Reviewerly/exHarmony huggingface dataset:
Efficient Batch-Mode Reinforcement Learning Using Extreme Learning Machines

As a class of batch-mode reinforcement learning (RL) methods for Markov decision problems with large or continuous state spaces, approximate policy iteration (API) has received increasing attention in the past decades. One open problem in the design of API algorithms is how to construct the basis functions or features for value function approximation (VFA). In this paper, we propose a novel batch-mode RL approach with randomly projected features for VFA. The proposed approach can be viewed as an extension of extreme learning machines (ELMs) to RL problems so it can be called ELM-API. The ELMs have been popularly studied in supervised learning problems, but there is not much work on the extension of ELMs to learning control problems. The proposed approach has advantages over the previous API algorithms in that the features for VFA can be quickly generated without complex parameter selection and the performance will be adaptive to different sample sets in batch-mode RL. In particular, the ELM-API approach can realize fast and efficient feature reconstruction when training sample sets are relatively small. Comprehensive simulation studies on two benchmark learning control problems were carried out to test the performance of API algorithms with different feature construction methods. It is shown that the ELM-API algorithm can obtain comparable or better performance than the previous API approaches. To further show the effectiveness of ELM-API in real-world applications, the simulation results on a more challenging high-dimensional lane-changing decision problem in dynamic traffic environment are also reported, which show the capability of the ELM-API algorithm in learning satisfactory lane-changing policies with high data efficiency.
Multi-objective optimal control for proactive decision making with temporal logic models

The operation of today’s robots entails interactions with humans, e.g., in autonomous driving amidst human-driven vehicles. To effectively do so, robots must proactively decode the intent of humans and concurrently leverage this knowledge for safe, cooperative task satisfaction: a problem we refer to as proactive decision making. However, simultaneous intent decoding and robotic control requires reasoning over several possible human behavioral models, resulting in high-dimensional state trajectories. In this paper, we address the proactive decision-making problem using a novel combination of formal methods, control, and data mining techniques. First, we distill high-dimensional state trajectories of human–robot interaction into concise, symbolic behavioral summaries that can be learned from data. Second, we leverage formal methods to model high-level agent goals, safe interaction, and information-seeking behavior with temporal logic formulas. Finally, we design a novel decision-making scheme that maintains a belief distribution over models of human behavior, and proactively plans informative actions. After showing several desirable theoretical properties, we apply our framework to a dataset of humans driving in crowded merging scenarios. For it, temporal logic models are generated and used to synthesize control strategies using tree-based value iteration and deep reinforcement learning. In addition, we illustrate how data-driven models of human responses to informative robot probes, such as from generative models such as conditional variational autoencoders, can be clustered with formal specifications. Results from simulated self-driving car scenarios demonstrate that data-driven strategies enable safe interaction, correct model identification, and significant dimensionality reduction.
Modelling Multi-Channel Emotions Using Facial Expression and Trajectory Cues for Improving Socially-Aware Robot Navigation

Using facial expressions and trajectory signals, we present an emotion-aware navigation algorithm for social robots. Our approach uses a combination of Bayesian-inference, CNN-based learning and the Pleasure-Arousal-Dominance model from psychology to estimate time-varying emotional behaviors of pedestrians from their faces and trajectories. For each pedestrian, these PAD characteristics are used to generate proxemic constraints. We use a multi-channel model to classify pedestrian features into four categories of emotions (happy, sad, angry, neutral). We observe an emotional detection accuracy of 85.33% in our validation results. In low-to medium-density environments, we formulate emotion-based proxemic constraints to perform socially conscious robot navigation. With Pepper, a social humanoid robot, we demonstrate the benefits of our algorithm in simulated environments with tens of pedestrians as well as in a real world setting.
DA-BERT: Enhancing Part-of-Speech Tagging of Aspect Sentiment Analysis Using BERT

With the development of Internet, text-based data from web have grown exponentially where the data carry large amount of valuable information. As a vital branch of sentiment analysis, the aspect sentiment analysis of short text on social media has attracted interests of researchers. Aspect sentiment classification is a kind of fine-grained textual sentiment classification. Currently, the attention mechanism is mainly combined with RNN (Recurrent Neural Network) or LSTM (Long Short-Term Memory) networks. Such neural network-based sentiment analysis model not only has a complicated computational structure, but also has computational dependence. To address the above problems and improve the accuracy of the target-based sentiment classification for short text, we propose a neural network model that combines deep-attention with Bidirectional Encoder Representations from Transformers (DA-BERT). The DA-BERT model can fully mine the relationships between target words and emotional words in a sentence, and it does not require syntactic analysis of sentences or external knowledge such as sentiment lexicon. The training speed of the proposed DA-BERT model has been greatly improved while removing the computational dependencies of RNN structure. Compared with LSTM, TD-LSTM, TC-LSTM, AT-LSTM, ATAE-LSTM, and PAT-LSTM, the results of experiments on the dataset SemEval2014 Task4 show that the accuracy of the DA-BERT model is improved by 13.63% on average where the word vector is 300 dimensions in aspect sentiment classification.
On the extension of digital ecosystems for scm and customs with distributed ledger technologies requirements analysis, innovation assessment, and prototype design for the lake constance region

Global supply chains represent the backbone of the modern manufacturing industry. Planning of global supply chains still represents a major hurdle, mainly because of the high complexity and unforeseen disruptions that have to be mastered for meeting the different logistics windows in a globally distributed production environment. Trust in supply chains is an additional challenge. A major - albeit sometimes overlooked - part of Supply Chain Management (SCM) is the management and integration of customs processes, clearing of tariffs, (re-)billing of customers, and fulfilling other legal requirements related to crossing borders, ranging from environmental standards over goods inspection to general paper work. With the exception of work offered by the World Customs Organization (WCO) the issue of customs and blockchain is still underrepresented in research and practice. In this paper, we look at innovations that drive the current ICT-enabled SCM research and how these can be combined with smart customs management. After a literature review and introduction to the state-of-the-art, we list potential trust-based innovations for SCM and customs in digital business ecosystems. Based upon the innovations we also describe a requirements analysis of existing distributed ledger technologies (requirements for system layout, system configuration, system governance). A description of the prototype for the Lake Constance region - on which we are currently working - concludes the paper.
First International Workshop on Languages for Modelling Variability (MODEVAR 2019)

Feature models were invented in 1990 and have been recognised as one of the main contributions to the software product line community. Although there have been several attempts to establish a sort of standard variability modelling language, there is still no consensus. There can be many motivations to have one but there is one that is very important: information sharing among researchers, tools or developers. This first international workshop is an interactive event where all participants shall share knowledge about how to build up a simple variability modelling language that all the community can agree on.
Gaussian Mixture Model (GMM) Based Dynamic Object Detection and Tracking

In this paper, we have addressed the problem of real-time detection and tracking of dynamic objects using quadrotors. We have developed a novel object detection algorithm by analyzing and matching the color and spacial features of the target from monocular image sequences. The proposed object detection algorithm can track the objects with high Frame Per Second (FPS) which is suitable for low-end onboard computers that are used in quad-rotors. In addition, we also estimate the position of the target object in real world so that the drone can track the object accurately. A rigorous experimental analysis is provided to show the efficacy of the proposed approach in indoor as well as outdoor environments.
Probabilistic Knowledge-Graph based Workflow Recommender for Network Management Automation

The move towards more complex and dynamic telecommunication networks renders the need for more automation in the management system stringent. This is especially true in the context of 5G networks, where understanding context for providing the right management recommendations is crucial. Our work focuses on gathering context from the current status of the network and correlating it with useful information from existing documents in the network provider and operator's domain. We propose an architecture that, based on collected and interlinked information, can provide automatic recommendations for addressing existing problems in the network. More specifically, our approach is based on creating a knowledge-graph to direct workflow recommendations for network incidents. We implemented this system using the actual OSS product documentation and real network events and we show that our approach adds new value for automated network management, including improved efficiency and customer experience.
Using Deep Learning to Detect Malicious URLs

With the development of energy internet, cyberattacks pose a great threat to the distribution of electricity. Specifically, malicious domains can cause tremendous damages to network security. Many researchers have tried to tackle the task of detecting DGA-generated domains with different approaches. In this paper, an algorithm based on the Deep Bidirectional LSTM model is constructed to address this problem. Before the final experiment with the deep learning model, this paper also presents traditional machine learning methods that classify malicious domains based on lexical features for comparison. The result shows that the DBLSTM classifier can achieve a 98.6% accuracy, performing much better than conventional machine learning approaches.
A Novel Encoding Scheme for Complex Neural Architecture Search

Recent boom in Image Processing can be attributed mostly to Artificial Neural networks, enormity of data and availability of high-performance hardware. ANNs need to be designed with care and a lot of tweaking is required according to the problem at hand. However, they can be designed automatically by deploying search algorithms given a set of possible values for hyperparameters. Previously, network design has been limited because of the inherent complexity of network diversity in terms of their style (e.g. VGG-style or ResNet-style). Aforementioned two network styles require different kinds of parameters hence limiting the search algorithm to output only a specific style of architecture. We propose a methodology which will allow the system to search for the best architecture with least possible constraints.
Performance Evaluation of Elliptic Curve Libraries on Automotive-Grade Microcontrollers

As cryptography is quickly entering the automotive domain, public-key cryptographic functions are a vital building block and are part of recent industry-proposed standards. Elliptic curves provide a more compact representation for public/private keys making them more suitable for embedded devices with limited amounts of memory. Nonetheless, they provide more compact signatures and open road for identity-based cryptographic primitives by exploiting the flexibility of bilinear pairings. In this work we carry a performance evaluation on some modern libraries, e.g., MIRACL, RELIC, and compare them to the more classical WolfSSL. The evaluation is carried on a state-of-the-art representative controller from the automotive industry, i.e., a 32 bit Infineon TC297. Having a crisper image on computational requirements is relevant for future automotive and industrial applications.
Physical Objects in AR Games – Offering a Tangible Experience

Most AR games focus primarily on static augmentation, leaving out the potential for rich and tangible interactions. Recently, with the creation of the Superhuman Sports genre, new AR games emerged with the goal to extend the sports experience by utilizing technology. With the understanding of various related projects towards physical object interaction in AR and with the rich selection of sensors in mobile devices, we want to present two innovative visions in the direction of AR Sports Games: 1.Floating Ball: A ball shaped object is augmented and while flying in a circular pattern, two players are competing against each other by interacting with it. 2.Augmented Drone: Resulting out of the experiences with the Floating Ball, a competitive game concept with a drone as a versatile augmented game ball will be presented. We want to utilize the results as a step towards a competitive multi-player AR Sports Game based on mobile devices. In both concepts, one device is functioning as a Tangible AR Interface with the task to let the player manipulate the flying path of the physical ball. For this purpose, the ball is incorporated into the augmented world to create a realistic, perceivable playing experience.
Cheetah

Modern database systems are growing increasingly distributed and struggle to reduce the query completion time with a large volume of data. In this poster, we propose to leverage programmable switches in the network to offload part of the query computation to the switch. While switches provide high performance, they also have many resource and programming constraints that make it hard to implement diverse database queries. To fit in these constraints, we introduce the concept of data pruning - filtering out entries which are guaranteed not to affect the output. The database system then runs the same query, but on the pruned data, which significantly reduces the processing time. We propose a set of pruning algorithms for a variety of queries. We implement our system, Cheetah, on a Barefoot Tofino switch and Spark. Our evaluation on the Berkeley AMPLab benchmark shows up to 3x improvement in the query completion time compared to Apache Spark.
EEG Can Be Used to Measure Embodiment When Controlling a Walking Self-Avatar

It has recently been shown that inducing the ownership illusion and then manipulating the movements of one's self-avatar can lead to compensatory motor control strategies in gait rehabilitation. In order to maximize this effect, there is a need for a method that measures, and monitors embodiment levels of participants immersed in VR to induce and maintain a strong ownership illusion. The objective of this study was to propose a novel approach to measuring embodiment by presenting visual feedback that conflicts with motor control to embodied subjects. Twenty healthy participants were recruited. During experimentations, participants wore an EEG cap and motion capture markers, with an avatar displayed in a HMD from a first-person perspective. They were cued to either perform, watch or imagine a single step forward or to initiate walking on the treadmill. For some of the trials, the avatar took a step with the contralateral limb or stopped walking before the participant stopped (modified feedback). Results show that subjective levels of embodiment correlate strongly with the difference in μ - ERS power over the motor and pre-motor cortex between the modified and non-modified feedback trials.
Histogram Learning in Image Contrast Enhancement

In this paper, we propose a novel contrast enhancement method utilizing a fully convolutional network (FCN) to learn the weighted histograms from input images. In this method, the enhanced image references are not required. The training images are synthesized by randomly adding illumination on different regions in the source images to simulate the input images with poor contrast in different regions, and to enlarge the scale of training image set. And with this data-driven strategy for learning the underlying ill-posed illumination information of each pixel, a novel weighted image histogram is developed. It not only describes the distribution of pixel intensity, but also contains the illumination information of input images. Consequently, the proposed method can fast and efficiently enhance the regions with poor contrast and have the regions with acceptable contrast preserved, which keeps vivid color and rich details of the enhanced images. Experimental results demonstrate the effectiveness of our proposed method in comparison with some state-of-the-art methods.
Estimation under copula-based Markov normal mixture models for serially correlated data

We propose an estimation method under a copula-based Markov model for serially correlated data. Motivated by the fat-tailed distribution of financial assets, we select a normal mixture distribution for the marginal distribution. Based on the normal mixture distribution for the marginal distribution and the Clayton copula for serial dependence, we obtain the corresponding likelihood function. In order to obtain the maximum likelihood estimators, we apply the Newton-Raphson algorithm with appropriate transformations and initial values. We conduct simulation studies to evaluate the performance of the proposed method. In the empirical analysis, the stock price of Dow Jones Industrial Average is analyzed for illustration.
An Analysis of Minimum Population Search on Large Scale Global Optimization

Minimum Population Search is a recently developed metaheuristic specifically designed to optimize high dimensional multi-modal functions. In this paper, Minimum Population Search is evaluated on the test functions provided for the 2013 LSGO competition in the IEEE Congress of Evolutionary Computation (CEC 2013). Furthermore, an analysis using some recent categorizations for exploitation and exploration, and especially the effects of selection, sheds light on how MPS performs better in high dimensions than popular metaheuristics such as Differential Evolution and Particle Swarm Optimization.
Parameter Tuning in MSER for Text Localization in Multi-lingual Camera-Captured Scene Text Images

Scene text detection and localization in camera-captured images has always posed a great challenge to the researchers due to its high complexity in understanding the texture and homogeneity of scene text images. The solution to this problem paves way for simplified text extraction and processing, thereby realizing wide range of applications. A very popular method, namely, maximally stable extremal region or MSER detection is used for localizing text since the text regions are considered to be more stable than other regions in an image. However, it involves manual selection of several parameters, limiting its usage in many practical applications. In this work, the relations among parameters, like image dimension, text size, and region area, are analyzed by experimenting on an in-house multi-lingual dataset having 300 images comprising English, Bangla, and Hindi texts and validating the outcome against images of standard datasets, like SVT and MSRA-TD500, which yields encouraging results.
A Hierarchical Framework for Coordinating Large-Scale Robot Networks

In this paper, we study the cooperative path planning and motion coordination problems of the multi-robot system with large number of robots, aiming for practical applications in robotic warehouses and automated transportation systems. Particularly, we solve the life-long planning problem and guarantee the coordination performance in the presence of robot motion uncertainties. A hierarchical path planning and motion coordination structure is presented. The environment is divided into several sectors and a traffic heat-map is presented to describe the current sector-level traffic condition. In path planning level, the sector-level path is calculated by considering the path distance, the current traffic condition and the current robot uncertainty. In motion coordination level, local cooperative A* algorithm and conflict-based searching strategy are utilized within each sector to generate the collision-free local path of each robot in a rolling planning manner. The effectiveness and practical applicability of the proposed approach are validated by simulations with more than one thousand robots and real experiments.
An automated change impact analysis approach for User Requirements Notation models

Requirements and their models often evolve to reflect changing needs, technologies, and regulations. The decision to implement proposed changes to requirements models relies on means to capture and analyze the potential impact of such changes. The User Requirements Notation (URN) is a standardized requirements modeling language that incorporates two complementary views based on Use Case Maps (UCM), for expressing scenarios and processes bound to architectural components, and on the Goal-oriented Requirement Language (GRL), for capturing the goals of actors and their relationships. This paper presents a new Change Impact Analysis (CIA) approach for URN models. Following a proposed change, this approach helps identify potentially impacted URN constructs within the selected GRL/UCM view, as well as throughout other view elements connected with URN links. This URN-oriented CIA approach is implemented as an extension of the jUCMNav modeling environment, and its applicability is demonstrated using an illustrative URN specification and three real and publicly available specifications. Furthermore, an empirical study involving 10 participants is used to assess the accuracy of this approach in identifying impacted URN elements upon specification changes. Results indicate excellent accuracy and a significant reduction in user-perceived difficulty when estimating the impact of changes in URN specifications.