Reviewerly/exHarmony huggingface dataset:
A Distributed Multiple Populations Framework for Evolutionary Algorithm in Solving Dynamic Optimization Problems

Aiming to dynamic optimization problems (DOPs), this paper develops a novel general distributed multiple populations (DMP) framework for evolutionary algorithms (EAs). DMP employs six strategies designed in three levels (i.e., population-level, subpopulation-level, and individual-level) to deal with different kinds of DOPs. First, the population-level subpopulation division estimation strategy in initialization phase rationally divides the whole population into several subpopulations to explore distinct subareas of search space sufficiently. Then, during the steady evolutionary process, diversity preservation in individual-level and population-level accelerates the responsiveness of the whole population to a new landscape, while subpopulation-level self-learning of elitist individuals promotes the exploitation of promising areas. Moreover, in subpopulation-level, the archive quality assurance technique avoids repeat exploring the same peaks by storing the locations of different peaks with low redundancy. When landscape variation occurs, in population-level, historical information containing excellent evolutionary pattern is recorded to guide the population evolution better in the new environment. DMP framework is easy to implement in various EAs due to its well generality and independence about operators and parameters of the embedded algorithm. Four DMP-EAs are accomplished in this paper whose basic algorithms are particle swarm optimization (PSO) and differential evolution (DE) with different settings. The performance of the four proposed DMP-EAs is evaluated on all the widely used complex DOP benchmarks from CEC 2009. The testing results indicate that the DMP-EAs generally significantly outperform many state-of-the-art dynamic EAs (DEAs) on most of DOP benchmarks.
PASCAL: An architecture for proactive auto-scaling of distributed services

One of the main characteristics that today makes cloud services so popular is their ability to be elastic, i.e., they can adapt their provisioning to variable workloads, thus increasing resource utilization and reducing operating costs. At the core of any elastic service lies an automatic scaling mechanism that drives provisioning on the basis of a given strategy. In this paper we propose PASCAL , an architecture for Proactive Auto-SCALing of generic distributed services. PASCAL combines a proactive approach, to forecast incoming workloads, with a profiling system, to estimate required provision. Scale-in/out operations are decided according to an application-specific strategy, which aims at provisioning the minimum number of resources needed to sustain the foreseen workload. The main novelties introduced with PASCAL architecture are: (i) a strategy to proactively auto-scale a distributed stream processing system (namely, Apache Storm) with the aim of load balancing operators through an accurate system performance estimation model, and (ii) a strategy to proactively auto-scale a distributed datastore (namely, Apache Cassandra), focused on how to choose when executing scaling actions on the basis of the time needed for the activation/deactivation of storage nodes so as to have the configuration ready when needed. We provide a prototype implementation of PASCAL for both use cases and, through an experimental evaluation conducted on a private cloud, we validate our approach and demonstrate the effectiveness of the proposed strategies in terms of saved resources and response time.
Refining Technology Threat Avoidance Theory

Understanding individual threat avoidance motivation and behavior is a critical component in designing effective cyber security solutions for both users and organizations. Technology threat avoidance theory (TTAT) asserts that individuals’ perceptions regarding their susceptibility to and the resulting severity of technology threats influence their awareness of the threats, which, in turn, influences their motivation and behavior to avoid them. While TTAT provides cogently and logically explains individuals’ technology threat motivations and behaviors, empirical tests have produced equivocal results particularly in terms of the influence of susceptibility and severity on threat perceptions. Due to these inconsistencies in the threat calculus involving susceptibility, severity, and threat, we need more work to improve and understand individual threat motivations. Additionally, TTAT does not account for individual differences such as risk propensity, distrust propensity, and impulsivity that have been shown to affect cyber security behavior. To address these gaps, we present an empirical assessment of a refined TTAT model, which includes individual differences and models the influence of susceptibility on threat perceptions as partially mediated by severity. Results indicate that, while perceived susceptibility is a significant predictor of threat perceptions, severity perceptions partially mediates its effect. Our results also support the inclusion of risk propensity and distrust propensity in the TTAT model as personal characteristics that significantly affect overall threat perceptions.
Introduction to Dynamic Linear Models for Time Series Analysis

Dynamic linear models (DLM) offer a very generic framework to analyse time series data. Many classical time series models can be formulated as DLMs, including ARMA models and standard multiple linear regression models. The models can be seen as general regression models where the coefficients can vary in time. In addition, they allow for a state space representation and a formulation as hierarchical statistical models, which in turn is the key for efficient estimation by Kalman formulas and by Markov chain Monte Carlo (MCMC) methods. A dynamic linear model can handle non-stationary processes, missing values and non-uniform sampling as well as observations with varying accuracies. This chapter gives an introduction to DLM and shows how to build various useful models for analysing trends and other sources of variability in geodetic time series.
Accelerated and Noise‐Resistant Protocol of Dissipation‐Based Knill–Laflamme–Milburn State Generation with Lyapunov Control

Abstract A protocol to generate steady Knill–Laflamme–Milburn (KLM) states of two Rydberg atoms with dissipation and Lyapunov control is proposed. Herein, combining with the quantum Zeno dynamics and the Rydberg antiblockade, a unique and steady solution of the master equation of the system is obtained, which satisfies the definition of KLM states. Furthermore, to polish the convergence rate, well‐designed additional coherent control (ACC) fields with Lyapunov control are added into the traditional dissipation process. Numerical simulation reveals that the steady target state generation is prominently accelerated and the protocol is robust against the stochastic noise errors caused by laser fields. It is hoped that the work may provide an alternative protocol to polish the generation of entanglement.
A note on the expressive power of deep rectified linear unit networks in high‐dimensional spaces

We investigate the ability of deep deep rectified linear unit (ReLU) networks to approximate multivariate functions. Specially, we establish the approximation error estimate on a class of bandlimited functions; in this case, ReLU networks can overcome the “curse of dimensionality.”
Designing for Digital Playing Out

We report on a design-led study in the UK that aimed to understand barriers to children (aged 5 to 14 years) 'playing out' in their neighbourhood and explore the potential of the Internet of Things (IoT) for supporting children's free play that extends outdoors. The study forms a design ethnography, combining observational fieldwork with design prototyping and co-creative activities across four linked workshops, where we used BBC micro:bit devices to co-create new IoT designs with the participating children. Our collective account contributes new insights about the physical and interactive features of micro:bits that shaped play, gameplay, and social interaction in the workshops, illuminating an emerging design space for supporting 'digital playing out' that is grounded in empirical instances. We highlight opportunities for designing for digital playing out in ways that promote social negotiation, supports varying participation, allows for integrating cultural influences, and accounts for the weaving together of placemaking and play.
CSED: Client-Side encrypted deduplication scheme based on proofs of ownership for cloud storage

As digital data are explosively generated nowadays, data management becomes a critical problem, which makes cloud storage services important and popular. In reality, the storage overhead can be reduced significantly by performing date deduplication. Among the outsourced data, some of them are very personal and sensitive, and should be prevented for any leakage. Generally, if clients conventionally encrypt the data, deduplication is lost. Message-locked encryption (MLE) is a cryptographic primitive supporting encrypted data deduplication. A secure client-side deduplication scheme can be built upon MLE to reduce both communication and computation overhead for cloud storage systems, where a client interacts with the cloud server to check the duplicate data and only the data which has not been outsourced by other clients before is required to be uploaded. However, existing client-side encrypted data deduplication schemes are confronted with brute-force attacks that can recover files falling into a known set. Furthermore, existing schemes are vulnerable to illegal content distribution attacks, where the adversary can distribute data to other users via the cloud server without detecting. In this paper, we propose a secure and efficient client-side encrypted data deduplication scheme (CSED). In CSED, a dedicated key server is introduced in generating MLE keys to resist brute-force attacks. We propose a Bloom filter-based proofs of ownership (PoW) mechanism and integrate it into CSED to resist illegal content distribution attacks. Moreover, a hierarchical storage architecture is employed to improve the I/O efficiency on the cloud server. Security analysis and performance evaluation demonstrate that CSED is secure and efficient.
Artist-Net: Decorating the Inferred Content With Unified Style for Image Inpainting

Recently, context learning networks have shown promise in filling large holes in natural images. These networks can decorate the predicted contents with high-frequency details by borrowing or copying neural information from the known region. However, this operation might introduce undesired content change in the synthesized region, especially when similar neural patterns cannot be found in the known region. To solve this problem, we present a network named Artist-Net to decompose an image into the content code and style code explicitly. The Artist-Net completes a corrupted image following the way an artist restores a damaged picture. It can produce more detailed content by inferring the content code of the corrupted images in the latent space since the dimension of the content space is lower than the original image. The Artist-Net can also keep style consistent over the entire image by decorating the inferred content code with the style code extracted from the known region. The experiments on multiple datasets, including structural and natural images demonstrate that the proposed network out-performs the existing ones in terms of content accuracy as well as texture details.
A Multidimensional IRT Approach for Dynamically Monitoring Ability Growth in Computerized Practice Environments

Adaptive learning systems have received an increasing attention as they enable to provide personalized instructions tailored to the behaviors and needs of individual learners. In order to reach this goal, it is desired to have an assessment system, monitoring each learner's ability change in real time. The Elo Rating System (ERS), a popular scoring algorithm for paired competitions, has recently been considered as a fast and flexible method that can assess learning progress in online learning environments. However, it has been argued that a standard ERS may be problematic due to the multidimensional nature of the abilities embedded in learning materials. In order to handle this issue, we propose a system that incorporates a multidimensional item response theory model (MIRT) in the ERS. The basic idea is that instead of updating a single ability parameter from the Rasch model, our method allows a simultaneous update of multiple ability parameters based on a compensatory MIRT model, resulting in a multidimensional extension of the ERS ("M-ERS"). To evaluate the approach, three simulation studies were conducted. Results suggest that the ERS that incorrectly assumes unidimensionality has a seriously lower prediction accuracy compared to the M-ERS. Accounting for both speed and accuracy in M-ERS is shown to perform better than using accuracy data only. An application further illustrates the method using real-life data from a popular educational platform for exercising math skills.
Recommendation model based on trust relations &amp; user credibility

Nowadays, the purchase of every product involves a lot of critical thinking. Every buyer goes through a lot of user reviews and rating before finalizing his purchase. They do this to ensure that the product they purchase is of good quality at minimum price possible. It is evident now that online r eviews are not that reliable because of fake reviews and review bots. Now you can even pay certain social media groups to give your product a fake good rating. Hence going just with the reviews of some stranger whom you do not know is not intelligent. So we propose a recommendation model based on the Trust Relations (TR) and User Credibility (UC) because it is human nature that a person feels more comfortable when he gets a review from a person he knows on a first name basis. Also, the credibility of the reviewer is an important factor while providing importance to the reviews because every person is different from other and can have different expertise. Our model takes into account the effect of credibility which is not used by any other recommendations models which increases the precision of the results of our model. We also propose the algorithm to calculate the credibility of any node in the network. The results are validated using a dataset and applying our proposed model and traditional average rating model which shows that our model performs better and gives precise results.
Grasshopper Optimization Algorithm: Theory, Literature Review, and Application in Hand Posture Estimation

This chapter covers the fundamental concepts of the recently proposed Grasshopper Optimization Algorithm (GOA). The inspiration, mathematical model, and the algorithm are presented in details. A brief literature review of this algorithm including different variants, improvement, hybrids, and applications are given too. The performance of GOA is tested on a set of test functions including unimodal, multi-modal, and composite. The results show the ability of GOA in improving the quality of a random population, transiting from exploration to exploitation, showing high coverage of the search space, and accelerating the convergence curve over the course of iterations. The chapter also applies the GOA algorithm to a challenging problem in the field of hand posture estimation. It is observed that GOA finds an accurate configuration for a 3D hand model to match a given hand image acquired from a camera.
Fast Query Answering over Existential Rules

Enhancing Datalog with existential quantification gives rise to Datalog ∃ , a powerful knowledge representation language widely used in ontology-based query answering. In this setting, a conjunctive query is evaluated over a Datalog ∃ program consisting of extensional data paired with so-called “existential” rules. Owing to their high expressiveness, such rules make the evaluation of queries undecidable, even when the latter are atomic. Decidable generalizations of Datalog by existential rules have been proposed in the literature (such as weakly acyclic and weakly guarded); but they pay the price of higher computational complexity, hindering the implementation of effective systems. Conversely, the results in this article demonstrate that it is definitely possible to enable fast yet powerful query answering over existential rules that strictly generalize Datalog by ensuring decidability without any complexity overhead. On the theoretical side, we define the class of parsimonious programs that guarantees decidability of atomic queries. We then strengthen this class to strongly parsimonious programs ensuring decidability also for conjunctive queries. Since parsimony is an undecidable property, we single out Shy, an easily recognizable class of strongly parsimonious programs that generalizes Datalog while preserving its complexity even under conjunctive queries. Shy also generalizes the class of linear existential programs, while it is uncomparable to the other main classes ensuring decidability. On the practical side, we exploit our results to implement DLV ∃ , an effective system for query answering over parsimonious existential rules. To assess its efficiency, we carry out an experimental analysis, evaluating DLV ∃ performances for ontology-based query answering on both real-world and synthetic ontologies.
Varying-parameter Zhang neural network for approximating some expressions involving outer inverses

A varying-parameter ZNN (VPZNN) neural design is defined for approximating various generalized inverses and expressions involving generalized inverses of complex matrices. The proposed model is termed as CVPZNN(A,F,G) and defined on the basis of the error function which includes three appropriate matrices A,F,G. The CVPZNN(A,F,G) evolution design includes so far defined VPZNN models for computing generalized inverses and also generates a number of matrix expressions involving these generalized inverses. Global and super-exponential convergence properties of the proposed model as well as behaviour of its equilibrium state are investigated. Main contribution of the defined model is its generality. Most important particular cases of the defined model are presented in order to show this fact explicitly. Presented simulation results illustrate generality and effectiveness of the discovered ZNN evolution design.
Factors and Barriers in Training Financial Management Professionals

The problems of training financial managers in Russian universities appear due to several reasons. Firstly, continuous changes in the business environment and complications of financial management in organizations. Secondly, the demand for creating new requirements for training financial managers under new educational standards. Thirdly, the uncertainty of employer approaches to assessment of key characteristics of a financial manager. Fourthly, differences in ideas about the competencies of a financial manager among employer and universities. In the last years, the demand for the profession of a financial manager around the world has fallen in comparison with the early 2000s. In regard to this, we decided to analyze the training of bachelor and master degree students in financial management, and demand for them in the labor market. Despite the existing trend of a falling popularity of degree programs in economics and management, the market demand for the graduates is high. The research goal was to find socially significant factors to influence the demand for financial managers. We used one of the Russian universities as a research environment. In total, 497 students participated in the survey, including graduate bachelor degree students and second year master degree students in management degree programs. The survey gave the following factors to influence the demand for financial managers in the labor market: university prestige, individual perception of the educational process, student interest for the professional activities, and a degree in management for employment. At the same time, the survey distinguished a number of barriers for personal fulfillment of the students, including: insufficient attention for non-major courses of the curriculum, absence of on-site practice to get a real life professional experience, and too much theory in major courses.
Violent Crowd Flow Detection Using Deep Learning

This research aims in detecting violent crowd flows in the context of Bangladesh. For this purpose, we have collected a dataset which includes both violent and non-violent crowd flows. Different deep learning algorithms and approaches have been applied on this dataset to detect scenarios which contain violence. Convolutional neural networks (CNN) and long short-term memory network (LSTM) based architectures have been experimented separately on this dataset and in combination as well. Moreover, a model that was already pre-trained on violent movie scenes has been used to leverage transfer learning which outperformed all other experimented approaches with an accuracy of 95.67%. Surprisingly, the sequence model alone or in combination with CNN has not performed well on this particular dataset. The proposed model is lightweight hence it can be deployed easily in any security systems consisting of CCTV cameras or unmanned aerial vehicles (UAVs).
Re-Thinking Untraceability in the CryptoNote-Style Blockchain

use of cryptographic techniques is not sufficient to protect transaction untraceability in the 94
Flexible Three-Dimensional Reconstruction via Structured-Light-based Visual Positioning and Global Optimization

Three-dimensional (3D) reconstruction using line structured light vision system commonly cooperates with motion restraint devices, such as parallel guide rail push-broom devices. In this study, we propose a visual positioning method to eliminate the motion constraint. An extended orthogonal iteration algorithm for visual positioning is proposed to obtain the precise position of the line structured light binocular camera system during movement. The algorithm uses the information acquired by the binocular camera, and produces a better positioning accuracy than the traditional vision localization algorithm. Furthermore, a global optimization method is proposed to calculate the poses of the camera relative to the world coordinate system at each shooting position. This algorithm effectively reduces the error accumulation and pose drift during visual positioning, and 3D information of the surface can be measured via the proposed free-moving line structured light vision system. The simulation and physical experiments performed herein validate the proposed method and demonstrate the significant improvement in the reconstruction accuracy: when the test distance is 1.5 m, the root mean square error of the point cloud is within 0.5 mm.