bias values trained by RBM were used to initialize a back propagation (BP) neural network. The RBM deep belief network was fine-tuned by the BP neural network to promote the initiative performance of network training, which helped to overcome local optimal limitation of the network due to the random initializing weight parameter. The dropout strategy has been put forward into the RBM network to solve the over-fitting of small sample spectral data. The experimental results show that the proposed method has excellent recognition performance of the ceramics by comparisons with some other ones.
Multi-Region Two-Stream Deep Architecture for Visual Power Monitoring Systems

Judging imaging quality is an important part of the maintenance of visual intelligent monitoring systems for electrical power scenes. However, accurate and efficient identification of possible abnormalities in imaging quality remains challenging. This paper proposes a novel multi-region two-stream deep architecture to improve judging abnormalities. The proposed architecture incorporates two-stream scheme and multi-region strategy to identify relevant information and explore hidden details. More specifically, in addition to color and intensity in the original images, the two-stream scheme uses high-frequency structure information from gradient images to enhance its performance. The multi-region strategy employs spatial pyramid random cropping and region fusion to handle locally non-uniform changes among categories: spatial pyramid random cropping characterizes images at different spatial pyramid levels, while region fusion focuses attention on cropped regions relevant to quality perception by using adaptive learning weights in a fully connected layer. In this way, the proposed strategy guides the framework to adequately and adaptively explore the discriminative regions hidden in the input images, and provides an end-to-end learning procedure. Experimental results demonstrate its strong performance for judging abnormalities, and the proposed method can be easily extended to the entire surveillance system.
Accelerated Path Tracing With GAN and Matrix Completion

Denoising-based techniques have recently been shown to be effective for accelerating path tracing rendering methods. However, there remains a problem which is input images need the minimum necessary samples number in order to ensure the quality of the output. In this paper, we propose a new accelerated path tracing approach with generative adversarial networks(GAN) and matrix completion. Unlike the methods based on denoising with neural network, we randomly render part of pixels of input image, which are much less than other methods. Next, we utilize the trained GAN to pre-complete the initializing missing pixels. Because of the accuracy and fast-convergence of GAN, our pre-completion results are more accurate than other methods. Then, according to the results of pre-completion, we present the pre-completed images as a low-rank matrix and make use of the matrix completion to recovers missing values accurately even in high details. To improve the efficiency of solving matrix completion, we modified the original weighted nuclear norm minimization with a parameter adjustment(PAWNNM) strategy. The result shows better visual quality, texture details and convergence efficiency than the state-of-the-art acceleration methods, especially the methods based on denoising with neural network.
A Barter and Combinatorial Auction Based Hierarchical Resource Trade Mechanism for Cybertwin Network

Recently, a cybertwin based cloud-centric network architecture for future generation networks was proposed, which can work in a distributed way through a realtime multi-agent trading platform to allocate hierarchical resource, such as computing, caching, communications resource. The current network operator centralized resource management mechanism, however, cannot sufficiently manage the resource in a flexible manner. In this work, we propose a barter and combinatorial auction based hierarchical resource trading mechanism for cybertwin network. A unified bid language is further presented to express various resource requests and supplies. We formulate the total payoff maximization problem as an integer programming problem. To solve the problem within a polynomial time, we transform it into a minimum cost multi-commodity flow and solve it efficiently. Simulation results show that the proposed model can obtain the optimal solution real-time.
Dynamic Multi-level Privilege Control in Behavior-based Implicit Authentication Systems Leveraging Mobile Devices

Implicit authentication (IA) is gaining popularity over recent years due to its use of user behavior as the main input, relieving users from explicit actions such as remembering and entering passwords. However, such convenience comes with a cost of authentication accuracy and delay which we propose to improve in this paper. Authentication accuracy deteriorates as users' behaviors change as a result of mood, age, a change of routine, etc. Current authentication systems handle failed authentication attempts by locking the users out of their mobile devices. It is unsuitable for IA whose accuracy deterioration induces a high false reject rate, rendering the IA system unusable. Furthermore, existing IA systems leverage computationally expensive machine learning, which can introduce a large authentication delay. It is challenging to improve the authentication accuracy of these systems without sacrificing authentication delay. In this paper, we propose a multi-level privilege control (MPC) scheme that dynamically adjusts users' access privilege based on their behavior change. MPC increases the system's confidence in users' legitimacy even when their behaviors deviate from historical data, thus improving authentication accuracy. It is a lightweight feature added to the existing IA schemes that helps avoid frequent and expensive retraining of machine learning models, thus improving authentication delay. We demonstrate that MPC increases authentication accuracy by 18.63% and reduces authentication delay by 7.02 minutes on average, using a public dataset that contains comprehensive user behavior data.
Main Content Extraction from Web Pages

The extraction of the main content of a web page is a major issue in text mining. It provides less noisy input content prior to fine grained natural language processing methods. We present an unsupervised learning method to extract the main textual content of a web page. It relies on three stages: a clustering step of text blocks within a single web page, a phase of selection of the clusters associated with the main content, and a classification phase carried out on the data labeled by the two previous steps. The overall method allows to extract the main content at scale since it is fully unsupervised and the complexity at prediction time is low. Experiments are conducted to validate the generalization of the classifier and the quality of the obtained results.
Hierarchical Colored Petri Nets for the Verification of SysML Designs- Activity-Based Slicing Approach

Requirements verification at early phases of the design process is one of the main challenges when developing critical and complex systems. In this paper, we focus on the verification of SysML functional requirements on activity diagrams. Our contribution consists in the proposition of a slicing based verification approach guided by the SysML relationships between requirements, blocks, and activities. The objective is to provide a verification methodology for complex systems with many components. Our approach is based on formalizing activity diagrams using Hierarchical Coloured Petri Nets (HCPNs). The proposed slicing permits to alleviate the verification process. For verifying a given requirement, the slicing consists in extracting a minimized fragment (slice) of the HCPNs, which is sound and sufficient to realize the verification. The approach is illustrated by a case study, where we specify and we verify a fire protection system for data-centers.
Pedestrian identification in infrared and visible images based on pose keypoints matching

Abstract The identification of persons in multi-spectral images that are not spatially aligned is a challenging process. A correct identification can improve the pedestrian detection task for machine vision applications. In this context we propose a person identification mechanism able to correctly find the same person in infrared and visible images. The main contribution of the paper is the keypoint based matcher that uses a deep learning based solution for finding relevant human pose keypoints and a local neighbour search for extracting the best candidates for person identification. For each of the two images, infrared and color we first perform pedestrian detection. Next, on each detected instance we extract the relevant keypoints for shoulders, hands and legs. A matching algorithm between the extracted keypoints is proposed in order to perform the identification of persons in the two images. We obtain an identification accuracy of 76% for pedestrians that have a medium height with respect to the image dimensions, and have a small occlusion degree.