OpenRaiser/Intern-Atlas huggingface dataset:
Achieving Phase Coherency and Gain Stability in Active Antenna Arrays for Sub-6 GHz FDD and TDD FD-MIMO: Challenges and Solutions
Venue: arXiv cs.IT 2019
Year: 2019

Massive MIMO has been the subject of intense interest in both academia and industry for the past few years. 3GPP standardization for cellular systems have adopted the principles of massive MIMO and categorized the use of large rectangular planar arrays at the base station as full-dimension MIMO (FD-MIMO) to operate in both TDD and FDD. Operating a large antenna array base station requires the system to overcome several implementation challenges caused by hardware impairments making practical solutions non-ideal and expensive to deploy at scale. It is important to learn from existing challenges and solutions in order to prepare for larger scale deployment for example with cell free massive MIMO. Hence in this paper, we specifically study the phase and amplitude instability due to RF impairments using measurements carried out in the lab and in the field in a commercial LTE network. We investigate the effect of phase and magnitude errors on the performance of FD-MIMO systems. We discuss and characterize various sources creating these errors including time varying phase drift from low cost local oscillator (LO) and internal temperature variations affecting frequency response of RF chains. The minimum requirements and tradeoffs of different LO architectures and calibration mechanisms for practical cellular deployment are discussed. We then provide details of a novel coherent LO distribution mechanism and related novel array calibration mechanism that can be applied to both TDD and FDD systems. Measurement results are provided to validate the performance of these methods used in a 2D full-connected hybrid beamforming array architecture called High Definition Active Antenna System (HDAAS). These results showcase the efficacy of the proposed methods which can easily be extended to other array architectures including sub-array hybrid beamforming and element-level digitization.
Neural Population Coding for Effective Temporal Classification
Venue: arXiv cs.NE 2019
Year: 2019

Neural encoding plays an important role in faithfully describing the temporally rich patterns, whose instances include human speech and environmental sounds. For tasks that involve classifying such spatio-temporal patterns with the Spiking Neural Networks (SNNs), how these patterns are encoded directly influence the difficulty of the task. In this paper, we compare several existing temporal and population coding schemes and evaluate them on both speech (TIDIGITS) and sound (RWCP) datasets. We show that, with population neural codings, the encoded patterns are linearly separable using the Support Vector Machine (SVM). We note that the population neural codings effectively project the temporal information onto the spatial domain, thus improving linear separability in the spatial dimension, achieving an accuracy of 95\% and 100\% for TIDIGITS and RWCP datasets classified using the SVM, respectively. This observation suggests that an effective neural coding scheme greatly simplifies the classification problem such that a simple linear classifier would suffice. The above datasets are then classified using the Tempotron, an SNN-based classifier. SNN classification results agree with the SVM findings that population neural codings help to improve classification accuracy. Hence, other than the learning algorithm, effective neural encoding is just as important as an SNN designed to recognize spatio-temporal patterns. It is an often neglected but powerful abstraction that deserves further study.
Heterogeneity-Aware Asynchronous Decentralized Training
Venue: arXiv cs.DC 2019
Year: 2019

Distributed deep learning training usually adopts All-Reduce as the synchronization mechanism for data parallel algorithms due to its high performance in homogeneous environment. However, its performance is bounded by the slowest worker among all workers, and is significantly slower in heterogeneous situations. AD-PSGD, a newly proposed synchronization method which provides numerically fast convergence and heterogeneity tolerance, suffers from deadlock issues and high synchronization overhead. Is it possible to get the best of both worlds - designing a distributed training method that has both high performance as All-Reduce in homogeneous environment and good heterogeneity tolerance as AD-PSGD? In this paper, we propose Ripples, a high-performance heterogeneity-aware asynchronous decentralized training approach. We achieve the above goal with intensive synchronization optimization, emphasizing the interplay between algorithm and system implementation. To reduce synchronization cost, we propose a novel communication primitive Partial All-Reduce that allows a large group of workers to synchronize quickly. To reduce synchronization conflict, we propose static group scheduling in homogeneous environment and simple techniques (Group Buffer and Group Division) to avoid conflicts with slightly reduced randomness. Our experiments show that in homogeneous environment, Ripples is 1.1 times faster than the state-of-the-art implementation of All-Reduce, 5.1 times faster than Parameter Server and 4.3 times faster than AD-PSGD. In a heterogeneous setting, Ripples shows 2 times speedup over All-Reduce, and still obtains 3 times speedup over the Parameter Server baseline.
Masking Salient Object Detection, a Mask Region-based Convolutional Neural Network Analysis for Segmentation of Salient Objects
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose a broad comparison between Fully Convolutional Networks (FCNs) and Mask Region-based Convolutional Neural Networks (Mask-RCNNs) applied in the Salient Object Detection (SOD) context. Studies in the SOD literature usually explore architectures based in FCNs to detect salient regions and objects in visual scenes. However, besides the promising results achieved, FCNs showed issues in some challenging scenarios. Fairly recently studies in the SOD literature proposed the use of a Mask-RCNN approach to overcome such issues. However, there is no extensive comparison between the two networks in the SOD literature endorsing the effectiveness of Mask-RCNNs over FCN when segmenting salient objects. Aiming to effectively show the superiority of Mask-RCNNs over FCNs in the SOD context, we compare two variations of Mask-RCNNs with two variations of FCNs in eight datasets widely used in the literature and in four metrics. Our findings show that in this context Mask-RCNNs achieved an improvement on the F-measure up to 47% over FCNs.
Enterprise API Security and GDPR Compliance: Design and Implementation Perspective
Venue: arXiv cs.CR 2019
Year: 2019

With the advancements in the enterprise-level business development, the demand for new applications and services is overwhelming. For the development and delivery of such applications and services, enterprise businesses rely on Application Programming Interfaces (APIs). In essence, API is a double-edged sword. On one hand, API provides ease of expanding the business through sharing value and utility, but on another hand it raises security and privacy issues. Since the applications usually use APIs to retrieve important data, therefore it is extremely important to make sure that an effective access control and security mechanism are in place , and the data does not fall into wrong hands. In this article, we discuss the current state of the enterprise API security and the role of Machine Learning (ML) in API security. We also discuss the General Data Protection Regulation (GDPR) compliance and its effect on the API security.
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Venue: arXiv cs.LG 2019
Year: 2019

Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples has raised concerns about applying deep learning to safety-critical applications. As a result, we have witnessed increasing interests in studying attack and defense mechanisms for DNN models on different data types, such as images, graphs and text. Thus, it is necessary to provide a systematic and comprehensive overview of the main threats of attacks and the success of corresponding countermeasures. In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for the three popular data types, i.e., images, graphs and text.
A Distributed Fair Machine Learning Framework with Private Demographic Data Protection
Venue: arXiv cs.LG 2019
Year: 2019

Fair machine learning has become a significant research topic with broad societal impact. However, most fair learning methods require direct access to personal demographic data, which is increasingly restricted to use for protecting user privacy (e.g. by the EU General Data Protection Regulation). In this paper, we propose a distributed fair learning framework for protecting the privacy of demographic data. We assume this data is privately held by a third party, which can communicate with the data center (responsible for model development) without revealing the demographic information. We propose a principled approach to design fair learning methods under this framework, exemplify four methods and show they consistently outperform their existing counterparts in both fairness and accuracy across three real-world data sets. We theoretically analyze the framework, and prove it can learn models with high fairness or high accuracy, with their trade-offs balanced by a threshold variable.
Q-Learning Based Aerial Base Station Placement for Fairness Enhancement in Mobile Networks
Venue: arXiv cs.NI 2019
Year: 2019

In this paper, we use an aerial base station (aerial-BS) to enhance fairness in a dynamic environment with user mobility. The problem of optimally placing the aerial-BS is a non-deterministic polynomial-time hard (NP-hard) problem. Moreover, the network topology is subject to continuous changes due to the user mobility. These issues intensify the quest to develop an adaptive and fast algorithm for 3D placement of the aerial-BS. To this end, we propose a method based on reinforcement learning to achieve these goals. Simulation results show that our method increases fairness among users in a reasonable computing time, while the solution is comparatively close to the optimal solution obtained by exhaustive search.
Identity-Aware Deep Face Hallucination via Adversarial Face Verification
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we address the problem of face hallucination by proposing a novel multi-scale generative adversarial network (GAN) architecture optimized for face verification. First, we propose a multi-scale generator architecture for face hallucination with a high up-scaling ratio factor, which has multiple intermediate outputs at different resolutions. The intermediate outputs have the growing goal of synthesizing small to large images. Second, we incorporate a face verifier with the original GAN discriminator and propose a novel discriminator which learns to discriminate different identities while distinguishing fake generated HR face images from their ground truth images. In particular, the learned generator cares for not only the visual quality of hallucinated face images but also preserving the discriminative features in the hallucination process. In addition, to capture perceptually relevant differences we employ a perceptual similarity loss, instead of similarity in pixel space. We perform a quantitative and qualitative evaluation of our framework on the LFW and CelebA datasets. The experimental results show the advantages of our proposed method against the state-of-the-art methods on the 8x downsampled testing dataset.
Multimodal Continuation-style Architectures for Human-Robot Interaction
Venue: arXiv cs.HC 2019
Year: 2019

We present an architecture for integrating real-time, multimodal input into a computational agent's contextual model. Using a human-avatar interaction in a virtual world, we treat aligned gesture and speech as an ensemble where content may be communicated by either modality. With a modified nondeterministic pushdown automaton architecture, the computer system: (1) consumes input incrementally using continuation-passing style until it achieves sufficient understanding the user's aim; (2) constructs and asks questions where necessary using established contextual information; and (3) maintains track of prior discourse items using multimodal cues. This type of architecture supports special cases of pushdown and finite state automata as well as integrating outputs from machine learning models. We present examples of this architecture's use in multimodal one-shot learning interactions of novel gestures and live action composition.
Two Computational Models for Analyzing Political Attention in Social Media
Venue: arXiv cs.SI 2019
Year: 2019

Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric. Existing methods for measuring attention, such as manual labeling according to established codebooks, are expensive and can be restrictive. We describe two computational models that automatically distinguish topics in politicians' social media content. Our models---one supervised classifier and one unsupervised topic model---provide different benefits. The supervised classifier reduces the labor required to classify content according to pre-determined topic list. However, tweets do more than communicate policy positions. Our unsupervised model uncovers both political topics and other Twitter uses (e.g., constituent service). These models are effective, inexpensive computational tools for political communication and social media research. We demonstrate their utility and discuss the different analyses they afford by applying both models to the tweets posted by members of the 115th U.S. Congress.
An ASP-based Approach for Attractor Enumeration in Synchronous and Asynchronous Boolean Networks
Venue: arXiv cs.AI 2019
Year: 2019

Boolean networks are conventionally used to represent and simulate gene regulatory networks. In the analysis of the dynamic of a Boolean network, the attractors are the objects of a special attention. In this work, we propose a novel approach based on Answer Set Programming (ASP) to express Boolean networks and simulate the dynamics of such networks. Our work focuses on the identification of the attractors, it relies on the exhaustive enumeration of all the attractors of synchronous and asynchronous Boolean networks. We applied and evaluated the proposed approach on real biological networks, and the obtained results indicate that this novel approach is promising.
Encoding Selection for Solving Hamiltonian Cycle Problems with ASP
Venue: arXiv cs.AI 2019
Year: 2019

It is common for search and optimization problems to have alternative equivalent encodings in ASP. Typically none of them is uniformly better than others when evaluated on broad classes of problem instances. We claim that one can improve the solving ability of ASP by using machine learning techniques to select encodings likely to perform well on a given instance. We substantiate this claim by studying the hamiltonian cycle problem. We propose several equivalent encodings of the problem and several classes of hard instances. We build models to predict the behavior of each encoding, and then show that selecting encodings for a given instance using the learned performance predictors leads to significant performance gains.
Design of a Solver for Multi-Agent Epistemic Planning
Venue: arXiv cs.AI 2019
Year: 2019

As the interest in Artificial Intelligence continues to grow it is becoming more and more important to investigate formalization and tools that allow us to exploit logic to reason about the world. In particular, given the increasing number of multi-agents systems that could benefit from techniques of automated reasoning, exploring new ways to define not only the world's status but also the agents' information is constantly growing in importance. This type of reasoning, i.e., about agents' perception of the world and also about agents' knowledge of her and others' knowledge, is referred to as epistemic reasoning. In our work we will try to formalize this concept, expressed through epistemic logic, for dynamic domains. In particular we will attempt to define a new action-based language for multi-agent epistemic planning and to implement an epistemic planner based on it. This solver should provide a tool flexible enough to be able to reason on different domains, e.g., economy, security, justice and politics, where reasoning about others' beliefs could lead to winning strategies or help in changing a group of agents' view of the world.
Research Report on Automatic Synthesis of Local Search Neighborhood Operators
Venue: arXiv cs.NE 2019
Year: 2019

Constraint Programming (CP) and Local Search (LS) are different paradigms for dealing with combinatorial search and optimization problems. Their complementary features motivated researchers to create hybrid CP/LS solutions, maintaining both the modeling capabilities of CP and the computational advantages of the heuristic-based LS approach. Research presented in this report is focused on developing a novel method to infer an efficient LS neighborhood operator based on the problem structure, as modeled in the CP paradigm. We consider a limited formal language that we call a Neighborhood Definition Language, used to specify the neighborhood operators in a fine-grained and declarative manner. Together with Logic Programming runtime called Noodle, it allows to automatically synthesize complex operators using a Grammar Evolution algorithm.
Exploring Reciprocal Attention for Salient Object Detection by Cooperative Learning
Venue: arXiv cs.CV 2019
Year: 2019

Typically, objects with the same semantics are not always prominent in images containing different backgrounds. Motivated by this observation that accurately salient object detection is related to both foreground and background, we proposed a novel cooperative attention mechanism that jointly considers reciprocal relationships between background and foreground for efficient salient object detection. Concretely, we first aggregate the features at each side-out of traditional dilated FCN to extract the initial foreground and background local responses respectively. Then taking these responses as input, reciprocal attention module adaptively models the nonlocal dependencies between any two pixels of the foreground and background features, which is then aggregated with local features in a mutual reinforced way so as to enhance each branch to generate more discriminative foreground and background saliency map. Besides, cooperative losses are particularly designed to guide the multi-task learning of foreground and background branches, which encourages our network to obtain more complementary predictions with clear boundaries. At last, a simple but effective fusion strategy is utilized to produce the final saliency map. Comprehensive experimental results on five benchmark datasets demonstrate that our proposed method performs favorably against the state-of-the-art approaches in terms of all compared evaluation metrics.
Segmentation of Robot Movements using Position and Contact Forces
Venue: arXiv cs.RO 2019
Year: 2019

In this paper, a method for autonomous segmentation of demonstrated robot movements is proposed. Position data is clustered into Gaussian mixture models (GMMs), and an initial set of segments is identified from the Gaussian basis functions. A Kalman filter is used to detect sudden changes in the contact force/torque measurements, and this is used to update and verify the initial segmentation points. The segmentation method is verified experimentally on an industrial robot.
SPARCAS: A Decentralized, Truthful Multi-Agent Collision-free Path Finding Mechanism
Venue: arXiv cs.RO 2019
Year: 2019

We propose a decentralized collision-avoidance mechanism for a group of independently controlled robots moving on a shared workspace. Existing algorithms achieve multi-robot collision avoidance either (a) in a centralized setting, or (b) in a decentralized setting with collaborative robots. We focus on the setting with competitive robots in a decentralized environment, where robots may strategically reveal their information to get prioritized. We propose the mechanism SPARCAS in this setting that, using principles of mechanism design, ensures truthful revelation of the robots' private information and provides locally efficient movement of the robots. It is free from collisions and deadlocks, and handles a dynamic arrival of robots. In practice, this mechanism scales well for a large number of robots where the optimal collision-avoiding path-finding algorithm (M*) does not scale. Yet, SPARCAS does not compromise the path optimality too much. Our mechanism prioritizes the robots in the order of their `true' higher needs, but for a higher payment. It uses monetary transfers which is small enough compared to the value received by the robots.
Using BERT for Word Sense Disambiguation
Venue: arXiv cs.CL 2019
Year: 2019

Word Sense Disambiguation (WSD), which aims to identify the correct sense of a given polyseme, is a long-standing problem in NLP. In this paper, we propose to use BERT to extract better polyseme representations for WSD and explore several ways of combining BERT and the classifier. We also utilize sense definitions to train a unified classifier for all words, which enables the model to disambiguate unseen polysemes. Experiments show that our model achieves the state-of-the-art results on the standard English All-word WSD evaluation.
Message Reduction in the Local Model is a Free Lunch
Venue: arXiv cs.DC 2019
Year: 2019

A new \emph{spanner} construction algorithm is presented, working under the \emph{LOCAL} model with unique edge IDs. Given an $n$-node communication graph, a spanner with a constant stretch and $O (n^{1 + \varepsilon})$ edges (for an arbitrarily small constant $\varepsilon > 0$) is constructed in a constant number of rounds sending $O (n^{1 + \varepsilon})$ messages whp. Consequently, we conclude that every $t$-round LOCAL algorithm can be transformed into an $O (t)$-round LOCAL algorithm that sends $O (t \cdot n^{1 + \varepsilon})$ messages whp. This improves upon all previous message-reduction schemes for LOCAL algorithms that incur a $\log^{Ω(1)} n$ blow-up of the round complexity.
Anomaly Detection As-a-Service
Venue: arXiv cs.SE 2019
Year: 2019

Cloud systems are complex, large, and dynamic systems whose behavior must be continuously analyzed to timely detect misbehaviors and failures. Although there are solutions to flexibly monitor cloud systems, cost-effectively controlling the anomaly detection logic is still a challenge. In particular, cloud operators may need to quickly change the types of detected anomalies and the scope of anomaly detection, for instance based on observations. This kind of intervention still consists of a largely manual and inefficient ad-hoc effort. In this paper, we present Anomaly Detection as-a-Service (ADaaS), which uses the same as-a-service paradigm often exploited in cloud systems to declarative control the anomaly detection logic. Operators can use ADaaS to specify the set of indicators that must be analyzed and the types of anomalies that must be detected, without having to address any operational aspect. Early results with lightweight detectors show that the presented approach is a promising solution to deliver better control of the anomaly detection logic.