Query: Ontology-based traffic scene modeling, traffic regulations dependent situational awareness and decision-making for automated vehicles

Relevant passages:
Ontology-based decision making on uncontrolled intersections and narrow roads <=SEP=> Many Advanced Driver Assistance Systems (ADAS) have been developed to improve car safety. However, it is still a challenging problem to make autonomous vehicles to drive safely on urban streets such as uncontrolled intersections (without traffic lights) and narrow roads. In this paper, we introduce a decision making system that can assist autonomous vehicles at uncontrolled intersections and narrow roads. We constructed a machine understandable ontology-based Knowledge Base, which contains maps and traffic regulations. The system makes decisions in comply with traffic regulations such as Right-Of-Way rules when it receives a collision warning signal. The decisions are sent to a path planning system to change the route or stop to avoid collisions.

Using Ontology-Based Traffic Models for More Efficient Decision Making of Autonomous Vehicles <=SEP=> The paper describes how a high-level abstract world model can be used to support the decision-making process of an autonomous driving system. The approach uses a hierarchical world model and distinguishes between a low-level model for the trajectory planning and a high-level model for solving the traffic coordination problem. The abstract world model used in the CyberCars-2 project is presented. It is based on a topological lane segmentation and introduces relations to represent the semantic context of the traffic scenario. This makes it much easier to realize a consistent and complete driving control system, and to analyze, evaluate and simulate such a system.

The Semantic Web Revisited <=SEP=> The article included many scenarios in which intelligent agents and bots undertook tasks on behalf of their human or corporate owners. Of course, shopbots and auction bots abound on the Web, but these are essentially handcrafted for particular tasks: they have little ability to interact with heterogeneous data and information types. Because we haven't yet delivered large-scale, agent-based mediation, some commentators argue that the semantic Web has failed to deliver. We argue that agents can only flourish when standards are well established and that the Web standards for expressing shared meaning have progressed steadily over the past five years

Pellet: A practical OWL-DL reasoner <=SEP=> In this report, we present Pellet: a complete and capable OWL-DL reasoner with acceptable to very good performance, extensive middleware, and a number of unique features. Pellet is written in Java and is open source under a very liberal license. It is used in a number of projects, from pure research to industrial settings. Pellet is the first sound and complete OWL-DL reasoner with extensive support for rea-soning with individuals (including nominal support and conjunctive query), user-defined datatypes, and debugging support for ontologies. It implements several extensions to OWL-DL including a combination formalism for OWL-DL ontologies, a non-monotonic operator, and preliminary support for OWL/Rule hybrid reasoning. It has proven to be a reliable tool for working with OWL-DL ontologies and experimenting with OWL extensions. In this paper we describe Pellet’s features, architecture and special capabilities, along with an empirical comparison of its performance against other leading OWL-DL reasoners.

RoadGraph: High level sensor data fusion between objects and street network <=SEP=> The RoadGraph is a graph based environmental model for driver assistance systems. It integrates information from different sources like digital maps, onboard sensors and V2X communication into one single model about the vehicle's environment. At the moment of information aggregation some function independent situation analysis is done. In this paper we look at techniques for lane-precise map-matching even with moderate GPS reception using distinct information sources. We also analyze the concepts of aggregating objects from different sources with a-priori knowledge of the street-layout. Results of this novelty approach are shown.

Hardware resource estimation for heterogeneous FPGA-based SoCs <=SEP=> The increasing complexity of recent System-on-Chip (SoC) designs introduces new challenges for design space exploration tools. In addition to the time-to-market challenge, designers need to estimate rapidly and accurately both performance and area occupation of complex and diverse applications. High-Level Synthesis (HLS) has been emerged as an attractive solution for designers to address these challenges in order to explore a large number of SoC configurations. In this paper, we target hybrid CPU-FPGA based SoCs. We propose a high-level area estimation tool based on an analytic model without requiring register-transfer level (RTL) implementations. This technique allows to estimate the required FPGA resources at the source code level to map an application to a hybrid CPU-FPGA system. The proposed model also enables a fast design exploration with different trade-offs through HLS optimization pragmas. Experimental results show that the proposed area analytic model provides an accurate estimation with a negligible error (less than 5+) compared to RTL implementations.

Defeasible Logic Programming: An Argumentative Approach <=SEP=> The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warranting the entailed conclusions. In DeLP an argumentation formalism will be used for deciding between contradictory goals. Queries will be supported by arguments that could be defeated by other arguments. A query q will succeed when there is an argument A for q that is warranted, i. e. the argument A that supports q is found undefeated by a warrant procedure that implements a dialectical analysis. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent’s knowledge and for providing an argumentation based reasoning mechanism to agents.

3D face recognition: a survey <=SEP=> 3D face recognition has become a trending research direction in both industry and academia. It inherits advantages from traditional 2D face recognition, such as the natural recognition process and a wide range of applications. Moreover, 3D face recognition systems could accurately recognize human faces even under dim lights and with variant facial positions and expressions, in such conditions 2D face recognition systems would have immense difficulty to operate. This paper summarizes the history and the most recent progresses in 3D face recognition research domain. The frontier research results are introduced in three categories: pose-invariant recognition, expression-invariant recognition, and occlusion-invariant recognition. To promote future research, this paper collects information about publicly available 3D face databases. This paper also lists important open problems.

The Distributed Dissimilar Redundancy Architecture of Fly-by-Wire Flight Control System <=SEP=> This article presents a distributed dissimilar redundancy architecture of fly-by-wire Flight Control System. It is based on many dissimilar flight control computer, redundancy structure which includes the cross arranged hydraulic and power supply, three types of data buses, lots of data correction monitors, a distributed control loop to avoid the correlated faults and achieve the rigid safety airworthiness requirements. A Fault Tree Analysis (FTA) is implemented to verify whether the system safety could meet system design targets.

How Positive Informational Social Influence Affects Consumers&#146; Decision of Internet Shopping? <=SEP=> Given the amount of perceived risk involved in Internet shopping, many potential Internet shoppers tend to wait and observe the experiences of others who have tried it before considering adopting it. This study explores how positive informational social influence affects consumers’ decision of Internet shopping using a laboratory experiment. The research results reveal that positive informational social influence reinforces the relationships between perceived ease of use and consumer attitude toward Internet shopping, as well as attitude and their intention to shop. Implications for the current investigation and future research directions are provided.

Prisoner's dilemma game model for e-commerce <=SEP=> This study investigates how the user’s exchanges affect people’s trust in electronic commerce and empower them to interact with foreign customers. Online exchanges often occur between strangers who cannot rely on past behavior or the prospect of future interactions to establish mutual trust. Game theorists have formalized this problem as a prisoner’s dilemma and predict mutual noncooperation. In this paper, we introduce a social network based model to promote the cooperation in the prisoner’s dilemma game. For this study, we implemented an agent-based simulation framework, which models different types of behaviors in online auctions. Agents who represent buyers or sellers are located in the nodes of a small world graph. Each link weight between the agent and its neighbor symbolizes how much it trusts this neighbor. A link with trust inferior to some cut-link threshold will be removed from the graph. Our results show that the evolved structure of the graph induces considerable variation in the level of cooperation and the profit in the e-commerce system. This shows that the outcome of our co-evolutionary game, in terms of cooperative behavior, strongly depends on topology but also on the update rule used in the trust between users. Co-evolution of game dynamics in interconnected networks is also studied, where two networks are interconnected, and players have interactions not only with others in the same network, but also with players in the other network. We will demonstrate that the interdependence between networks promotes the cooperation in both networks. But, the degree of promotion changes as a function of interdependence

Flexible design of a wearable lower limb exoskeleton robot <=SEP=> Wearable exoskeleton robot is a kind of humanoid service robot to help the elderly and the patients with walking dysfunction, it is also an effective medical rehabilitation method to help patients who have walking disorders due to central neural system damage. This paper focuses on the flexible mechanism design of wearable lower limb exoskeleton robot. A wearable exoskeleton robot prototype was developed which can assist human walking. This paper analyzes the role of the major joints of walking human by experimental studies based on bionic design methods from human anatomy and bone surgery. We first analyze parameters of joint movements in a gait cycle, then we design a preliminary bionic model, finally we proposed a control strategy including a pair of electric crutches for the exoskeleton robot.

The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity <=SEP=> Recently, there has been an upsurge of attention focused on bias and its impact on specialized artificial intelligence (AI) applications. Allegations of racism and sexism have permeated the conversation as stories surface about search engines delivering job postings for well-paying technical jobs to men and not women, or providing arrest mugshots when keywords such as "black teenagers" are entered. Learning algorithms are evolving; they are often created from parsing through large datasets of online information while having truth labels bestowed on them by crowd-sourced masses. These specialized AI algorithms have been liberated from the minds of researchers and startups, and released onto the public. Yet intelligent though they may be, these algorithms maintain some of the same biases that permeate society. They find patterns within datasets that reflect implicit biases and, in so doing, emphasize and reinforce these biases as global truth. This paper describes specific examples of how bias has infused itself into current AI and robotic systems, and how it may affect the future design of such systems. More specifically, we draw attention to how bias may affect the functioning of (1) a robot peacekeeper, (2) a self-driving car, and (3) a medical robot. We conclude with an overview of measures that could be taken to mitigate or halt bias from permeating robotic technology.

Model Learning for Look-ahead Exploration in Continuous Control <=SEP=> We propose an exploration method that incorporates look-ahead search over basic learnt skills and their dynamics, and use it for reinforcement learning (RL) of manipulation policies . Our skills are multi-goal policies learned in isolation in simpler environments using existing multigoal RL formulations, analogous to options or macroactions. Coarse skill dynamics, i.e., the state transition caused by a (complete) skill execution, are learnt and are unrolled forward during lookahead search. Policy search benefits from temporal abstraction during exploration, though itself operates over low-level primitive actions, and thus the resulting policies does not suffer from suboptimality and inflexibility caused by coarse skill chaining. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parametrized skills as building blocks of the policy itself, as opposed to guiding exploration. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parameterized skills as building blocks of the policy itself, as opposed to guiding exploration.

A monad for deterministic parallelism <=SEP=> We present a new programming model for deterministic parallel computation in a pure functional language. The model is monadic and has explicit granularity, but allows dynamic construction of dataflow networks that are scheduled at runtime, while remaining deterministic and pure. The implementation is based on monadic concurrency, which has until now only been used to simulate concurrency in functional languages, rather than to provide parallelism. We present the API with its semantics, and argue that parallel execution is deterministic. Furthermore, we present a complete work-stealing scheduler implemented as a Haskell library, and we show that it performs at least as well as the existing parallel programming models in Haskell.

A Lightweight Semantic Web-based Approach for Data Annotation on IoT Gateways <=SEP=> Internet of Things (IoT) applications rely on networks composed of set of heterogeneous sensors and smart devices, which have the capability to constantly, observe the surroundings and gather data. This heterogeneity is reflected on raw data gathered by such type of systems. Consequently, the task of high-level IoT applications to interpret such data and detect events in the real world is more complex. Moreover, data heterogeneity leads to the lack of interoperability between IoT applications. Semantic Web (SW) technologies have been widely adopted to model and integrate data from different sources on the web; extending them to the IoT domain can be used to mitigate the aforementioned challenges. Semantically annotating IoT data is a fundamental step toward developing smarter and interoperable IoT applications. However, this type of process requires a large amount of computing resources, especially in scenarios where a large number of sensors is expected to be involved such as smart city. To address these challenges, we propose a lightweight semantic annotation approach that can be implemented on resource-constrained IoT gateways connected to a limited number of sensors. To evaluate the feasibility of the proposed approach, we have carried out a set of experimentations using a middleware prototype implementation. Several benchmarks are considered such as: Data size, response time, and resource utilization. c © 2017 The Authors. Published by Elsevier B.V. .

MI&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification <=SEP=> A CNN method for sentiment classification task in Task 4A of SemEval 2017 is presented. To solve the problem of word2vec training word vector slowly, a method of training word vector by integrating word2vec and Convolutional Neural Network (CNN) is proposed. This training method not only improves the training speed of word2vec, but also makes the word vector more effective for the target task. Furthermore, the word2vec adopts a full connection between the input layer and the projection layer of the Continuous Bag-of-Words (CBOW) for acquiring the semantic information of the original sentence.

Born Digital: Understanding the First Generation of Digital Natives <=SEP=> Perhaps you have seen them on the train in the morning, adjusting their iPods and texting with their preternaturally agile thumbs. Or perhaps one sits in the cubicle next to yours, cycling through a seemingly endless loop of news feeds, YouTube videos, cell phone calls, and work. Perhaps you have one living in your home, capable of reprogramming the wireless router and leaping insurmountable DVR instructions in a single, tech-savvy bound.

Data Gloves for Sign Language Recognition System <=SEP=> Communication between deaf-dumb and a normal person have always been a challenging task . About 9 billion people in the world come into this category which is quite large number to be ignored. As deaf-dumb people use sign language for their communication which is difficult to understand by the normal people. This paper aims at eradicating the communication barrier between them by developing an embedded system which will translate the hand gestures into

A Comparative Evaluation of Log-Based Process Performance Analysis Techniques <=SEP=> Process mining has gained traction over the past decade and an impressive body of research has resulted in the introduction of a variety of process mining approaches measuring process performance. Having this set of techniques available, organizations might find it difficult to identify which approach is best suited considering context, performance indicator, and data availability. In light of this challenge, this paper aims at introducing a framework for categorizing and selecting performance analysis approaches based on existing research. We start from a systematic literature review for identifying the existing works discussing how to measure process performance based on information retrieved from event logs. Then, the proposed framework is built starting from the information retrieved from these studies taking into consideration different aspects of performance analysis.

Benchmarking Web API Quality <=SEP=> Web APIs are increasingly becoming an integral part of web or mobile applications. As a consequence, performance characteristics and availability of the APIs used directly impact the user experience of end users. Still, quality of web APIs is largely ignored and simply assumed to be sufficiently good and stable. Especially considering geo-mobility of today’s client devices, this can lead to negative surprises at runtime. In this work, we present an approach and toolkit for benchmarking the quality of web APIs considering geo-mobility of clients. Using our benchmarking tool, we then present the surprising results of a geo-distributed 3-month benchmark run for 15 web APIs and discuss how application developers can deal with volatile quality both from an architectural and engineering point of view.

Iterative localization refinement in convolutional neural networks for improved object detection <=SEP=> Accurate region proposals are of importance to facilitate object localization in the existing convolutional neural network (CNN)-based object detection methods. This paper presents a novel iterative localization refinement (ILR) method which, undertaken at a mid-layer of a CNN architecture, iteratively refines region proposals in order to match as much ground-truth as possible. The search for the desired bounding box in each iteration is first formulated as a statistical hypothesis testing problem and then solved by a divide-and-conquer paradigm. The proposed ILR is not only data-driven, free of learning, but also compatible with a variety of CNNs. Furthermore, to reduce complexity, an approximate variant based on a refined sampling strategy using linear interpolation is addressed. Simulations show that the proposed method improves the main state-of-the-art works on the PASCAL VOC 2007 dataset.

A Multimodal Analysis of Floor Control in Meetings <=SEP=> The participant in a human-to-human communication who controls the floor bears the burden of moving the communication process along. Change in control of the floor can happen through a number of mechanisms, including interruptions, delegation of the floor, and so on. This paper investigates floor control in multiparty meetings that are both audio and video taped; hence, we analyze patterns of speech (e.g., the use of discourse markers) and visual cues (e.g, eye gaze exchanges) that are often involved in floor control changes. Identifying who has control of the floor provides an important focus for information retrieval and summarization of meetings. Additionally, without understanding who has control of the floor, it is impossible to identify important events such as challenges for the floor. In this paper, we analyze multimodal cues related to floor control in two different meetings involving five participants each.

Transaction-Cost Economics: The Governance of Contractual Relations <=SEP=> Transaction-Cost Economics: The Governance of Contractual Relations Author(s): Oliver E. Williamson Reviewed work(s): Source: Journal of Law and Economics, Vol. 22, No. 2 (Oct., 1979), pp. 233-261 Published by: The University of Chicago Press for The Booth School of Business of the University of Chicago and The University of Chicago Law School Stable URL: http://www.jstor.org/stable/725118 . Accessed: 07/01/2013 16:49

Product Classification in E-Commerce using Distributional Semantics <=SEP=> Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction. To address the above challenges, we propose a new distributional semantics representation for document vector formation. We also develop a new two-level ensemble approach utilizing (with respect to the taxonomy tree) a path-wise, node-wise and depth-wise classifiers for error reduction in the final product classification. Our experiments show the effectiveness of the distributional representation and the ensemble approach on data sets from a leading e-commerce platform and achieve better results on various evaluation metrics compared to earlier approaches.

Missing data analyses: a hybrid multiple imputation algorithm using Gray System Theory and entropy based on clustering <=SEP=> Researchers and practitioners who use databases usually feel that it is cumbersome in knowledge discovery or application development due to the issue of missing data. Though some approaches can work with a certain rate of incomplete data, a large portion of them demands high data quality with completeness. Therefore, a great number of strategies have been designed to process missingness particularly in the way of imputation. Single imputation methods initially succeeded in predicting the missing values for specific types of distributions. Yet, the multiple imputation algorithms have maintained prevalent because of the further promotion of validity by minimizing the bias iteratively and less requirement on prior knowledge to the distributions. This article carefully reviews the state of the art and proposes a hybrid missing data completion method named Multiple Imputation using Gray-system-theory and Entropy based on Clustering (MIGEC). Firstly, the non-missing data instances are separated into several clusters. Then, the imputed value is obtained after multiple calculations by utilizing the information entropy of the proximal category for each incomplete instance in terms of the similarity metric based on Gray System Theory (GST). Experimental results on University of California Irvine (UCI) datasets illustrate the superiority of MIGEC to other current achievements on accuracy for either numeric or categorical attributes under different missing mechanisms. Further discussion on real aerospace datasets states MIGEC is also applicable for the specific area with both more precise inference and faster convergence than other multiple imputation methods in general.

Analysing and improving the diagnosis of ischaemic heart disease with machine learning <=SEP=> Ischaemic heart disease is one of the world's most important causes of mortality, so improvements and rationalization of diagnostic procedures would be very useful. The four diagnostic levels consist of evaluation of signs and symptoms of the disease and ECG (electrocardiogram) at rest, sequential ECG testing during the controlled exercise, myocardial scintigraphy, and finally coronary angiography (which is considered to be the reference method). Machine learning methods may enable objective interpretation of all available results for the same patient and in this way may increase the diagnostic accuracy of each step. We conducted many experiments with various learning algorithms and achieved the performance level comparable to that of clinicians. We also extended the algorithms to deal with non-uniform misclassification costs in order to perform ROC analysis and control the trade-off between sensitivity and specificity. The ROC analysis shows significant improvements of sensitivity and specificity compared to the performance of the clinicians. We further compare the predictive power of standard tests with that of machine learning techniques and show that it can be significantly improved in this way.

Does Microfinance Really Help the Poor ? New Evidence from Flagship Programs in Bangladesh <=SEP=> The microfinance movement has built on innovations in financial intermediation that reduce the costs and risks of lending to poor households. Replications of the movement’s flagship, the Grameen Bank of Bangladesh, have now spread around the world. While programs aim to bring social and economic benefits to clients, few attempts have been made to quantify benefits rigorously. This paper draws on a new cross-sectional survey of nearly 1800 households, some of which are served by the Grameen Bank and two similar programs, and some of which have no access to programs. Households that are eligible to borrow and have access to the programs do not have notably higher consumption levels than control households, and, for the most part, their children are no more likely to be in school. Men also tend to work harder, and women less. More favorably, relative to controls, households eligible for programs have substantially (and significantly) lower variation in consumption and labor supply across seasons. The most important potential impacts are thus associated with the reduction of vulnerability, not of poverty per se. The consumption-smoothing appears to be driven largely by income-smoothing, not by borrowing and lending. The evaluation holds lessons for studies of other programs in low-income countries. While it is common to use fixed effects estimators to control for unobservable variables correlated with the placement of programs, using fixed effects estimators can exacerbate biases when, as here, programs target their programs to specific populations within larger communities.

IUI workshop on interactive machine learning <=SEP=> Many applications of Machine Learning (ML) involve interactions with humans. Humans may provide input to a learning algorithm (in the form of labels, demonstrations, corrections, rankings or evaluations) while observing its outputs (in the form of feedback, predictions or executions). Although humans are an integral part of the learning process, traditional ML systems used in these applications are agnostic to the fact that inputs/outputs are from/for humans.
 However, a growing community of researchers at the intersection of ML and human-computer interaction are making interaction with humans a central part of developing ML systems. These efforts include applying interaction design principles to ML systems, using human-subject testing to evaluate ML systems and inspire new methods, and changing the input and output channels of ML systems to better leverage human capabilities. With this Interactive Machine Learning (IML) workshop at IUI 2013 we aim to bring this community together to share ideas, get up-to-date on recent advances, progress towards a common framework and terminology for the field, and discuss the open questions and challenges of IML.

NLAST: A natural language assistant for students <=SEP=> This paper presents a system that works as an assistant for students in their learning process. The assistant system has two main parts: an Android application and a server platform. The Android application is a chatterbot (i.e., an agent intended to conduct a conversation in natural language with a human being) based on AIML, one of the more successful languages for developing conversational agents. The chatterbot acts as an intermediation agent between a student and the server platform. The server platform contains four repositories and a recommender (which are part of a bigger professor assessment system). The final objective of the assistant system is to make a student able to carry out several actions related to his/her learning and assessment processes, such as: to consult exam questions from a repository, to receive recommendations about learning material, to ask questions about a course, and to check his/her own assessed exams. These actions are carried out through and Android application using a natural language interface (by voice or typing). The purpose of this development is to facilitate the access to this information through a friendly interface.