Reviewerly/exHarmony huggingface dataset:
A Nonagonal Framework of Regulation in Talent Development (NFRTD)

Regulatory processes are pervasive on many levels in talent development, yet neither a systematic analysis of their role in talent development let alone a comprehensive model have been presented to date. The aim of this article is to demonstrate the diversity and complexity of regulatory processes in talent development and to provide a conceptual framework. The proposed nonagonal framework of regulation in talent development (NFRTD) distinguishes nine dimensions for the analysis of regulation in talent development: regulatory network (neutral, co-operative, competitive, hybrid, and panarchic), regulatory function (homeostasis and homeorhesis), control type (external, internal-automatic, and internal-controlled), regulatory type (open-loop control and closed-loop control), regulatory form (iterative and recursive), regulatory activities (preventive, detective, and corrective), regulated structures (exogenous and endogenous learning resources), regulatory side-effects (neutral, synergetic, destructive, catalytic, exploitative, and allostatic), and regulatory externalities (neutral, iatrogenic, and autocatalytic). The last section discusses the status of the nonagonal framework and hints at some fields of application.
Hypervisor-Based White Listing of Executables

We describe an efficient system for ensuring code integrity of an operating system (OS), both its own code and application code. The proposed system can protect from an attacker who has full control over the OS kernel. An evaluation of the system's performance suggests the induced overhead is negligible.
Development of a method for fraud detection in heterogeneous data during installation of mobile applications

A method for fraud detection when installing mobile applications was proposed. The developed method, in contrast to existing ones, uses all available data regardless of their types, dimensions, and discrepancies and converts such data into homogeneous coefficients based on the proposed scaling method. This approach allows one to improve accuracy of task solution and build an open to expansion knowledge base with characteristics of fraudsters and rules of detecting fraudulent users. A system of scales for converting heterogeneous data into homogeneous coefficients has been developed which has enabled construction of a mathematical model of the scaling process. The algorithm of scaling heterogeneous data sets based on the proposed scales and the mathematical model of the process of scaling large arrays of heterogeneous data has been developed which has made it possible to reduce the whole data set to two homogeneous groups. The algorithms of processing the resulting groups of homogeneous data and detection of fraudulent users were offered. The developed algorithms using coefficients of similarity between user characteristics form fingerprints of fraudsters, determine characteristics and dependences of fraudsters which allows one to increase efficiency and speed of the process of fraudster detection. A scheme of the fraud detection process which was used in the intelligent system of automatic detection of fraudsters for carrying out of experimental studies was proposed. According to the results of experimental study, accuracy of fraudster detection was 99.14 % for a given representative sample. The results of experimental studies have shown effectiveness of automatic detection of fraudsters and the possibility of expanding formats and characteristics of fraudsters based on intelligent analysis and knowledge bases.
Dictionary learning enhancement framework: Learning a non-linear mapping model to enhance discriminative dictionary learning methods

In this paper, a new framework is presented to enhance the reconstruction and discrimination capabilities of existing discriminative dictionary learning methods. In the proposed framework, a non-linear mapping model is introduced to learn a feature space in a way that any standard discriminative dictionary learning algorithms could achieve higher classification accuracies. The proposed feature mapping process targets to boost the standard dictionary learning methods by facilitating their optimization process. The mapping model uses a modified autoencoder network to provide a higher level of reconstruction and discrimination capabilities for the discriminative dictionary learning methods. The proposed dictionary learning enhancement (DLE) framework could be applied to any discriminative dictionary learning methods with the embedded discriminative term in their objective functions. Our experiments on several real-world image datasets demonstrate that the proposed framework could improve the classification accuracies of standard discriminative dictionary learning methods.
Diverse image annotation with missing labels

Automatic image annotation aims at assigning a set of textual labels to an image that describes its semantics. In real-world datasets with large vocabularies, each image is usually annotated with only a subset of all possible relevant labels. This leads to the problem of learning with missing labels. Moreover, several existing approaches for image annotation aim at producing a set which contains as many relevant labels as possible for a given image. However, such a set is usually unnecessary and contains redundant labels. This leads to the task of diverse image annotation, which aims at predicting labels that are collectively representative as well as diverse. In this paper, we study a new task called diverse image annotation with missing labels (DIAML), which is a fusion of these two practical aspects of the conventional image annotation task; i.e., diverse image annotation, and image annotation with missing labels. For this, we also propose a new k-nearest neighbours (kNN) based algorithm, called per-label k-nearest neighbours (or PL-kNN), that addresses both these challenges simultaneously. For a given image, rather than identifying its neighbours in the feature-space as done in the conventional kNN algorithm, PL-kNN first creates diverse partitions of the (training) samples based on label information, and then predicts the confidence for each label using a fixed number of nearest neighbours from the corresponding partition. The label-specific partitioning and neighbourhood selection steps inherently address the issue of missing labels as well. Extensive experiments on benchmark datasets show that though conceptually simple, the proposed method consistently outperforms state-of-the-art methods that address either of these two (sub-)tasks, thus establishing a strong baseline for the new DIAML task.
Aircraft Pose Estimation Based on Geometry Structure Features and Line Correspondences

A robust and accurate aircraft pose estimation method is proposed in this paper. The aircraft pose reflects the flight status of the aircraft and accurate pose measurement is of great importance in many aerospace applications. This work aims to establish a universal framework to estimate the aircraft pose based on generic geometry structure features. In our method, line features are extracted to describe the structure of an aircraft in single images and the generic geometry features are exploited to form line groups for aircraft structure recognition. Parallel line clustering is utilized to detect the fuselage reference line and bilateral symmetry property of aircraft provides an important constraint for the extraction of wing edge lines under weak perspective projection. After identifying the main structure of the aircraft, a planes intersection method is used to obtain the 3D pose parameters based on the established line correspondences. Our proposed method can increase the measuring range of binocular vision sensors and has the advantage of not relying on 3D models, cooperative marks or other feature datasets. Experimental results show that our method can obtain reliable and accurate pose information of different types of aircraft.
Application of Apriori and FP-growth algorithms in soft examination data analysis

With the continuous development of internet and information technology, human beings need to process a lot of information and data. When processing a large amount of information, data mining technology must be used. In order to better mine the required data information quickly based on condition ma tching, an optimized Apriori and FP - Growth association rule mining algorithm is proposed. Based on the algorithm flow and evaluation model, an optimization and up-date scheme is proposed, an effective data transmission evaluation model is established by effectively evaluating the state of data analysis, and the corresponding evaluation results are given. By introducing the idea of improved decomposition database to reduce the collection of infrequent databases, the algorithm adaptability is improved. In order to verify the feasibility and reliability of the method, the case experiment is demonstrated. Based on the experimental results, the algorithm is more effective in actual operation efficiency and data mining precision.
Memes Evolution in a Memetic Variant of Particle Swarm Optimization

In this work, a coevolving memetic particle swarm optimization (CoMPSO) algorithm is presented. CoMPSO introduces the memetic evolution of local search operators in particle swarm optimization (PSO) continuous/discrete hybrid search spaces. The proposed solution allows one to overcome the rigidity of uniform local search strategies when applied to PSO. The key contribution is that memes provides each particle of a PSO scheme with the ability to adapt its exploration dynamics to the local characteristics of the search space landscape. The objective is obtained by an original hybrid continuous/discrete meme representation and a probabilistic co-evolving PSO scheme for discrete, continuous, or hybrid spaces. The coevolving memetic PSO evolves both the solutions and their associated memes, i.e. the local search operators. The proposed CoMPSO approach has been experimented on a standard suite of numerical optimization benchmark problems. Preliminary experimental results show that CoMPSO is competitive with respect to standard PSO and other memetic PSO schemes in literature, and its a promising starting point for further research in adaptive PSO local search operators.
An Entropy-Assisted Particle Swarm Optimizer for Large-Scale Optimization Problem

Diversity maintenance is crucial for particle swarm optimizer’s (PSO) performance. However, the update mechanism for particles in the conventional PSO is poor in the performance of diversity maintenance, which usually results in a premature convergence or a stagnation of exploration in the searching space. To help particle swarm optimization enhance the ability in diversity maintenance, many works have proposed to adjust the distances among particles. However, such operators will result in a situation where the diversity maintenance and fitness evaluation are conducted in the same distance-based space. Therefore, it also brings a new challenge in trade-off between convergence speed and diversity preserving. In this paper, a novel PSO is proposed that employs competitive strategy and entropy measurement to manage convergence operator and diversity maintenance respectively. The proposed algorithm was applied to the large-scale optimization benchmark suite on CEC 2013 and the results demonstrate the proposed algorithm is feasible and competitive to address large scale optimization problems.
Image-based translucency transfer through correlation analysis over multi-scale spatial color distribution

This paper introduces an image-based material transfer framework which only requires single input and reference images as an ordinary color transfer method. In contrast to previous material transfer methods, we focus on transferring the appearances of translucent objects. There are two challenging problems in such material transfer for translucent objects. First, the appearances of translucent materials are characterized by not only colors but also their spatial distribution. Unfortunately, the traditional color transfer methods can hardly handle the translucency because they only consider the colors of the objects. Second, temporal coherency in the transferred results cannot be handled by the traditional methods and furthermore by recent neural style transfer methods, as we demonstrated in this paper. To address these problems, we propose a novel image-based material transfer method based on the analysis of spatial color distribution. We focus on “subbands,” which represent multi-scale image structures, and find that the correlation between color distribution and subbands is a key feature for reproducing the appearances of translucent materials. Our method relies on a standard principal component analysis, which harmonizes the correlation of input and reference images to reproduce the translucent appearance. Considering the spatial color distribution in the input and reference images, our method can be naturally applied to video sequences in a frame-by-frame manner without any additional pre-/post-process. Through experimental analyses, we demonstrate that the proposed method can be applied to a broad variety of translucent materials, and their resulting appearances are perceptually similar to those of the reference images.
Abstraction for Non-ground Answer Set Programs

We address the issue of abstraction, a widely used notion to simplify problems, in the context of Answer Set Programming (ASP), which is a highly expressive formalism and a convenient tool for declarative problem solving. We introduce a method to automatically abstract non-ground ASP programs given an abstraction over the domain, which ensures that each original answer set is mapped to some abstract answer set. We discuss abstraction possibilities on several examples and show the use of abstraction to gain insight into problem instances, e.g., domain details irrelevant for problem solving; this makes abstraction attractive for getting to the essence of the problem. We also provide a tool implementing automatic abstraction from an input program.
Anomaly detection for time series using temporal convolutional networks and Gaussian mixture model

Anomaly detection, as an important research field in the analysis of time series, has practical and significant applications in many occasions, such as network security, medical health, Internet of Things (IoT), fault diagnosis and so on. However, due to the inherent characteristics of time series, such as tremendous data volumes, the imbalance of normal data and abnormal data, additional constraints and challenges are added for anomaly detection for time series. We present a novel anomaly detection framework, which applies temporal convolutional networks to extract features of time series and combined Gaussian mixture model with Bayesian inference to detect anomalies of systems. In order to evaluate the effectiveness of our approach, experiments are carried out on two typical time series datasets including EEG dataset and current dataset of electrical equipment. The experiments indicate that temporal convolutional network can contribute to extracting salient features of time series and Gaussian mixture model with Bayesian inference has good generalization and reliability for anomaly detection. Meanwhile, the designed architecture and analysis approach of anomaly detection reveal the method's effectiveness and generalization in the feature extraction and anomaly detection for other time series.
Sequeval: An Offline Evaluation Framework for Sequence-Based Recommender Systems

Recommender systems have gained a lot of popularity due to their large adoption in various industries such as entertainment and tourism. Numerous research efforts have focused on formulating and advancing state-of-the-art of systems that recommend the right set of items to the right person. However, these recommender systems are hard to compare since the published evaluation results are computed on diverse datasets and obtained using different methodologies. In this paper, we researched and prototyped an offline evaluation framework called Sequeval that is designed to evaluate recommender systems capable of suggesting sequences of items. We provide a mathematical definition of such sequence-based recommenders, a methodology for performing their evaluation, and the implementation details of eight metrics. We report the lessons learned using this framework for assessing the performance of four baselines and two recommender systems based on Conditional Random Fields (CRF) and Recurrent Neural Networks (RNN), considering two different datasets. Sequeval is publicly available and it aims to become a focal point for researchers and practitioners when experimenting with sequence-based recommender systems, providing comparable and objective evaluation results.
A CCA-secure multi-conditional proxy broadcast re-encryption scheme for cloud storage system

Conditional proxy re-encryption allows a semi-trusted third-party proxy to convert Alice's ciphertext that meets a certain condition into another set of ciphertext. Meanwhile, the proxy cannot obtain any valuable information in the process. Nevertheless, a single re-encryption condition is difficult to meet the needs of practical applications, and the existing conditional proxy re-encryption scheme cannot flexibly control the conversion conditions. To solve this problem, this paper proposes a multi-conditional proxy broadcast re-encryption (MC-PBRE) scheme for file sharing systems. In our scheme, a single user can delegate the decryption rights of the encrypted file to a group of users. At the same time, users can set any number of conditions to control the user group decryption permissions. This paper demonstrates the security of the scheme for chosen-ciphertext attacks in the standard model. Furthermore, the program can resist collusion attacks, which means that the agent cannot collude with the user group to get the private key of the principal.
Solving Gravity Anomaly Matching Problem Under Large Initial Errors in Gravity Aided Navigation by Using an Affine Transformation Based Artificial Bee Colony Algorithm

Gravity aided inertial navigation system (GAINS), which uses earth gravitational anomaly field for navigation, holds strong potential as an underwater navigation system. The gravity matching algorithm is one of the key factors in GAINS. Existing matching algorithms cannot guarantee the matching accuracy in the matching algorithms based gravity aided navigation when the initial errors are large. Evolutionary algorithms, which are mostly have the ability of global optimality and fast convergence, can be used to solve the gravity matching problem under large initial errors. However, simply applying evolutionary algorithms to GAINS may lead to false matching. Therefore, in order to deal with the underwater gravity matching problem, it is necessary to improve the traditional evolutionary algorithms. In this paper, an affine transformation based artificial bee colony (ABC) algorithm, which can greatly improve the positioning precision under large initial errors condition, is developed. The proposed algorithm introduces affine transformation to both initialization process and evolutionary process of ABC algorithm. The single-point matching strategy is replaced by the strategy of matching a sequence of several consecutive position vectors. In addition, several constraints are introduced to the process of evolution by using the output characteristics of the inertial navigation system (INS). Simulations based on the actual gravity anomaly base map have been performed for the validation of the proposed algorithm.
Weather Radar Echo Super-Resolution Reconstruction Based on Nonlocal Self-Similarity Sparse Representation

Weather radar echo plays an important role in early warning and timely forecasting of severe weather. However, the radar echo may not be accurate enough to predict or analyze small-scale weather phenomenon due to the degradation of the observed radar. In order to solve this problem, some radar echo super-resolution reconstruction algorithms have been proposed, but the algorithm may result in an excessively smooth edge and detail in a local region. To reconstruct radar echo with better edges and finer details, a novel nonlocal self-similarity sparse representation (NSSR) model is proposed. The NSSR model is based on the sparse representation of weather radar echoes to better reconstruct the echo edge and detail information. We exploit the radar echo nonlocal self-similarity to recover more realistic details based on the NSSR model. Experiment results demonstrate that the proposed NSSR outperforms current general-purpose radar echo super-resolution approaches on both visual effects and objective radar echo quality.
A hybrid iterated local search algorithm with adaptive perturbation mechanism by success-history based parameter adaptation for differential evolution (SHADE)

The iterated local search (ILS) is exceptionally successful in combinatorial solution spaces. However, few research works have reported on the application of ILS in continuous problems. In this article, a new hybrid population-based iterated local search (HILS) algorithm is proposed for solving numerical optimization problems. The proposed hybrid method introduces both success-history based parameter adaptation for differential evolution (SHADE) and limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) as the perturbation and local search strategy, respectively, and integrates the benefits of exploration capability of SHADE and local search performance of LBFGS. The simulated annealing type of acceptance criterion is adopted to balance the exploration and exploitation of ILS. The proposed HILS is tested against the CEC2017 benchmark functions, which were used to evaluate the performance of the proposed algorithm in solving numerical optimization problems. The experimental results show the effectiveness and efficiency of the HILS.
<i>Qué perfección</i>: Complimenting Behaviour among Ecuadorian Teenage Girls on Instagram

Placencia’s contribution to the volume complements Eslami et al.’s chapter in that it looks at compliments exchanged on Instagram, a social networking site aimed explicitly at sharing pictures. The study analyzes comments on solo shots posted by older Ecuadorian teenage girls over a period of 12 months, and the interpretation of the responses is backed up with informal interviews. The pictures analyzed by Placencia serve as invitations for positive assessment, and it is argued that the complimenters compete with each other to offer the most original ‘gift’ in the form of a compliment. Their creativity is conveyed through lexical choices, use of irony, mock impoliteness, and macho talk with sexual connotations (used in jest). The compliments are characterized by exaggeration and high emotional involvement, expressed through different means. They tend to be brief, with a strong reliance on ellipsis and pictorial forms. The affordances of the medium contribute to the multimodal character of the compliments, reflected in the use of emojis, prosodic orthography, and hashtags, linking the compliments with images.