Reviewerly/exHarmony huggingface dataset:
Automatically detecting missing cleanup for ungraceful exits

Software encounters ungraceful exits due to either bugs in the interrupt/signal handler code or the intention of developers to debug the software. Users may suffer from ”weird” problems caused by leftovers of the ungraceful exits. A common practice to fix these problems is rebooting, which wipes away the stale state of the software. This solution, however, is heavyweight and often leads to poor user experience because it requires restarting other normal processes. In this paper, we design SafeExit, a tool that can automatically detect and pinpoint the root causes of the problems caused by ungraceful exits, which can help users fix the problems using lightweight solutions. Specifically, SafeExit checks the program exit behaviors in the case of an interrupted execution against its expected exit behaviors to detect the missing cleanup behaviors required for avoiding the ungraceful exit. The expected behaviors are obtained by monitoring the program exit under a normal execution. We apply SafeExit to 38 programs across 10 domains. SafeExit finds 133 types of cleanup behaviors from 36 programs and detects 2861 missing behaviors from 292 interrupted executions. To predict missing behaviors for unseen input scenarios, SafeExit trains prediction models using a set of sampled input scenarios. The results show that SafeExit is accurate with an average F-measure of 92.5%.
Real-time human cross-race aging-related face appearance detection with deep convolution architecture

Human age estimation (AE) is an emerging research topic in computer vision and machine learning and has attracted increasing amount of research due its wide potential applications. In the process of human aging, facial appearances change from glabrous to crinkly similarly across all races, from European, Hispanic and African to Asian. To specially explore the relationships between aging and facial appearances across races, this paper is devoted to determining the correspondence between facial aging and facial appearances. Specifically, we first extract appearance vector features from facial images with their spatial structure preserved. Then, we propose to select the aging-related features shared by different races to explore their aging-related common facial regions, while removing redundant features. Thirdly, we improve the proposed model by incorporating potential cross-race relationships in an automated learning manner. Additionally, we extend our model with deep convolution architecture. Finally, we evaluate the proposed methodologies on a large face aging database with real-time efficiency.
The Ensemble of Strategies and Perturbation Parameter in Self-organizing Migrating Algorithm Solving CEC 2019 100-Digit Challenge

This paper presents the result of the "Ensemble of Strategies and Perturbation Parameter in Self-organizing Migrating Algorithm" (ESP-SOMA) on CEC 2019 100-Digit Challenge. This algorithm reached score around 52 out of maximum 100 with only a few parameters to set. ESP-SOMA has manifested notable potential in this demanding challenge.
Meaning Extraction in a Domotic Assistant Agent Interacting by Means of Natural Language

This paper presents a software architecture to let users interact with their smart home devices, through the commonly used social network channels. The software, called FABULOS, is the result of the combination of components used to interact with devices and social networks, and a rule-based artificial intelligence, which implements the base logic for the automation. The core of the software architecture is the translation service, which has the objective of extracting the meaning of the sentences provided by text and voice messages by users in natural language, transforming them into proper device commands. In order to achieve this, the proposed solution relies on an approach which has the capability to interpret and extract the meaning of the intentions plus the entities involved, associated to the sentences sent by the users. A description of a realistic case-study, which shows an example of how the proposed software behaves in a real interaction with an user, is also included in the paper.
Visual object tracking based on discriminant DCT features

In this paper, a visual object tracking method is proposed based on sparse 2-dimensional discrete cosine transform (2D DCT) coefficients as discriminative features. To select the discriminative DCT coefficients, we give two propositions. The propositions select the features based on estimated mean of feature distributions in each frame. Some intermediate tracking instances are obtained by (a) computing feature similarity using kernel, (b) finding the maximum classifier score computed using ratio classifier, and (c) combinations of both. Another intermediate tracking instance is obtained using incremental subspace learning method. The final tracked instance amongst the intermediate instances are selected by using a discriminative linear classifier learned in each frame. The linear classifier is updated in each frame using some of the intermediate tracked instances. The proposed method has a better tracking performance as compared to state-of-the-art video trackers in a dataset of 50 challenging video sequences.
Reachable set for Dubins car and its application to observation problem with incomplete information

A three-dimensional reachable set “at the instant” for a controlled object called the Dubins car is considered. The turn angular velocity of the linear velocity vector is a control variable. Along with the case when, according to the statement of the problem, rotation is possible in both directions, the cases of one-sided rotation are studied. Three-dimensional images of reachable sets are presented. Sufficiency of the Pontryagin maximum principle conditions for the control leading to the boundary of the reachable set is analyzed. Possibility of using reachable sets in the problem of motion observation under conditions of inaccurate measurements of the geometric position is considered.
Prediction Maps for Real-Time 3D Footstep Planning in Dynamic Environments

Perception of the local environment is a precondition for mobile robots to navigate safely in dynamic environments. Most robots, i.e., humanoids and smaller wheeled robots rely on planar regions. For humanoids, a simple 2D occupancy map as environment representation on which a path is planned is hereby not sufficient since they can step over and onto objects and therefore need height information. Considering dynamic obstacles introduces another level of complexity, since they can lead to necessary replanning or collisions at later stages. In this paper, we present a framework that first extracts planar regions in height maps and detects dynamic obstacles. Our system then uses this information to create a set of prediction maps, in which paths can be efficiently planned in real time at low CPU cost. We show in simulation and real-world experiments that our framework keeps run times well under 10ms for one computation cycle and allows for foresighted real-time 3D footstep planning.
Composing Distributed Data-Intensive Web Services Using Distance-Guided Memetic Algorithm

Web services are fundamental elements of distributed computing and allow rapid development of distributed applications. Data-intensive Web services handle an enormous amount of data created by different companies. Data-intensive Web service compositions (DWSC) must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously. Evolutionary Computing (EC) techniques allow for the creation of compositions that meets both requirements. However, current approaches to Web service composition have overlooked the impact of data transmission and the distribution of services, rendering them ineffective when applied to distributed data-intensive Web service composition DWSC. Especially, those approaches failed to consider important information from the problem that enables us to quickly determine the suitability of any solution. In this paper, we propose an EC-based algorithm with novel crossover operators to effectively address the above challenges. An evaluation is carried out and the results show that our proposed method is more effective than the existing methods.
Joint Label-Density-Margin Space and Extreme Elastic Net for Label-Specific Features

The label-specific features learning is a kind of framework for extracting the specific features of each label for classification. At present, the label-specific features algorithm is generally based on the original label space to find a particular feature set. This kind of extraction method for label-specific features has a general adaptation when the label density is balanced. However, in most multi-label data sets, the number of positive and negative labels varies greatly, and the label density is unbalanced. And there is an inherent correlation between each label. When extracting label-specific features, the method needs to consider issues such as imbalanced label density and label correlations. Based on this, a label-specific features learning method based on Joint Label-density-margin Space and Extreme Elastic Net (JLSE2N) is proposed in this paper. Firstly, to enlarge the margin between the positive and negative labels in the original label space, the positive and negative label density of the total example are calculated. And the information of unbalanced label density is integrated into the label space, thus forming a priori knowledge of the label-density-margin space. Subsequently, to quickly extract the label-specific features, the elastic net regularization extreme learning machine is used for the first time to obtain the label-specific features. The transformed label density space is used to calculate the cosine similarity and is added to the L2 regularization term to consider the correlation between pairs of labels. The L1 regularization term in the elastic net can generate the sparse weight matrix to extract the required label-specific features. We compare the proposed method with five well-known algorithms on 11 benchmark data sets. Experimental analysis shows that our proposed method is superior to the state-of-the-art algorithms for multi-label learning.
Memetic Evolution Strategy for Reinforcement Learning

Neuroevolution (i.e., training neural network with Evolution Computation) has successfully unfolded a range of challenging reinforcement learning (RL) tasks. However, existing neuroevolution methods suffer from high sample complexity, as the black-box evaluations (i.e., accumulated rewards of complete Markov Decision Processes (MDPs)) discard bunches of temporal frames (i.e., time-step data instances in MDP). Actually, these temporal frames hold the Markov property of the problem, that benefits the training of neural network as well by temporal difference (TD) learning. In this paper, we propose a memetic reinforcement learning (MRL) framework that optimizes the RL agent by leveraging both black-box evaluations and temporal frames. To this end, an evolution strategy (ES) is associated with Q learning, where ES provides diversified frames to globally train the agent, and Q learning locally exploits the Markov property within frames to refresh the agent. Therefore, MRL conveys a novel memetic framework that allows evaluation free local search by Q learning. Experiments on classical control problem verify the efficiency of the proposed MRL, that achieves significantly faster convergence than canonical ES.
Grammatical Evolution-based ensembles for algorithmic trading

The literature on trading algorithms based on Grammatical Evolution commonly presents solutions that rely on static approaches. Given the prevalence of structural change in financial time series, that implies that the rules might have to be updated at predefined time intervals. We introduce an alternative solution based on an ensemble of models which are trained using a sliding window. The structure of the ensemble combines the flexibility required to adapt to structural changes with the need to control for the excessive transaction costs associated with over-trading. The performance of the algorithm is benchmarked against five different comparable strategies that include the traditional static approach, the generation of trading rules that are used for single time period and are subsequently discarded, and three alternatives based on ensembles with different voting schemes. The experimental results, based on market data, show that the suggested approach offers very competitive results against comparable solutions and highlight the importance of containing transaction costs.
Bayesian Learning of Infinite Asymmetric Gaussian Mixture Models for Background Subtraction

Background subtraction plays an important role in many video-based applications such as video surveillance and object detection. As such, it has drawn much attention in the computer vision research community. Utilizing a Gaussian mixture model (GMM) has especially shown merit in solving this problem. However, a GMM is not ideal for modeling asymmetrical data. Another challenge we face when applying mixture models is the correct identification of the right number of mixture components to model the data at hand. Hence, in this paper, we propose a new infinite mathematical model based on asymmetric Gaussian mixture models. We also present a novel background subtraction approach based on the proposed infinite asymmetric Gaussian mixture (IAGM) model with a non-parametric learning algorithm. We test our proposed model on the challenging Change Detection dataset. Our evaluations show comparable to superior results with other methods in the literature.
Rainfall Estimation from Traffic Cameras

We propose and evaluate a method for the estimation of rainfall from images from a network of traffic cameras and rain gauges. The method trains a neural network for each camera under the supervision of the rain gauges and interpolates the results to estimate rainfall at any location. We study and evaluate variants of the method that exploit feature extraction and various interpolation methods. We empirically and comparatively demonstrate the superiority of a hybrid approach and of the inverse distance weighting interpolation for an existing comprehensive network of publicly accessible weather stations and traffic cameras.
Brain Storm Optimization Algorithm Based on Improved Clustering Approach Using Orthogonal Experimental Design

The brain storm optimization (BSO) algorithm is a new and promising swarm intellgience paradigm, inspired from the behaviors of the human process of brainstorming. The noverty of BSO lies in the clustering mechanism where the ideas are clustered into a set of groups and each idea learns from experiences of one inter-cluster or two intra-cluster neighbors. However, this mechanism is inefficient to deal with complex optimiaiton problems. In this paper, we propose an improved BSO algorithm called OSBSO using orthogonal experimental design (OED) strategy, which aims to discover useful search experiences for improving the convergence and solution accurancy. In OSBSO, two new clustering procedures are developed, i.e., orthogonal initialization and orthogonal clustering. The orthogonal initialization aims to improve the uniformity of the initial cluster centers in the objective space instead of the decision space, which can enhance the convergence performance. The orthogonal clustering uses the information between inter- cluster and intra-cluster indviduals to alleviate the evolution stagnation of clusters. Experiments are conducted on a set of the CEC2017 benchmark functions and the results verify the effectivenss and efficiency of OSBSO.
Understanding Synchronization Costs for Distributed ML on Transient Cloud Resources

Cloud platforms often execute parallel batch applications, such as distributed machine learning (ML), that include numerous synchronization barriers. These barriers, which prevent any task from advancing beyond a specified point until all tasks have reached that point, significantly degrade application performance by reducing it to that of the slowest "straggler" task. To address the problem, researchers have proposed numerous straggler mitigation techniques, including speculatively re-executing straggler tasks and various relaxations of strict barrier semantics. While these techniques improve parallel application performance, they incur a cost in terms of the resources wasted re-executing tasks or waiting. Importantly, these costs, which are often implicit in prior work that targets dedicated resources, become explicit in the cloud, which charges for resources at fine-grained intervals. In addition, the cost difference between techniques is exacerbated in cloud platforms, since they charge substantially less for transient resources that effectively yield a probabilistic performance across a wide range. While transient resources' low list price is attractive, revocations increase the frequency and severity of stragglers, which decreases parallel job performance and increases overall execution cost. To better understand the cost of synchronization, we develop simple analytical models of different straggler mitigation techniques and compare their cost and performance on on-demand and transient resources. Our analysis shows that i) transient servers offer complex tradeoffs compared to on-demand servers, and can result in higher overall costs despite their highly discounted price due to their probabilistic performance; ii) common approaches to straggler mitigation, which is a well-studied problem, are less effective using transient servers that cause frequent and severe stragglers; and iii) a recent approach to flexible synchronization offers the best cost and performance.
Constructions of de Bruijn sequences from a full-length shift register and an irreducible LFSR

In this paper, we study the construction of de Bruijn sequences. A method is given to stretch a de Bruijn sequence of order n to a de Bruijn sequence of order n+k with the help of a k-stage irreducible LFSR. It is shown that there is a correspondence between the cycle structure of an LFSR(l) and that of an NFSR(f⁎l), where f is the characteristic function of a de Bruijn sequence of order n and the LFSR(l) is a k-stage irreducible LFSR. Besides, an efficient algorithm is given to construct a class of de Bruijn sequences whose time complexity is 2n+1O(k) and memory requirement is O(2n+1).
Encouraging Rehabilitation Trials: The Potential of 360° Immersive Instruction Videos

Despite the rapid growth of the VR/AR/ XR applications in the health-care sector, enhancing health and well-being with innovative technologies often is a challenge. Part of the challenge is the limited knowledge transfer between the healthcare, technology, the patients demands, and how these demands could be appropriately met. The current study addressed this challenge when exploring the potential of 360° immersive instruction videos in encouraging rehabilitation trials. A professional VR/ video maker studio created the video for the purpose of the current research. A rehabilitation therapist was recorded while performing rehabilitation exercise as it is done in the real life practice. Patients currently in various rehabilitation trials (motor vs. cardiovascular) were exposed to the 360° immersive instruction video. Their experience was compared with the control group, i.e. healthy people. The VR experience and the rehabilitation exercise experience were evaluated as measures of the Virtual Reality Rehabilitation (VRR) impact. Results showed that 360° immersive videos engaged patients well, irrespective of the rehabilitation trial they are currently in. Regression modelling further demonstrated that the more people liked the VR experience, the more they enjoyed the rehabilitation activity. The more the rehabilitation activity was enjoyed, the more people were satisfied with the VRR. Current outcomes are discussed in the framework of a model of VRR impact, which is a solid base for long-term exercise adherence. The model could be implemented to successfully develop immersive instructional videos as efficient tools in the course of physical rehabilitation trials, and thus, to enhance health and well-being.
A Closed Itemset Property based Multi-objective Evolutionary Approach for Mining Frequent and High Utility Itemsets

Mining frequent and high utility itemsets from a transactional database is a significant task in the field of data mining and has attracted increasing attention in the past several years. Recently, researchers focus on designing multiobjective evolutionary algorithms (MOEAs) for the task of mining frequent and high utility itemsets, which has shown promising performance. In this paper, we continue this research line by further exploring the potential of MOEAs for mining frequent and high utility itemsets. Tb be specific, we suggest a closed itemset property based multi-objective evolutionary approach, termed as CP-MOEA, where two individual updating strategies are designed for improving the quality of mining frequent and high utility itemsets. We find that if the superset of an itemset is closed, then this itemset must be dominated by its superset, termed as closed itemset property. The proposed two individual updating strategies exploit this property of closed itemset to guide the evolution of the population at certain times. The experimental results on six real datasets demonstrate the effectiveness of the proposed algorithm CP-MOEA comparing to the state-of-the-art baseline.
Phase estimation using homodyne detection for continuous variable quantum key distribution

In order to ensure the security of the local oscillator (LO) in continuous variable quantum key distribution (CV-QKD), the LO in CV-QKD based on the local LO (LLO) scheme no longer passes through the quantum channel. For the LLO scheme using homodyne detection, two orthogonal quadratures of reference pulse can only be measured sequentially, which, as a result, each signal pulse should be accompanied by a pair of identical reference pulses. However, due to the phase shift between two adjacent pulses, it is difficult to prepare dual reference pulses with the same phase relative to the LO. In this paper, we propose an improved method to get around this difficulty, which is demonstrated experimentally for a transmitter and a receiver connected by a 50 km single mode fiber. The phase noise measured in the experiment is 2.0×10−3rad2, which is comparable to the recent experimental results using heterodyne detection. This method simplifies the preparation of phase reference pulses and removes the obstacles for the application of homodyne detection in the LLO scheme. In addition, this method can also be used for the compensation of quadrature imbalance in heterodyne detection, then the performance of heterodyne detection can be improved.