Reviewerly/exHarmony huggingface dataset:
Quality Evaluation of Self-Adaptive Systems

Self-adaptive systems (SAS) can adapt their behavior to suit user preferences or contexts, as well as monitor their performance and adjust it if necessary. In addition to adaptation operations, self-adaptive systems communicate with sensors, actuators, and other devices. Due to the complexity and dynamism of SAS, many situations can compromise the functioning of the system, such as faults in adaptations, low performance to execute tasks, and context inconsistencies. To prevent the system of these problems, it is essential to ensure high levels of quality. However, due to the peculiarities of these systems, there are still challenges to perform quality evaluations in these systems. In this sense, this paper proposes a discussion about the quality evaluation of self-adaptive systems in the last years. As a result, we identify challenges, limitations and research opportunities related to SAS quality evaluation.
Distribution of attention in a gallery segment on the National Socialists’ Führer cult: diving deeper into visitors’ cognitive exhibition experiences using mobile eye tracking

Mobile eye tracking recordings of 20 visitors of the gallery section 'Der Führer' of the Dokumentation Obersalzberg (Germany) were analyzed to determine the distribution of the visitors' attention on a detailed level of individual exhibition elements consisting of single objects, pictures, texts, and labels. It was found that most of these elements were only briefly scanned but with a substantial variability in gaze behavior both across participants and exhibition elements. In particular, the objects and pictures primarily attracted the visitors' attention but did not necessarily lead to elaborate inspection. In contrast, the information texts were less often noticed by the visitors, but if they were noticed, the visitors tended to spend substantial time reading them. The study demonstrates that notwithstanding methodological drawbacks such as the lack of unobtrusiveness of recording or highly time-consuming coding of raw data, mobile eye tracking allows to analyze visitors' attention in a gallery with unprecedented precision.
Jerk limited planner for real-time applications requiring variable velocity bounds

The generation of velocity references for autonomous vehicles is a known complex problem. The degree of complexity depends on the requirements to be fulfilled. First of all, in some applications of interest, trajectories need to be planned in real time. Furthermore, for smoothness reasons one may desire them to be jerk limited and, finally, they must satisfy velocity bounds that are variable along the path. Since planners proposed in the literature do not simultaneously fulfill the above mentioned characteristics, this paper proposes a novel generator which is able to efficiently evaluate almost minimum-time references. Despite the heuristic nature of the planner, an extensive test campaign has proved that obtained solutions are only slightly suboptimal w.r.t. the ones provided by a nonlinear solver, i.e., an algorithm for the global optimization whose computational times are incompatible with the online requirement.
Risk Susceptibility of Brain Tumor Classification to Adversarial Attacks

Discovery of adversarial attacks on deep neural networks, have exposed the vulnerabilities of these networks, wherein they often entirely fail to classify the attack generated images. While deep learning networks have generated promising results in performing brain tumor classification, there has been no analysis of their susceptibility to adversarial attacks. Vulnerability to adversarial attacks can render the deep neural networks useless for practical medical application. In this paper, a study has been performed to determine extent of white box adversarial attacks on convolutional neural networks used for brain tumor classification. Three different adversarial attacks are implemented on the network, namely Noise generated, Fast Gradient Sign, and Virtual Adversarial Training methods. The performance of the network under these attacks is analyzed and discussed. Furthermore, in the paper it is shown how these networks perform when trained on the adversarial attack generated images, which could be a possible solution to prevent the failure of the classification networks against adversarial attacks.
Formal foundations for responsible application integration

Enterprise Application Integration (EAI) constitutes the cornerstone in enterprise IT landscapes that are characterized by heterogeneity and distribution. Starting from established Enterprise Integration Patterns (EIPs) such as Content-based Router and Aggregator, EIP compositions are built to describe, implement, and execute integration scenarios. The EIPs and their compositions must be correct at design and runtime in order to avoid functional errors or incomplete functionalities. However, current EAI system vendors use many of the EIPs as part of their proprietary integration scenario modeling languages that are not grounded on any formalism. This renders correctness guarantees for EIPs and their composition impossible. Thus this work advocates responsible EAI based on the formalization, implementation, and correctness of EIPs. For this, requirements on an EIP formalization are collected and based on these requirements an extension of db-net, i.e., timed db-net , is proposed, fully equipped with execution semantics. It is shown how EIPs can be realized based on timed db-nets and how the correctness of these realizations can be shown. Moreover, the simulation of EIP realizations based on timed db-nets is enabled which is essential for later implementation. The concepts are evaluated in many ways, including a proof-of-concept implementation and case studies. The EIP formalization based on timed db-nets constitutes the first step towards responsible EAI.
Co-located Collaborative Block-Based Programming

With the increasing need to teach programming to novices using collaborative methods like pair programming, it is important to understand how different input devices can help support collaborative learning. In this paper we present Multi-Device Grace, the first application to explore block-based programming in a cross-device environment consisting of digital tabletops, mobile tablets, and laptops. We conducted a user study (n = 18) to explore how cross-device environments can support co-located collaborative block-based programming. The study used Tiled Grace, an existing block-based programming language, and our extensions: Tabletop Grace (designed for tabletops) and Mobile Grace (designed for tablets). Our results show that the majority of participants felt they were able to collaborate quickly and easily, and the cross device interaction would be particularly beneficial in an education setting.
Healthcare Information Exchange Through Blockchain-Based Approaches

Blockchain is one of the fastest growing and most important technologies in the world. Most of the people think that blockchain is all about cryptocurrency or bitcoin, but it is beyond that. It is a technology that creates immutable and distributable data records that are shared between peers in network database systems and records digital events in such a way that it cannot be altered or recognized until it reaches the recipient. In recent times, many of the industries are using blockchain as a tool to innovate their functionality. Some of the well-known industries are banking sector, real estate, healthcare, internet of things, insurance, and many more. Out of these industries, healthcare is one of the industries that is adopting blockchain very rapidly. This chapter will discuss the blockchain and how it has transformed the healthcare industry.
An Efficient Micropayment Channel on Ethereum

Blockchain protocols for cryptocurrencies offer secure payment transactions, yet their throughput pales in comparison to centralized payment systems such as VISA. Moreover, transactions incur fees that relatively hinder the adoption of cryptocurrencies for simple daily payments. Micropayment channels are second layer protocols that allow efficient and nearly unlimited number of payments between parties at the cost of only two transactions, one to initiate it and the other one to close it. Typically, the de-facto approach for micropayment channels on Ethereum is to utilize digital signatures which incur a constant gas cost but still relatively high due to expensive elliptic curve operations. Recently, ElSheikh et al. have proposed a protocol that utilizes hash chain which scales linearly with the channel capacity and has a lower cost compared to the digital signature based channel up to a capacity of 1000 micropayments. In this paper, we improve even more and propose a protocol that scales logarithmically with the channel capacity. Furthermore, by utilizing a variant of Merkle tree, our protocol does not require the payer to lock the entire balance at the channel creation which is an intrinsic limitation with the current alternatives. To assess the efficiency of our protocol, we carried out a number of experiments, and the results prove a positive efficiency and an overall low cost. Finally, we release the source code for prototype on Github ( https://github.com/HSG88/Payment-Channel ).
NCV Filter Design for Radar Tracking of Maneuvering Targets with LFM Waveforms

Modern radars employ linear frequency modulated (LFM) waveforms for target tracking as the range-Doppler coupling enables better track accuracy in range. When tracking maneuvering targets with a nearly constant velocity (NCV) Kalman filter, the selection of the process noise variance is complicated by the fact that the process errors are modeled as white Gaussian, while target maneuvers are deterministic or highly correlated in time. In recent years, the deterministic tracking index was introduced and used to develop a relationship between the maximum acceleration of the target and the process noise variance that minimizes the maximum mean squared error (MMSE) in position. A lower bound on the process noise variance was also expressed in terms of the maximum acceleration and deterministic tracking index. In this paper, the design methods for NCV Kalman filters are extended to radar tracking with linear frequency modulation (LFM) waveforms. The effectiveness of the design methods are confirmed via Monte Carlo simulations.
Cascaded Fully Convolutional DenseNet for Automatic Kidney Segmentation in Ultrasound Images

Although the extraction of traditional features has achieved good performance in kidney segmentation, the automatic kidney segmentation in whole ultrasound images is still challenging. Due to the characteristics of the kidney ultrasound images such as obvious noise, severe redundancy information and low image contrast, it is difficult to achieve accurate segmentation of the kidney by directly using the fully convolutional dense network (FC-DenseNet). Therefore, we propose a cascaded FC-DenseNet, that is, a model of coarse-to-fine segmentation. By training the coarse segmentation model, prediction probability that the pixel becomes the correct class label is calculated, and the proposal region of the kidney in the ultrasound image is obtained. By training the fine segmentation model, the proposal region of kidney is finely segmented and restored back to the original size. Compared with the original FC-DenseNet, cascaded FC-DenseNet has better performance in kidney segmentation. The mean intersection over union is increased by 1.43%, and the samples that are difficult to segment could be better segmented.
Markov Models of Cyber Kill Chains with Iterations

A understanding of the nature of targeted cyber-attack processes is needed to defend against this kind of cyber threats. Generally, the models describing processes of targeted cyber attacks are called in the literature as cyber kill chains or rarely cyber-attacks life cycles. Despite the fact that cyber-attacks have random nature, almost no stochastic models of cyber kill chains bases on the theory of stochastic processes have been proposed so far. This work, attempting to fill this deficiency, proposes to start using Markov processes for modeling some cyber-attack kill chains. In this paper two example theoretical models of cycles of returning cyber-attacks are proposed which have been generally named as the models of cyber kill chains with iterations. Presented models are based on homogeneous continuous time Markov chains.
Label Correlation Guided Deep Multi-View Image Annotation

Automatic image annotation is an important technique which has been widely applied in many fields such as social network image analysis and retrieval, face recognition and so on. Multi-view image annotation aims to utilize multi-view complementary information to achieve more effective annotation results. However, the existing multi-view image annotation methods cannot well handle the complex and diversified multi-view feature, and the label correlation is also ignored. In this paper, we propose an image annotation method by integrating deep multi-view latent space learning and label correlation guided image annotation into a unified framework, which is termed as Label Correlation guided Deep Multi-view image annotation (LCDM) method. LCDM first learns a consistent multi-view representation via deep matrix factorization, which well captures multi-view complementary information. Then, label correlation is exploited to improve the discriminating power of the classifiers. We propose a unified objective function so that multi-view data representation and classifiers can be jointly learned. Extensive experimental results on various image datasets demonstrate the effectiveness of our method.
A survey of the QR code phishing: the current attacks and countermeasures

Quick response (QR) code gained popularity and has been adapted for various applications such as a pointer to digital information and authentication. While the code gives convenience as a physical pointer to the digital world, it can be manipulated to divert the intended destination of the link to a malicious site. Thus, QR codes can be easily exploited by phishers to launch phishing attacks. Here, the current phishing attacks that utilise the QR code as a vector are surveyed and categorised. The recent countermeasures for such attacks are surveyed as well. It is also found that, current countermeasures are insufficient and face challenges like barcode-in-barcode attacks, high overhead solutions and limited data space in the code. In comparison to the amount of work done in web and email phishing detection, QR code phishing detection still inadequate. This paper hopes to shed light on the recent phishing attacks using QR code and the countermeasures proposed to tackle these attacks.
Immersive Virtual Reality for Assisting in Inclusive Architectural Design

Immersive Virtual Reality (IVR) technology is capable of simulating highly realistic environments and affecting behavioral realism via the creation of the feeling of immersion in a virtual world. Because of that IVR has got a great potential as a tool for evaluation of architectural designs. IVR allows to navigate in the designed space and interact with its elements as if they were real. In this paper, we demonstrate that IVR can assist during the Inclusive Design process and improve the architects’ understanding of the needs of different users, especially those that are older or disabled. Inclusive design promotes products and environments that are inclusive for all people. Architects require in-depth insight into how particular groups of people experience the designed environment and how they interact with it. IVR can help with that.
Chinese Event Detection Based on Multi-Feature Fusion and BiLSTM

With the rapid development of the Internet, the number of Internet users has grown rapidly, and the Internet has become more and more influential on people's lives.As a result, the amount of network text is increasing rapidly, and it is difficult to extract interested event information from it only by manual reading.Therefore, event extraction technique automatically extracting useful information from a large amount of unstructured texts becomes increasingly important.Event detection is the first step of event extraction task and plays a vital role in it.However, current event detection research lacks comprehensive consideration of the context of the trigger words.A Chinese event detection method based on multi-feature fusion and BiLSTM is proposed in this paper.The contextual information of word is divided into sentence-level and document-level in the method.The contextual information is captured based on BiLSTM model.At the same time, a word representation method suitable for trigger word classification tasks is proposed in this paper.The word representation incorporates semantic information, grammar information, and document-level context information of word.The word vectors in the sentence are sequentially inputted into BiLSTM model to obtain output vectors containing sentence-level contextual information.Finally, output vectors of BiLSTM are inputted into the Softmax classifier to realize the identification of the trigger words.The experimental results show that Chinese Event Detection Based on Multi-feature Fusion and BiLSTM method proposed in this paper has high accuracy.
A novel topic-based framework for recommending long tail products

Recommender systems have attracted tremendous research due to their distinctive properties in finding appropriate products/services for users facing the information overload problem. However, traditional recommendation techniques such as collaborative filtering usually recommend best-selling products to consumers, the long tail products that account for a large portion of total sales are ignored by the traditional recommender system. Thus, how to recommend niche products to users in the e-commerce platform is a critical issue. This paper presents a novel topic-based framework for recommending the long tail products. The framework has two components: the item profile extractor and the similar item extractor. The item profile extractor is essentially a novel topic model that can simultaneously extract product aspects and consumer sentiment from online reviews, especially works well on the long tail products with a few comments. In the similar item extractor, an enhanced k-medoids method specialized for textual data and numeric data is designed for identifying equivalent substitutes that can be recommended to customers in their purchase decisions. Extensive empirical evaluations on real-world datasets demonstrate the superiority of proposed framework compared with other state-of-the-art techniques.
Security in Many-Core SoCs Leveraged by Opaque Secure Zones

This paper presents an original approach to protect the execution of applications with security constraints in many-core systems. The proposed method includes three defense mechanisms. The first one is the application admission into the many-core using ECDH and MAC techniques. The second is the spatial reservation of computation and communication resources, resulting in an Opaque Secure Zone (OSZ). The key feature enabling the runtime creation of OSZs is a rerouting mechanism responsible for deviating any traffic traversing an OSZ. The last mechanism is the access to peripherals using a secure protocol to open access points in the OSZ border, and lightweight encryption mechanisms.
Berry phase estimation in gate-based adiabatic quantum simulation

Gate-based quantum computers can in principle simulate the adiabatic dynamics of a large class of Hamiltonians. Here we consider the cyclic adiabatic evolution of a parameter in the Hamiltonian. We propose a quantum algorithm to estimate the Berry phase and use it to classify the topological order of both single-particle and interacting models, highlighting the differences between the two. This algorithm is immediately extensible to any interacting topological system. Our results evidence the potential of near-term quantum hardware for the topological classification of quantum matter.
Online Bipartite Matching with Amortized <i>O</i> (log <sup>2</sup> <i>n</i> ) Replacements

In the online bipartite matching problem with replacements, all the vertices on one side of the bipartition are given, and the vertices on the other side arrive one-by-one with all their incident edges. The goal is to maintain a maximum matching while minimizing the number of changes (replacements) to the matching. We show that the greedy algorithm that always takes the shortest augmenting path from the newly inserted vertex (denoted the SAP protocol) uses at most amortized O (log 2 n ) replacements per insertion, where n is the total number of vertices inserted. This is the first analysis to achieve a polylogarithmic number of replacements for any replacement strategy, almost matching the Ω (log n ) lower bound. The previous best strategy known achieved amortized O (√ n ) replacements [Bosek, Leniowski, Sankowski, Zych, FOCS 2014]. For the SAP protocol in particular, nothing better than the trivial O ( n ) bound was known except in special cases. Our analysis immediately implies the same upper bound of O (log 2 n ) reassignments for the capacitated assignment problem, where each vertex on the static side of the bipartition is initialized with the capacity to serve a number of vertices. We also analyze the problem of minimizing the maximum server load. We show that if the final graph has maximum server load L , then the SAP protocol makes amortized O (min { L log 2 n , √ n log n }) reassignments. We also show that this is close to tight, because Ω (min { L , √ n }) reassignments can be necessary.
A comparative study of high-recall real-time semantic segmentation based on swift factorized network

Semantic Segmentation (SS) is the task to assign a semantic label to each pixel of the observed images, which is of crucial significance for autonomous vehicles, navigation assistance systems for the visually impaired, and augmented reality devices. However, there is still a long way for SS to be put into practice as there are two essential challenges that need to be addressed: efficiency and evaluation criterions for practical application. For specific application scenarios, different criterions need to be adopted. Recall rate is an important criterion for many tasks like autonomous vehicles. For autonomous vehicles, we need to focus on the detection of the traffic objects like cars, buses, and pedestrians, which should be detected with high recall rates. In other words, it is preferable to detect it wrongly than miss it, because the other traffic objects will be dangerous if the algorithm miss them and segment them as safe roadways. In this paper, our main goal is to explore possible methods to attain high recall rate. Firstly, we propose a real-time SS network named Swift Factorized Network (SFN). The proposed network is adapted from SwiftNet, whose structure is a typical U-shape structure with lateral connections. Inspired by ERFNet and Global Convolution Networks (GCNet), we propose two different blocks to enlarge valid receptive field. They do not take up too much calculation resources, but significantly enhance the performance compared with the baseline network. Secondly, we explore three ways to achieve higher recall rate, i.e loss function, classifier and decision rules. We perform a comprehensive set of experiments on state-of-the-art datasets including CamVid and Cityscapes. We demonstrate that our SS convolutional neural networks reach excellent performance. Furthermore, we make a detailed analysis and comparison of the three proposed methods on the promotion of recall rate.