Reviewerly/exHarmony huggingface dataset:
DiBS: Differentiable Bayesian Structure Learning

Bayesian structure learning allows inferring Bayesian network structure from data while reasoning about the epistemic uncertainty -- a key element towards enabling active causal discovery and designing interventions in real world systems. In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation. Building on recent advances in variational inference, we use DiBS to devise an efficient method for approximating posteriors over structural models. Contrary to existing work, DiBS is agnostic to the form of the local conditional distributions and allows for joint posterior inference of both the graph structure and the conditional distribution parameters. This makes our method directly applicable to posterior inference of nonstandard Bayesian network models, e.g., with nonlinear dependencies encoded by neural networks. In evaluations on simulated and real-world data, DiBS significantly outperforms related approaches to joint posterior inference.
Distributive Laws for Lawvere Theories (Invited Talk)

Distributive laws give a way of combining two algebraic structures expressed as monads; in this work we propose a theory of distributive laws for combining algebraic structures expressed as Lawvere theories. We propose four approaches, involving profunctors, monoidal profunctors, an extension of the free finite-product category 2-monad from Cat to Prof, and factorisation systems respectively. We exhibit comparison functors between CAT and each of these new frameworks to show that the distributive laws between the Lawvere theories correspond in a suitable way to distributive laws between their associated finitary monads. The different but equivalent formulations then provide, between them, a framework conducive to generalisation, but also an explicit description of the composite theories arising from distributive laws.
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization

A common pain point in differentially private machine learning is the significant runtime overhead incurred when executing Differentially Private Stochastic Gradient Descent (DPSGD), which may be as large as two orders of magnitude. We thoroughly demonstrate that by exploiting powerful language primitives, including vectorization, just-in-time compilation, and static graph optimization, one can dramatically reduce these overheads, in many cases nearly matching the best non-private running times. These gains are realized in two frameworks: JAX and TensorFlow. JAX provides rich support for these primitives as core features of the language through the XLA compiler. We also rebuild core parts of TensorFlow Privacy, integrating features from TensorFlow 2 as well as XLA compilation, granting significant memory and runtime improvements over the current release version. These approaches allow us to achieve up to 50x speedups in comparison to the best alternatives. Our code is available at this https URL.
Background/Foreground Separation: Guided Attention based Adversarial Modeling (GAAM) versus Robust Subspace Learning Methods

Background-Foreground separation and appearance generation is a fundamental step in many computer vision applications. Existing methods like Robust Subspace Learning (RSL) suffer performance degradation in the presence of challenges like bad weather, illumination variations, occlusion, dynamic backgrounds and intermittent object motion. In the current work we propose a more accurate deep neural network based model for background-foreground separation and complete appearance generation of the foreground objects. Our proposed model, Guided Attention based Adversarial Model (GAAM), can efficiently extract pixel-level boundaries of the foreground objects for improved appearance generation. Unlike RSL methods our model extracts the binary information of foreground objects labeled as attention map which guides our generator network to segment the foreground objects from the complex background information. Wide range of experiments performed on the benchmark CDnet2014 dataset demonstrate the excellent performance of our proposed model.
Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings

This work studies the statistical limits of uniform convergence for offline policy evaluation (OPE) problems with model-based methods (for episodic MDP) and provides a unified framework towards optimal learning for several well-motivated offline tasks. Uniform OPE $\sup_\Pi|Q^\pi-\hat{Q}^\pi|<\epsilon$ is a stronger measure than the point-wise OPE and ensures offline learning when $\Pi$ contains all policies (the global class). In this paper, we establish an $\Omega(H^2 S/d_m\epsilon^2)$ lower bound (over model-based family) for the global uniform OPE and our main result establishes an upper bound of $\tilde{O}(H^2/d_m\epsilon^2)$ for the \emph{local} uniform convergence that applies to all \emph{near-empirically optimal} policies for the MDPs with \emph{stationary} transition. Here $d_m$ is the minimal marginal state-action probability. Critically, the highlight in achieving the optimal rate $\tilde{O}(H^2/d_m\epsilon^2)$ is our design of \emph{singleton absorbing MDP}, which is a new sharp analysis tool that works with the model-based approach. We generalize such a model-based framework to the new settings: offline task-agnostic and the offline reward-free with optimal complexity $\tilde{O}(H^2\log(K)/d_m\epsilon^2)$ ($K$ is the number of tasks) and $\tilde{O}(H^2S/d_m\epsilon^2)$ respectively. These results provide a unified solution for simultaneously solving different offline RL problems.
Continuous Curvature Turns Based Method for Least Maximum Curvature Path Generation of Autonomous Vehicle

Safe driving and stable paths are essential for the autonomous vehicle navigation, that large and fast steering angle should be avoided. This paper addresses a local path generation problem for autonomous vehicles while the least maximum curvature and the shortest length are obtained with limited curvature rate. The properties of the novel feasible paths based on continuous curvature turns are investigated. And a simple and fast computational method is presented to solve the problem by iterative procedure. The simulation in this paper shows that, compared with recent researches, the performance of least maximum curvature and shortest length is obtained by the novel local path planner, and the driving behavior is closer to the human driver operation.
Temporally Abstract Partial Models

Humans and animals have the ability to reason and make predictions about different courses of action at many time scales. In reinforcement learning, option models (Sutton, Precup \& Singh, 1999; Precup, 2000) provide the framework for this kind of temporally abstract prediction and reasoning. Natural intelligent agents are also able to focus their attention on courses of action that are relevant or feasible in a given situation, sometimes termed affordable actions. In this paper, we define a notion of affordances for options, and develop temporally abstract partial option models, that take into account the fact that an option might be affordable only in certain situations. We analyze the trade-offs between estimation and approximation error in planning and learning when using such models, and identify some interesting special cases. Additionally, we demonstrate empirically the potential impact of partial option models on the efficiency of planning.
A Shared E-Learning Resources Database Using Big Data and Cloud Environment

The world has entered into the era of Big Data unwittingly. Although no universally accepted definition can be given as of now, the term “Big Data” is commonly referred to as extensive datasets, primarily in the characteristics of 5 Vs: value, variability, variety, velocity, and volume, which require scalable architectures for efficient storage, manipulation, and analysis [1]. Higher education institutions are encouraged to construct a shared E-learning resources database based on Big Data and Cloud because of its convenience and cost-savings to E-learning teachers in terms of data crawling, storage, analysis, as well as data processing, optimization and sharing [2]. This article introduces the conceptual underlying frameworks for developing a shared E-learning resources database based on Big Data and Cloud as well as its prospective benefits to teachers, students, and university-corporation connections. Its prospective application holds a vision to assist associated teachers for improved efficiency and effectiveness of data acquisition, teaching material preparation and processing, user (students) satisfaction and prominent university-corporation connections.
A Survey on Multimodal Sentiment Analysis

Multimodal sentiment analysis is one of the core research topics in the field of natural language processing.Firstly,the research background of multimodal sentiment analysis is introduced.Two sub-topics of multimodal sentiment analysis,narrative multimodal sentiment analysis and interactive multimodal sentiment analysis,are proposed.Then,the development and the research progress at home and abroad are summarized based on the mentioned two sub-topics.Finally,the existing scientific problems of interactive modeling in this field are summarized,and the future development trend is discussed.
ASSESSMENT OF ALGORITHMIC THINKING OF SLOVAK AND HUNGARIAN SECONDARY SCHOOL STUDENTS: RESULTS OF A PILOT STUDY

In today’s knowledge and information based economy there is a high labor demand for individuals who have the necessary expertise in STEM (science, technology, engineering and mathematics) areas. However, numerous international research studies have indicated that interest and motivation toward STEM subjects among primary and secondary school children in the member countries of the European Union has declined, and their scientific and mathematical competence (knowledge, skills, abilities and attitudes) fall behind the needs of modern highly technological society. Consequently, the majority of secondary school students may choose not to continue their studies in science and technology. Since their absence from the labor market could negatively affect the economic development, it is important to identify the reasons of students’ low interest in STEM subjects. This is one of the main research objectives of an on-going larger project which studies the students’ natural-scientific, mathematical, algorithmic and problem-solving thinking, understanding difficulties, and their learning styles and attitudes towards STEM sciences, including computer science. The study sample will consist of secondary school students from one Hungarian county (Komárom-Esztergom megye), one Slovak region (Nitrianský kraj) and southern Slovakia where the Hungarian minority lives as a regional majority. The proposed research instrument (STEM assessment test) was developed with a total of 20 tasks, 4-4 tasks were chosen from school subjects of informatics, mathematics, biology, chemistry, and physics. The reliability of this instrument was tested by conducting a pilot study on a small sample (n = 130) of students both from Hungary and Slovakia. In this paper some results, analyses and conclusions of this empirical pilot study are presented, focusing particularly on assessment of four computer science tasks related to execution of given algorithm, analysis of given algorithm, creation of an optimal algorithm, and problem solving and logical thinking.
Text Classification Feature extraction using SVM

Text Classification is an automated procedure of ordering Text into classifications. We can characterize Emails into spam or non-spam, nourishment into frank or not sausage, and so on. Text Classification should be possible with the assistance of Natural Language Processing and various calculations. The center target of this exploration is to examine the presentation of classification calculations utilizing the Scopus data set. In-text classification, classification, and highlight extraction from the archive using extricated highlights are the significant issues for diminishing the exhibitions in various calculations. In this paper, displays of classification calculations, for example, KNN and Support vector Machine(SVM), indicated better improvement utilizing Bayesian lift and sacking. The presentation results were broken down through chosen classification calculations over various archives from Scopus. They were analyzed using F-measure and delivered examination networks to assess exactness, accuracy, and review utilizing N.B. and SUPPORT VECTOR MACHINE (SVM) classifier. Further, information pre-processing and cleaning steps are incited on the chose data set, and class unevenness issues are examined to build the presentation of text classification calculations. Test results indicated exhibitions over 9% utilizing SVM and uncovered better when contrasted with KNN.
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability

Generalization is a central challenge for the deployment of reinforcement learning (RL) systems in the real world. In this paper, we show that the sequential structure of the RL problem necessitates new approaches to generalization beyond the well-studied techniques used in supervised learning. While supervised learning methods can generalize effectively without explicitly accounting for epistemic uncertainty, we show that, perhaps surprisingly, this is not the case in RL. We show that generalization to unseen test conditions from a limited number of training conditions induces implicit partial observability, effectively turning even fully-observed MDPs into POMDPs. Informed by this observation, we recast the problem of generalization in RL as solving the induced partially observed Markov decision process, which we call the epistemic POMDP. We demonstrate the failure modes of algorithms that do not appropriately handle this partial observability, and suggest a simple ensemble-based technique for approximately solving the partially observed problem. Empirically, we demonstrate that our simple algorithm derived from the epistemic POMDP achieves significant gains in generalization over current methods on the Procgen benchmark suite.
Retraction Note to: Simulation of rainfall process in mountainous regions and sports athletes’ fatigue recovery based on convolutional neural network

Convolution neural network is applied in image processing, and it can also carry out deep learning according to image classification. Its main advantage is that it can directly convolute the pixels related to the image, and then extract the main image features from the image. Of course, this way of processing may be closer to that of the human brain system. In this paper, the design and parameter optimization of convolution neural network are deeply studied and discussed. At the same time, the field monitoring and experimental methods are combined with historical data to further study the rainfall and runoff and sediment yield. Through the research and analysis, it is found that athletes can appropriately improve their training performance, competitive ability, and sports ability when choosing more difficult training. High-intensity training can appropriately exceed the normal psychological and bearing capacity of the human body, but if it is excessive, it will lead to physical fatigue. In the normal training process, we should consider the level of training intensity and pay attention to relaxation and recovery exercise. If we want to make the trainer recover in a short time, we should use effective means to carry out reasonable training and improve the sports ability. In the training to a certain time, the body organs may decline in function, so in the daily hard training, we should strictly grasp all aspects of scientific and comprehensive development, and coaches and athletes should timely solve the problem of recovery after sports.
A Local Planning Method Based on Graph Optimization Framework

To generate a collision free local trajectory respect to the dynamic and kinematic constrains of car-like mobile robots, a local planning method is proposed in this paper. The method generates a local path in the graph optimization framework of the Timed Elastic Band (TEB) algorithm firstly. The generated local path is made of a series of waypoints and concerned with collision avoidance, curvature constraints. Then some post-processes are offered: The fifth-order splines are used to get a smooth path through these waypoints; Finally, shortest time trajectory generation algorithm along the given path generates a shortest time trajectory satisfying the dynamic and kinematic constraints.
To study on employee engagement and their impact on employee performance

Employee Engagement refers the level of commitment and involvement of the employees towards their organization and its values. The aim of this study is to study the impact of employee engagement on employee performance and also to study the factors affecting employee engagement. The factors are pay and benefits, Health and safety, Performance appraisal, performance of the company, communication, training, leadership, grievance procedure, career development and other benefits. The research design of the study is causal and descriptive in nature. The data are collected through structured questionnaire and the sample size is 100 employees. For the analysis of the data descriptive statistic, Kolmogorav-simirnov test and cross tabulation. The result of the study discovered that employees are satisfied with overall performance of the company. All the factors of Employee engagement are affecting the employee performance in the positive way at the organization.
Crafting temporality in design

This chapter describes the generative role that a practice-based research inquiry can play in incorporating temporality into the design of longer-term human–technology relations. We draw attention to how first-person experiences of time, over time in the design process, are of great importance when designing slow technologies. A practice-based research approach enables creative practitioners to attend to how temporality is shaped and manifested through the crafting of a design artifact which represents a key benefit of this approach and a key area for future research. Through an example of crafting temporality into Chronoscope, we introduce our designer-researcher approach to reveal how first-person perspectives among our design team could benefit and support practice-based research. Four main points are described to position our designer-researcher approach through articulating the motivation and design decisions that follow the goal of embodying temporality across the Chronoscope design process. Finally, we highlight how Chronoscope exemplifies a designer-researcher approach to craft slowness and temporality into practices and provide four lessons for future design-oriented researchers and practitioners to apply similar concepts and perspectives to a broader range of disciplines. Our experience of practice-based research is from an academic perspective in North America. Our thinking aligns with the principles of practice-based research stated in the definitions of this handbook.
Approximation Algorithms for LCS and LIS with Truly Improved Running Times

Longest common subsequence ($\mathsf{LCS}$) is a classic and central problem in combinatorial optimization. While $\mathsf{LCS}$ admits a quadratic time solution, recent evidence suggests that solving the problem may be impossible in truly subquadratic time. A special case of $\mathsf{LCS}$ wherein each character appears at most once in every string is equivalent to the longest increasing subsequence problem ($\mathsf{LIS}$) which can be solved in quasilinear time. In this work, we present novel algorithms for approximating $\mathsf{LCS}$ in truly subquadratic time and $\mathsf{LIS}$ in truly sublinear time. Our approximation factors depend on the ratio of the optimal solution size over the input size. We denote this ratio by $\lambda$ and obtain the following results for $\mathsf{LCS}$ and $\mathsf{LIS}$ without any prior knowledge of $\lambda$. $\bullet$ A truly subquadratic time algorithm for $\mathsf{LCS}$ with approximation factor $\Omega(\lambda^3)$. $\bullet$A truly sublinear time algorithm for $\mathsf{LIS}$ with approximation factor $\Omega(\lambda^3)$. Triangle inequality was recently used by [Boroujeni, Ehsani, Ghodsi, HajiAghayi and Seddighin SODA 2018] and [Charkraborty, Das, Goldenberg, Koucky and Saks FOCS 2018] to present new approximation algorithms for edit distance. Our techniques for $\mathsf{LCS}$ extend the notion of triangle inequality to non-metric settings.
Optimized distributed fusion filtering for singular systems with fading measurements and stochastic nonlinearity

&lt;abstract&gt;&lt;p&gt;In this paper, the problem of optimized distributed fusion filtering is considered for a class of multi-sensor singular systems in the presence of fading measurements and stochastic nonlinearity. By utilizing the standard singular value decomposition, the multi-sensor stochastic singular systems are simplified to two reduced-order nonsingular subsystems (RONSs). The local filters (LFs) with corresponding error covariance matrices are proposed for RONSs via the innovation analysis approach. Then, on the basis of the matrix-weighted fusion estimation algorithm, the distributed fusion filters (DFFs) are designed for RONSs with multiple sensors in the linear minimum variance sense. Moreover, the DFFs are obtained by utilizing the state transformation for original singular systems. It can be observed that the DFFs have better accuracy in contrast with the LFs. Finally, an illustrate example is put forward to verify the feasibility of the proposed fusion filtering scheme.&lt;/p&gt;&lt;/abstract&gt;
Key Aspects of Mobile Digital Education

Emergent technologies and pedagogies are interdependent in the research field of M-learning. Mobile learning can be mainly evaluated by addressing usability, technology availability, and connectivity; learning effectiveness, curriculum integration, and users satisfaction, since its affordances are ubiquity, flexibility, accessibility, immediacy, interactivity, motivation, engagement, and contextuality. In this chapter, key aspects of mobile learning are identified in the analysis of seamless learning to develop the skills of 21st century learners. Flexible adaptation is here analysed to deepen learners choice of personal control of the learning environments. Technology adoption is instead related to the perceived usefulness and the ease of use of a technology device to encounter personalized and self-regulated learning. Mobile devices are seen as effective learning tools and easy/quick ways to access knowledge in synchronous and asynchronous collaboration among learners and teachers. Mobile learning has made learning more learner-centered, even if individual or social, and fosters experiential learning, integrated into daily life. Frontier pedagogies of mobile learning are grounded into experimental models of education of bioeducational sciences, which interpret learning like an adaptive process and knowledge structures as both individual and collective, within digital and mobile learning environments.