Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability and Control of a Rider–Bicycle System: Analysis and Experiments

We present stability and control analysis of a rider–bicycle system under human steering and body movements. The dynamic model of rider–bicycle interactions is first constructed to integrate the rider’s body movement with the moving bicycle platform. We then present human balance control strategies based on human riding experiments. The closed-loop system stability is analyzed and discussed. Quantitative influences of the bicycle physical parameters, the human control gains, and the time delays are also analyzed and discussed. Extensive experiments are conducted to validate the human control models and demonstrate human balance performance using the bikebot, an instrumented bicycle platform. The presented modeling and analysis results can be potentially used for further development of bicycle-assisted rehabilitation for postural balance patients. Note to Practitioners—Understanding human balance and control of bicycles is crucial for not only designing bicycle-based rehabilitation devices but also studying physical human–machine interactions for healthcare automation. This paper takes the rider and bicycle as an example of physical human–machine interactions to understand how human use their limbs and body movement to stabilize an unstable platform (i.e., bicycles). We develop an instrumented bicycle system, called bikebot, to conduct human riding experiments. Using experimental data, we build the dynamic models for human steering and leaning control actions. Using these actuation models, stability analyses are conducted for rider–bicycle interactions and then validated by experiments. We also obtain the stability results by perturbing human visual and sensorimotor feedback. These results reveal that the visual feedback and the time delay in sensorimotor feedback mechanism play critical roles in stabilizing the unstable bicycle platform.
Complexity and stable evolution of circuits

We consider the viability problem for random dynamical systems, in particular, for circuits. A system is viable only if the system state stays in a prescribed domain Π of a phase space. We assume that the circuit structure is coded by a code evolving in time. We introduce the notion of stable evolution of the code and the system: evolution is stable if there is a δ > 0 such that the probability PT to be in Π within time interval [0, T ] satisfies PT > δ as T →∞. We show that for certain large classes of systems, the stable evolution has the following fundamental property: the Kolmogorov complexity of the code cannot be bounded by a constant as time t → ∞. For circuit models, we describe examples of stable evolution of complicated boolean networks for a difficult case when the domain Π is unknown. We dedicate this paper to Professor Grisha Mints. We admire the breadth of his interests.
An Hybrid Algorithm to Simulate Mice Following Residential Walls

Commensal rodents such as mice that live in the proximity of human are well-known pests that can transmit diseases to man. An agent-based model is presented to formalize the spread of rodents in human habitations. We focus in this paper on the specific behaviour of mice consisting in walking along the walls (thigmotaxis). In order to simulate this behaviour we combine a cognitive architecture with an algorithm simulating rodents’ moves following residential walls. The cognitive model and the ‘wall following’ algorithm developed are successively described. Simulation configurations are tested to evaluate the robustness of the ‘wall following’ algorithm alone. In a data poor context this simple combined algorithm, using a discrete space, fulfils the basic needs to simulate a plausible mouse activity, towards a detailed representation of mice moves within a fluctuating environment.
Reinforcement Learning Agent under Partial Observability for Traffic Light Control in Presence of Gridlocks

Bangkok is notorious for its chronic traffic congestion due to the rapid urbanization and the haphazard city plan. The Sathorn Road network area stands to be one of the most critical areas where gridlocks are a normal occurrence during rush hours. This stems from the high volume of demand imposed by the dense geographical placement of 3 big educational institutions and the insufficient link capacity with strict routes. Current solutions place heavy reliance on human traffic control expertises to prevent and disentangle gridlocks by consecutively releasing each queue length spillback through inter-junction coordination. A calibrated dataset of the Sathorn Road network area in a microscopic road traffic simulation package SUMO (Simulation of Urban MObility) is provided in the work of Chula-Sathorn SUMO Simulator (Chula-SSS). In this paper, we aim to utilize the Chula-SSS dataset with extended vehicle flows and gridlocks in order to further optimize the present traffic signal control policies with reinforcement learning approaches by an artificial agent. Reinforcement learning has been successful in a variety of domains over the past few years. While a number of researches exist on using reinforcement learning with adaptive traffic light control, existing studies often lack pragmatic considerations concerning application to the physical world especially for the traffic system infrastructure in developing countries, which suffer from constraints imposed from economic factors. The resultant limitation of the agent’s partial observability of the whole network state at any specific time is imperative and cannot be overlooked. With such partial observability constraints, this paper has reported an investigation on applying the Ape-X Deep Q-Network agent at the critical junction in the morning rush hours from 6 AM to 9 AM with practically occasional presence of gridlocks. The obtainable results have shown a potential value of the agent’s ability to learn despite physical limitations in the traffic light control at the considered intersection within the Sathorn gridlock area. This suggests a possibility of further investigations on agent applicability in trying to mitigate complex interconnected gridlocks in the future.
An Efficient Message Filtering Strategy Based on Asynchronous ADMM with L1 Regularization

With the increment of data scale, distributed machine learning has received more and more attention. However, as the data grows, the dimension of the dataset will increase rapidly, which leads to the increment of the communication traffic in the distributed computing cluster and decreases the performance of the distributed algorithms. This paper proposes a message filtering strategy based on asynchronous alternating direction method of multipliers (ADMM), which can effectively reduce the communication time of the algorithm while ensuring the convergence of the algorithm. In this paper, a soft threshold filtering strategy based on L1 regularization is proposed to filter the parameter of master node, and a gradient truncation filtering strategy is proposed to filter the parameter of slave node. Besides, we update the algorithm asynchronously to reduce the waiting time of the master node. Experiments on large-scale sparse data show that our algorithm can effectively reduce the traffic of messages and make the algorithm reach convergence in a shorter time.
Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach

Knowledge on adversaries during military missions at sea heavily influences decision making, making identification of unknown vessels an important task. Identification of surrounding vessels based on visual data offers an alternative to AIS information (Automatic Identification System), the current standard in vessel identification, which can be spoofed. One visual approach employs human expertise and manually identifies vessels guided by a ship catalog. In order to minimize or potentially eliminate human error and performance limitations, there is strong interest in developing an automated vessel classification pipeline. One such pipeline is currently being developed at TNO, capable of classifying over 500 separate classes. A crucial part of the classification pipeline is retrieving an accurate contour of a vessel from a digital image. To address this important challenge, this thesis proposes an advanced deep learning pipeline to automatically segment the vessel image into background (e.g. sky and sea) and the object of interest (a vessel). Deep learning models based on Fully Convolutional Neural Networks (FCNs) have achieved high performance on the task of semantic segmentation. Several networks such as CRF-RNN, PSPNet, DeepLab and Mask R-CNN are employed to determine a baseline performance. We will focus on identifying the cause of poor or failing segmentations and aim to construct a robust network capable of handling these challenges. By sampling disturbances, caused by ship distance and camera noise, augmented data sets are built to tune networks to input from on-site images. Additionally, experiments are done to evaluate the influence of different levels of disturbances. Previous approaches implementing the CRF-RNN network achieved top 1 and top 5 classification accuracies of 31.1% and 44.0% respectively. Employing the DeepLab network, trained to convergence on artificial noise augmented data, we report top 1 and top 5 accuracy of 68.9% and 88.8% respectively. Additionally, implementing an ensemble of classifiers, performance is increased to 73.0% and 91.7% for top 1 and top 5 accuracy respectively. This best result is comparable to the classification results with human annotated ship silhouettes. The human performance accuracy is 73.4% on top 1, and 91.3% on top 5 classification performance. Finally, we show that training on a collection of different levels of image disturbances results in a network that is robust against increasing disturbance in images, while retaining performance on clean images.
Detection of e-Commerce Anomalies using LSTM-recurrent Neural Networks

As the e-commerce sales grow in global retail sector year by year, detecting anomalies that occur in the most important key performance indicators (KPI) in real-time has become a critical requirement for e-commerce companies. Such anomalies that may arise from software updates, server failures, or incorrect price entries cause substantial revenue loss in the meantime until they are detected with their root-causes. In this paper, we present a comparative analysis of various anomaly detection methods in detecting e-commerce anomalies. For this purpose, we first present the univariate analysis of six commonly used anomaly detection methods on two important KPIs of an e-commerce website. The highest F1 Scores and recall values on the test sets of both KPIs are obtained using Long-Short Term Memory (LSTM) network, showing that LSTM fits better to the dynamics of e-commerce KPIs than time-series based prediction methods. Then, in addition to the univariate analysis of the methods, we feed the campaign information into LSTM network considering that campaigns have significant effects on the values of KPIs in e-commerce domain and this information can be helpful to prevent false positives that may occur in the campaign periods. The results also show that constructing a multivariate LSTM by feeding the campaign information as an additional input improves the adaptability of the model to sudden changes occurring in campaign periods.
Improved joint compatibility data association approach for simultaneous localization and map building(SLAM)

数据火联是SLAM的难点之一. 联合相容分枝定界算法(JCBB)虽能得到可靠的关联结果. 但计算馈随观测数日的增加而迅速提高. 实时应用受到限制. 通过给定联合相容配对个数的上限. 对JCBB算法进行了改进,提出了一种快速JCBB算法(FJCBB). FJCBB与JCBB有相同的关联效果. 但计算量随观测数Fj的增加变化缓慢. 当观测数目较大时,FJCBB的计算时间大幅减小,优势十分明显. 最后,通过大量实验,验证r该方法的有效性.
Detecting Anomalous Energy Consumption from Profiles

Controlling and reducing electric energy consumption is a critical issue across all over the countries for human wellbeing. However, approaches to achieve energy consumption reduction of individual occupants have not been established yet as both problems of collecting a large amount data of energy consumption and constructing prediction models are challenging. Here we show a case-study of energy consumption analysis, in which households with anomalous energy consumption can be completely detected using a questionnaire about their profiles without seeing actual energy consumption. Our approach is based on simple data mining techniques of outlier detection and decision trees, hence it can be easily implemented in the condominium housing market.
Unsupervised feature selection with the largest angle coding

ABSTRACT In many areas such as machine learning, data mining and computer vision, feature selection is a crucial and challenging task to find a relevant feature subset of the original features. Unsupervised feature selection is a type of feature selection which preforms the task without label information. Many unsupervised feature selection methods select the top rank features without the analysis of the differences among features, so they cannot select a feature subset with strong generality. With the analysis of the differences among features in unsupervised feature selection, original dataset can be described more comprehensively by selected features. In this paper, we propose the difference degree matrix and a new method called unsupervised feature selection with the largest angle coding (FSAC). The difference degree matrix is used to describe the difference degree of the distributions of the data points on every two features and FSAC is an effective feature selection method. Different from existing unsupervised feature selection methods, FSAC selects features through the analysis of the differences among features and the self-representation of the difference degree matrix. To make the self-representation of the difference degree matrix more useful and reduce the redundant and noisy features, -norm constraint is added into the objective function of FSAC to guarantee the feature selection matrix sparse in the rows. Experimental results on different real-world datasets show that the promising performance of FSAC outperforms the state-of-the-arts. We also analyse the sensitivity of the parameter in the objective function.
Implementation of a Map-Reduce based Context-Aware Recommendation Engine for Social Music Events

In our modern ubiquitously connected world the amount of ever available product and service information within our daily lives is exploding. Powerful client devices, such as smartphones and tablets allow the users to get access to an unlimited amount of information on every product or service available. As the amount of available information on products by far exceeds the users time to examine and filter detailed pieces of information in every situation, we expect that client-centric and context-aware information filtering is one of the thriving topics within the next years. A popular approach is to combine context-awareness with traditional recommendation engines in order to evaluate the relevance of a large amount of items for a given user situation. The goal is to proactively evaluate the situation of a user in order to automatically propose relevant products. Within this work we describe a general approach and the implementation of a software framework that combines traditional recommendation methods with a variable number of context dimensions, such as location or social context. The main contribution of this work is to show how to use a MapReduce programming model for aggregating the necessary information for calculating fast context-aware recommendations as well as how to overcome a typical cold start problem. The use-case at the end of this work evaluates the practical benefit of our general framework to introduce a client-centric, MapReducebased recommendation engine for real-time recommending music events and festivals. Keywords–context awareness, context aware recommendation, decision support, recommendation system.
Sequential decision problems in online education

This thesis is concerned with the study of sequential decision problems motivated by the challenge of selecting questions to give to students in an online educational environment. In online education there is the potential to develop personalized and adaptive learning environments, where students can receive individualized sequences of questions which update as the student is observed to be struggling or flourishing. In order to achieve this personalization, we must learn about how good each question is, while simultaneously giving students good questions. Multi-armed bandits are a popular technique for sequential decision making under uncertainty. Due to their online nature and their ability to balance the trade-off between exploitation and exploration, multi-armed bandits lend themselves naturally to this problem of adaptively selecting questions in education software. However, due to the complexity of the educational problem, standard approaches to multi-armed bandits cannot be applied directly. In this thesis variants of the multi-armed bandit problem specifically motivated by the issues arising in the educational domain are considered. Particular focus will be placed on ton the statistical and mathematical foundations of such approaches.
Nonlinear observer for hydropower system

Estimation of unmeasured states plays an essential role in the design of control systems as well as for monitoring of hydropower plants. The standard Kalman filter gives the optimum state estimates for linear systems. However, this optimality is not relevant for nonlinear models and a choice between stochastic and deterministic approaches is not so obvious in this case. Thus the application of a nonlinear observer in a hydropower system is of interest here as an alternative to the widely used extended Kalman filter. This paper provides a study and design of a reduced order nonlinear observer to estimate the states of a hydropower system. Implementation of the nonlinear observer is done in OpenModelica and added to our in-house hydropower Modelica library — OpenHPL, where different models for hydropower systems are assembled. Simulations and analysis of the designed observer are done in Python using a Python API for operating OpenModelica simulations.
Proceedings of the 2008 Conference on Semantics in Text Processing

Thanks to both statistical approaches and finite state methods, natural language processing (NLP), particularly in the area of robust, open-domain text processing, has made considerable progress in the last couple of decades. It is probably fair to say that NLP tools have reached satisfactory performance at the level of syntactic processing, be the output structures chunks, phrase structures, or dependency graphs. Therefore, the time seems ripe to extend the state-of-the-art and consider deep semantic processing as a serious task in wide-coverage NLP. This is a step that normally requires syntactic parsing, as well as integrating named entity recognition, anaphora resolution, thematic role labelling and word sense disambiguation, and other lower levels of processing for which reasonably good methods have already been developed. The goal of the STEP workshop is to provide a forum for anyone active in semantic processing of text to discuss innovative technologies, representation issues, inference techniques, prototype implementations, and real applications. The preferred processing targets are large quantities of texts---either specialised domains, or open domains such as newswire text, blogs, and wikipedia-like text. Implemented rather than theoretical work is emphasised in STEP. Featuring in STEP 2008 workshop is a "shared task" on comparing semantic representations as output by state-of-the-art NLP systems. Participants were asked to supply a (small) text, before the workshop. The test data for the shared task is composed out of all the texts submitted by the participants, allowing participants to "challenge" each other. The output of these systems will be judged on a number of aspects by a panel of experts in the field, during the workshop.
Empowered by Innovation: Unravelling Determinants of Idea Implementation in Open Innovation Platforms

Companies use crowdsourcing to solve specific problems or to search for innovation. By using open innovation platforms, where community members propose ideas, companies can better serve customer needs. So far, it remains unclear which factors influence idea implementation in crowd sourcing context. With the research idea that we present here, we aim to get a better understanding of the success and failure of ideas by examining relationships between characteristics of ideators, characteristics of ideas and the likelihood of implementation. In order to test the methodological approach that we propose in this paper in which we investigate for business relevant innovativeness as well as sentiment based on text analytics, data including unstructured text was mined from Dell IdeaStorm using webcrawling and scraping techniques. Some relevant hypotheses that we define in this paper were confirmed on the Dell IdeaStorm dataset but in order to generalize our findings we want to apply to the Leg o dataset in our current work in progress. Possible implications of our novel research idea can be used to fill theoretical gaps in marketing literature, help companies to better structure their search for innovation and for ideators to better understand factors contributing to successful idea generation.
The Diffusion of Telehealth: System-Level Conditions for Successful Adoption

ii ACKNOWLEDGMENTS . iii LIST OF TABLES . vii LIST OF ABBREVIATIONS . viii CHAPTER I INTRODUCTION . 1 Research Question . 2 Hypotheses . 3 Methodological Approach . 4 Contributions to the Literature . 6 Summary . 7 CHAPTER II – LITERATURE REVIEW . 10 Introduction . 10 Health Care Burden . 11 History of Telehealth. 14 Review of Reviews . 16 Significance of the Study . 19 Theoretical Approaches . 24 Broad Theoretical Foundations . 24 Previous Empirical Approaches . 36 v Applications of Telehealth . 37 Barriers to Telehealth Adoption . 44 Relative Advantage . 49 Compatibility. 57 Complexity . 62 Observability . 66 Significance of the Research . 70 Chapter Summary . 71 CHAPTER III METHODOLOGY . 73 Introduction . 73 Dependent Variables and Measurement . 75 Independent Variables and Measurement . 77 Quantitative Approach . 86 Conclusions . 88 CHAPTER IV – PRESENTATION OF RESULTS . 90 Introduction . 90 Data Collection . 91 Presentation of Results . 92 Regression Results . 99 Conclusions . 107 vi CHAPTER V – ANALYSIS . 108 Analysis of Results . 108 Literature and Theory Alignment . 114 Key Findings . 117 Conclusions . 121 CHAPTER VI – CONCLUSIONS . 123 Review of Hypotheses . 124 H1: Relative Advantage Affects Successful Telehealth Adoption . 124 H2: Compatibility Affects Successful Telehealth Adoption . 127 H3: Complexity Affects Successful Telehealth Adoption . 130 H4: Observability Affects Successful Telehealth Adoption. 131 Limitations of Research . 132 Recommendations . 133 Recommendations for Policy . 133 Recommendations for Practice . 135 Conclusions . 136 REFERENCES . 139