Reviewerly/exHarmony huggingface dataset:
Gradient method for determining non-informative features on the basis of a homogeneous criterion with a positive degree

Abstract The choice of informative features is one of the main tasks in the problem of pattern recognition and the effectiveness of its solution in many respects depends on the criterion of information content used. Some methods for choosing informative features are implemented by eliminating non-informative features from the feature space. A single criterion has not been developed to select a set of non-informative features. Therefore, the criterion is selected from the formulation of a practical problem. Currently, several methods have been developed for selecting features that are focused on the use of a specific information content criterion. To date, more than 50 homogeneous criteria with zero order have been developed, but in many practical tasks the task of choosing non-informative features requires the use of homogeneous criteria with a positive degree. The paper proposes a gradient method and an algorithm for determining non-informative features based on a homogeneous criterion with a positive degree.
A Systematic Review on Application of Deep Learning Techniques for Software Quality Predictive Modeling

Software quality prediction is the process of evaluating the software developed for various metrics like defect prediction, bug localisation, effort estimation etc. To evaluate these metrics a myriad of techniques have been developed in the literature, from manual assessment to application of machine learning and statistical testing. These methodologies, however, had lower accuracy in determining SQPMs due to their inability to model the complex relationships in the training data. With the wide emergence of deep learning, not only has the accuracy of the pre-existing models enhanced, but it has also opened doors for new metrics that could be evaluated and automated. This study performs a systematic literature review of research papers published from January 1990 to January 2019 that used deep learning to evaluate software quality prediction metrics (SQPM). The paper identifies 20 primary studies and 7 categories of application of deep learning in SQPM. Models using deep learning techniques significantly outperform other traditional methodologies in almost all studies. The concept and external threats to the models are limited, however the time taken to train these models is large. The techniques, currently predominantly applied for defect prediction, have shown promising results in other diverse software engineering fields like code search and effort estimation by modeling the source code efficiently. There is, hence, scope for incorporating deep learning further with pragmatic use and diverse application. The need to find scalable solutions, however, still persists.
Multi-threshold Image Segmentation by Improved Lion Swarm Optimization Algorithm

Abstract In this paper we propose an improved Lion Swarm Optimization (ILSO) algorithm for multi-threshold image segmentation. We introduce the global search method of Artificial Bee Colony optimization (ABC) algorithm and revise the updating function of LSO algorithm to improve the global and local search performance of LSO algorithm. We introduce a sign to record the number of times an individual falls into the local optimum, and attenuate it so that the algorithm can jump out of the local optimum more quickly in the early stage and accelerate the convergence speed in the later stage. The maximum inter-class variance criterion is selected as the fitness function to solve the Multi-threshold image segmentation problem by ILSO. Experiment results show this algorithm can obtain ideal image segmentation result. And when the dimension of the problem is higher, the advantage of the improved Lion Swarm Optimization algorithm proposed in this paper is more obvious.
Learning Monocular 3D Vehicle Detection Without 3D Bounding Box Labels

The training of deep-learning-based 3D object detectors requires large datasets with 3D bounding box labels for supervision that have to be generated by hand-labeling. We propose a network architecture and training procedure for learning monocular 3D object detection without 3D bounding box labels. By representing the objects as triangular meshes and employing differentiable shape rendering, we define loss functions based on depth maps, segmentation masks, and ego- and object-motion, which are generated by pre-trained, off-the-shelf networks. We evaluate the proposed algorithm on the real-world KITTI dataset and achieve promising performance in comparison to state-of-the-art methods requiring 3D bounding box labels for training and superior performance to conventional baseline methods.
Incremental Structure-Evolving Intelligent Systems with Advanced Interpretational Properties

The paper considers some problems related to interpretability of evolving intelligent systems and online design and optimization methods for such systems under streaming data control. In the proposed online approach, the main criteria are the modeling accuracy, which is based on mean squared error minimization, and the set of the interpretability criteria, some of which are the complexity and the consistency of the knowledge base from fuzzy system. The novel incremental algorithm of fuzzy model structure reformation based on incremental clustering procedure is presented. The possibility of development of the novel immunological approach to intelligent data processing based on proposed evolving systems is considered.
A Testbed for Hardware-assisted Online Profiling of IoT devices

The widespread application of the Internet of Things (IoT) has put forward higher requirements for the reliability of the IoT devices. Traditional testing methods, while able to get a rough approximation of the performance of IoT devices, often fail to extract detailed runtime execution traces of applications from the resource-constrained devices. Hardware-assisted tracing can make it easier for IoT developers to obtain the rich running information of IoT devices with limited overhead, which brings new possibility to fully evaluate the software (i.e. firmware) of the IoT. For the hardware-assisted tracing data, existing offline analysis methods have severely limited the observable time span due to a large amount of tracing data. In this paper, we propose an FPGA-based online profiling testbed to carry out real-time processing of tracing data and implement continuous observation of IoT devices. Our preliminary experiment shows that the testbed has superior performance in terms of runtime trace capturing and sampling frequency. Finally, the potential applications of the captured traces are discussed.
Effective Use of Personal Health Records to Support Emergency Services

Smart City systems capture and exchange information with the aim to improve public services. Particularly, healthcare data could help emergency services to plan resources and make life-saving decisions. However, the delivery of healthcare information to emergency bodies must be balanced against the concerns related to citizens' privacy. Besides, emergency services face challenges in interpreting this data; the heterogeneity of sources and a large amount of information available represent a significant barrier. In this paper, we focus on a case study involving the use of personal health records to support emergency services in the context of a fire building evacuation. We propose a methodology involving a knowledge engineering approach and a common-sense knowledge base to address the problem of deriving useful information from health records and, at the same time, preserve citizens' privacy. We perform extensive experiments involving a synthetic dataset of health records and a curated gold standard to demonstrate how our approach allows us to identify vulnerable people and interpret their particular needs while avoiding the disclosure of personal information.
Heterogeneous Systems Modelling with Adaptive Traffic Profiles and Its Application to Worst-Case Analysis of a DRAM Controller

Computing Systems are evolving towards more complex, hetero-geneous systems where multiple computing cores and accelera-tors on the same system concur to improve computing resources utilization, resources re-use and the efficiency of data sharing across workloads. Such complex systems require equally complex tools and models to design and engineer them so that their use-case requirements can be satisfied. Adaptive Traffic Profiles (ATP) introduce a fast prototyping technology, which allows one to model the dynamic memory behavior of computer system de-vices when executing their workloads. ATP defines a standard file format and comes with an open source transaction generator engine written in C++. Both ATP files and the engine are porta-ble and pluggable to different host platforms, to allow workloads to be assessed with various models at different levels of abstraction. We present here the ATP technology developed at Arm and published in [5]. We present a case-study involving the usage of ATP, namely the analysis of the worst-case latency at a DRAM controller, which is assessed via two separate toolchains, both using traffic modelling encoded in ATP.
Usage for Inside Design by AR and VR Technology

The Article defines the usage of AR & VR for interior beautifying. This innovative phase of digital/arithmetical technology, virtual techniques had a greater impression on enhancing the picturing of architectural based applications. Thus, the new technology of Augmented Reality and Virtual Reality contracts more benefits of the digital architectural plan as well as construction fields. Augmented Reality & Virtual Reality is deliberated as one of the substantial design approaches for internal design. In this view, the virtual equipment can be displayed and modified by choosing the appropriate style, look or placement of the furniture agreeing of the user have an interactive capability. The article describes about the significance of the applications that increase the quality of furniture arrangements.
Object recognition for UAV navigation in complex environment

Impressive progress in technical characteristics of modern unmanned aerial vehicles (UAV) provides new opportunities for their exploiting in different applications and missions which were impossible earlier. The growing applicability of UAVs is based on high performance of modern computers and latest advances in sensor data processing techniques. Recent decades modern convolutional neural network (CNN) models have demonstrated the state of the art performance in many computer vision problems seemed to be solved properly only by a human. The study is aimed at developing a deep learning techniques for UAV autonomous navigation in complex environment in obstacle avoidance mode. Such kind of navigation is required for cargo delivery or rescue mission in urban, industrial or forestry environment when global geo-positioning system can be unavailable. For navigating in complex environment UAV have to recognize objects of observed scene and to estimate distance for possible obstacle. The proposed technique to solve these tasks exploits deep learning approach for image segmentation and depth map estimation using an image of the observed scene. The convolutional neural network model is developed capable to predict depth map of the observed scene along with scene segmentation according the predefined object classes. The proposed neural network architecture is based on generative adversarial model with generative part translating an input color image into an output voxel model. The aim of the discriminative part is to estimate how close the output to real data and to penalize false output. Both generative and discriminative parts are trained simultaneously on the specially prepared dataset. Evaluation on the testing part of the prepared dataset has demonstrated the ability of the developed neural network model to perform segmentation of unobserved complex scenes containing several objects and estimating depth map for this scene. The proposed neural network architecture provides high generalization ability for new scenes.
An Improved AdaBoost Algorithm for Hyperparameter Optimization

Abstract AdaBoost algorithm is a typical Boosting algorithm, which belongs to a successful representative in the Boosting family. This algorithm can upgrade a weak classifier with a better classification effect than random classification to a strong classifier with high classification accuracy, where n_estimators represents the number of iterations of the base classifier. If the value is too large, it will easily cause the model to overfit, if it is too small, it is easy. The model is under-fitting, and the parameter setting is not set randomly, but according to the current status of the data set. Aiming at the problem that the number of iterations in the AdaBoost algorithm is uncertain, this paper introduces a Bayesian optimization algorithm for hyperparameter tuning, which makes the value of hyper parameter in AdaBoost algorithm suitable for the current data set, and finally obtains a hyperparameter optimization AdaBoost algorithm. The experiment result shows the method that adopt Bayesian optimization algorithm for hyperparameter optimization and apply the optimized hyperparameter value to the AdaBoost algorithm does not only improves the classification accuracy of the AdaBoost algorithm, but also avoids overfitting and underfitting of the model.
Readily Design and Try-On Garments by Manipulating Segmentation Images

Recently, fashion industries have introduced artificial intelligence to provide new services, and research to combine fashion design and artificial intelligence has been continuously conducted. Among them, generative adversarial networks that synthesize realistic-looking images have been widely applied in the fashion industry. In this paper, a new apparel image is created using a generative model that can apply a new style to a desired area in a segmented image. It also creates a new fashion image by manipulating the segmentation image. Thus, interactive fashion image manipulation, which enables users to edit images by controlling segmentation images, is possible. This allows people to try new styles without the pain of inconvenient travel or changing clothes. Furthermore, they can easily determine which color and pattern suits the clothes they wear more, or whether the clothes other people wear match their clothes. Therefore, user-centered fashion design is possible. It is useful for virtually trying on or recommending clothes.
Monitoring for control in role-oriented self-adaptive systems

Self-adaptive Systems (SASs) are one way to address the ever-growing complexity of software systems by allowing the system to react on changes in its operating environment. In today's systems, self-adaptation is typically realized with a control loop, for which the MAPE-K feedback loop is a prominent example. Research uses the notion of patterns to describe the distribution and decentralization of individual control loop components or control loops and their underlying managed subsystems. While there are some well-accepted standards about which components a managed sub-system has to implement so that it can interact with the control loop, research still lacks best practices for communication within and across control loops. This paper aims to identify several research challenges that exist currently in this domain. Furthermore, ideas on upcoming research to create distributed SASs that rely on roles, benchmarking and inter- and intra-loop communication for control loops will be presented. Furthermore, ongoing work on a self-adaptive distributed benchmarking application will be discussed. Finally, an evaluation strategy will be presented to provide evidence for viable results to the community.
From equivalence queries to PAC learning: The case of implication theories

In Angluin's exact-learning framework, equivalence queries can be simulated by stochastic equivalence testing to achieve a probably approximately correct identification of an unknown concept. We present an analysis of the number of samples that need to be generated in the process leading to a theoretical improvement on an earlier approach. We apply this modification to a previously known probably approximately correct algorithm for computing implication bases with an implication oracle and evaluate its performance in terms of the number of queries to the oracle on artificial and real-world data.
Intelligent Data Fusion and Multi-Agent Coordination for Target Allocation

This paper addresses the fusion processing techniques for multi-sensor data perceived through the infrared sensors of military surveillance robots, and proposes their decision-theoretic coordination to effectively monitor multiple targets. To combine the multi-sensor data from the distributed battlefield robots, a set of fusion rules are used to formulate a combined prediction from the multi-source data. The possible type of a target is estimated through the fusion rules. For the identification of targets, agents need to keep track of targets for continuous situation awareness. The coordination of the agents with limited range of surveillance is indispensable for their successful monitoring of multiple targets. For dynamic and flexible coordination, our agents follow the decision-theoretic approach. We implement a military simulator to compare the capabilities of fusion processing and those of coordination, and conduct experiments with our framework in distributed and uncertain battlefield environments. The experimental results show that the fusion process of multi-sensor data from military robots can improve the performance of estimation of the type of a target, and our coordinated agents outperform agents using random strategy for their target selection in various military scenarios.
Adaptive Pointwise-Pairwise Learning-to-Rank for Content-based Personalized Recommendation

This paper extends the standard pointwise and pairwise paradigms for learning-to-rank in the context of personalized recommendation, by considering these two approaches as two extremes of a continuum of possible strategies. It basically consists of a surrogate loss that models how to select and combine these two approaches adaptively, depending on the context (query or user, pair of items, etc.). In other words, given a training instance, which is typically a triplet (a query/user and two items with different preferences or relevance grades), the strategy adaptively determines whether it is better to focus on the “most preferred” item (pointwise - positive instance), on the “less preferred” one (pointwise - negative instance) or on the pair (pairwise), or on anything else in between these 3 extreme alternatives. We formulate this adaptive strategy as minimizing a particular loss function that generalizes simultaneously the traditional pointwise and pairwise loss functions (negative log-likelihood) through a mixture coefficient. This coefficient is formulated as a learnable function of the features associated to the triplet. Experimental results on several real-world news recommendation datasets show clear improvements over several pointwise, pairwise, and listwise approaches.
Exploratory analysis of cultural influences on requirements engineering activities based on stakeholders’ profile

Requirements Engineering (RE) is a critical process in the development of any software. It requires intensive communication among many stakeholders in order to identify software requirements accurately. As RE activities require intensive communication, cultural influences might need to be considered to improve these activities and to avoid issues that might affect the software project outcomes. In our work, we explored the influence of national culture on RE activities. We conducted an empirical study in two countries that have different cultural profiles: Saudi Arabia and Australia. To explore this issue, we conducted 41 face-to-face interviews. To consolidate the collected data, we conducted also 30 follow-up interviews with the same practitioners. The aim of our study was to empirically identify the set of cultural influences affecting RE activities, and analyse their implication on RE activities. This paper reports the results of the analysis of participants’ responses with respect to their industrial profiles, in terms of three factors: the type of software methodology applied by the participants (Traditional vs. Agile), the size of their company/organisation (small, medium, and large), and its type (private vs. government). The results showed a number of significant differences in the cultural influences that determine the type of software development methodology, i.e., Traditional or Agile, being applied within the corresponding company. In this paper, we discuss the identified differences and the factors that determine them. This in-depth analysis provides a broader examination of the influence of culture on RE activities, which might assist RE practitioners to select the most suitable RE practices/methodology to overcome any negative effects of these influences.
Acoustic features exploration and examination for voice spoofing counter measures with boosting machine learning techniques

Automatic Speaker verification systems are vulnerable to spoofing attacks. We propose our anti-spoofing system. It uses some acoustic features. The set of binary classifiers includes XGBoost tree boosting algorithm. ASV Spoof 2015 corpus is utilized in the experiments as the main database for anti-spoofing systems training. The pretreatment of acoustic features is essential for better performance of the system. Obtained results demonstrate that the proposed system can provide a good accuracy. High evaluation performance can be obtained using the combination between MFCC, LogFBE and SSC features. The attained metrics values such 97.80% for the accuracy, 97.12% for the precision, 98.50% for the Recall and 97.81% for the F1-mesure validate the performance of the proposed technique.
A Model Learning Approach For Low Light Image Restoration

We study the problem of low light image restoration through contrast enhancement and denoising. We approach this problem by learning a model that relates a noisy low light and well lit image pair. The low light image is modeled to suffer from contrast distortion and additive noise. In particular, we model the loss of contrast through a global parametric function, which enables the estimation of the underlying noise. We then use a pair of convolutional neural network (CNN) models to learn the noise and the parameters of a function to achieve contrast enhancement. This contrast enhancement function is modeled as a linear combination of multiple gamma enhancers. We show through extensive evaluations that our Low Light Image Model for Enhancement Network (LLIMENet) achieves superior restoration performance when compared to other methods on several publicly available datasets.
Multistate Constrained Invariant Kalman Filter For Rolling Shutter Camera And Imu Calibration

In this paper, we propose a novel algorithm for joint calibration of a rolling shutter camera and inertial sensors. Specifically, the proposed method performs online calibration to estimate the camera intrinsics, the readout time, the extrinsics and the time offset between the camera and the IMU, as well as IMU's bias. By employing a multi-state constrained invariant Kalman filter, the proposed method processes the measurements of IMU and rolling shutter camera in a decoupled way, which greatly reduces the computational complexity of Jacobian matrices and further improves the robustness and accuracy. Experiments on public datasets demonstrate that the proposed approach can accurately calibrate the considered parameters and perform better than state-of-the-art methods in terms of accuracy and robustness.