Reviewerly/exHarmony huggingface dataset:
Predicting Replication Time in Cassandra

With the rise of new high volume data emitters it becomes important to log the data for further cognitive and analysis tasks. The storage capabilities and the speed at which data can be inserted or retrieved play a major role in real time systems. Cassandra and other distributed storage systems are designed to store multiple number of replicas of each data set to ensure availability. However, due to heavy load the replication time may be high and if the system crashes the data integrity would be compromised. In this paper we have built a machine learning model that predicts how the replication time behaves under different read/write workloads. In addition we have shown the outcome of machine learning model which predicts the replication time and also analyses the important parameters which affect replication time the most.
An Evaluation of a Bootstrapped Neuro-Evolution Approach

The optimisation of candidate solution policies using Evolution Strategies (ES) have indicated that these strategies are viable alternatives to common Reinforcement Learning techniques and provide the inherent traits of ES when solving partially observable Markov decision process (POMDP) problems. This paper presents a novel ES method called, Bootstrapped Neuro-Evolution (BNE), for the evolution of policies for POMDP problems. This method trains a simulator and evolves a population of policies in a concurrent process that was adapted from the Evolutionary Robotics’ algorithm, the Bootstrapped Neuro-Simulator. The findings of this study indicate that BNE can be used to successfully evolve closed-loop control policies and train simulators that can be used to evaluate a partial fitness function of a policy for POMDP. This partial fitness function aids in the evolutionary process without inherently increasing the sample complexity of the algorithm.
Evidence Theory Based Combination of Frequent Chronicles for Failure Prediction

A chronicle is a kind of temporal pattern mined from a set of sequences made-up of time-stamped events. It has been shown recently that such knowledge is effective in sketching machines’ behaviours in industry. However, chronicles that describe a same new sequence of events could be multiple and conflictual. To predict nature and time interval of future events, we need to consider all the chronicles that match a new sequence. In this paper, we introduce a new approach, called FCP, that uses the evidence theory and chronicle mining to classify sequences. The approach has been evaluated on both synthetic and real-world data sets and compared to baseline state-of-the-art approaches.
Recommendations In The Virtual Societies: Some Considerations

We aim to show how the classical tool of social recommendation, massively used in social interactions, has to be more deeply analyzed within the new digitally infrastructured societies. We state that within these highly dynamic contexts it is fundamental to restructure the concept of recommendation. We tested our solutions by means of a multi-agent social simulation, identifying when it is more convenient to combine inferential processes with recommendations, i.e. focusing on recommending categories of agents rather than specific individuals. We found that within open networks and in the presence of not so reliable recommenders, category’s recommendations are better. The results we obtained are in agreement with the literature and can be of important interest for the development of this sector.
Skeleton Action Recognition Based on Singular Value Decomposition

This work introduces new method using the singular value decomposition (SVD) to recognise human activities from skeleton motion sequences. The primary focus was on different activity durations, inaccurate placement of the joints and loss of information about position of the joints. For that we needed to develop a robust model. At first, the pose features are created for description of skeleton pose per frame, that is created by directional vectors to alljoint pairwise combinations without repetition. The data timeline is divided in logical hierarchical parts using temporal pyramid decomposition. This helps to code the time information. On each temporal part, SVD is computed from each pose feature. From the final decomposition of SVD, the singular values representing variance and the right singular vectors representing the fitted planes in space-time are used to characterise the pose feature trajectory. The support vector machine classifier uses these features for recognition. Some of the results of the experiments are similar, and some outperform the results of the state-of-theart methods. The next analysis verified the robustness of the proposed method to Gaussian noise and to the loss of coordinates data of joints. The primary contribution of proposed method is robustness against noise and information loss with preservation of the state-of-the-art results.
Analysing the South African Junior Primary Education System with Agent‐based Simulation Modelling

Abstract The state and quality of the South African education system is an increasing concern. Even more than two decades after the Apartheid era, the consequence of the segregation and discrimination is evident in the country's education system. In order to address this matter, an agent‐based simulation model with the ability to model the academic progression of grade one to four learners from various socio‐economic backgrounds, based on existing data, is proposed. The model aims to effectively simulate the academic progression of learners over a four‐year period, as well as assisting intervention planning in identifying the effect that certain social or socio‐economic intervention strategies may have on the academic progression of learners in the South African education system. The agent‐based model allows for a graphical output visualizing the academic progression of agents and the effects that the activation of certain intervention strategies have on this progression.
RL Based Network Accelerator Compiler for Joint Compression Hyper-Parameter Search

Although compression techniques like pruning or quantization are beneficial for accelerators' energy efficiency, the large search space makes finding the appropriate compression scheme difficult. Besides, most existing works ignore the combination of both pruning and quantization. In this paper, we propose a reinforcement learning (RL) based joint compression framework to find the appropriate pruning ratio and quantization bit-width for accelerators. By interacting with the energy model of the target accelerator, the RL agent can learn the effect of compression scheme on both accuracy and energy efficiency. Through a long trial-and-error process, the agent can finally reach an optimal trade-off between accuracy and energy efficiency. Compared with control groups whose compression hyper-parameters are not jointly optimized, the proposed framework can achieve at least 25% energy reduction with higher accuracy or much higher accuracy with small disadvantages on energy. Compared with 8-bit quantized baseline, the framework can achieve 90% and 85% energy reduction on Cifar10 and Cifar100 respectively.
Attention-Based Interaction Trajectory Prediction

Trajectory prediction is a hot topic in the field of computer vision and has a wide range of applications. Trajectory prediction refers to predicting the future trajectory of a target based on its past trajectory. This paper proposes a method based on graph neural network and attention mechanism, in order to update trajectory characteristics by implement global pedestrian interaction. And, a direct relationship between history and future is introduced with the attention module for reducing error propagation. The method was evaluated on several real-world crowd datasets, the results demonstrate the effectiveness of our method.
One-Class Classification for Selecting Synthetic Datasets in Meta-Learning

Algorithm selection is a challenging task in machine learning. Meta-learning treats algorithm selection as a supervised learning task, in which training examples (i.e., meta-examples) are generated from experiments performed with a set of candidate algorithms in several datasets. The small availability of real datasets in some domains can make it difficult to generate good sets of meta-examples. An alternative is the use of synthetic datasets. Unfortunately, not all synthetic datasets can be considered equally relevant and representative compared to real datasets. Thus simply adopting a high number of arbitrary synthetic datasets increases the computational cost of performing experiments, without necessarily improving the quality of meta-learning. In this paper, we treat the selection of relevant synthetic datasets for meta-learning as an One-Class Classification (OCC) problem. In this problem, it is assumed the availability of instances associated to a single class of interest (the positive class) and a large set of unlabelled instances (the unknown class). The objective is to classify which unlabelled instances most likely belong to the positive class. In our context, OCC techniques are used to select the most relevant synthetic datasets (unknown class), by considering the real datasets (positive class) available. In our work, we conducted experiments in a case study in which we adopted a data manipulation procedure to produce synthetic datasets and two OCC techniques for dataset selection. The results revealed that it was actually possible to select a reduced number of synthetic datasets while maintaining or even increasing meta-learning performance.
Numeric Data Augmentation using Structural Constraint Wasserstein Generative Adversarial Networks

Some recent studies have suggested using GANs for numeric data generation such as to generate data for completing the imbalanced numeric data. Considering the significant difference between the dimensions of the numeric data and images, as well as the strong correlations between features of numeric data, the conventional GANs normally face an overfitting problem, consequently leads to an ill-conditioning problem in generating numeric and structured data. This paper studies the constrained network structures between generator G and discriminator D in WGAN, designs several structures including isomorphic, mirror and self-symmetric structures. We evaluates the performances of the constrained WGANs in data augmentations, taking the non-constrained GANs and WGANs as the baselines. Experiments prove the constrained structures have been improved in 16/20 groups of experiments. In twenty experiments on four UCI Machine Learning Repository datasets, Australian Credit Approval data, German Credit data, Pima Indians Diabetes data and SPECT heart data facing five conventional classifiers. Especially, Isomorphic WGAN is the best in 15/20 experiments. Finally, we theoretically proves that the effectiveness of constrained structures by the directed graphic model (DGM) analysis.
Multidimensional Period Recovery

Multidimensional data are widely used in real-life applications. Intel’s new brand of SSDs, called 3D XPoint, is an example of three-dimensional data. Motivated by a structural analysis of multidimensional data, we introduce the multidimensional period recovery problem, defined as follows. The input is a d-dimensional text array, with dimensions \(n_1 \times n_2 \times \dots \times n_d\), that contains corruptions, while the original text without the corruptions is periodic. The goal is then to report the period of the original text. We show that, if the number of corruptions is at most Open image in new window , where \(\epsilon > 0\) and \(p_1 \times \cdots \times p_d\) are the period’s dimensions, then the amount of possible period candidates is \(O(\log N)\), where \(N = \varPi _{i=1}^{d}n_i\). The independency of this bound of the number of dimensions is a surprising key contribution of this paper. We present an \(O(\varPi _{i=1}^{d} n_i \varPi _{i=1}^{d} \log n_i)\) algorithm, for any constant dimension d, (linear time up to logarithmic factor) to report these candidates. The tightness of the bound on the number of errors enabling a small size candidate set is demonstrated by showing that if the number of errors is equal to Open image in new window , a family of texts with \(\varTheta (N)\) period candidates can be constructed for any dimension \(d \ge 2\).
A hybrid PSO-parallel fuzzy brain emotional learning classifier for medical diseases diagnosis

This study presents a fuzzy brain emotional learning classifier (FBELC), combined with a modified particle swarm optimization (PSO) algorithm, that allows a network to automatically determine the optimum values for a reward signal and a classification threshold. The designed FBELC model imitates the brain decision process including the emotion information. To verify the predictive performance, a novel fitness function based on the accuracy of the training and cross-validation datasets is used for a PSO algorithm. This PSO-FBELC model is used to diagnose breast tumors and heart diseases. A comparison of simulations using the proposed PSO-FBELC with other processes shows that the proposed model performs better in terms of recognition accuracy.
Visual Authentication Scheme Based on Reversible Degradation and QR Code

Two-Dimensional barcodes are used as data authentication storage tool on several cryptographic architectures. This article describes a novel meaningful image authentication method for data validation using the Meaningless Reversible Degradation concept and QR Codes. The system architecture use the Meaningless Reversible Degradation algorithm, systematic Reed-Solomon error correction codes, meaningful images, and QR Codes. The encoded images are the secret key for visual validation. The proposed work encodes any secret image file up to 3.892 Bytes and is decoded using data stored in a QR Code and a digital file retrieved through a wireless connection on a mobile device. The QR Code carries partially distorted and stream ciphered bits. The QR Code version is defined in conformity with the secret image file size. Once the QR Code data is decoded, the authenticating party retrieves a previous created Reed-Solomon redundancy file to correct the QR Code stored data. Finally, the secret image is decoded for user visual identification. A regular QR Code reader cannot decode any meaningful information when the QR Code is scanned. The presented cryptosystem improves the redundancy download file size up to 50% compared to a plaintext image transmission.
Completion of Missing Labels for Multi-Label Annotation by a Unified Graph Laplacian Regularization

The task of image annotation is becoming enormously important for efficient image retrieval from the web and other large databases. However, huge semantic information and complex dependency of labels on an image make the task challenging. Hence determining the semantic similarity between multiple labels on an image is useful to understand any incomplete label assignment for image retrieval. This work proposes a novel method to solve the problem of multi-label image annotation by unifying two different types of Laplacian regularization terms in deep convolutional neural network (CNN) for robust annotation performance. The unified Laplacian regularization model is implemented to address the missing labels efficiently by generating the contextual similarity between labels both internally and externally through their semantic similarities, which is the main contribution of this study. Specifically, we generate similarity matrices between labels internally by using Hayashi's quantification method-type III and externally by using the word2vec method. The generated similarity matrices from the two different methods are then combined as a Laplacian regularization term, which is used as the new objective function of the deep CNN. The Regularization term implemented in this study is able to address the multi-label annotation problem, enabling a more effectively trained neural network. Experimental results on public benchmark datasets reveal that the proposed unified regularization model with deep CNN produces significantly better results than the baseline CNN without regularization and other state-of-the-art methods for predicting missing labels.
Bioinspired object motion filters as the basis of obstacle negotiation in micro aerial systems

All animals and robots that move in the world must navigate to a goal while clearing obstacles. Using vision to accomplish such task has several advantages in cost and payload, which explains the prevalence of biological visual guidance. However, the computational overhead has been an obvious concern when increasing number of pixels and frames that need to be analyzed in real-time for a machine vision system. The use of motion vision and optic flow has been a popular bio-inspired solution for this problem. However, many early-stage motion detection approaches rely on special hardware (e.g. event-cameras) or extensive computation (e.g. dense optic flow map). Here we demonstrate a method to combine an insect vision inspired object motion filter model with simple visual guidance rules to fly through a cluttered environment. We have implemented a complete feedback control loop in a micro racing drone and achieved proximaldistal object separation through only two object motion filters. We discuss the key constraints and the scalability of this approach for future development.
Single Image Super-Resolution Using Depth Map as Constraint

Single image super-resolution based on the deep neural network has achieved great performance recently, but generating photo-realistic images remains a challenging problem. To tackle this issue, we propose a method that uses depth maps as a constraint to get better visual quality. Specifically, we propose a self-adaptive feature transform (AFT) layer, which can perform affine transformation on the feature map based on the depth map to constrain the plausible solution space of the SR image. Furthermore, we propose a hierarchical residual multi-scale fusion block to improve the representational ability of the network. Experimental results on benchmark datasets demonstrate that our method is superior to other perceptually-oriented SISR methods in terms of visual quality and also achieves state-of-the-art performance on quantitative metrics.
Identification the Model of Electric Power Generation by Small Hydroelectric Power Station Based on Artificial Bee Colony Algorithm

Solution the task of parametric identification the mathematical model of electric power generation by a small hydroelectric power station based on artificial bee colony algorithm are proposed in this article.
Psychological and Pedagogical Diagnostics in the System of Pedagogical Support for Development of Social Adaptability of Higher School Students

The paper substantiates the need for the formation (along with professional competencies) of so-called soft skills in higher education institutions, including an individual’s ability to effective social adaptation – social adaptability. The essence of the concept of social adaptability and its structure are revealed. The role of psychological and pedagogical diagnostics in the successful support for development of stu-dents’ social adaptability is shown. The paper pre-sents psychological and pedagogical methods that allow determining the level of social adaptability. It reveals content of step-by-step activities to deter-mine the level of students’ social adaptability as the basis for effective implementation of pedagogical support. The results of diagnostics of students’ social adaptability are presented. Three groups of students are identified and characterized depending on the level of their social adaptability: impulsive-intuitive, pro-creative or creative.
Identifying compatibility-related APIs by exploring biased distribution in Android apps

With the prosperity of Android, developers need to deal with the compatibility issues among different devices, which is costly. In this paper, we propose an automated and general approach named ICARUS to identify compatibility-related APIs in Android apps. The insight of our approach is that the compatibility-related API has the biased distribution among code segments, which is similar to the distribution of keywords among documents. It motivates us to leverage statistical features to discriminate compatibility-related APIs from normal APIs. Experimental results on apps demonstrate the effectiveness of our work.
UCQ-Rewritings for disjunctive knowledge and queries with negated atoms

In this paper, we study the problem of query rewriting for disjunctive existential rules. Query rewriting is a well-known approach for query answering on knowledge bases with incomplete data. We propose a rewriting technique that uses negative constraints and conjunctive queries to remove the disjunctive components of disjunctive existential rules. This process eventually generates new non-disjunctive rules, i.e., existential rules. The generated rules can then be used to produce new rewritings using existing rewriting approaches for existential rules. With the proposed technique we are able to provide complete UCQ-rewritings for union of conjunctive queries with universally quantified negation. We implemented the proposed algorithm in the Completo system and performed experiments that evaluate the viability of the proposed solution.
Guaranteed Cost Control for Visual Servoing Systems of Wheeled Mobile Robots

Since wheeled mobile robots play an important role in the field of domestic medical service, this paper studies the problem of guaranteed cost control for visual servoing systems of wheeled mobile robots with input constraints. First, a cascade model is derived for a class of mobile robot via image-based visual servoing method, in which the first stage subsystem considers the angular velocity as the input, and the second stage subsystem recognizes the angular velocity as the system parameter. Since the angular velocity of the system is a bounded time-varying parameter, the angular velocity is treated as an uncertain parameter in the second stage subsystem. In this case, the second stage subsystem is transferred into a linear time-invariant system with parameter uncertainties. Sufficient conditions are obtained by choosing a proper Lyapunov function, ensuring that the closed-loop system is asymptotically stable. Furthermore, a minimization problem with three linear matrix inequalities (LMIs) is proposed to obtain a state feedback controller. Finally, simulations and experiments are provided to show the effectiveness of the proposed method.
NRS: Non-Reliability Suppression for False Positives Elimination in YOLOv3

Non-maximum suppression (NMS) is essential as a post-processing step to eliminate the false positives caused by redundant detections for a single object. However, NMS cannot eliminate all the redundant detections which have different classification results. In order to address this problem, this paper proposes a novel post-processing step, non-reliability suppression (NRS). On PASCAL VOC, the proposed NRS reduces 28.4% false positives with only a 0.3% decrease of true positives for a YOLOv3 detector when compared with the traditional greedy-NMS. Because the computational complexity of NRS is low, the addition of NRS does not require much computational burden.