Reviewerly/exHarmony huggingface dataset:
Two-level information hiding for quantum images using optimal LSB

As a primary branch of information hiding, quantum image steganography has become one of the most popular areas of study in the field of security. In this paper, the two-level embedding approach for information hiding is surveyed using an optimal least significant bit (LSB) quantum steganography algorithm, which only modifies at most one qubit of the LSBs of each pixel to perform embedding. The first level is to hide the encrypted quantum secret image into a quantum watermark image, and the second level is to embed the quantum watermark image into a quantum cover image. Using the optimal LSB-based algorithm, the double embedding can make the position of embedding have a certain randomness, thus increasing security. In addition, the quantum secret image can be reconstructed by a series of inverse operations in the recovery phase; only the stego image and the key can extract the quantum secret image. The simulated experimental results and analysis demonstrate that the proposed scheme produces images of good visual quality, robustness and high security.
Towards Fact-Checking through Crowdsourcing

The content creation process on the Internet changed with the emergence of Social Networks, with the volume of information overgrowing. Unfortunately, social networks have shown a high potential for disseminating hoaxes and, more recently, fake news. This fact encouraged the emergence of fact-checking organizations, usually carried out by journalism organizations. However, they have limitations to meet the enormous demand arising from the immense volume of fake news currently on the Internet. Thus, this study proposes a crowd-based solution as a way to meet this high demand for fact-checking. The proposed fact-checking process uses crowdsourcing to performing the filtering, analysis and classification of news. In this way, we can use a checking mechanism that may scale proportionally to the creation of fake news.
Comparing Performance Potentials of Classical and Intuitionistic Fuzzy Systems in Terms of <i>Sculpting the State Space</i>

This article provides new application-independent perspectives about the performance potential of an intuitionistic (I-) fuzzy system over a (classical) Takagi-Sugeno-Kang (TSK) fuzzy system. It does this by extending sculpting the state-space works from a TSK fuzzy system to an I-fuzzy system. It demonstrates that, for piecewise-linear membership functions (trapezoids and triangles), an I-fuzzy system always has significantly more first-order rule partitions of the state space-the coarse sculpting of the state space-than does a TSK fuzzy system, and that some I-fuzzy systems also have more second-order rule partitions of the state space-the fine sculpting of the state space-than does a TSK fuzzy system. It is the author's conjecture that for piecewise-linear membership functions (trapezoids and triangles): it is the always significantly greater coarse (and possibly fine) sculpting of the state space that provides an I-fuzzy system with the potential to outperform a TSK fuzzy system, and that a type-1 I-fuzzy system has the potential to outperform an interval type-2 fuzzy system.
Support vector machines for classification of low birth weight in Indonesia

Abstract This paper proposes support vector machines (SVMs), which is currently one of the most popular algorithms in machine learning (ML), in order to classify the low birth weight (LBW) data. The main objectives of this study are to predict the classification of LBW data in Indonesia based on the SVMs andto compare the performance of the proposed SVMs with the binary logistic regression as the most common model for classification of LBW data. The obtained samples were based on the results of Indonesian Demographic and Health Survey in 2012. The results showed that SVMs with four kernel functions (linear, radial, polynomial and hyperbolic tangent) were fit well to the LBW data in Indonesia. Furthermore, the constructed SVMs based on linear kernel function had the best performance among the SVMs with the other proposed kernel functions. This research also concluded that the SVMs based on linear kernel competed well with thebinary logistic regression forclassification LBW data in Indonesia.
Blockchain Scheme Based on Evolutionary Proof of Work

In recent years, applications of the Blockchain concept, esp. as ledger for bitcoin transactions, has already resulted in huge amounts of wasted electrical energy for performing the Proof-of-Work tasks (cryptographic puzzles). Here, we consider an alternative concept to have this energy used at least for a useful purpose, the solution of real-world optimization problems. By means of the Traveling Salesperson Problem as model problem, we propose a concept to use optimization algorithms in an iterative manner to provide the Proof-of-Work needed to expand the Blockchain by a new block. The basic idea is to improve the tour cost for the best tour found for block n, extended by adding one more city, as a requirement for the inclusion of a new block in the Blockchain. This allows for the design of limited Blockchains, solving the underlying combinatorial optimization problems at the same time. Independently, it calls in for new efficient optimization algorithms and can serve as a real-world contest. It is discussed that metaheuristic algorithms perform an attractive class of optimization algorithms that can be used for the proposed approach. Numerical experiments also demonstrate the growth in problem complexity being handled by a binary PSO, which is a basic requirement for the full concept to work in practice.
Quantum Structure for Modelling Emotion Space of Robots

Utilising the properties of quantum mechanics, i.e., entanglement, parallelism, etc., a quantum structure is proposed for representing and manipulating emotion space of robots. This quantum emotion space (QES) provides a mechanism to extend emotion interpretation to the quantum computing domain whereby fewer resources are required and, by using unitary transformations, it facilitates easier tracking of emotion transitions over different intervals in the emotion space. The QES is designed as an intuitive and graphical visualisation of the emotion state as a curve in a cuboid, so that an “emotion sensor” could be used to track the emotion transition as well as its manipulation. This ability to use transition matrices to convey manipulation of emotions suggests the feasibility and effectiveness of the proposed approach. Our study is primarily influenced by two developments. First, the massive amounts of data, complexity of control, planning and reasoning required for today’s sophisticated automation processes necessitates the need to equip robots with powerful sensors to enable them adapt and operate in all kinds of environments. Second, the renewed impetus and inevitable transition to the quantum computing paradigm suggests that quantum robots will have a role to play in future data processing and human-robot interaction either as standalone units or as part of larger hybrid systems. The QES proposed in this study provides a quantum mechanical formulation for quantum emotion as well as a platform to process, track, and manipulate instantaneous transitions in a robot’s emotion. The new perspective will open broad areas, such as applications in emotion recognition and emotional intelligence for quantum robots.
Measuring the Information Security Awareness Level of Government Employees Through Phishing Assessment

As an important institutional element, government information security is not only related to technical issues but also to human resources. Various types of information security instruments in an institution cannot provide maximum protection as long as employees still have a low level of information security awareness. This study aims to measure the level of information security awareness of government employees through case studies at the Directorate General of ABC (DG ABC) in Indonesia. This study used two methods, behavior approach through phishing simulation and knowledge approach through a questionnaire on a Likert scale. The simulation results were analyzed on a percentage scale and compared to the results of the questionnaire to determine the level of employees' information security awareness and determine which method was the best. Results show a significant relationship between the simulation results and the questionnaire results. Among the employees who opened the email, 69% clicked on the link that led to the camouflage page and through the questionnaire, it was found that the information security awareness level of DG ABC employees was at the level of 79.32% which was the lower limit of the GOOD category.
Multi-agent based intuitionistic fuzzy logic healthcare decision support system

The present study is intended to provide a practical Decision Support System framework, based on Multi Agent System (MAS) and Intuitionistic Fuzzy Logic (IFL), useful for implementation in the healthcare area. The major objective consists in enabling the implementing of the conceived incremental pr oject design, dubbed “smart healthcare”, in public or private healthcare centers (hospitals, polyclinics, etc.) through mobile cloud computing technology. In this regard, the present work is conceived to involve a thorough investigation and discussion of the IFL efficiency scope, as integrated into MAS architecture, for the purpose of detecting the patient’s health status in Intensive Care Units (ICUs). To this end, an implementation of the Intuitionistic Fuzzy Logic Decision Support System (IFLDSS), based on the Modified Early Warning Score (MEWS) standard, is carried out in the Tunisian Sfax city based ESSALEMA polyclinic. The proposed solution’s achieved results appears to reveal that the IFLDSS proves to display a commanding capacity in detecting uncertainty related to the ICU patients’ deteriorating cases. On comparing the IFL provided performance to that exhibited by the IFLDSS application, the findings appears to reveal well that the IFL MEWS appears to achieve remarkably higher accuracy scores than those provided by the MEWS standard.
Characterizing the Redundancy of DarkWeb .onion Services

The Darkweb hosts a number of legal and illegal services that are hard to reach due to the lack, by design, of a regular 'name service' as those operated by DNSs on the clearnet. This difficulty, together with the continuous appearance of decoy mirror services and replicated domains, severely limits the investigation capabilities of researchers and law enforcement agencies interested in measuring the Darkweb phenomenon as a whole. To address this issue we developed MASSDEAL, a tool for the automated exploration of the Darkweb that automatically learns about new or previous unseen services and measures repeatedly across time and space (i.e. across multiple deployments of the same service). Relying on data collected over more than 20 thousand darkweb services sampled from September 2018 to January 2019, we perform and report an extensive analysis of service redundancy and measure the appearance of .onion mirrors as well as providing an estimation of the infrastructural redundancy behind those systems.
Revocable Cloud-Assisted Attribute-Based Signcryption in Personal Health System

PHR System is a favorable platform for personal health information exchange.In order to ensure that the personal information is not falsified and leaked by malicious users, we use the attributebased signcryption technology to provide secure and reliable data protection.At the same time, in order to prevent users from accessing the data in the system by collusion of attributes, we proposes a revocable cloud-assisted attribute-based signcryption scheme which using the broadcast encryption technology and key segmentation technology realize user revocation function.Moreover, the proposed scheme is proven to be confidentiality and unforgeability under chosen plaintext attack in the random oracle model.And the experimental evaluation indicates that the proposed scheme is practical and feasible.
Enhancing fragility of zero-based text watermarking utilizing effective characters list

Text is an important medium used for sharing information worldwide. For a text document, digital watermarking is an efficient way for copyright protection, authentication, tamper proofing, to name but a few. In this paper, a zero-based watermarking approach is proposed for document authentication and tamper detection. To enhance the fragility of watermark, the proposed text watermarking approach can be comfortably utilized – based on the Effective Characters List (ECL) for watermark generation. The ECL method is generated for English text zero-watermarking by maintaining the contents of the original document and constructing the watermark by formulating the smooth transition between the selected characters in the documents. The evaluation of the proposed watermarking approach is based on three famous watermarking attacks including deletion, insertion, and reordering with an accuracy of 80.76%, 80.36%, and 88.1%, respectively. For a fair evaluation, a comparison is put forth with a recent zero-based watermarking method - clearly showing that the proposed method outperforms existing with greater accuracy.
A teaching method based on storytelling of a student social activity in renewable energy education

In recent years, renewable energy sources have been of great interest, because they are reversible, available, the environment-friendly and cheaper than conventional fossil fuels. Therefore, suitable policies to expand the use of this type of energy in all countries are important which requires precise planning and investment. One of the first important steps toward these planning is a proper introduction of this kind of energy in the society. Education in schools, universities and mass media can play a major role in building a culture and introduce renewable energy sources. The purpose of this paper is to present and introduce an innovative scheme to introduce renewable energies. This idea is based on a fictional story with real photos of the mountaineering. Because the type and method of teaching is an important factor in the proper induction of concepts to learners, in this paper, after studying the different methods of learning and teaching in different ways, this plan has been proposed. To evaluate the effectiveness, the project has been applied to three groups of students at undergraduate and postgraduate levels. Polls from these groups show acceptable satisfaction from the plan.
How metaphors matter: an ethnography of blockchain-based re-descriptions of the world

This paper explores the role of metaphors in the production of re-descriptions of the world within the framework of technological design processes. Drawing on a collaborative ethnography with the Economic Space Agency (ECSA), a start-up developing post-blockchain technology, this paper illustrates how metaphors mimic the toponymy of decentralized material infrastructures, while simultaneously pushing forward ‘posthuman’ values that are expected to become fixated through software. Through an analysis of a ‘collection’ of metaphors produced by ECSA, this paper sheds light on the work performed by specific vocabularies, within technological communities, in shaping a symbiotic relationship between futuristic politics and material culture.
Web Quality of Experience from Encrypted Packets

Pervasive encryption makes it hard for ISPs to manage their network. Yet, to avoid user churn at times of shrinking revenues, ISPs must be able to assess the quality of experience they are delivering to their customers. The case of the Web is particularly complex, with a plethora of recently proposed in-browser metrics that aim at capturing the page visual rendering quality (e.g. Above the Fold and SpeedIndex). In this demo, we showcase that such metrics can be estimated quite accurately just from streams of encrypted packets, using classic supervised learning techniques.
Rethinking Regex engines to address ReDoS

Regular expressions (regexes) are a powerful string manipulation tool. Unfortunately, in programming languages like Python, Java, and JavaScript, they are unnecessarily dangerous, implemented with worst-case exponential matching behavior. This high time complexity exposes software services to regular expression denial of service (ReDoS) attacks.
Edge-Cloud Orchestration: Strategies for Service Placement and Enactment

As devices existing at the edge of the network improve in their processing and data storage capacity, there is increasing potential to host and enact services on such devices. A workflow that was traditionally enacted on a data centre can be fragmented across both edge and data centre hosted resources. The following aspects are investigated in this work: (i) mechanisms for dividing a workflow across edge and cloud/data centre resources; (ii) service hosting environments that can be shared across edge and data centre resources; (iii) performance metrics that can influence service placement and selection. An "edge orchestrator" is a resource manager that makes such decisions on the behalf of a user application, and which may be centralised or distributed. An industry scenarios is used to illustrate decision points that influence such choices within an edge orchestrator. The overall objective considered is the completion of the workflow within some deadline constraint by the edge orchestrator.
Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents

Neuroevolution is a process of training neural networks (NN) through an evolutionary algorithm, usually to serve as a state-to-action mapping model in control or reinforcement learning-type problems. This paper builds on the Neuro Evolution of Augmented Topologies (NEAT) formalism that allows designing topology and weight evolving NNs. Fundamental advancements are made to the neuroevolution process to address premature stagnation and convergence issues, central among which is the incorporation of automated mechanisms to control the population diversity and average fitness improvement within the neuroevolution process. Insights into the performance and efficiency of the new algorithm is obtained by evaluating it on three benchmark problems from the Open AI platform and an Unmanned Aerial Vehicle (UAV) collision avoidance problem.
Embodying an Extra Virtual Body in Augmented Reality

Presence and the sense of embodiment are essential concepts for the experience of our self and virtual bodies, but there is little quantitative evidence for a relation between these, and this relation becomes more complicated when there are real and virtual bodies in augmented reality (AR). We investigate the experience of body ownership, agency, self-location and self-presence in AR where users can see their real body and a virtual body from behind. Active arm movement congruency and virtual anthropomorphism are varied. We found significant effects of movement congruency but not anthropomorphism, a strong correlation between self-presence and body ownership, and a moderate correlation between self-presence and agency and self-location.
The End to the Myth of Individual Programmer Productivity

One often-quoted truism in software engineering is that good programmers are "much much better" than bad programmers. The size of "much much better" is widely debated, but ranges such as 10 times more productive are often cited as conservative estimates. This article argues that such statements are misleading and miss numerous important effects. Based on the studies described later, it would appear that some programmers are not inherently exceedingly better than others.
Towards an Uncertainty-Aware Visualization in the Digital Humanities

As visualization becomes widespread in a broad range of cross-disciplinary academic domains, such as the digital humanities (DH), critical voices have been raised on the perils of neglecting the uncertain character of data in the visualization design process. Visualizations that, purposely or not, obscure or remove uncertainty in its different forms from the scholars’ vision may negatively affect the manner in which humanities scholars regard computational methods as useful tools in their daily work. In this paper, we address the issue of uncertainty representation in the context of the humanities from a theoretical perspective, in an attempt to provide the foundations of a framework that allows for the construction of ecological interface designs which are able to expose the computational power of the algorithms at play while, at the same time, respecting the particularities and needs of humanistic research. To this end, we review past uncertainty taxonomies in other domains typically related to the humanities and visualization, such as cartography and GIScience. From this review, we select an uncertainty taxonomy related to the humanities that we link to recent research in visualization for the DH. Finally, we bring a novel analytics method developed by other authors (Progressive Visual Analytics) into question, which we argue can be a good candidate to resolve the aforementioned difficulties in DH practice.
A Blockchain Construct for Energy Trading against Sybil Attacks

In recent years, vehicular networks have been drawing special attention because of its significant potential role in the future smart city. Safety is a crucial status in vehicular networks, especially in energy trading where security of transactional data and safety against critical attacks is of most concern. The Sybil attack is one where an adversary can create multiple fake identities, become a part of the P2P network and try to manipulate the decision of the entire network in accordance to his own will. Thus in view to enhance the security of system, this paper focuses on a Sybil attack mitigation method based on blockchain and it's Proof of Work (PoW) consensus. Also a framework of automated privacy-preserving selection of charging stations (CS) based on pricing and the distance to the electric vehicle (EV) is presented. A blockchain based approach increases the transparency between EV and CS while preserving the privacy of the EV owners.
2GesturePIN: Securing PIN-Based Authentication on Smartwatches

Smartwatches offer new capabilities to develop sophisticated applications that make daily life easier and more convenient for consumers and are becoming increasingly ubiquitous. The kind of services these devices are capable to provide include applications for mobile payment, ticketing, identification, access control, etc. While this makes modern smartwatches very powerful devices, it also makes them very attractive targets for attackers. PINs and Pattern Lock have been widely used in smartwatches for user authentication, however, those types of passwords are not robust against various forms of attacks, such as side channel, phishing, smudge, shoulder surfing, and videorecording attacks. In this work, we propose 2GesturePIN, a new authentication method that allows users to authenticate securely to their smartwatches and sensitive services through solely two gestures. It leverages the rotating bezel or the crown which are the most intuitive channels to interact with a smartwatch. 2GesturePIN enhances the resilience of the regular PIN to common attacks while maintaining a high level of usability.