Reviewerly/exHarmony huggingface dataset:
A novel approach to solve AI planning problems in graph transformations

The aim of AI planning is to solve the problems with no exact solution available. These problems usually have a big search space, and planning may not find plans with the least actions and in the shortest time. Recent researches show that using suitable heuristics can help to find desired plans. In planning problems specified formally through graph transformation system (GTS), there are dependencies between applied rules (actions) in the search space. This fact motivates us to solve the planning problem for a small goal (instead of the main goal), extract dependencies from the searched space, and use these dependencies to solve the planning problem for the main goal. In GTS based systems, the nodes of a state (really is a graph) can be grouped due to their type. To create a small (refined) goal, we use a refinement technique to remove the predefined percent of nodes from each group of the main goal. Bayesian Optimization Algorithm (BOA) is then used to solve the planning problem for the refined goal. BOA is an Estimation of Distribution Algorithm (EDA) in which Bayesian networks are used to evolve the solution populations. Actually, a Bayesian network is learned from the current population, and then this network is employed to generate the next population. Since the last Bayesian network learned in BOA has the knowledge about dependencies between applied rules, this network can be used to solve the planning problem for the main goal. Experimental results on four well-known planning domains confirm that the proposed approach finds plans with the least actions and in the lower time compared with the state-of-the-art approaches.
PLASMA

Developers are always on the lookout for simple solutions to manage their applications on cloud platforms. Major cloud providers have already been offering automatic elasticity management solutions (e.g., AWS Lambda, Azure durable function) to users. However, many cloud applications are stateful --- while executing, functions need to share their state with others. Providing elasticity for such stateful functions is much more challenging, as a deployment/elasticity decision for a stateful entity can strongly affect others in ways which are hard to predict without any application knowledge. Existing solutions either only support stateless applications (e.g., AWS Lambda) or only provide limited elasticity management (e.g., Azure durable function) to stateful applications.
Quality Metrics for Symmetric Graph Drawings

In this paper, we present a framework for quality metrics that measure symmetry, that is, how faithfully a drawing of a graph displays the ground truth geometric automorphisms as symmetries. The quality metrics are based on group theory as well as geometry. More specifically, we introduce two types of symmetry quality metrics for displaying: (1) a single geometric automorphism as a symmetry (axial or rotational) and (2) a group of geometric automorphisms (cyclic or dihedral). We also present algorithms to compute the symmetry quality metrics in O(n log n) time. We validate our symmetry quality metrics using deformation experiments. We then use the metrics to evaluate existing graph layouts to compare how faithfully they display geometric automorphisms of a graph as symmetries.
Design and Evaluation of an Authentication Framework for Wearable Devices

The demand for wearable technology devices has changed in recent years, due to the fact that wearable devices (WDs) make our lives easier and more comfortable than ever before. WDs are often seen as an extension to mobile devices such as smartphones even thought the size, shape and physical specifications, i.e., the form factors are completely different. The core features of WDs are always accessible and available to users while sole input devices, e.g. keyboard does not exist. In this case, the traditional authentication methods in which users type in passwords on the system can not apply to authenticate WDs. Therefore, this paper introduces an Authentication Framework for Wearable Devices (AFWD) that basically includes an authentication model designed for WDs, capable of creating reliable and secure authentication techniques. Subjective and objective assessment of the AFWD showed that it provides usability, deployability, and security and that people are willing to use it if available. Such a framework is crucial for Intelligent Transportation Systems (ITS) as it provides reliability and trustworthiness of the data transmitted via WDs.
ReviewerNet: A visualization platform for the selection of academic reviewers

We propose ReviewerNet, an online, interactive visualization system aimed to improve the reviewer selection process in the academic domain. Given a paper submitted for publication, we assume that good candidate reviewers can be chosen among the authors of a small set of pertinent papers; ReviewerNet supports the construction of such set of papers, by visualizing and exploring a literature citation network. The system helps journal editors and Program Committee members to select reviewers that do not have any conflict-of-interest and are representative of different research groups, by visualising the careers and co-authorship relations of candidate reviewers. The system is publicly available, and is demonstrated in the field of Computer Graphics.
A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

The artificial bee colony (ABC) algorithm, which has been widely studied for years, is a stochastic algorithm for solving global optimization problems. Taking advantage of the information of a global best solution, the Gbest-guided artificial bee colony (GABC) algorithm goes further by modifying the solution search equation. However, the coefficient in its equation is based only on a numerical test and is not suitable for all problems. Therefore, we propose a novel algorithm named the Gbest-guided ABC algorithm with gradient information (GABCG) to make up for its weakness. Without coefficient factors, a new solution search equation based on variable gradients is established. Besides, the gradients are also applied to differentiate the priority of different variables and enhance the judgment of abandoned solutions. Extensive experiments are conducted on a set of benchmark functions with the GABCG algorithm. The results demonstrate that the GABCG algorithm is more effective than the traditional ABC algorithm and the GABC algorithm, especially in the latter stages of the evolution.
Neural Distributed Ledger

The neural distributed ledger so lution presented here adopts a ledger-of-ledgers approach to perform the interconnection of multiple ledgers. Beyond interledger operability, it also enables the development of custom blockchain-based solutions, providing controlled costs and scalability while retaining decentralization and security.
The Efficiency of Training a Teacher at Higher Education Institutions of Different Profiles

&lt;p style="text-align:justify"&gt;The article reveals the problem of empirical research of training higher education institutions teachers in the context of the master’s degree programme. This study specifies and adapts the methods of diagnostics, criteria for conducting teacher training which are reasonable, psychologically and pedagogically approved and could be implemented in the institutions of higher education of different profiles. It was found out that the training of a teacher within the boundaries of the master’s degree programme of institutions of higher education is an actual task of three-level training of a specialist in Ukraine and requires a differentiated approach for institutions of higher education of various profiles. The components of the programme, based on their comparative characteristics, the status of teacher training in higher education institutions of different profiles with the application of certain criteria, indicators and levels were determined. Comparative analysis of the results of determining the levels of activity empathy and pedagogical reflection formation at the final stage of the study showed objective differences in teacher training in the context of the master’s degree programme of institutions of higher education of different profiles. The perspective directions of the problem under consideration are the study of the didactic conditions of effective teacher training for the master's degree in higher education and the development of the Concept of master’s training in higher education institutions on this basis, the analysis of the influence of the main factors on the formation of a teacher of a higher educational institution in the context of a master’s degree programme, in the profile professional training.&lt;/p&gt;
Smart community security monitoring based on artificial intelligence and improved machine learning algorithm

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218.
CRTL: Context Restoration Transfer Learning for Cross-Domain Recommendations

Among different recommendation techniques, collaborative filtering usually suffers from limited performance due to the data sparsity problem. Transfer learning presents an unprecedented opportunity to alleviate this issue by transferring rating patterns from the source domain to the target domain. However, an issue occurs in some situations when the source and target domains have unrelated parts to each other, such that a negative transfer may reduce the learning performance in the target domain. To address this issue, in this article, we present a novel model called cluster rating bias restoration to divide the learning process into two stages. First, we employ traditional transfer learning methods to adapt and reconstruct the target rating matrix. Second, we propose a cluster rating bias restoration framework specifically to minimize the effect of the negative transfer from the source domain and to restore the context-information-based rating bias trend in the target domain. Experiments on real-world datasets show that our proposed model outperforms the state-of-the-art alternatives.
Front Camera Eye Tracking For Mobile VR

User fixations is a fast and natural input method for VR interaction. Previous attempts for mobile eye tracking in VR were limited due to low accuracy, long processing time and the need for hardware add-ons such as anti-reflective lens coating and IR emitters. We present an innovative mobile VR eye tracking methodology, utilizing only the captured images of the front-facing (selfie) camera through the headset’s lens, without any modifications. The system enhances the low-quality camera-captured images that suffer from low contrast and poor lighting by applying a pipeline of customized low level image enhancements to suppress obtrusive reflections due to the headset lenses. We proceed to calibration and linear gaze mapping between the estimated iris centroids and physical pixels on the screen resulting to iris tracking in real-time. A preliminary study confirms that the presented eye tracking methodology performs comparably to eye trackers in commercial VR headsets when the eyes move in the central part of the headset’s field of view.
Two-photon bunching inside a quantum memory cell

We demonstrate the effect of interference, similar to the Hong-Ou-Mandel effect, for two single-photon pulses by writing them sequentially inside a quantum memory cell in a tripod configuration.
A New Orientation Estimation Method Based on Rotation Invariant Gradient for Feature Points

For many remote sensing image applications, orientation estimation is a crucial step for feature points extraction and matching, but it has attracted little attention. Due to the intensity differences between remote sensing image pairs, it is difficult to estimate the orientations of corresponding points accurately, resulting in performance degradation of feature matching. Thus, encountering the intensity differences, a robust spatial structure description for feature regions, and an effective calculation manner from description to orientation play a key role in accurate orientation estimation. To this end, in this letter, we first define a plausible orientation for feature points by the total gradient, offering an effective way to convert the gradient trend of the feature region to orientation. Therefore, we further propose a novel orientation estimation method, in which the rotation invariant gradient is introduced to improve the accuracy of gradient calculation and robustness of spatial structure description. Experimental results on multisensor remote sensing images demonstrate that our method increases the orientation estimation accuracy remarkably and outperforms other orientation estimation methods by a large margin, and effectively improves the performance of feature matching.
Modulating The Gait Of A Real-Time Self-Avatar To Induce Changes In Stride Length During Treadmill Walking

In virtual reality, it is possible to simulate visual self-representation by mapping one’s body movements with an avatar. The acceptance of the virtual body as part of one’s own body creates an ownership illusion. The purpose of this study was to investigate the effects of increasing a self-avatar’s stride length on the stride length of embodied users. Nine healthy subjects walked on a treadmill while viewing their self-avatar in a virtual mirror through an HMD, while the stride length of the avatar was manipulated. No statistically significant changes in stride length were found but the results show a clear trend towards a gradual increase in stride length following increased stride length of the avatar, and a residual after-effect.
A Constrained Path Redirection for Passive Haptics

Navigation with passive haptic feedback can enhance users’ immersion in virtual environments. We propose a constrained path redirection method to provide users with corresponding haptic feedback at the right time and place. We have quantified the VR exploration practicality in a study and the results show advantages over steer-to-center method in terms of presence, and over Steinicke’s method in terms of matching errors and presence.
Intelligent Transportation System and Future of Road Safety

World health organization recognized road accidents as one of the social and public health problems. Intelligent transportation system which is totally based on advanced telecommunication and information technology is already being used to overcome this problem. However, the constantly increasing fatal accident on road, requires usage of technology to predict human behavior and future interaction. This paper therefore looks into modem technologies, such as computer vision and artificial intelligence to identify solutions for the detection of real-time human behavior and interaction on road.
Perceived Usefulness and Easiness of Information and Communication Technologies and Volunteering among Older Adults

This study investigates the association between older adults' perception of usefulness and easiness (PUE) of Information Communication and Technologies (ICTs) and volunteering, and if this association differs across their income status. Data were obtained from the 2012 wave of the Health and Retirement Study (HRS), and the sample was restricted to respondents aged between 60 and 84 (N = 901) and who completed the 2012 HRS technology module. Multinomial logistic regression was employed to examine the independent and joint influence of PUE of ICTs and of low-income status on volunteering. The results show that only people with high PUE engaged in more than 100 hours of volunteering among older adults after controlling for covariates. The positive effect of high PUE was found to be more significant in the low-income group. This is the first known research to investigate the PUE of ICTs and volunteering among older adults. This study expands the knowledge of volunteering among older adults by exploring ICTs which can be considered as one of the most influential macrosocial changes in the current society. Moreover, our findings provide some insights and an empirical foundation in volunteering programs for older adults of different PUE levels.
An asymmetric dynamic multiparty quantum secret sharing against active attacks

We propose a dynamic quantum secret sharing (DQSS) protocol based on traveling single-qubits where any number of players can join or leave the protocol according to the dealer’s and players’ will. The protocol can resist collusion attacks of dishonest players by using an honesty test based on some unambiguous unitary operations. Specifically, the proposed protocol is secure against active attacks from dishonest players, which is different from existing protocols that consider only passive attacks based on the assumption that players are honest but curious. By using the asymmetric strategy, the efficiency of our DQSS protocol can be made asymptotically close to 100%. Compared with previous DQSS protocols, our protocol is securer, more efficient and more practical.
Mapping distributions in the entropy-parametric space

Abstract The paper discusses the possibility of approximate identification of asymmetric models in the space of information and probability signs of the sample data distributions. The paper contains a description of the features of the method for displaying asymmetric models in the entropy-parametric space of distribution signs. In particular a brief analysis of the shortcomings of the mapping of distributions in the space of parametric signs of asymmetry and kurtosis is given. For the construction a space of distribution signs, the author is proposed an entropy coefficient for asymmetric distributions that it is used as an independent interval informational estimate for observation sample data distribution. The material is illustrated by the example of a widespread family of generalized gamma distribution.
Compact and efficient encodings for planning in factored state and action spaces with learned Binarized Neural Network transition models

In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs) to learn complex state transition models of planning domains with discretized factored state and action spaces. In order to directly exploit this transition structure for planning, we present two novel compilations of the learned factored planning problem with BNNs based on reductions to Weighted Partial Maximum Boolean Satisfiability (FD-SAT-Plan+) as well as Binary Linear Programming (FD-BLP-Plan+). Theoretically, we show that our SAT-based Bi-Directional Neuron Activation Encoding is asymptotically the most compact encoding relative to the current literature and supports Unit Propagation (UP) – an important property that facilitates efficiency in SAT solvers. Experimentally, we validate the computational efficiency of our Bi-Directional Neuron Activation Encoding in comparison to an existing neuron activation encoding and demonstrate the ability to learn complex transition models with BNNs. We test the runtime efficiency of both FD-SAT-Plan+ and FD-BLP-Plan+ on the learned factored planning problem showing that FD-SAT-Plan+ scales better with increasing BNN size and complexity. Finally, we present a finite-time incremental constraint generation algorithm based on generalized landmark constraints to improve the planning accuracy of our encodings through simulated or real-world interaction.
Refined identification of hybrid traffic in DNS tunnels based on regression analysis

DNS (Domain Name System) tunnels almost obscure the true network activities of users, which makes it challenging for the gateway or censorship equipment to identify malicious or unpermitted network behaviors. An efficient way to address this problem is to conduct a temporal-spatial analysis on the tunnel traffic. Nevertheless, current studies on this topic limit the DNS tunnel to those with a single protocol, whereas more than one protocol may be used simultaneously. In this paper, we concentrate on the refined identification of two protocols mixed in a DNS tunnel. A feature set is first derived from DNS query and response flows, which is incorporated with deep neural networks to construct a regression model. We benchmark the proposed method with captured DNS tunnel traffic, the experimental results show that the proposed scheme can achieve identification accuracy of more than 90%. To the best of our knowledge, the proposed scheme is the first to estimate the ratios of two mixed protocols in DNS tunnels.
A New Swarm Algorithm Based on Orcas Intelligence for Solving Maze Problems

In this paper, a new swarm intelligence algorithm based on orcas behaviors is proposed for problem solving. The algorithm called Orcas Algorithm (OA) consists in simulating the orcas life style and in particular their social organization, their echolocation practice and their hunting techniques. The experimental study we conducted tested the designed algorithm as well as recent state-of-the-art evolutionary algorithms for comparison purposes. The experiments were performed using a public dataset describing mazes with four level of complexity. The overall obtained results clearly show the superiority of OA algorithm over the others. This finding opens the way to other problems to solve to benefit from OA robustness.
Performance Evaluation of Physical Attacks against E2E Autoencoder over Rayleigh Fading Channel

The use of Deep Learning (DL) in wireless communication systems is becoming very popular. As an example to the use of DL, the end-to-end (E2E) communication system can be implemented as an autoencoder. However, security and robustness are the main challenges due to the weaknesses of the autoencoders against physical adversarial attacks. Some works have been devoted to addressing these issues using only Additive White Gaussian Noise (AWGN) channel model. In this paper, we investigate the vulnerabilities of autoencoder E2E using a more realistic Rayleigh channel model with fast-fading and slow-fading characteristics studied separately. We apply white-box and black-box adversarial attacks to show in which extent this system is weak against adversaries. We use AWGN channel model as a benchmark to analyze our results. The results show that the adversary has more destructive impacts on the system that involves Rayleigh channel than AWGN channels as it causes a larger block error rate. Also, the black-box attack affects the system of Rayleigh model similar to jamming attacks.
A Hybrid Visualization Approach to Perform Analysis of Feature Spaces

In this paper, we propose a hybrid visualization by combining a projection based approach with star plot visualization to inspect feature spaces. While the projection based visualization is used to depict the instances similarities from high-dimensional spaces onto a bi-dimensional space, the star plot visual metaphor enables inspection of features (attributes) relationship. By inspecting feature spaces, analysts can assess their quality and analyze which features contribute for the formation of clusters. To validate our proposal, we demonstrate how to improve feature spaces to generate more cohesive clusters, as well as how to analyze deep learning features of distinct Convolutional Neural Network (CNN) architectures.