OpenRaiser/Intern-Atlas huggingface dataset:
Sim2Real View Invariant Visual Servoing by Recurrent Control
Venue: arXiv cs.CV 2017
Year: 2017

Humans are remarkably proficient at controlling their limbs and tools from a wide range of viewpoints and angles, even in the presence of optical distortions. In robotics, this ability is referred to as visual servoing: moving a tool or end-point to a desired location using primarily visual feedback. In this paper, we study how viewpoint-invariant visual servoing skills can be learned automatically in a robotic manipulation scenario. To this end, we train a deep recurrent controller that can automatically determine which actions move the end-point of a robotic arm to a desired object. The problem that must be solved by this controller is fundamentally ambiguous: under severe variation in viewpoint, it may be impossible to determine the actions in a single feedforward operation. Instead, our visual servoing system must use its memory of past movements to understand how the actions affect the robot motion from the current viewpoint, correcting mistakes and gradually moving closer to the target. This ability is in stark contrast to most visual servoing methods, which either assume known dynamics or require a calibration phase. We show how we can learn this recurrent controller using simulated data and a reinforcement learning objective. We then describe how the resulting model can be transferred to a real-world robot by disentangling perception from control and only adapting the visual layers. The adapted model can servo to previously unseen objects from novel viewpoints on a real-world Kuka IIWA robotic arm. For supplementary videos, see: https://fsadeghi.github.io/Sim2RealViewInvariantServo
Improving User's Experience through Simultaneous Multi-WLAN Connections
Venue: arXiv cs.NI 2017
Year: 2017

In highly-dense IEEE 802.11 deployments, areas covered by multiple Wireless Local Area Networks (WLANs) will be common. This opens the door for stations equipped with multiple IEEE 802.11 interfaces to use several WLANs simultaneously, which not only may improve user experience, achieving a better connection with higher throughput and resilience; but it may also improve the network utilization. In this paper we investigate such a scenario. First, using a test-bed, consisting of a single station equipped with two interfaces and two access points, we observe that the file transfer time between the station and a destination server can be significantly reduced, studying with special attention the case in which both links do not have the same available bandwidth. Then, using a Markovian model that captures the scenario's dynamics in presence of multiple stations, we observe that in addition to improve individual station's performance, we can also improve the utilization of a multi-Access Points network despite increasing the contention level.
Improving DRAM Performance by Parallelizing Refreshes with Accesses
Venue: arXiv cs.AR 2017
Year: 2017

Modern DRAM cells are periodically refreshed to prevent data loss due to leakage. Commodity DDR DRAM refreshes cells at the rank level. This degrades performance significantly because it prevents an entire rank from serving memory requests while being refreshed. DRAM designed for mobile platforms, LPDDR DRAM, supports an enhanced mode, called per-bank refresh, that refreshes cells at the bank level. This enables a bank to be accessed while another in the same rank is being refreshed, alleviating part of the negative performance impact of refreshes. However, there are two shortcomings of per-bank refresh. First, the per-bank refresh scheduling scheme does not exploit the full potential of overlapping refreshes with accesses across banks because it restricts the banks to be refreshed in a sequential round-robin order. Second, accesses to a bank that is being refreshed have to wait. To mitigate the negative performance impact of DRAM refresh, we propose two complementary mechanisms, DARP (Dynamic Access Refresh Parallelization) and SARP (Subarray Access Refresh Parallelization). The goal is to address the drawbacks of per-bank refresh by building more efficient techniques to parallelize refreshes and accesses within DRAM. First, instead of issuing per-bank refreshes in a round-robin order, DARP issues per-bank refreshes to idle banks in an out-of-order manner. Furthermore, DARP schedules refreshes during intervals when a batch of writes are draining to DRAM. Second, SARP exploits the existence of mostly-independent subarrays within a bank. With minor modifications to DRAM organization, it allows a bank to serve memory accesses to an idle subarray while another subarray is being refreshed. Extensive evaluations show that our mechanisms improve system performance and energy efficiency compared to state-of-the-art refresh policies and the benefit increases as DRAM density increases.
Deep Unsupervised Clustering Using Mixture of Autoencoders
Venue: arXiv cs.LG 2017
Year: 2017

Unsupervised clustering is one of the most fundamental challenges in machine learning. A popular hypothesis is that data are generated from a union of low-dimensional nonlinear manifolds; thus an approach to clustering is identifying and separating these manifolds. In this paper, we present a novel approach to solve this problem by using a mixture of autoencoders. Our model consists of two parts: 1) a collection of autoencoders where each autoencoder learns the underlying manifold of a group of similar objects, and 2) a mixture assignment neural network, which takes the concatenated latent vectors from the autoencoders as input and infers the distribution over clusters. By jointly optimizing the two parts, we simultaneously assign data to clusters and learn the underlying manifolds of each cluster.
Methodological Framework for Determining the Land Eligibility of Renewable Energy Sources
Venue: arXiv cs.OH 2017
Year: 2017

The quantity and distribution of land which is eligible for renewable energy sources is fundamental to the role these technologies will play in future energy systems. As it stands, however, the current state of land eligibility investigation is found to be insufficient to meet the demands of the future energy modelling community. Three key areas are identified as the predominate causes of this; inconsistent criteria definitions, inconsistent or unclear methodologies, and inconsistent dataset usage. To combat these issues, a land eligibility framework is developed and described in detail. The validity of this framework is then shown via the recreation of land eligibility results found in the literature, showing strong agreement in the majority of cases. Following this, the framework is used to perform an evaluation of land eligibility criteria within the European context whereby the relative importance of commonly considered criteria are compared.
Interference Exploitation Precoding Made Practical: Closed-Form Solutions with Optimal Performance
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose closed-form precoding schemes with optimal performance for constructive interference (CI) exploitation in the multiuser multiple-input single-output (MU-MISO) downlink. We first consider an optimization where we maximize the distance between the constructive region and the detection thresholds. The cases of both strict and non-strict phase rotation are considered and can further be formulated as convex optimization problems. For optimization with strict phase rotation, we mathematically derive the optimal beamforming structure with Lagrangian and Karush-Kuhn-Tucker (KKT) conditions. By formulating its dual problem, the optimization problem is further shown to be equivalent to a quadratic programming (QP) over a simplex, which can be solved more efficiently. We then extend our analysis to the case of non-strict phase rotation, where it is mathematically shown that a K-dimensional optimization for non-strict phase rotation is equivalent to a 2K-dimensional optimization for strict phase rotation in terms of the problem formulation. The connection with the conventional zero-forcing (ZF) precoding is also discussed. Based on the above analysis, we further propose an iterative closed-form scheme to obtain the optimal beamforming matrix, where within each iteration a closed-form solution can be obtained. Numerical results validate our analysis and the optimality of the proposed iterative scheme, and further show that the proposed closed-form scheme is more efficient than the conventional QP algorithms with interior-point methods, which motivates the use of CI beamforming in practical wireless systems.
A Comparative Study of Different Source Code Metrics and Machine Learning Algorithms for Predicting Change Proneness of Object Oriented Systems
Venue: arXiv cs.SE 2017
Year: 2017

Change-prone classes or modules are defined as software components in the source code which are likely to change in the future. Change-proneness prediction is useful to the maintenance team as they can optimize and focus their testing resources on the modules which have a higher likelihood of change. Change-proneness prediction model can be built by using source code metrics as predictors or features within a machine learning classification framework. In this paper, twenty one source code metrics are computed to develop a statistical model for predicting change-proneness modules. Since the performance of the change-proneness model depends on the source code metrics, they are used as independent variables or predictors for the change-proneness model. Eleven different feature selection techniques (including the usage of all the $21$ proposed source code metrics described in the paper) are used to remove irrelevant features and select the best set of features. The effectiveness of the set of source code metrics are evaluated using eighteen different classiffication techniques and three ensemble techniques. Experimental results demonstrate that the model based on selected set of source code metrics after applying feature selection techniques achieves better results as compared to the model using all source code metrics as predictors. Our experimental results reveal that the predictive model developed using LSSVM-RBF yields better result as compared to other classification techniques
Human Action Recognition: Pose-based Attention draws focus to Hands
Venue: arXiv cs.CV 2017
Year: 2017

We propose a new spatio-temporal attention based mechanism for human action recognition able to automatically attend to the hands most involved into the studied action and detect the most discriminative moments in an action. Attention is handled in a recurrent manner employing Recurrent Neural Network (RNN) and is fully-differentiable. In contrast to standard soft-attention based mechanisms, our approach does not use the hidden RNN state as input to the attention model. Instead, attention distributions are extracted using external information: human articulated pose. We performed an extensive ablation study to show the strengths of this approach and we particularly studied the conditioning aspect of the attention mechanism. We evaluate the method on the largest currently available human action recognition dataset, NTU-RGB+D, and report state-of-the-art results. Other advantages of our model are certain aspects of explanability, as the spatial and temporal attention distributions at test time allow to study and verify on which parts of the input data the method focuses.
Overview of the Triple Scoring Task at the WSDM Cup 2017
Venue: arXiv cs.IR 2017
Year: 2017

This paper provides an overview of the triple scoring task at the WSDM Cup 2017, including a description of the task and the dataset, an overview of the participating teams and their results, and a brief account of the methods employed. In a nutshell, the task was to compute relevance scores for knowledge-base triples from relations, where such scores make sense. Due to the way the ground truth was constructed, scores were required to be integers from the range 0..7. For example, reasonable scores for the triples "Tim Burton profession Director" and "Tim Burton profession Actor" would be 7 and 2, respectively, because Tim Burton is well-known as a director, but he acted only in a few lesser known movies. The triple scoring task attracted considerable interest, with 52 initial registrations and 21 teams who submitted a valid run before the deadline. The winning team achieved an accuracy of 87%, that is, for that fraction of the triples from the test set (which was revealed only after the deadline) the difference to the score from the ground truth was at most 2. The best result for the average difference from the test set scores was 1.50.
Unifying Map and Landmark Based Representations for Visual Navigation
Venue: arXiv cs.CV 2017
Year: 2017

This works presents a formulation for visual navigation that unifies map based spatial reasoning and path planning, with landmark based robust plan execution in noisy environments. Our proposed formulation is learned from data and is thus able to leverage statistical regularities of the world. This allows it to efficiently navigate in novel environments given only a sparse set of registered images as input for building representations for space. Our formulation is based on three key ideas: a learned path planner that outputs path plans to reach the goal, a feature synthesis engine that predicts features for locations along the planned path, and a learned goal-driven closed loop controller that can follow plans given these synthesized features. We test our approach for goal-driven navigation in simulated real world environments and report performance gains over competitive baseline approaches.
A Deep Reinforcement Learning-Based Framework for Content Caching
Venue: arXiv cs.IT 2017
Year: 2017

Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, this work presents a DRL-based framework with Wolpertinger architecture for content caching at the base station. The proposed framework is aimed at maximizing the long-term cache hit rate, and it requires no knowledge of the content popularity distribution. To evaluate the proposed framework, we compare the performance with other caching algorithms, including Least Recently Used (LRU), Least Frequently Used (LFU), and First-In First-Out (FIFO) caching strategies. Meanwhile, since the Wolpertinger architecture can effectively limit the action space size, we also compare the performance with Deep Q-Network to identify the impact of dropping a portion of the actions. Our results show that the proposed framework can achieve improved short-term cache hit rate and improved and stable long-term cache hit rate in comparison with LRU, LFU, and FIFO schemes. Additionally, the performance is shown to be competitive in comparison to Deep Q-learning, while the proposed framework can provide significant savings in runtime.
Practically-Self-Stabilizing Vector Clocks in the Absence of Execution Fairness
Venue: arXiv cs.DC 2017
Year: 2017

Vector clock algorithms are basic wait-free building blocks that facilitate causal ordering of events. As wait-free algorithms, they are guaranteed to complete their operations within a finite number of steps. Stabilizing algorithms allow the system to recover after the occurrence of transient faults, such as soft errors and arbitrary violations of the assumptions according to which the system was designed to behave. We present the first, to the best of our knowledge, stabilizing vector clock algorithm for asynchronous crash-prone message-passing systems that can recover in a wait-free manner after the occurrence of transient faults. In these settings, it is challenging to demonstrate a finite and wait-free recovery from (communication and crash failures as well as) transient faults, bound the message and storage sizes, deal with the removal of all stale information without blocking, and deal with counter overflow events (which occur at different network nodes concurrently). We present an algorithm that never violates safety in the absence of transient faults and provides bounded time recovery during fair executions that follow the last transient fault. The novelty is that in the absence of execution fairness, the algorithm guarantees a bound on the number of times in which the system might violate safety (while existing algorithms might block forever due to the presence of both transient faults and crash failures). Since vector clocks facilitate a number of elementary synchronization building blocks (without requiring remote replica synchronization) in asynchronous systems, we believe that our analytical insights are useful for the design of other systems that cannot guarantee execution fairness.
An Economic Study of the Effect of Android Platform Fragmentation on Security Updates
Venue: arXiv cs.CR 2017
Year: 2017

Vendors in the Android ecosystem typically customize their devices by modifying Android Open Source Project (AOSP) code, adding in-house developed proprietary software, and pre-installing third-party applications. However, research has documented how various security problems are associated with this customization process. We develop a model of the Android ecosystem utilizing the concepts of game theory and product differentiation to capture the competition involving two vendors customizing the AOSP platform. We show how the vendors are incentivized to differentiate their products from AOSP and from each other, and how prices are shaped through this differentiation process. We also consider two types of consumers: security-conscious consumers who understand and care about security, and naïve consumers who lack the ability to correctly evaluate security properties of vendor-supplied Android products or simply ignore security. It is evident that vendors shirk on security investments in the latter case. Regulators such as the U.S. Federal Trade Commission have sanctioned Android vendors for underinvestment in security, but the exact effects of these sanctions are difficult to disentangle with empirical data. Here, we model the impact of a regulator-imposed fine that incentivizes vendors to match a minimum security standard. Interestingly, we show how product prices will decrease for the same cost of customization in the presence of a fine, or a higher level of regulator-imposed minimum security.
ReabsNet: Detecting and Revising Adversarial Examples
Venue: arXiv cs.LG 2017
Year: 2017

Though deep neural network has hit a huge success in recent studies and applica- tions, it still remains vulnerable to adversarial perturbations which are imperceptible to humans. To address this problem, we propose a novel network called ReabsNet to achieve high classification accuracy in the face of various attacks. The approach is to augment an existing classification network with a guardian network to detect if a sample is natural or has been adversarially perturbed. Critically, instead of simply rejecting adversarial examples, we revise them to get their true labels. We exploit the observation that a sample containing adversarial perturbations has a possibility of returning to its true class after revision. We demonstrate that our ReabsNet outperforms the state-of-the-art defense method under various adversarial attacks.
A Response-Function-Based Coordination Method for Transmission-Distribution-Coupled AC OPF
Venue: arXiv cs.SY 2017
Year: 2017

With distributed generation highly integrated into the grid, the transmission-distribution-coupled AC OPF (TDOPF) becomes increasingly important. This paper proposes a response-function-based coordination method to solve the TDOPF. Different from typical decomposition methods, this method employs approximate response functions of the power injections with respect to the bus voltage magnitude in the transmission-distribution (T-D) interface to reflect the "reaction" of the distribution to the transmission system control. By using the response functions, only one or two iterations between the transmission system operator (TSO) and the distribution system operator(s) (DSO(s)) are required to attain a nearly optimal TDOPF solution. Numerical tests confirm that, relative to a typical decomposition method, the proposed method does not only enjoy a cheaper computational cost but is workable even when the objectives of the TSO and the DSO(s) are in distinct scales.
TFW, DamnGina, Juvie, and Hotsie-Totsie: On the Linguistic and Social Aspects of Internet Slang
Venue: arXiv cs.CL 2017
Year: 2017

Slang is ubiquitous on the Internet. The emergence of new social contexts like micro-blogs, question-answering forums, and social networks has enabled slang and non-standard expressions to abound on the web. Despite this, slang has been traditionally viewed as a form of non-standard language -- a form of language that is not the focus of linguistic analysis and has largely been neglected. In this work, we use UrbanDictionary to conduct the first large-scale linguistic analysis of slang and its social aspects on the Internet to yield insights into this variety of language that is increasingly used all over the world online. We begin by computationally analyzing the phonological, morphological and syntactic properties of slang. We then study linguistic patterns in four specific categories of slang namely alphabetisms, blends, clippings, and reduplicatives. Our analysis reveals that slang demonstrates extra-grammatical rules of phonological and morphological formation that markedly distinguish it from the standard form shedding insight into its generative patterns. Next, we analyze the social aspects of slang by studying subject restriction and stereotyping in slang usage. Analyzing tens of thousands of such slang words reveals that the majority of slang on the Internet belongs to two major categories: sex and drugs. We also noted that not only is slang usage not immune to prevalent social biases and prejudices but also reflects such biases and stereotypes more intensely than the standard variety.
Source-side Prediction for Neural Headline Generation
Venue: arXiv cs.CL 2017
Year: 2017

The encoder-decoder model is widely used in natural language generation tasks. However, the model sometimes suffers from repeated redundant generation, misses important phrases, and includes irrelevant entities. Toward solving these problems we propose a novel source-side token prediction module. Our method jointly estimates the probability distributions over source and target vocabularies to capture a correspondence between source and target tokens. The experiments show that the proposed model outperforms the current state-of-the-art method in the headline generation task. Additionally, we show that our method has an ability to learn a reasonable token-wise correspondence without knowing any true alignments.