OpenRaiser/Intern-Atlas huggingface dataset:
Online Interaction Detection for Click-Through Rate Prediction
Venue: arXiv cs.LG 2021
Year: 2021

Click-Through Rate prediction aims to predict the ratio of clicks to impressions of a specific link. This is a challenging task since (1) there are usually categorical features, and the inputs will be extremely high-dimensional if one-hot encoding is applied, (2) not only the original features but also their interactions are important, (3) an effective prediction may rely on different features and interactions in different time periods. To overcome these difficulties, we propose a new interaction detection method, named Online Random Intersection Chains. The method, which is based on the idea of frequent itemset mining, detects informative interactions by observing the intersections of randomly chosen samples. The discovered interactions enjoy high interpretability as they can be comprehended as logical expressions. ORIC can be updated every time new data is collected, without being retrained on historical data. What's more, the importance of the historical and latest data can be controlled by a tuning parameter. A framework is designed to deal with the streaming interactions, so almost all existing models for CTR prediction can be applied after interaction detection. Empirical results demonstrate the efficiency and effectiveness of ORIC on three benchmark datasets.
Efficient Realistic Data Generation Framework leveraging Deep Learning-based Human Digitization
Venue: arXiv cs.CV 2021
Year: 2021

The performance of supervised deep learning algorithms depends significantly on the scale, quality and diversity of the data used for their training. Collecting and manually annotating large amount of data can be both time-consuming and costly tasks to perform. In the case of tasks related to visual human-centric perception, the collection and distribution of such data may also face restrictions due to legislation regarding privacy. In addition, the design and testing of complex systems, e.g., robots, which often employ deep learning-based perception models, may face severe difficulties as even state-of-the-art methods trained on real and large-scale datasets cannot always perform adequately due to not having been adapted to the visual differences between the virtual and the real world data. As an attempt to tackle and mitigate the effect of these issues, we present a method that automatically generates realistic synthetic data with annotations for a) person detection, b) face recognition, and c) human pose estimation. The proposed method takes as input real background images and populates them with human figures in various poses. Instead of using hand-made 3D human models, we propose the use of models generated through deep learning methods, further reducing the dataset creation costs, while maintaining a high level of realism. In addition, we provide open-source and easy to use tools that implement the proposed pipeline, allowing for generating highly-realistic synthetic datasets for a variety of tasks. A benchmarking and evaluation in the corresponding tasks shows that synthetic data can be effectively used as a supplement to real data.
AdaptiveFog: A Modelling and Optimization Framework for Fog Computing in Intelligent Transportation Systems
Venue: arXiv cs.NI 2021
Year: 2021

Fog computing has been advocated as an enabling technology for computationally intensive services in smart connected vehicles. Most existing works focus on analyzing the queueing and workload processing latencies associated with fog computing, ignoring the fact that wireless access latency can sometimes dominate the overall latency. This motivates the work in this paper, where we report on a five-month measurement study of the wireless access latency between connected vehicles and a fog/cloud computing system supported by commercially available LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE networks that implement a fog/cloud infrastructure. AdaptiveFog's main objective is to maximize the service confidence level, defined as the probability that the latency of a given service type is below some threshold. To quantify the performance gap between different LTE networks, we introduce a novel statistical distance metric, called weighted Kantorovich-Rubinstein (K-R) distance. Two scenarios based on finite- and infinite-horizon optimization of short-term and long-term confidence are investigated. For each scenario, a simple threshold policy based on weighted K-R distance is proposed and proved to maximize the latency confidence for smart vehicles. Extensive analysis and simulations are performed based on our latency measurements. Our results show that AdaptiveFog achieves around 30% to 50% improvement in the confidence levels of fog and cloud latencies, respectively.
Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins
Venue: arXiv cs.LG 2021
Year: 2021

Physics-informed dynamical system models form critical components of digital twins of the built environment. These digital twins enable the design of energy-efficient infrastructure, but must be properly calibrated to accurately reflect system behavior for downstream prediction and analysis. Dynamical system models of modern buildings are typically described by a large number of parameters and incur significant computational expenditure during simulations. To handle large-scale calibration of digital twins without exorbitant simulations, we propose ANP-BBO: a scalable and parallelizable batch-wise Bayesian optimization (BBO) methodology that leverages attentive neural processes (ANPs).
Curious Explorer: a provable exploration strategy in Policy Learning
Venue: arXiv cs.LG 2021
Year: 2021

Having access to an exploring restart distribution (the so-called wide coverage assumption) is critical with policy gradient methods. This is due to the fact that, while the objective function is insensitive to updates in unlikely states, the agent may still need improvements in those states in order to reach a nearly optimal payoff. For this reason, wide coverage is used in some form when analyzing theoretical properties of practical policy gradient methods. However, this assumption can be unfeasible in certain environments, for instance when learning is online, or when restarts are possible only from a fixed initial state. In these cases, classical policy gradient algorithms can have very poor convergence properties and sample efficiency. In this paper, we develop Curious Explorer, a novel and simple iterative state space exploration strategy that can be used with any starting distribution $ρ$. Curious Explorer starts from $ρ$, then using intrinsic rewards assigned to the set of poorly visited states produces a sequence of policies, each one more exploratory than the previous one in an informed way, and finally outputs a restart model $μ$ based on the state visitation distribution of the exploratory policies. Curious Explorer is provable, in the sense that we provide theoretical upper bounds on how often an optimal policy visits poorly visited states. These bounds can be used to prove PAC convergence and sample efficiency results when a PAC optimizer is plugged in Curious Explorer. This allows to achieve global convergence and sample efficiency results without any coverage assumption for REINFORCE, and potentially for any other policy gradient method ensuring PAC convergence with wide coverage. Finally, we plug (the output of) Curious Explorer into REINFORCE and TRPO, and show empirically that it can improve performance in MDPs with challenging exploration.
Distributed Matrix Tiling Using A Hypergraph Labeling Formulation
Venue: arXiv cs.DC 2021
Year: 2021

Partitioning large matrices is an important problem in distributed linear algebra computing (used in ML among others). Briefly, our goal is to perform a sequence of matrix algebra operations in a distributed manner (whenever possible) on these large matrices. However, not all partitioning schemes work well with different matrix algebra operations and their implementations (algorithms). This is a type of data tiling problem. In this work we consider a theoretical model for a version of the matrix tiling problem in the setting of hypergraph labeling. We prove some hardness results and give a theoretical characterization of its complexity on random instances. Additionally we develop a greedy algorithm and experimentally show its efficacy.
Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes
Venue: arXiv cs.HC 2021
Year: 2021

This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. First, it presents a Big-Data driven methodology that studies the multimodal components of user interactions and analyzes the data from Bluetooth Low Energy (BLE) beacons and BLE scanners to detect a user's indoor location in a specific activity-based zone during Activities of Daily Living. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data from diverse behavioral patterns to detect the zone-based indoor location of a user in any Internet of Things (IoT)-based environment. These two approaches achieved performance accuracies of 81.36% and 81.13%, respectively, when tested on a dataset. Third, it presents a methodology to detect the spatial coordinates of a user's indoor position that outperforms all similar works in this field, as per the associated root mean squared error - one of the performance evaluation metrics in ISO/IEC18305:2016- an international standard for testing Localization and Tracking Systems. Finally, it presents a comprehensive comparative study that includes Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, to address the challenge of identifying the optimal machine learning approach for Indoor Localization.
Diffusion Priors In Variational Autoencoders
Venue: arXiv cs.LG 2021
Year: 2021

Among likelihood-based approaches for deep generative modelling, variational autoencoders (VAEs) offer scalable amortized posterior inference and fast sampling. However, VAEs are also more and more outperformed by competing models such as normalizing flows (NFs), deep-energy models, or the new denoising diffusion probabilistic models (DDPMs). In this preliminary work, we improve VAEs by demonstrating how DDPMs can be used for modelling the prior distribution of the latent variables. The diffusion prior model improves upon Gaussian priors of classical VAEs and is competitive with NF-based priors. Finally, we hypothesize that hierarchical VAEs could similarly benefit from the enhanced capacity of diffusion priors.
SAT Based Analogy Evaluation Framework for Persian Word Embeddings
Venue: arXiv cs.CL 2021
Year: 2021

In recent years there has been a special interest in word embeddings as a new approach to convert words to vectors. It has been a focal point to understand how much of the semantics of the the words has been transferred into embedding vectors. This is important as the embedding is going to be used as the basis for downstream NLP applications and it will be costly to evaluate the application end-to-end in order to identify quality of the used embedding model. Generally the word embeddings are evaluated through a number of tests, including analogy test. In this paper we propose a test framework for Persian embedding models. Persian is a low resource language and there is no rich semantic benchmark to evaluate word embedding models for this language. In this paper we introduce an evaluation framework including a hand crafted Persian SAT based analogy dataset, a colliquial test set (specific to Persian) and a benchmark to study the impact of various parameters on the semantic evaluation task.
NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs
Venue: arXiv cs.DC 2021
Year: 2021

Deep Neural Networks (DNNs) may be partitioned across the edge and the cloud to improve the performance efficiency of inference. DNN partitions are determined based on operational conditions such as network speed. When operational conditions change DNNs will need to be repartitioned to maintain the overall performance. However, repartitioning using existing approaches, such as Pause and Resume, will incur a service downtime on the edge. This paper presents the NEUKONFIG framework that identifies the service downtime incurred when repartitioning DNNs and proposes approaches for reducing edge service downtime. The proposed approaches are based on 'Dynamic Switching' in which, when the network speed changes and given an existing edge-cloud pipeline, a new edge-cloud pipeline is initialised with new DNN partitions. Incoming inference requests are switched to the new pipeline for processing data. Two dynamic switching scenarios are considered: when a second edge-cloud pipeline is always running and when a second pipeline is only initialised when the network speed changes. Experimental studies are carried out on a lab-based testbed to demonstrate that Dynamic Switching reduces the downtime by at least an order of magnitude when compared to a baseline using Pause and Resume that has a downtime of 6 seconds. A trade-off in the edge service downtime and memory required is noted. The Dynamic Switching approach that requires the same amount of memory as the baseline reduces the edge service downtime to 0.6 seconds and to less than 1 millisecond in the best case when twice the amount of memory as the baseline is available.
Near-Optimal Deterministic Single-Source Distance Sensitivity Oracles
Venue: arXiv cs.DS 2021
Year: 2021

Given a graph with a source vertex $s$, the Single Source Replacement Paths (SSRP) problem is to compute, for every vertex $t$ and edge $e$, the length $d(s,t,e)$ of a shortest path from $s$ to $t$ that avoids $e$. A Single-Source Distance Sensitivity Oracle (Single-Source DSO) is a data structure that answers queries of the form $(t,e)$ by returning the distance $d(s,t,e)$. We show how to deterministically compress the output of the SSRP problem on $n$-vertex, $m$-edge graphs with integer edge weights in the range $[1,M]$ into a Single-Source DSO of size $O(M^{1/2}n^{3/2})$ with query time $\widetilde{O}(1)$. The space requirement is optimal (up to the word size) and our techniques can also handle vertex failures. Chechik and Cohen [SODA 2019] presented a combinatorial, randomized $\widetilde{O}(m\sqrt{n}+n^2)$ time SSRP algorithm for undirected and unweighted graphs. Grandoni and Vassilevska Williams [FOCS 2012, TALG 2020] gave an algebraic, randomized $\widetilde{O}(Mn^ω)$ time SSRP algorithm for graphs with integer edge weights in the range $[1,M]$, where $ω<2.373$ is the matrix multiplication exponent. We derandomize both algorithms for undirected graphs in the same asymptotic running time and apply our compression to obtain deterministic Single-Source DSOs. The $\widetilde{O}(m\sqrt{n}+n^2)$ and $\widetilde{O}(Mn^ω)$ preprocessing times are polynomial improvements over previous $o(n^2)$-space oracles. On sparse graphs with $m=O(n^{5/4-\varepsilon}/M^{7/4})$ edges, for any constant $\varepsilon > 0$, we reduce the preprocessing to randomized $\widetilde{O}(M^{7/8}m^{1/2}n^{11/8})=O(n^{2-\varepsilon/2})$ time. This is the first truly subquadratic time algorithm for building Single-Source DSOs on sparse graphs.
Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification
Venue: arXiv cs.CV 2021
Year: 2021

This paper presents new designs of graph convolutional neural networks (GCNs) on 3D meshes for 3D object segmentation and classification. We use the faces of the mesh as basic processing units and represent a 3D mesh as a graph where each node corresponds to a face. To enhance the descriptive power of the graph, we introduce a 1-ring face neighbourhood structure to derive novel multi-dimensional spatial and structure features to represent the graph nodes. Based on this new graph representation, we then design a densely connected graph convolutional block which aggregates local and regional features as the key construction component to build effective and efficient practical GCN models for 3D object classification and segmentation. We will present experimental results to show that our new technique outperforms state of the art where our models are shown to have the smallest number of parameters and consietently achieve the highest accuracies across a number of benchmark datasets. We will also present ablation studies to demonstrate the soundness of our design principles and the effectiveness of our practical models.
When Video Classification Meets Incremental Classes
Venue: arXiv cs.CV 2021
Year: 2021

With the rapid development of social media, tremendous videos with new classes are generated daily, which raise an urgent demand for video classification methods that can continuously update new classes while maintaining the knowledge of old videos with limited storage and computing resources. In this paper, we summarize this task as Class-Incremental Video Classification (CIVC) and propose a novel framework to address it. As a subarea of incremental learning tasks, the challenge of catastrophic forgetting is unavoidable in CIVC. To better alleviate it, we utilize some characteristics of videos. First, we decompose the spatio-temporal knowledge before distillation rather than treating it as a whole in the knowledge transfer process; trajectory is also used to refine the decomposition. Second, we propose a dual granularity exemplar selection method to select and store representative video instances of old classes and key-frames inside videos under a tight storage budget. We benchmark our method and previous SOTA class-incremental learning methods on Something-Something V2 and Kinetics datasets, and our method outperforms previous methods significantly.
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction
Venue: arXiv cs.CL 2021
Year: 2021

Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts. Existing approaches, such as table filling and pairwise scoring, have shown impressive performance on various information extraction tasks, but they are difficult to scale to datasets with longer input texts because of their second-order space/time complexities with respect to the input length. In this work, we propose a Hybrid Span Generator (HySPA) that invertibly maps the information graph to an alternating sequence of nodes and edge types, and directly generates such sequences via a hybrid span decoder which can decode both the spans and the types recurrently in linear time and space complexities. Extensive experiments on the ACE05 dataset show that our approach also significantly outperforms state-of-the-art on the joint entity and relation extraction task.
Biologically Inspired Model for Timed Motion in Robotic Systems
Venue: arXiv cs.RO 2021
Year: 2021

The goal of this work is the development of a motion model for sequentially timed movement actions in robotic systems under specific consideration of temporal stabilization, that is maintaining an approximately constant overall movement time (isochronous behavior). This is demonstrated both in simulation and on a physical robotic system for the task of intercepting a moving target in three-dimensional space. Motivated from humanoid motion, timing plays a vital role to generate a naturalistic behavior in interaction with the dynamic environment as well as adaptively planning and executing action sequences on-line. In biological systems, many of the physiological and anatomical functions follow a particular level of periodicity and stabilization, which exhibit a certain extent of resilience against external disturbances. A main aspect thereof is stabilizing movement timing against limited perturbations. Especially human arm movement, namely when it is tasked to reach a certain goal point, pose or configuration, shows a stabilizing behavior. This work incorporates the utilization of an extended Kalman filter (EKF) which was implemented to predict the target position while coping with non-linear system dynamics. The periodicity and temporal stabilization in biological systems was artificially generated by a Hopf oscillator, yielding a sinusoidal velocity profile for smooth and repeatable motion.
Genre determining prediction: Non-standard TAM marking in football language
Venue: arXiv cs.CL 2021
Year: 2021

German and French football language display tense-aspect-mood (TAM) forms which differ from the TAM use in other genres. In German football talk, the present indicative may replace the pluperfect subjunctive. In French reports of football matches, the imperfective past may occur instead of a perfective past tense-aspect form. We argue that the two phenomena share a functional core and are licensed in the same way, which is a direct result of the genre they occur in. More precisely, football match reports adhere to a precise script and specific events are temporally determined in terms of objective time. This allows speakers to exploit a secondary function of TAM forms, namely, they shift the temporal perspective. We argue that it is on the grounds of the genre that comprehenders predict the deviating forms and are also able to decode them. We present various corpus studies where we explore the functioning of these phenomena in order to gain insights into their distribution, grammaticalization and their functioning in discourse. Relevant factors are Aktionsart properties, rhetorical relations and their interaction with other TAM forms. This allows us to discuss coping mechanisms on the part of the comprehender. We broaden our understanding of the phenomena, which have only been partly covered for French and up to now seem to have been ignored in German.
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study
Venue: arXiv cs.AI 2021
Year: 2021

We introduce a procedural content generation (PCG) framework at the intersections of experience-driven PCG and PCG via reinforcement learning, named ED(PCG)RL, EDRL in short. EDRL is able to teach RL designers to generate endless playable levels in an online manner while respecting particular experiences for the player as designed in the form of reward functions. The framework is tested initially in the Super Mario Bros game. In particular, the RL designers of Super Mario Bros generate and concatenate level segments while considering the diversity among the segments. The correctness of the generation is ensured by a neural net-assisted evolutionary level repairer and the playability of the whole level is determined through AI-based testing. Our agents in this EDRL implementation learn to maximise a quantification of Koster's principle of fun by moderating the degree of diversity across level segments. Moreover, we test their ability to design fun levels that are diverse over time and playable. Our proposed framework is capable of generating endless, playable Super Mario Bros levels with varying degrees of fun, deviation from earlier segments, and playability. EDRL can be generalised to any game that is built as a segment-based sequential process and features a built-in compressed representation of its game content.