Reviewerly/exHarmony huggingface dataset:
Distributed Value-Function Learning with Linear Convergence Rates

In this paper we develop a fully decentralized algorithm for policy evaluation with off-policy learning and linear function approximation. The proposed algorithm is of the variance reduced kind and achieves linear convergence with O(1) memory requirements. We consider the case where a collection of agents have distinct and fixed size datasets gathered following different behavior policies (none of which is required to explore the full state space) and they all collaborate to evaluate a common target policy. The network approach allows all agents to converge to the optimal solution even in situations where neither agent can converge on its own without cooperation. We provide simulations to illustrate the effectiveness of the method in a Linear Quadratic Regulator (LQR) problem.
Fast Instance and Semantic Segmentation Exploiting Local Connectivity, Metric Learning, and One-Shot Detection for Robotics

Semantic scene understanding is important for autonomous robots that aim to navigate dynamic environments, manipulate objects, or interact with humans in a natural way. In this paper, we address the problem of jointly performing semantic segmentation as well as instance segmentation in an online fashion, so that autonomous robots can use this information on-the-go and without sacrificing accuracy. We achieve this by exploiting a local connectivity prior of objects in the real world and a multi-task convolutional neural network architecture. The network identifies the individual object instances and their classes without region proposals or pre-segmentation of the images into individual classes. We implemented and thoroughly evaluated our approach, and our experiments suggest that our method can be used to accurately segment instance masks of objects and identify their class in an online fashion.
Evolutionary Optimization of Hyperparameters in Deep Learning Models

Recently, deep learning is one of the most popular techniques in artificial intelligence. However, to construct a deep learning model, various components must be set up, including activation functions, optimization methods, a configuration of model structure called hyperparameters. As they affect the performance of deep learning, researchers are working hard to find optimal hyperparameters when solving problems with deep learning. Activation function and optimization technique play a crucial role in the forward and backward processes of model learning, but they are set up in a heuristic way. The previous studies have been conducted to optimize either activation function or optimization technique, while the relationship between them is neglected to search them at the same time. In this paper, we propose a novel method based on genetic programming to simultaneously find the optimal activation functions and optimization techniques. In genetic programming, each individual is composed of two chromosomes, one for the activation function and the other for the optimization technique. To calculate the fitness of one individual, we construct a neural network with the activation function and optimization technique that the individual represents. The deep learning model found through our method has 82.59% and 53.04% of accuracies for the CIFAR-10 and CIFAR-100 datasets, which outperforms the conventional methods. Moreover, we analyze the activation function found and confirm the usefulness of the proposed method.
Parallel quantum trajectories via forking for sampling without redundancy

The computational cost of preparing a quantum state can be substantial depending on the structure of data to be encoded. Many quantum algorithms require repeated sampling to find the answer, mandating reconstruction of the same input state for every execution of an algorithm. Thus, the advantage of quantum computation can diminish due to redundant state initialization. We present a framework based on quantum forking that bypasses this fundamental issue and expedites a family of tasks that require sampling from independent quantum processes. Quantum forking propagates an input state to multiple quantum trajectories in superposition, and a weighted power sum of individual results from each trajectories is obtained in one measurement via quantum interference. The significance of our work is demonstrated via applications to implementing non-unitary quantum channels, studying entanglement and benchmarking quantum control. A proof-of-principle experiment is implemented on the IBM and Rigetti quantum cloud platforms.
Real-Time Single Object Detection on The UAV

The demand for mission critical tasks, especially for tracking on the UAVs, has been increasing due to their superior mobility. Out of necessity, the ability of processing large images emerges for object detection or tracking with UAVs. As such, the requirements of low latency and lack of Internet access under some circumstances become the major challenges. In this paper, we present a modeling method of CNN that is dedicated to single object detection on the UAV without any transfer learning model. Not limited to the features learned by the transfer learning model, the single object can be selected arbitrarily and specifically, even can be distinguished from those other objects in the same category. Our modeling method introduces the inducing neural network that follows the traditional CNN and plays the role of guiding the training in a fast and efficient way with respect to the training convergence and the model capacity. Using the dataset released by DAC 2018, which contains 98 classes and 96,408 images taken by UAVs, we present how our modeling method develops the inducing neural network that integrates multi-task learning drawn from the state-of-the-art works to achieve about 50% of IoU (Intersection over Union of the ground-truth bounding boxes and predicted bounding boxes) and 20 FPS running on NVIDIA Jetson TX2. Experimental results demonstrated fast inference of an image in size of 720x1280 and the UAV navigated itself to track the target (car) using the inference result.
CognitiveEMS

This paper presents our preliminary results on development of a Cognitive assistant system for Emergency Medical Services (CognitiveEMS) that aims to improve situational awareness and safety of first responders. CognitiveEMS integrates a suite of smart wearable sensors, devices, and analytics for real-time collection and analysis of in-situ data from incident scene and delivering dynamic data-driven insights to responders on the most effective response actions to take. We present the overall architecture of CognitiveEMS pipeline for processing information collected from the responder, which includes stages for converting speech to text, extracting medical and EMS protocol specific concepts, and modeling and execution of an EMS protocol. The performance of the pipeline is evaluated in both noise-free and noisy incident environments. The experiments are conducted using two types of publicly-available real EMS data: short radio calls and post-incident patient care reports. Three different noise profiles are considered for simulating the noisy environments: cafeteria, people talking, and emergency sirens. Noise was artificially added at 3 intensity levels of low, medium, and high to pre-recorded audio data. The results show that the i) state-of-the-art speech recognition tools such as Google Speech API are quite robust to low and medium noise intensities; ii) in the presence of high noise levels, the overall recall rate in medical concept annotation is reduced; and iii) the effect of noise often propagates to the final decision making stage and results in generating misleading feedback to responders.
An EEG-Based Study on Perception of Video Distortion Under Various Content Motion Conditions

Human perception sensitivity to video distortion is vital for visual quality assessment (VQA). Different from the perception mechanism of image distortion that has been thoroughly studied, the perception of video distortion is inevitably influenced by motion of dynamic content due to the characteristics of the human visual system (HVS). In this paper, electroencephalography (EEG) is used as a novel psychophysiological method to study the human perception sensitivity to quantification-aroused video distortion under various content motion conditions. For this purpose, we conduct experiments to record the EEG signals of the subjects when they are watching distorted videos. According to the feature analysis of EEG data, the P300 component aroused by human perception of video quality change is selected as the indicator of human perception of distortion. By the means of classification based on linear discriminant analysis (LDA), it is found that the separability of the P300 component, which is measured by the area under curve (AUC) of the receiver operating characteristic (ROC), is positively correlated with the perceptibility of distortion. The correlation provides a valid psychophysiological method, which is exempt from being influenced by subjective bias due to human high-level cognitive activities, for evaluating distortion perceptibility. In addition, the regression analysis results demonstrate a sigmoid-typed quantitative relation between the perceptibility of distortion and separability of the P300 component. Based on such relation, the perceptibility thresholds of distortion corresponding to various content motion speeds are calibrated by EEG signals and it is found that the content motion speed has a significant impact on distortion perceptibility.
Document Embeddings vs. Keyphrases vs. Terms for Recommender Systems: A Large-Scale Online Evaluation

Many recommendation algorithms are available to digital library recommender system operators. The effectiveness of algorithms is largely unreported by way of online evaluation. We compare a standard term-based recommendation approach to two promising approaches for related-article recommendation in digital libraries: document embeddings, and keyphrases. We evaluate the consistency of their performance across multiple scenarios. Through our recommender-system as-a-service Mr. DLib, we delivered 33.5M recommendations to users of Sowiport and Jabref over the course of 19 months, from March 2017 to October 2018. The effectiveness of the algorithms differs significantly between Sowiport and Jabref (Wilcoxon rank-sum test; p <; 0.05). There is a ~400% difference in effectiveness between the best and worst algorithm in both scenarios separately. The best performing algorithm in Sowiport (terms) is the worst performing in Jabref. The best performing algorithm in Jabref (keyphrases) is 70% worse in Sowiport, than Sowiport's best algorithm (click-through rate; 0.1% terms, 0.03% keyphrases).
Secure Open Federation of IoT Platforms Through Interledger Technologies - The SOFIE Approach

The lack of interoperability among IoT platforms has led to a fragmented environment, where the users and society as a whole suffer from lock-ins, lack of privacy, and reduced functionality. This paper presents SOFIE, a solution for federating the existing IoT platforms in an open and secure manner using Distributed Ledger Technologies (DLTs) and without requiring modifications to the IoT platforms, and describes how SOFIE is used to enable two complex real life pilots: food supply chain tracking from field to fork and electricity distribution grid balancing with guided electrical vehicle (EV) charging. SOFIE's main contribution is to provide interoperability between IoT systems while also enabling new functionality and business models
Optimized least-squares support vector machine for predicting aero-optic imaging deviation based on chaotic particle swarm optimization

In the time-averaged flow field, aero-optic imaging deviation (AOID) and its influencing variables are connected by complicated and strongly coupled nonlinear time-dependent relationships that present many difficulties for fast calculation of AOID. To achieve real-time online AOID compensation in practice, chaotic particle swarm optimization (CPSO) and the least-squares support vector machine (LSSVM) are combined to construct a predictive AOID model for supersonic aircraft in flight. With its strong global-search capability, the CPSO algorithm is used to optimize the parameters of the LSSVM predictive model. First, a chaotic sequence is used to initialize the particle positions, thereby enhancing search diversity. The premature convergence of normal particle swarm optimization (PSO) is then countered by using CPSO. If PSO falls into a local optimum, the extreme position of the population is adjusted and the current search trajectory of the particles is disturbed so that the particles search new neighborhoods and paths, thereby increasing the probability of escaping the local optimum. The simulation results show that the predictive accuracy of the CPSO-LSSVM model exceeds that of the LSSVM model alone or the back-propagation neural network model. The CPSO-LSSVM model is effective at compensating for AOID within a limited range, and it avoids the disadvantages of traditional geometrical-optics calculations, namely that they are time-consuming, laborious, and error-prone. When the training data of LSSVM is enough, any AOID can be estimated by the CPSO-LSSVM model.
A feature extraction model based on discriminative graph signals

Classification finds wide applications in artificial intelligence and expert systems. Feature extraction is a key step for classifier learning. However, the relation among samples is usually ignored in classical feature extraction models. Recently, feature extraction based on graph signal processing that makes use of the relation among samples has attracted great attention. It is a common assumption that the classification information is smooth and of low frequency in these studies. We point out that it is the discrimination ability that essentially makes a good classification feature instead of smoothness. This new perspective prompts us to introduce the concept of discriminative graph signal, and then, based on this concept, we propose a novel feature extraction model for supervised classification. To improve the classification ability for multi-class problems, a generalized model is proposed to extract multiple discriminative signals and an algorithm is also presented to compute the multiple discriminative signals simultaneously. On five publicly available UCI datasets, our proposed method outperforms the existing methods in terms of performance. Finally some drawbacks are discussed and future research directions are also provided.
Sparsely Self-Supervised Generative Adversarial Nets for Radio Frequency Estimation

Radio frequency (RF) estimation plays a significant role in cellular network's planning and optimization. The conventional methods for RF estimation are mainly based on radio propagation models, which suffer from low accuracy and coarse granularity. Distinguished from existing methods, we propose sparsely self-supervised generative adversarial nets (SS-GAN), a novel data-driven model to generate the RF maps of an area from irregularly distributed measurement samples. SS-GAN meticulously adopts the standard GAN framework, where the generator learns the distribution of true observations under the guidance of the discriminator that discriminates whether the input data is from real samples or from generated outputs. Competition in the minmax game between generator and discriminator drives them to improve their capability, until the generator is indistinguishable from the true RF distribution. Specifically, on top of the GAN framework, SS-GAN carries out a variety of operations to enhance the estimation: (1) In addition to observations of the measured RF coverage and RF interference, SS-GAN also employs a collection of crucial auxiliary information (e.g., geographic data) as additional input features to the GAN framework so as to precisely characterize the RF environment; (2) To dampen the training instability, a new lightweight reconstruction loss is introduced to the objective function of SS-GAN rather than solely using the adversarial loss, which aims to impose a weak supervision on the generated RF maps according to estimation accuracy; (3) Moreover, SS-GAN designs an innovative sparsely self-supervised (SS) learning mechanism that facilitates the validation of the estimated results for a model lacking direct ground truth knowledge. Extensive experiments on a real-world 4G LTE dataset demonstrate that SS-GAN can substantially improve the estimation accuracy over the state-of-the-art baselines. Comparison results are presented through visualized case studies and quantitative statistics.
Visual Saliency Prediction Based on Deep Learning

Human eye movement is one of the most important functions for understanding our surroundings. When a human eye processes a scene, it quickly focuses on dominant parts of the scene, commonly known as a visual saliency detection or visual attention prediction. Recently, neural networks have been used to predict visual saliency. This paper proposes a deep learning encoder-decoder architecture, based on a transfer learning technique, to predict visual saliency. In the proposed model, visual features are extracted through convolutional layers from raw images to predict visual saliency. In addition, the proposed model uses the VGG-16 network for semantic segmentation, which uses a pixel classification layer to predict the categorical label for every pixel in an input image. The proposed model is applied to several datasets, including TORONTO, MIT300, MIT1003, and DUT-OMRON, to illustrate its efficiency. The results of the proposed model are quantitatively and qualitatively compared to classic and state-of-the-art deep learning models. Using the proposed deep learning model, a global accuracy of up to 96.22% is achieved for the prediction of visual saliency.
Towards a Profiling View for Unsupervised Traffic Classification by Exploring the Statistic Features and Link Patterns

In this paper, we study the network traffic classification task. Different from existing supervised methods that rely heavily on the labeled statistic features in a long period (e.g., several hours or days), we adopt a novel view of unsupervised profiling to explore the flow features and link patterns in a short time window (e.g., several seconds), dealing with the zero-day traffic problem. Concretely, we formulate the traffic identification task as a graph co-clustering problem with topology and edge attributes, and proposed a novel Hybrid Flow Clustering (HFC) model. The model can potentially achieve high classification performance, since it comprehensively leverages the available information of both features and linkage. Moreover, the two information sources integrated in HFC can also be utilized to generate the profiling for each flow category, helping to reveal the deep knowledge and semantics of network traffic. The effectiveness of the model is verified in the extensive experiments on several real datasets of various scenarios, where HFC achieves impressive results and presents powerful application ability.
Real-Time Stencil Printing Optimization Using a Hybrid Multi-Layer Online Sequential Extreme Learning and Evolutionary Search Approach

This article aims to develop a dynamic optimization model performing real-time control of a stencil printing process (SPP) by maintaining the optimal printer parameter settings. In a surface mount technology (SMT) assembly line, stencil printing is a major process that affects the yield of printed circuit boards (PCBs). During printing, environmental changes may induce the PCB’s printing results to deviate from initial optimal outcomes. To consistently improve the system performance, a real-time adaptation of the printer settings is an effective and cost-efficient approach. This research proposes a hybrid online optimization model by using online learning to predict real-time SPP volumes and an evolutionary search (ES) technique to determine the optimal settings. The prediction model investigates the printing volumes’ transfer efficiency (TE) in averages and standard deviations (SDs) with relevant features. From the model selection of the online-based learning, the multi-layer online sequential extreme learning machine (MOSELM) shows outstanding prediction performance with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> values of 97% for volume averages and 81% for SDs. From the real implicational results, the system achieves a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C_{pk}=2.8$ </tex-math></inline-formula> , outperforming other advanced models. The proposed framework exhibits a good balance between accuracy and retraining efficiency, promising effective SMT assembly dynamic control.
Towards Learning Abstract Representations for Locomotion Planning in High-dimensional State Spaces

Ground robots which are able to navigate a variety of terrains are needed in many domains. One of the key aspects is the capability to adapt to the ground structure, which can be realized through movable body parts coming along with additional degrees of freedom (DoF). However, planning respective locomotion is challenging since suitable representations result in large state spaces. Employing an additional abstract representation-which is coarser, lower-dimensional, and semantically enriched-can support the planning. While a desired robot representation and action set of such an abstract representation can be easily defined, the cost function requires large tuning efforts. We propose a method to represent the cost function as a CNN. Training of the network is done on generated artificial data, while it generalizes well to the abstraction of real world scenes. We further apply our method to the problem of search-based planning of hybrid driving-stepping locomotion. The abstract representation is used as a powerful informed heuristic which accelerates planning by multiple orders of magnitude.
Posterior-thresholding feature extraction for paralinguistic speech classification

The standard approach for handling computational paralinguistic speech tasks is to extract several thousand utterance-level features from the speech excerpts, and use machine learning methods such as Support Vector Machines and Deep Neural Networks (DNNs) for the actual classification task. In contrast, Automatic Speech Recognition handles the speech signal in small, equal-sized parts called frames. Although the speech community has developed techniques for efficient frame classification, these efforts have mostly been ignored in computational paralinguistics. In this study we propose a simple, three-step technique to utilize frame-level DNN training know-how in computational paralinguistics. We show that this method by itself provides good accuracy scores, and by combining it with the standard paralinguistic classification approach, we get close to the performance of heavyweight, state-of-the-art techniques such as Fisher vector analysis. However, our approach has the advantage that it can be easily realized by using standard speech recognition tools. To demonstrate the generic applicability of this three-step method proposed, we performed our experiments on four different corpora containing different paralinguistic tasks. Overall, we were able to achieve improvements over the baseline score in all four cases, leading to relative error reductions of up to 19%.