Reviewerly/exHarmony huggingface dataset:
Short-term detection of volcanic unrest at Mt. Etna by means of a multi-station warning system

Early-warning assessment of a volcanic unrest requires that accurate information from monitoring is continuously gathered before volcanic activity starts. Seismic data are an optimal source of such information, overcoming safety problems due to dangerous conditions for field surveys or cloud cover that may hinder visibility. We designed a multi-station warning system based on the classification of patterns of the background seismic radiation, so-called volcanic tremor, by using Self-Organizing Maps (SOM) and fuzzy clustering. The classifier automatically detects patterns that are typical footprints of volcanic unrest. The issuance of the SOM colors on DEM allows their geographical visualization according to the stations of detection; this spatial location makes it possible to infer areas potentially impacted by eruptive phenomena. Tested at Mt. Etna (Italy), the classifier forecasted in hindsight patterns associated with fast-rising magma (typical of lava fountains) as well as a relatively long lead time of the outburst (lava flows from eruptive fractures). Receiver Operating Characteristics (ROC) curves gave an Area Under the Curve (AUC) ∼0.8 indicative of a good detection accuracy that cannot be achieved from a mere random choice.
Vicious circle principle, aggregates, and formation of sets in ASP based languages

The paper introduces an extension of the original Answer Set Prolog (ASP) by several set constructs including aggregates, defined as functions on sets. The new language, called Alog allows creating sets based on the Vicious Circle Principle by Poincaré and Russell which eliminates a number of problems found in existing extensions of ASP by aggregates. We argue that, despite the fact that Alog is not as expressive as other extensions of ASP by aggregates, clarity of its syntax and semantics, addition of several new set-based constructs, and simplicity and the ease of use make it a viable competitor to these languages. We also study a number of important properties of the language and show how ideas used in its design can be utilized to generalize and simplify the definition of another important extension of ASP by aggregates.
Instrumenting and Analyzing Fabrication Activities, Users, and Expertise

The recent proliferation of fabrication and making activities has introduced a large number of users to a variety of tools and equipment. Monitored, reactive and adaptive fabrication spaces are needed to provide personalized information, feedback and assistance to users. This paper explores the sensorization of making and fabrication activities, where the environment, tools, and users were considered to be separate entities that could be instrumented for data collection. From this exploration, we present the design of a modular system that can capture data from the varied sensors and infer contextual information. Using this system, we collected data from fourteen participants with varying levels of expertise as they performed seven representative making tasks. From the collected data, we predict which activities are being performed, which users are performing the activities, and what expertise the users have. We present several use cases of this contextual information for future interactive fabrication spaces.
A Study on Machine Learning for Steganalysis

Data security is very important when sensitive data are transmitted over the Internet. Steganography and steganalysis techniques can solve the problem of copyright, ownership, and detection malicious data. Steganography is to hide secret data without distortion and steganalysis is to detect the presence of hidden data. In this paper, steganography and steganalysis techniques are described together with machine learning frameworks to show that machine learning framework can be used to detect the secret data hiding in image using steganography algorithms.
Refugees &amp; HCI SIG

Currently, the United Nations High Commissioner for refugees estimates that there are around 65.8 million forcibly displaced people worldwide [16]. As digital technologies have become more available, humanitarian researchers and organizations have begun to explore how technologies may be used to address refugee needs under the umbrella of Digital Humanitarianism. Interest in refugee and humanitarian contexts has also been expressed within the HCI community through the organization of workshops at conferences. While previous engagements within the HCI community have focused on our experiences of working within refugee contexts as well as developing a common research agenda, we have yet to explore how HCI research fits within wider humanitarian research and in relation to digital humanitarianism. This SIG invites HCI researchers to engage in discussions on situating HCI research within humanitarian research and response.
Improving backtracking search algorithm with variable search strategies for continuous optimization

The backtracking search algorithm (BSA), a relatively new evolutionary algorithm (EA), has been shown to be a competitive alternative to other population-based algorithms. To effectively solve a variety of optimization problems, this paper suggests ten mutation strategies and compares the performance of selection mechanisms in employing these strategies. Moreover, following the original BSA design, new parameters of historical mean and best positions are proposed in order to implement several additional mutation strategies. In addition, as recommended in the literature, a one-dimensional crossover scheme is enacted for greedy strategies in order to prevent premature convergence. Furthermore, three settings for search factors of mutation strategies are proposed. As a result, improved BSA versions that employed, respectively, ten and four mutation strategies were found to significantly facilitate the ability of BSA to handle optimization tasks of different characteristics. The experimental results show that the proposed versions outperformed the basic BSA in terms of achieving high convergence speed in the early stage, reaching the convergence precision and plateau with better scores, and performing perfectly on tests of composition functions. In addition, the improved BSA versions outperformed five popular, nature-inspired algorithms in terms of achieving the best convergence precision and performing perfectly on six composition functions.
Reversible data hiding in encrypted images with somewhat homomorphic encryption based on sorting block-level prediction-error expansion

This paper proposes a reversible data hiding scheme for encrypted images with SHE (Somewhat Homomorphic Encryption) based on sorting block-level prediction-error expansion (SBPEE). The image provider divides the image into 2 × 2 image blocks and uses SHE to protect the content of the image. In encrypted images, the data hider embeds the secret data with the method of SBPEE, which is based on the pixel redundancy within each block. And the sorting technique shows a good performance at low embedding rate. With the watermarked encrypted image, if the receiver has only the data hiding key, he can extract the additional data. If the receiver has both the data hiding key and the encryption key, he can extract the additional data correctly and recover the original image without loss. The experimental results show that the performance of the method is better than that of the existing similar methods when the embedding rate is low and is consistent with the existing methods when the embedding rate is high.
Secure over-the-air firmware updating for automotive electronic control units

This work presents secure over-the-air firmware updating that brings a homogenized updating process across OEMs, suppliers and sub-tiers, removing at the same time the costs for individual security precautions and cryptographic countermeasures for each individual component or sub-system. The objective is to overcome all attacks to the servers, to the networks and to the diverse electronic control units (ECUs) in modern vehicles.
A Finite-State-Machine model driven service composition architecture for internet of things rapid prototyping

The emerging field of Internet of Things (IoT) offers an unprecedented opportunity for a wide spectrum of applications. However, most applications have been integrating IoT devices through proprietary mechanisms and with closed technology stacks. The monolithic, mostly vendor-specific development architecture leads to soaring customization costs and limited component reusability. It impedes the full-fledged IoT applications in cross-organizational, general-purpose and rapid-changing scenarios. This research intends to provide a coherent architecture that enables interoperable, low-cost and user-customizable IoT rapid prototyping. Under this architecture, each IoT component, either a physical device or a control logic, is abstracted into an independent web service that described by a set of transferable states. By concatenating a valid chain of state transfers between web services, IoT components are further assembled into customizable applications. In this research, a Finite-State-Machine (FSM) model driven architecture is established and a typical implementation of the proposed architecture, i.e. the Hyper Sensor Markup Language (HSML) is provided. We also discuss two practical use cases and related evaluations.
Self-Efficacy-Based Game Design to Encourage Security Behavior Online

Eliciting cybersecurity behavior change in users has been a difficult task. Although most users have concerns about their safety online, few take precautions. Transformational games offer a promising avenue for cybersecurity behavior change. To date, however, studies typically focus on entertainment value instead of investigating the effectiveness and design potential of games in cybersecurity. As a first step to filling this gap, we present the design of Hacked Time, a desktop game that aims to encourage cybersecurity behavior change by translating self-efficacy theory into the game's design. As cybersecurity games are a relatively novel area, our design aims to serve as a prototype for mapping specific behavior change principles relevant to this area onto game design practice.
Data-Driven Software Architecture for Analyzing Confidentiality

Preservation of confidentiality has become a crucial quality property of software systems that software vendors have to consider in each development phase. Especially, neglecting confidentiality constraints in the software architecture leads to severe issues in later phases that often are hard to correct. In contrast to the implementation phase, there is no support for systematically considering confidentiality in architectural design phases by means of data processing descriptions. To fill this gap, we introduce data flows in an architectural description language to enable simple definition of confidentiality constraints. Afterwards, we transform the software architecture specification to a logic program to find violated confidentiality constraints. In a case study-based evaluation, we apply the analysis to sixteen scenarios to show the accuracy of the approach.
FQI: feature‐based reduced‐reference image quality assessment method for screen content images

In this study, a reduced-reference image-quality-assessment (IQA) method for screen content images, named as feature-quality-index (FQI) is proposed. The proposed method is based on the fact that the human visual system is more sensitive towards change in features than intensity or structure. Reduced features from the reference and distorted images are first extracted. In order to find the preserved features in the distorted image, a feature matching process with a reduced number of distance calculations is proposed, namely reduced-distance method. To reflect the importance of the matched features and their distance, the inner product between the normalised scale and distance vector is obtained. Extensive comparisons are performed on two available benchmark databases namely SIQAD and QACS, with eight reduced-reference, and nine full-reference state-of-the-art IQA techniques to demonstrate the consistency, accuracy, and robustness of the proposed FQI. The subjective evaluation of mean opinion score shows that FQI outperforms the current state-of-the-art IQA techniques.
A Survey of User-Centred Approaches for Smart Home Transfer Learning and New User Home Automation Adaptation

Recent smart home applications enhance the quality of people's home experiences by detecting their daily activities and providing them services that make their daily life more comfortable and safe. Human activity recognition is one of the fundamental tasks that a smart home should accomplish. However, there are still several challenges for such recognition in smart homes, with the target home adaptation process being one of the most critical, since new home environments do not have sufficient data to initiate the necessary activity recognition process. The transfer learning approach is considered the solution to this challenge, due to its ability to improve the adaptation process. This paper endeavours to provide a concrete review of user-centred smart homes along with the recent advancements in transfer learning for activity recognition. Furthermore, the paper proposes an integrated, personalised system that is able to create a dataset for target homes using both survey and transfer learning approaches, providing a personalised dataset based on user preferences and feedback.
Arabic Text Summarization using Firefly Algorithm

In this research, we propose the use of Firefly algorithm for the extraction of summaries of single Arabic documents. The proposed approach is compared with two evolutionary approaches that use genetic algorithms and harmony search. The EASC Corpus and the ROUGE toolkit are used for the evaluation of the proposed approach. Experimental results showed that the proposed approach achieved competitive and even higher ROUGE scores in comparison with the two state-of-the-art approaches.
Towards the Application of Blockchain technology for Smart Grids in Kazakhstan

The concept of smart grid infrastructure is already being implemented and progressing rapidly. The number of countries are transforming the traditional power grid so that it will be able to maintain the smart grid requirements. Smart grid is known as an evolutionary power grid which is proposed to make intelligent decisions on its own based on a current state of electrical power system. Microgrid (MG) is a "building block" of smart grid which supposed to provide wider control capability into the system, thus allowing to operate without connection to the grid. However, the remote and autonomous control of the smart grid may result in problems related to security. The main purpose of this paper is to consider various application scenarios of the Blockchain technology in smart grid. The utmost example is smart contract feature. It allows to reduce cost and increase security of trading operations without including third parties. In other words, direct communication between seller and consumer is implemented. Particularly, paper explain how the security and resilience of the system can be improved by the application of Blockchain technology. Furthermore, decentralized machine-to-machine interaction scheme is proposed. First of all, auction based electricity trading platform is established in Jade platform, and further on electricity trading history is stored in blockchain. The proposed case study shows implementation possibility of smart contracts in smart grid environment.
A Comprehensive Survey on Privacy and Security Issues in Cloud Computing, Internet of Things and Cloud of Things

This journal article deals with the most important existing problems of security and privacy of the Cloud Computing (CC), Internet of Things (IoT) and Cloud of Things (CoT) concepts especially confidentiality issues. With the evolution of ubiquitous computing, everything is connected everywhere, therefore these concepts have been widely studied in the literature. However, due to the systems complexity and the difficulty to control each access attempt, intrusions and vulnerabilities will be more recurrent. To tackle this issue, researchers have been focused on various approaches enforcing security and privacy. In the present article, risk factors and solutions regarding these technologies are reviewed then current and future trends are discussed.
JeL

We present JeL-a bio-responsive immersive installation for interpersonal synchronization through breathing. In JeL, two users are immersed in a virtual underwater environment, where their individual breathing controls the movement of a jellyfish. As users synchronize their breathing, a virtual glass sponge-like structure starts to grow, representing the users' physiological synchrony. JeL explores a novel form of interpersonal interaction in virtual reality that aims to connect users to their physiological state through biofeedback, to each other through physiological synchronization, and to nature through connecting with a jellyfish and collaboratively growing a glass sponge-inspired sculpture. This form of immersive, bio-responsive interaction could ultimately be used to encourage self-awareness, a feeling of connectedness, and consequently pro-social and pro-environmental attitudes. Here, we describe the motivation, inspiration, design elements, and future work involved in bringing this system to fruition.
Joint Transformation Learning via the L<sub>2,1</sub>-Norm Metric for Robust Graph Matching

Establishing correspondence between two given geometrical graph structures is an important problem in computer vision and pattern recognition. In this paper, we propose a robust graph matching (RGM) model to improve the effectiveness and robustness on the matching graphs with deformations, rotations, outliers, and noise. First, we embed the joint geometric transformation into the graph matching model, which performs unary matching over graph nodes and local structure matching over graph edges simultaneously. Then, the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm is used as the similarity metric in the presented RGM to enhance the robustness. Finally, we derive an objective function which can be solved by an effective optimization algorithm, and theoretically prove the convergence of the proposed algorithm. Extensive experiments on various graph matching tasks, such as outliers, rotations, and deformations show that the proposed RGM model achieves competitive performance compared to the existing methods.
Breaking Virtual Memory Protection and the SGX Ecosystem with Foreshadow

Foreshadow is a speculative execution attack that allows adversaries to subvert the security guarantees of Intel's Software Guard eXtensions (SGX). Foreshadow allows access to data across process boundaries, and allows virtual machines (VMs) to read the physical memory belonging to other VMs or the hypervisor.
Quantifying privacy vulnerability of individual mobility traces: A case study of license plate recognition data

Emerging smart transportation applications are calling for publishing and sharing individual-based mobility trace data sets to researchers and practitioners; in the meanwhile, however, privacy issues have become a major concern given that true identities of individuals can be easily revealed from these data sets. Data synthesis In this paper, we quantitatively measure the risk of privacy disclosure in mobility trace data set caused by re-identification attacks based on the concept of k-anonymity. Using a one-month license plate recognition (LPR) data set collected in Guangzhou, China, we examine a variety of factors determining the degree of anonymity of an individual, including the temporal granularity and the size of the published data, local v.s. non-local vehicles, and continuous v.s. non-continuous observations. We find that five spatiotemporal records are enough to uniquely identify about 90% of individuals, even when the temporal granularity is set to be half a day. To publish LPR data without compromising privacy, we propose a suppression solution and a generalization solution and quantify the privacy-and-utility trade-off of them. Our results show that the suppression solution, which removes sensitive records, have a notable performance on privacy protection. The average individual anonymity identified by three spatiotemporal records increases by more than 20% at the cost of losing less than 8% of the data. We also propose a bintree-based adaptive time interval cloaking algorithm as a generalization solution. To meet a specific anonymity constraint, this algorithm adjusts the temporal resolution adaptively based on traffic counts under the principle of minimal information loss. We find that the generalization algorithm performs extremely well in satisfying different user-specified anonymity constraints and it is more flexible and reliable than the traditional uniform time interval cloaking method. We also find a strong correlation between the resulting temporal accuracy of data anonymized by the algorithm and the traffic condition. This study serves as a reminder to relevant agencies and data owners about the privacy vulnerability in individual-based mobility trace data sets and provides methodological guidance when publishing and sharing such sensitive data set.
Empirical Study on Human Evaluation of Complex Argumentation Frameworks

In abstract argumentation, multiple argumentation semantics have been proposed that allow to select sets of jointly acceptable arguments from a given argumentation framework, i.e. based only on the attack relation between arguments. The existence of multiple argumentation semantics raises the question which of these semantics predicts best how humans evaluate arguments. Previous empirical cognitive studies that have tested how humans evaluate sets of arguments depending on the attack relation between them have been limited to a small set of very simple argumentation frameworks, so that some semantics studied in the literature could not be meaningfully distinguished by these studies. In this paper we report on an empirical cognitive study that overcomes these limitations by taking into consideration twelve argumentation frameworks of three to eight arguments each. These argumentation frameworks were mostly more complex than the argumentation frameworks considered in previous studies. All twelve argumentation framework were systematically instantiated with natural language arguments based on a certain fictional scenario, and participants were shown both the natural language arguments and a graphical depiction of the attack relation between them. Our data shows that grounded and CF2 semantics were the best predictors of human argument evaluation. A detailed analysis revealed that part of the participants chose a cognitively simpler strategy that is predicted very well by grounded semantics, while another part of the participants chose a cognitively more demanding strategy that is mostly predicted well by CF2 semantics.
World From Blur

What can we tell from a single motion-blurred image? We show in this paper that a 3D scene can be revealed. Unlike prior methods that focus on producing a deblurred image, we propose to estimate and take advantage of the hidden message of a blurred image, the relative motion trajectory, to restore the 3D scene collapsed during the exposure process. To this end, we train a deep network that jointly predicts the motion trajectory, the deblurred image, and the depth one, all of which in turn form a collaborative and self-supervised cycle that supervise one another to reproduce the input blurred image, enabling plausible 3D scene reconstruction from a single blurred image. We test the proposed model on several large-scale datasets we constructed based on benchmarks, as well as real-world blurred images, and show that it yields very encouraging quantitative and qualitative results.