Reviewerly/exHarmony huggingface dataset:
A Data-driven Framework for Proactive Intention-Aware Motion Planning of a Robot in a Human Environment

For safe and efficient human-robot interaction, a robot needs to predict and understand the intentions of humans who share the same space. Mobile robots are traditionally built to be reactive, moving in unnatural ways without following social protocol, hence forcing people to behave very differently from human-human interaction rules, which can be overcome if robots instead were proactive. In this paper, we build an intention-aware proactive motion planning strategy for mobile robots that coexist with multiple humans. We propose a framework that uses Hidden Markov Model (HMM) theory with a history of observations to: i) predict future states and estimate the likelihood that humans will cross the path of a robot, and ii) concurrently learn, update, and improve the predictive model with new observations at run-time. Stochastic reachability analysis is proposed to identify multiple possibilities of future states and a control scheme that leverages temporal virtual physics inspired by spring-mass systems is proposed to enable safe proactive motion planning. The proposed approach is validated with simulations and experiments involving an unmanned ground vehicle (UGV) performing go-to-goal operations in the presence of multiple humans, demonstrating improved performance and effectiveness of online learning when compared to reactive obstacle avoidance approaches.
A Hybrid Photovoltaic Power Prediction Model Based on Multi-source Data Fusion and Deep Learning

With the rapid development of the world economy, environmental and energy problems have become increasingly prominent. This fact motivates the exploitation of renewable energy, such as solar power. Solar photovoltaic (PV) power has been integrated into modern smart grids on a large scale. Accurate photovoltaic prediction is the key to ensure the stable operation of the power grid. The single prediction model is difficult to be universal due to the limitations of the model. Therefore, this paper proposes a hybrid ultra-short-term photovoltaic power prediction model based on multi-source data fusion and deep learning. First, the satellite cloud images, historical power sequences, and numerical weather prediction (NWP) data are fused. Then, the photovoltaic power prediction models are established by using appropriate deep learning methods (convolutional neural networks (CNN), long-short term memory (LSTM) networks, and Extreme Gradient Boosting (XGBoost)) according to different data. Finally, the three models are combined to get the final prediction result. The results show that the proposed method in this paper has better forecasting performance than other single models.
SIBI Sign Language Recognition Using Convolutional Neural Network Combined with Transfer Learning and non-trainable Parameters

Sign Language Recognition (SLR) is a complex classification problem to be solved. Every language has their own syntax and grammar. New model combination and dataset is required to developed new SLR system for different syntax and grammar. In this study, we implemented a Convolutional Neural Network (CNN) model which is Inflated 3D model combined with transfer learning method from ImageNet and Kinectic dataset to overcome small dataset problems. There is no public dataset available for SIBI Dataset. Therefore, we collected the dataset by our-self using a mobile phone camera with the following specification. Samsung Galaxy S6 Camera, 16-megapixel Sony Exmore RS IMX240 sensor. The camera is in static position. We used 200 videos as our dataset with 10 words (classes) and 2 signers. We split the dataset into 3 parts (training, validation, testing). After several training and testing with 5 different froze layer combination. The highest validation accuracy was 100% and the highest testing accuracy is 97.50%. The best result was obtained by using a model with the most froze inception module.
Evolving simple and accurate symbolic regression models via asynchronous parallel computing

In machine learning, reducing the complexity of a model can help to improve its computational efficiency and avoid overfitting. In genetic programming (GP), the model complexity reduction is often achieved by reducing the size of evolved expressions. However, previous studies have demonstrated that the expression size reduction does not necessarily prevent model overfitting. Therefore, this paper uses the evaluation time – the computational time required to evaluate a GP model on data – as the estimate of model complexity. The evaluation time depends not only on the size of evolved expressions but also their composition, thus acting as a more nuanced measure of model complexity than the expression size alone. To discourage complexity, this study employs a novel method called asynchronous parallel GP (APGP) that introduces a race condition in the evolutionary process of GP; the race offers an evolutionary advantage to the simple solutions when their accuracy is competitive. To evaluate the proposed method, it is compared to the standard GP (GP) and GP with bloat control (GP+BC) methods on six challenging symbolic regression problems. APGP produced models that are significantly more accurate (on 6/6 problems) than those produced by both GP and GP+BC. In terms of complexity control, APGP prevailed over GP but not over GP+BC; however, GP+BC produced simpler solutions at the cost of test-set accuracy. Moreover, APGP took a significantly lower number of evaluations than both GP and GP+BC to meet a target training fitness in all tests. Our analysis of the proposed APGP also involved: (1) an ablation study that separated the proposed measure of complexity from the race condition in APGP and (2) the study of an initialisation scheme that encourages functional diversity in the initial population that improved the results for all the GP methods. These results question the overall benefits of bloat control and endorse the employment of both the evaluation time as an estimate of model complexity and the proposed APGP method for controlling it.
A Repository of Method Fragments for Agent-Oriented Development of Learning-Based Edge Computing Systems

The upcoming avenue of IoT, with its massive generated data, makes it really hard to train centralized systems with machine learning in real time. This problem can be addressed with learning-based edge computing systems where the learning is performed in a distributed way on the nodes. In particular, this work focuses on developing multi-agent systems for implementing learning-based edge computing systems. The diversity of methodologies in agent-oriented software engineering reflects the complexity of developing multi-agent systems. The division of the development processes into method fragments facilitates the application of agent-oriented methodologies and their study. In this line of research, this work proposes a database for implementing a repository of method fragments considering the development of learning-based edge computing systems and the information recommended by the FIPA technical committee. This repository makes method fragments available from different methodologies, and computerizes certain metrics and queries over the existing method fragments. This work compares the performance of several combinations of dimensionality reduction methods and machine learning techniques (i.e., support vector regression, k-nearest neighbors, and multi-layer perceptron neural networks) in a simulator of a learning-based edge computing system for estimating profits and customers.
Scalable mining of maximal quasi-cliques

Given a user-specified minimum degree threshold γ , a γ -quasiclique is a subgraph g = (V g , E g ) where each vertex ν ∈ V g connects to at least γ fraction of the other vertices (i.e., ⌈ γ · (| V g |- 1)⌉ vertices) in g. Quasi-clique is one of the most natural definitions for dense structures useful in finding communities in social networks and discovering significant biomolecule structures and pathways. However, mining maximal quasi-cliques is notoriously expensive. In this paper, we design parallel algorithms for mining maximal quasi-cliques on G-thinker, a distributed graph mining framework that decomposes mining into compute-intensive tasks to fully utilize CPU cores. We found that directly using G-thinker results in the straggler problem due to (i) the drastic load imbalance among different tasks and (ii) the difficulty of predicting the task running time. We address these challenges by redesigning G-thinker's execution engine to prioritize long-running tasks for execution, and by utilizing a novel timeout strategy to effectively decompose long-running tasks to improve load balancing. While this system redesign applies to many other expensive dense subgraph mining problems, this paper verifies the idea by adapting the state-of-the-art quasi-clique algorithm, Quick, to our redesigned G-thinker. Extensive experiments verify that our new solution scales well with the number of CPU cores, achieving 201× runtime speedup when mining a graph with 3.77M vertices and 16.5M edges in a 16-node cluster.
Optimized Web Service Composition Using Evolutionary Computation Techniques

In service computing, Quality of Service (QoS)-aware web service composition is considered as one of the influential traits. To embrace this, an optimal method for predicting QoS values of web service is implemented where credibility evaluation is computed by accumulating reputation and trustworthiness. An automatic approach for weight calculation is invoked to calculate the weight of QoS attributes; it improves WS QoS values. QoS value is optimized by using Genetic Algorithm. Services with high QoS values are taken as candidate services for service composition. Instead of just selecting services randomly for service composition, cuckoo-based algorithm is used to identify optimal web service combination. Cuckoo algorithm realizes promising combinations by replacing the best service in lieu of worst service and by calculating the fitness score of each composition. A comparative study proved that it can provide the best service to end-users, as cuckoo selects only service composition with high fitness score.
Reinforcement Learning for Datacenter Congestion Control

We approach the task of network congestion control in datacenters using Reinforcement Learning (RL). Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorithms have shown practical potential in this domain. Evidently, the most popular recent deployments rely on rule-based heuristics that are tested on a predetermined set of benchmarks. Consequently, these heuristics do not generalize well to newly-seen scenarios. Contrarily, we devise an RL-based algorithm with the aim of generalizing to different configurations of real-world datacenter networks. We overcome challenges such as partial-observability, nonstationarity, and multi-objectiveness. We further propose a policy gradient algorithm that leverages the analytical structure of the reward function to approximate its derivative and improve stability. We show that this scheme outperforms alternative popular RL approaches, and generalizes to scenarios that were not seen during training. Our experiments, conducted on a realistic simulator that emulates communication networks' behavior, exhibit improved performance concurrently on the multiple considered metrics compared to the popular algorithms deployed today in real datacenters. Our algorithm is being productized to replace heuristics in some of the largest datacenters in the world.
Linguistic Features for Detecting Fake Reviews

Online reviews play an integral part for success or failure of businesses. Prior to purchasing services or goods, customers first review the online comments submitted by previous customers. However, it is possible to superficially boost or hinder some businesses through posting counterfeit and fake reviews. This paper explores a natural language processing approach to identify fake reviews. We present a detailed analysis of linguistic features for distinguishing fake and trustworthy online reviews. We study 15 linguistic features and measure their significance and importance towards the classification schemes employed in this study. Our results indicate that fake reviews tend to include more redundant terms and pauses, and generally contain longer sentences. The application of several machine learning classification algorithms revealed that we were able to discriminate fake from real reviews with high accuracy using these linguistic features.
Tripartite Graph Regularized Latent Low-Rank Representation for Fashion Compatibility Prediction

In recent years, an increasing online shopping demand has greatly promoted the innovation and development of the fashion industry. Visual fashion analysis has become a prospective research topic in computer vision and multimedia fields. Among these studies, fashion compatibility analysis is required in many real applications, such as fashion recommendation, matching, and retrieval. However, learning fashion compatibility is nontrivial, not only due to the uncertain and sparse dependencies among fashion items but also the latent and mutual associations among multiple factors such as color, texture, style, and functionality. To better predict fashion compatibility, in this paper, we proposed a tripartite graph regularized latent low-rank representation method, named TGRLLR, for fashion compatibility prediction. In TGRLLR, to learn more low-dimensional and effective representations, we considered the latent low-rank representation by decomposing the original feature matrix in both the column and row directions to tackle the problem of insufficient observations. On this basis, we simultaneously exploited different regularization strategies to encode the structured correlations among features, the high-order relationships among items, and the geometrical structures of outfits for more informative representations. Extensive experiments conducted on a real-world dataset demonstrate the effectiveness of our proposed method compared with state-of-the-art methods.
An Approach to Support Automated Deployment of Applications on Heterogeneous Cloud-HPC Infrastructures

Complex applications, which include microservices, computationally intensive batch jobs, and sophisticated interaction with the external environment, demand for heterogeneous computational infrastructures that range from cloud to HPC and edge computing. In this context, a crucial problem is to facilitate the work of DevOps teams in i. the conception of the right operational architecture for the application, ii. its transformation into infrastructural code that automates its deployment, taking into account the peculiarities of each of the diverse infrastructures involved in this, and iii. its operation. The SODALITE framework aims at addressing this scenario. This paper presents the main features offered by the first version of the framework, currently focusing on managing cloud and HPC clusters, and shows them in practice through a relevant case study.
An effective combination of loss gradients for multi-task learning applied on instance segmentation and depth estimation

Advanced driver assistance systems are responsible for assisting decision making and can play an important role in safety and traffic efficiency. Such systems require robust perception methods to handle complex urban scenes, and one way to achieve this is through instance segmentation. However, due to the difficulty in separating overlapping objects into different instances, this task becomes very challenging. For this, several authors proposed CNN-based methods and used depth information to enhance the instance segmentation performance. A promising way to explore this information is by adopting a multi-task learning approach, in which multiple tasks are learned simultaneously by sharing the same architecture. Usually, this combination is made by the weighted sum of loss functions, in which the weight of each task is defined manually. Nonetheless, when tasks have different natures with variation in the order of magnitude, performing this combination during training so that all tasks converge towards their optimal solution is not trivial. Aiming to get the best possible solution, we modeled the multi-task learning as a multiobjective optimization problem and, as the main contribution of this paper, we proposed a greedy approach to find the weighting coefficients for each task, performing a trade-off between tasks that allow the optimization of multiple loss functions. Experimental results showed that it is possible to enhance instance segmentation when depth information is properly explored. Moreover, not only did depth information help instance segmentation, but also did the instance segmentation help the depth estimations, achieving better performance compared to single-task models.
Scalable Graph Neural Networks with Deep Graph Library

Learning from graph and relational data plays a major role in many applications including social network analysis, marketing, e-commerce, information retrieval, knowledge modeling, medical and biological sciences, engineering, and others. Recently, Graph Neural Networks (GNNs) have emerged as a promising new learning framework capable of bringing the power of deep representation learning to graph and relational data. This ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations. In practice, many of the real-world graphs are very large. It is urgent to have scalable solutions to train GNN on large graphs efficiently.
SmartEar: Rhythm-Based Tap Authentication Using Earphone in Information-Centric Wireless Sensor Network

The rapid development of the information-centric wireless sensor network (ICWSN) has solved the challenges of information transmission and processing caused by the accelerated growth of wearable devices and the wide deployment of the Internet of Things (IoT) recently. The privacy security is also a growing problem. The existing works use earphones, covert, and user-friendly wearable devices, for user authentication. However, some of the earphone-based authentication solutions need to customize special earphones, which are not universal. Other solutions use microphones and speakers of earphones for authentication, which are susceptible to changes in the auricle’s internal environment, resulting in a decline in performance. To solve this problem, a new authentication solution based on the existing commercial earphones is proposed to authenticate a user by tapping on the earphone rhythmically. This rhythmic tap behavior causes a change of the signal waveform of the built-in accelerometer in the earphone. Based on this, we design a pipeline to authenticate the user’s identity. We first design an event detection algorithm to segment the tap signal accurately. Then, we use the global features calculated based on the event detection algorithm and local features extracted from the convolutional neural network (CNN) for building an authentication model using the Naive Bayes (NB) classifier. Finally, 20 users are recruited to evaluate the experiment and the recognition accuracy reaches 98%. Moreover, we extend the experiment to prove that it has a good performance against the different attacks and is robust in different scenarios.
Anomaly detection using state‐space models and reinforcement learning

The early detection of anomalies associated with changes in the behavior of structures is important for ensuring their serviceability and safety. Identifying anomalies from monitoring data is prone to false and missed alarms due to the uncertain nature of the infrastructure responses' dependency on external factors such as temperature and loading. Existing anomaly detection strategies typically rely on univariate threshold values and disregard the planning horizon in the context of decision making. This paper proposes an anomaly detection framework that combines the interpretability of existing Bayesian dynamic linear models, a particular form of state-space models, with the long-term planning ability of reinforcement learning. The new framework provides (a) reinforcement learning formalism for anomaly detection in Bayesian dynamic linear models, (b) a method for simulating anomalies with respect to its height, duration, and time of occurrence, and (c) a method for quantifying anomaly detectability. The potential of the new framework is demonstrated on monitoring data collected on a bridge in Canada. The results show that the framework is able to detect real anomalies that were known to have occurred, as well as synthetic anomalies.
Active &amp; Idle Virtual Machine Migration Algorithm- a new Ant Colony Optimization approach to consolidate Virtual Machines and ensure Green Cloud Computing

Energy efficiency in cloud data-centers is an incredibly significant issue in recent cloud computing research. High consumption of power and improper utilization of physical resources are the main drawbacks in cloud architecture. The idle virtual machines tend to consume 50%-70% of the total server energy which ultimately leads to an imbalance and lack of enough power for the actively working machines. In this paper, a new evolutionary computational approach of the Ant Colony System (ACS) algorithm has been applied to address such problem. Inspired by the promising performance of Ant Colony Optimization (ACO) algorithm, one similar but more efficient algorithm has been developed that not only deals with the problem of high consumption of energy but also addresses the Virtual Machine Placement (VMP) problem. This new concept has been named the Active & Idle Virtual Machine Migration (AIVMM) algorithm. It effectively migrates the idle virtual machines from an actively working server and places them in an inactive server with the objective of reducing power interruption for the active machines. The results depict that the AIVMM when implemented with OEMACS results in a hybrid algorithm which outperforms the conventional methods and offers more significant savings of data center energy and resources.
Achieving criticality for reservoir computing using environment-induced explosive death

The network of oscillators coupled via a common environment has been widely studied due to its great abundance in nature. We exploit the occurrence of explosive oscillation quenching in a network of non-identical oscillators coupled to each other indirectly via an environment for efficient reservoir computing. At the very edge of explosive transition, the reservoir achieves criticality maximizing its information processing capacity. The efficiency of the reservoir at different configurations is determined by the computational accuracy for different tasks performed by it. We analyze the dependence of accuracy on the dynamical behavior of the reservoir in terms of an order parameter symbolizing the desynchronization of the system. We found that the reservoir achieves the criticality in the steady-state region right at the edge of the hysteresis area. By computing the entropy of the reservoir for different tasks, we confirm that maximum accuracy corresponds to the edge of chaos or the edge of stability for this reservoir.