OpenRaiser/Intern-Atlas huggingface dataset:
Understanding Feature Selection and Feature Memorization in Recurrent Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we propose a test, called Flagged-1-Bit (F1B) test, to study the intrinsic capability of recurrent neural networks in sequence learning. Four different recurrent network models are studied both analytically and experimentally using this test. Our results suggest that in general there exists a conflict between feature selection and feature memorization in sequence learning. Such a conflict can be resolved either using a gating mechanism as in LSTM, or by increasing the state dimension as in Vanilla RNN. Gated models resolve this conflict by adaptively adjusting their state-update equations, whereas Vanilla RNN resolves this conflict by assigning different dimensions different tasks. Insights into feature selection and memorization in recurrent networks are given.
Unsupervised Bi-directional Flow-based Video Generation from one Snapshot
Venue: arXiv cs.CV 2019
Year: 2019

Imagining multiple consecutive frames given one single snapshot is challenging, since it is difficult to simultaneously predict diverse motions from a single image and faithfully generate novel frames without visual distortions. In this work, we leverage an unsupervised variational model to learn rich motion patterns in the form of long-term bi-directional flow fields, and apply the predicted flows to generate high-quality video sequences. In contrast to the state-of-the-art approach, our method does not require external flow supervisions for learning. This is achieved through a novel module that performs bi-directional flows prediction from a single image. In addition, with the bi-directional flow consistency check, our method can handle occlusion and warping artifacts in a principled manner. Our method can be trained end-to-end based on arbitrarily sampled natural video clips, and it is able to capture multi-modal motion uncertainty and synthesizes photo-realistic novel sequences. Quantitative and qualitative evaluations over synthetic and real-world datasets demonstrate the effectiveness of the proposed approach over the state-of-the-art methods.
Topological Information-Theoretic Belief Space Planning with Optimality Guarantees
Venue: arXiv cs.RO 2019
Year: 2019

Determining a globally optimal solution of belief space planning (BSP) in high-dimensional state spaces is computationally expensive, as it involves belief propagation and objective function evaluation for each candidate action. Our recently introduced topological belief space planning t-bsp instead performs decision making considering only topologies of factor graphs that correspond to posterior future beliefs. In this paper we contribute to this body of work a novel method for efficiently determining error bounds of t-bsp, thereby providing global optimality guarantees or uncertainty margin of its solution. The bounds are given with respect to an optimal solution of information theoretic BSP considering the previously introduced topological metric which is based on the number of spanning trees. In realistic and synthetic simulations, we analyze tightness of these bounds and show empirically how this metric is closely related to another computationally more efficient t-bsp metric, an approximation of the von Neumann entropy of a graph, which can achieve online performance.
The Role of Artificial Intelligence (AI) in Adaptive eLearning System (AES) Content Formation: Risks and Opportunities involved
Venue: arXiv cs.AI 2019
Year: 2019

Artificial Intelligence (AI) plays varying roles in supporting both existing and emerging technologies. In the area of Learning and Tutoring, it plays key role in Intelligent Tutoring Systems (ITS). The fusion of ITS with Adaptive Hypermedia and Multimedia (AHAM) form the backbone of Adaptive eLearning Systems (AES) which provides personalized experiences to learners. This experience is important because it facilitates the accurate delivery of the learning modules in specific to the learner capacity and readiness. AES types vary, with Adaptive Web Based eLearning Systems (AWBES) being the popular type because of wider access offered by the web technology.The retrieval and aggregation of contents for any eLearning system is critical whichis determined by the relevance of learning material to the needs of the learner.In this paper, we discuss components of AES, role of AI in AES content aggregation, possible risks and available opportunities.
Energy Efficiency Analysis of Collaborative Compressive Sensing Scheme in Cognitive Radio Networks
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we investigate the energy efficiency of conventional collaborative compressive sensing (CCCS) scheme, focusing on balancing the tradeoff between energy efficiency and detection accuracy in cognitive radio environment. In particular, we derive the achievable throughput, energy consumption and energy efficiency of the CCCS scheme, and formulate an optimization problem to determine the optimal values of parameters which maximize the energy efficiency of the CCCS scheme. The maximization of energy efficiency is proposed as a multi-variable, non-convex optimization problem, and we provide approximations to reduce it to a convex optimization problem. We highlight that errors due to these approximations are negligible. Later, we analytically characterize the tradeoff between dimensionality reduction and collaborative sensing performance of the CCCS scheme -- the implicit tradeoff between energy saving and detection accuracy, and show that the loss due to compression can be recovered through collaboration which improves the overall energy efficiency.
Bounding Inefficiency of Equilibria in Continuous Actions Games using Submodularity and Curvature
Venue: arXiv cs.GT 2019
Year: 2019

Games with continuous strategy sets arise in several machine learning problems (e.g. adversarial learning). For such games, simple no-regret learning algorithms exist in several cases and ensure convergence to coarse correlated equilibria (CCE). The efficiency of such equilibria with respect to a social function, however, is not well understood. In this paper, we define the class of valid utility games with continuous strategies and provide efficiency bounds for their CCEs. Our bounds rely on the social function being a monotone DR-submodular function. We further refine our bounds based on the curvature of the social function. Furthermore, we extend our efficiency bounds to a class of non-submodular functions that satisfy approximate submodularity properties. Finally, we show that valid utility games with continuous strategies can be designed to maximize monotone DR-submodular functions subject to disjoint constraints with approximation guarantees. The approximation guarantees we derive are based on the efficiency of the equilibria of such games and can improve the existing ones in the literature. We illustrate and validate our results on a budget allocation game and a sensor coverage problem.
Clockless Spin-based Look-Up Tables with Wide Read Margin
Venue: arXiv cs.ET 2019
Year: 2019

In this paper, we develop a 6-input fracturable non-volatile Clockless LUT (C-LUT) using spin Hall effect (SHE)-based Magnetic Tunnel Junctions (MTJs) and provide a detailed comparison between the SHE-MTJ-based C-LUT and Spin Transfer Torque (STT)-MTJ-based C-LUT. The proposed C-LUT offers an attractive alternative for implementing combinational logic as well as sequential logic versus previous spin-based LUT designs in the literature. Foremost, C-LUT eliminates the sense amplifier typically employed by using a differential polarity dual MTJ design, as opposed to a static reference resistance MTJ. This realizes a much wider read margin and the Monte Carlo simulation of the proposed fracturable C-LUT indicates no read and write errors in the presence of a variety of process variations scenarios involving MOS transistors as well as MTJs. Additionally, simulation results indicate that the proposed C-LUT reduces the standby power dissipation by 5.4-fold compared to the SRAM-based LUT. Furthermore, the proposed SHE-MTJ-based C-LUT reduces the area by 1.3-fold and 2-fold compared to the SRAM-based LUT and the STT-MTJ-based C-LUT, respectively.
RT-Gang: Real-Time Gang Scheduling Framework for Safety-Critical Systems
Venue: arXiv cs.DC 2019
Year: 2019

In this paper, we present RT-Gang: a novel real-time gang scheduling framework that enforces a one-gang-at-a-time policy. We find that, in a multicore platform, co-scheduling multiple parallel real-time tasks would require highly pessimistic worst-case execution time (WCET) and schedulability analysis - even when there are enough cores - due to contention in shared hardware resources such as cache and DRAM controller. In RT-Gang, all threads of a parallel real-time task form a real-time gang and the scheduler globally enforces the one-gang-at-a-time scheduling policy to guarantee tight and accurate task WCET. To minimize under-utilization, we integrate a state-of-the-art memory bandwidth throttling framework to allow safe execution of best-effort tasks. Specifically, any idle cores, if exist, are used to schedule best-effort tasks but their maximum memory bandwidth usages are strictly throttled to tightly bound interference to real-time gang tasks. We implement RT-Gang in the Linux kernel and evaluate it on two representative embedded multicore platforms using both synthetic and real-world DNN workloads. The results show that RT-Gang dramatically improves system predictability and the overhead is negligible.
Hand Pose Estimation: A Survey
Venue: arXiv cs.CV 2019
Year: 2019

The success of Deep Convolutional Neural Networks (CNNs) in recent years in almost all the Computer Vision tasks on one hand, and the popularity of low-cost consumer depth cameras on the other, has made Hand Pose Estimation a hot topic in computer vision field. In this report, we will first explain the hand pose estimation problem and will review major approaches solving this problem, especially the two different problems of using depth maps or RGB images. We will survey the most important papers in each field and will discuss the strengths and weaknesses of each. Finally, we will explain the biggest datasets in this field in detail and list 22 datasets with all their properties. To the best of our knowledge this is the most complete list of all the datasets in the hand pose estimation field.
Learning Modular Safe Policies in the Bandit Setting with Application to Adaptive Clinical Trials
Venue: arXiv cs.LG 2019
Year: 2019

The stochastic multi-armed bandit problem is a well-known model for studying the exploration-exploitation trade-off. It has significant possible applications in adaptive clinical trials, which allow for dynamic changes in the treatment allocation probabilities of patients. However, most bandit learning algorithms are designed with the goal of minimizing the expected regret. While this approach is useful in many areas, in clinical trials, it can be sensitive to outlier data, especially when the sample size is small. In this paper, we define and study a new robustness criterion for bandit problems. Specifically, we consider optimizing a function of the distribution of returns as a regret measure. This provides practitioners more flexibility to define an appropriate regret measure. The learning algorithm we propose to solve this type of problem is a modification of the BESA algorithm [Baransi et al., 2014], which considers a more general version of regret. We present a regret bound for our approach and evaluate it empirically both on synthetic problems as well as on a dataset from the clinical trial literature. Our approach compares favorably to a suite of standard bandit algorithms.
COMIC: Towards A Compact Image Captioning Model with Attention
Venue: arXiv cs.CV 2019
Year: 2019

Recent works in image captioning have shown very promising raw performance. However, we realize that most of these encoder-decoder style networks with attention do not scale naturally to large vocabulary size, making them difficult to be deployed on embedded system with limited hardware resources. This is because the size of word and output embedding matrices grow proportionally with the size of vocabulary, adversely affecting the compactness of these networks. To address this limitation, this paper introduces a brand new idea in the domain of image captioning. That is, we tackle the problem of compactness of image captioning models which is hitherto unexplored. We showed that, our proposed model, named COMIC for COMpact Image Captioning, achieves comparable results in five common evaluation metrics with state-of-the-art approaches on both MS-COCO and InstaPIC-1.1M datasets despite having an embedding vocabulary size that is 39x - 99x smaller. The source code and models are available at: https://github.com/jiahuei/COMIC-Compact-Image-Captioning-with-Attention
Error Exponents of Typical Random Trellis Codes
Venue: arXiv cs.IT 2019
Year: 2019

In continuation to an earlier work, where error exponents of typical random codes were studied in the context of general block coding, with no underlying structure, here we carry out a parallel study on typical random, time-varying trellis codes for general discrete memoryless channels, focusing on a certain range of low rates. By analyzing an upper bound to the error probability of the typical random trellis code, using the method of types, we first derive a Csiszar-style error exponent formula (with respect to the constraint length), which allows to easily identify and characterize properties of good codes and dominant error events. We also derive a Gallager-style form of this error exponent, which turns out to be related to the expurgated error exponent. The main result is further extended to channels with memory and mismatch.
Pseudocodeword-based Decoding of Quantum Stabilizer Codes
Venue: arXiv cs.IT 2019
Year: 2019

It has been shown that graph-cover pseudocodewords can be used to characterize the behavior of sum-product algorithm (SPA) decoding of classical codes. In this paper, we leverage and adapt these results to analyze SPA decoding of quantum stabilizer codes. We use the obtained insights to formulate modifications to the SPA that overcome some of its weaknesses.
Liver Pathology Simulation: Algorithm for Haptic Rendering and Force Maps for Palpation Assessment
Venue: arXiv cs.GR 2019
Year: 2019

Preoperative gestures include tactile sampling of the mechanical properties of biological tissue for both histological and pathological considerations. Tactile properties used in conjunction with visual cues can provide useful feedback to the surgeon. Development of novel cost effective haptic-based simulators and their introduction in the minimally invasive surgery learning cycle can absorb the learning curve for your residents. Receiving pre-training in a core set of surgical skills can reduce skill acquisition time and risks. We present the integration of a real-time surface stiffness adjustment algorithm and a novel paradigm -- force maps -- in a visuo-haptic simulator module designed to train internal organs disease diagnostics through palpation.
Using Causal Analysis to Learn Specifications from Task Demonstrations
Venue: arXiv cs.AI 2019
Year: 2019

Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human-robot interaction. In this work we show that it is possible to learn a generative model for distinct user behavioral types, extracted from human demonstrations, by enforcing clustering of preferred task solutions within the latent space. We use this model to differentiate between user types and to find cases with overlapping solutions. Moreover, we can alter an initially guessed solution to satisfy the preferences that constitute a particular user type by backpropagating through the learned differentiable model. An advantage of structuring generative models in this way is that it allows us to extract causal relationships between symbols that might form part of the user's specification of the task, as manifested in the demonstrations. We show that the proposed method is capable of correctly distinguishing between three user types, who differ in degrees of cautiousness in their motion, while performing the task of moving objects with a kinesthetically driven robot in a tabletop environment. Our method successfully identifies the correct type, within the specified time, in 99% [97.8 - 99.8] of the cases, which outperforms an IRL baseline. We also show that our proposed method correctly changes a default trajectory to one satisfying a particular user specification even with unseen objects. The resulting trajectory is shown to be directly implementable on a PR2 humanoid robot completing the same task.
The StreetLearn Environment and Dataset
Venue: arXiv cs.AI 2019
Year: 2019

Navigation is a rich and well-grounded problem domain that drives progress in many different areas of research: perception, planning, memory, exploration, and optimisation in particular. Historically these challenges have been separately considered and solutions built that rely on stationary datasets - for example, recorded trajectories through an environment. These datasets cannot be used for decision-making and reinforcement learning, however, and in general the perspective of navigation as an interactive learning task, where the actions and behaviours of a learning agent are learned simultaneously with the perception and planning, is relatively unsupported. Thus, existing navigation benchmarks generally rely on static datasets (Geiger et al., 2013; Kendall et al., 2015) or simulators (Beattie et al., 2016; Shah et al., 2018). To support and validate research in end-to-end navigation, we present StreetLearn: an interactive, first-person, partially-observed visual environment that uses Google Street View for its photographic content and broad coverage, and give performance baselines for a challenging goal-driven navigation task. The environment code, baseline agent code, and the dataset are available at http://streetlearn.cc
Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs
Venue: arXiv cs.LG 2019
Year: 2019

This paper reviews graph convolutional neural networks (GCNNs) through the lens of edge-variant graph filters. The edge-variant graph filter is a finite order, linear, and local recursion that allows each node, in each iteration, to weigh differently the information of its neighbors. By exploiting this recursion, we formulate a general framework for GCNNs which considers state-of-the-art solutions as particular cases. This framework results useful to i) understand the tradeoff between local detail and the number of parameters of each solution and ii) provide guidelines for developing a myriad of novel approaches that can be implemented locally in the vertex domain. One of such approaches is presented here showing superior performance w.r.t. current alternatives in graph signal classification problems.
Teaching HCI Design in a Flipped Learning M.Sc. Course Using Eye-Tracking Peer Evaluation Data
Venue: arXiv cs.HC 2019
Year: 2019

This paper presents experiences from a flipped classroom M.Sc. course on Human-Computer Interaction (HCI). The students that finished successfully this course participated in twelve short workshops, based on a flipped classroom model. Each workshop focused on a specific HCI activity, while before the workshops, a two-hour lecture was used to introduce the students in the flipped learning concept. All the rest of the educational material was offered to the students online before each workshop. Such material was mainly short lectures from the professor, in the form of videos uploaded in the course's YouTube channel and documents delivered using the university learning management system (LMS). For each workshop the students had to be prepared to participate, which was tested using brief quizzes before the start of specific workshops. The activity presented in this paper was the design and evaluation of an interactive system. The students were asked to form six groups comprising of three to four students each. Then a system's description, vague enough to stimulate creativity, was randomly assigned to each group. This activity presented in this paper was the longest activity of the entire course and it was conducted in four consequent workshops. The paper presents the setting of this experiment, the peer assessment method and the use of eye-tracking data collected and analysed to aid the students towards improving their design. The students created a working model of the system with limited functionality and improved this model using eye-tracking data from the peer evaluation of this model. The use of these data offered them the insight to improve their models and to undergo design changes. The paper presents samples of the progress made between various versions of the models and concludes presenting the preliminary positive results of the students qualitative evaluation of this experiment.
Secure Data Offloading Strategy for Connected and Autonomous Vehicles
Venue: arXiv cs.NI 2019
Year: 2019

Connected and Automated Vehicles (CAVs) are expected to constantly interact with a network of processing nodes installed in secure cabinets located at the side of the road -- thus, forming Fog Computing-based infrastructure for Intelligent Transportation Systems (ITSs). Future city-scale ITS services will heavily rely upon the sensor data regularly off-loaded by each CAV on the Fog Computing network. Due to the broadcast nature of the medium, CAVs' communications can be vulnerable to eavesdropping. This paper proposes a novel data offloading approach where the Random Linear Network Coding (RLNC) principle is used to ensure the probability of an eavesdropper to recover relevant portions of sensor data is minimized. Our preliminary results confirm the effectiveness of our approach when operated in a large-scale ITS networks.
Opportunistic View Materialization with Deep Reinforcement Learning
Venue: arXiv cs.DB 2019
Year: 2019

Carefully selected materialized views can greatly improve the performance of OLAP workloads. We study using deep reinforcement learning to learn adaptive view materialization and eviction policies. Our insight is that such selection policies can be effectively trained with an asynchronous RL algorithm, that runs paired counter-factual experiments during system idle times to evaluate the incremental value of persisting certain views. Such a strategy obviates the need for accurate cardinality estimation or hand-designed scoring heuristics. We focus on inner-join views and modeling effects in a main-memory, OLAP system. Our research prototype system, called DQM, is implemented in SparkSQL and we experiment on several workloads including the Join Order Benchmark and the TPC-DS workload. Results suggest that: (1) DQM can outperform heuristic when their assumptions are not satisfied by the workload or there are temporal effects like period maintenance, (2) even with the cost of learning, DQM is more adaptive to changes in the workload, and (3) DQM is broadly applicable to different workloads and skews.
Efficient Millimeter-Wave Infrastructure Placement for City-Scale ITS
Venue: arXiv cs.NI 2019
Year: 2019

Millimeter Waves (mmWaves) will play a pivotal role in the next-generation of Intelligent Transportation Systems (ITSs). However, in deep urban environments, sensitivity to blockages creates the need for more sophisticated network planning. In this paper, we present an agile strategy for deploying road-side nodes in a dense city scenario. In our system model, we consider strict Quality-of-Service (QoS) constraints (e.g. high throughput, low latency) that are typical of ITS applications. Our approach is scalable, insofar that takes into account the unique road and building shapes of each city, performing well for both regular and irregular city layouts. It allows us not only to achieve the required QoS constraints but it also provides up to $50\%$ reduction in the number of nodes required, compared to existing deployment solutions.