OpenRaiser/Intern-Atlas huggingface dataset:
UAV Visual Teach and Repeat Using Only Semantic Object Features
Venue: arXiv cs.RO 2018
Year: 2018

We demonstrate the use of semantic object detections as robust features for Visual Teach and Repeat (VTR). Recent CNN-based object detectors are able to reliably detect objects of tens or hundreds of categories in a video at frame rates. We show that such detections are repeatable enough to use as landmarks for VTR, without any low-level image features. Since object detections are highly invariant to lighting and surface appearance changes, our VTR can cope with global lighting changes and local movements of the landmark objects. In the teaching phase, we build a series of compact scene descriptors: a list of detected object labels and their image-plane locations. In the repeating phase, we use Seq-SLAM-like relocalization to identify the most similar learned scene, then use a motion control algorithm based on the funnel lane theory to navigate the robot along the previously piloted trajectory. We evaluate the method on a commodity UAV, examining the robustness of the algorithm to new viewpoints, lighting conditions, and movements of landmark objects. The results suggest that semantic object features could be useful due to their invariance to superficial appearance changes compared to low-level image features.
Waveform Modeling and Generation Using Hierarchical Recurrent Neural Networks for Speech Bandwidth Extension
Venue: arXiv cs.SD 2018
Year: 2018

This paper presents a waveform modeling and generation method using hierarchical recurrent neural networks (HRNN) for speech bandwidth extension (BWE). Different from conventional BWE methods which predict spectral parameters for reconstructing wideband speech waveforms, this BWE method models and predicts waveform samples directly without using vocoders. Inspired by SampleRNN which is an unconditional neural audio generator, the HRNN model represents the distribution of each wideband or high-frequency waveform sample conditioned on the input narrowband waveform samples using a neural network composed of long short-term memory (LSTM) layers and feed-forward (FF) layers. The LSTM layers form a hierarchical structure and each layer operates at a specific temporal resolution to efficiently capture long-span dependencies between temporal sequences. Furthermore, additional conditions, such as the bottleneck (BN) features derived from narrowband speech using a deep neural network (DNN)-based state classifier, are employed as auxiliary input to further improve the quality of generated wideband speech. The experimental results of comparing several waveform modeling methods show that the HRNN-based method can achieve better speech quality and run-time efficiency than the dilated convolutional neural network (DCNN)-based method and the plain sample-level recurrent neural network (SRNN)-based method. Our proposed method also outperforms the conventional vocoder-based BWE method using LSTM-RNNs in terms of the subjective quality of the reconstructed wideband speech.
Anticipating epileptic seizures through the analysis of EEG synchronization as a data classification problem
Venue: arXiv cs.LG 2018
Year: 2018

Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting about 0.5--0.8\% of the world population. Several studies investigated the relationship between seizures and brainwave synchronization patterns, pursuing the possibility of identifying interictal, preictal, ictal and postictal states. In this work, we introduce a graph-based model of the brain interactions developed to study synchronization patterns in the electroencephalogram (EEG) signals. The aim is to develop a patient-specific approach, also for a real-time use, for the prediction of epileptic seizures' occurrences. Different synchronization measures of the EEG signals and easily computable functions able to capture in real-time the variations of EEG synchronization have been considered. Both standard and ad-hoc classification algorithms have been developed and used. Results on scalp EEG signals show that this simple and computationally viable processing is able to highlight the changes in the synchronization corresponding to the preictal state.
Evaluation of Interactive Machine Learning Systems
Venue: arXiv cs.AI 2018
Year: 2018

The evaluation of interactive machine learning systems remains a difficult task. These systems learn from and adapt to the human, but at the same time, the human receives feedback and adapts to the system. Getting a clear understanding of these subtle mechanisms of co-operation and co-adaptation is challenging. In this chapter, we report on our experience in designing and evaluating various interactive machine learning applications from different domains. We argue for coupling two types of validation: algorithm-centered analysis, to study the computational behaviour of the system; and human-centered evaluation, to observe the utility and effectiveness of the application for end-users. We use a visual analytics application for guided search, built using an interactive evolutionary approach, as an exemplar of our work. Our observation is that human-centered design and evaluation complement algorithmic analysis, and can play an important role in addressing the "black-box" effect of machine learning. Finally, we discuss research opportunities that require human-computer interaction methodologies, in order to support both the visible and hidden roles that humans play in interactive machine learning.
XZero: On Practical Cross-Technology Interference-Nulling for LTE-U/WiFi Coexistence
Venue: arXiv cs.NI 2018
Year: 2018

LTE-U/WiFi coexistence can be significantly improved by placing so-called coexistence gaps in space through cross-technology interference-nulling (CTIN) from LTE-U BS towards WiFi nodes. Such coordinated co-existence scheme requires, for the exchange of control messages, a cross-technology control channel (CTC) between LTE-U and WiFi networks which was presented recently. However, it is unclear how a practical CTIN operates in the absence of channel state information which is needed for CTIN but cannot be obtained from the CTC. We present XZero, the first practical CTIN system that is able to quickly find the suitable precoding configuration used for interference nulling without having to search the whole space of angular orientations. XZero performs a tree-based search to find the direction for the null beam(s) by exploiting the feedback received from the WiFi AP on the tested null directions. We have implemented a prototype of XZero using SDR platform for LTE-U and commodity hardware for WiFi and evaluated its performance in a large indoor testbed. Evaluation results reveal on average a reduction by 15.7 dB in interference-to-noise ratio at the nulled WiFi nodes when using a ULA with four antennas. Moreover, XZero has a sub-second reconfiguration delay which is up to 10x smaller as compared to naive exhaustive linear search.
The Historic Development of the Zooarchaeological Database OssoBook and the xBook Framework for Scientific Databases
Venue: arXiv cs.DB 2018
Year: 2018

In this technical report, we describe the historic development of the zooarchaeological database OssoBook and the resulting framework xBook, a generic infrastructure for distributed, relational data management that is mainly designed for the needs of scientific data. We describe the concepts of the architecture and its most important features. We especially point out the Server-Client architecture, the synchronization process, the Launcher application, and the structure and features of the application.
Learning Symmetric and Low-energy Locomotion
Venue: arXiv cs.LG 2018
Year: 2018

Learning locomotion skills is a challenging problem. To generate realistic and smooth locomotion, existing methods use motion capture, finite state machines or morphology-specific knowledge to guide the motion generation algorithms. Deep reinforcement learning (DRL) is a promising approach for the automatic creation of locomotion control. Indeed, a standard benchmark for DRL is to automatically create a running controller for a biped character from a simple reward function. Although several different DRL algorithms can successfully create a running controller, the resulting motions usually look nothing like a real runner. This paper takes a minimalist learning approach to the locomotion problem, without the use of motion examples, finite state machines, or morphology-specific knowledge. We introduce two modifications to the DRL approach that, when used together, produce locomotion behaviors that are symmetric, low-energy, and much closer to that of a real person. First, we introduce a new term to the loss function (not the reward function) that encourages symmetric actions. Second, we introduce a new curriculum learning method that provides modulated physical assistance to help the character with left/right balance and forward movement. The algorithm automatically computes appropriate assistance to the character and gradually relaxes this assistance, so that eventually the character learns to move entirely without help. Because our method does not make use of motion capture data, it can be applied to a variety of character morphologies. We demonstrate locomotion controllers for the lower half of a biped, a full humanoid, a quadruped, and a hexapod. Our results show that learned policies are able to produce symmetric, low-energy gaits. In addition, speed-appropriate gait patterns emerge without any guidance from motion examples or contact planning.
Unsupervised learning from videos using temporal coherency deep networks
Venue: arXiv cs.CV 2018
Year: 2018

In this work we address the challenging problem of unsupervised learning from videos. Existing methods utilize the spatio-temporal continuity in contiguous video frames as regularization for the learning process. Typically, this temporal coherence of close frames is used as a free form of annotation, encouraging the learned representations to exhibit small differences between these frames. But this type of approach fails to capture the dissimilarity between videos with different content, hence learning less discriminative features. We here propose two Siamese architectures for Convolutional Neural Networks, and their corresponding novel loss functions, to learn from unlabeled videos, which jointly exploit the local temporal coherence between contiguous frames, and a global discriminative margin used to separate representations of different videos. An extensive experimental evaluation is presented, where we validate the proposed models on various tasks. First, we show how the learned features can be used to discover actions and scenes in video collections. Second, we show the benefits of such an unsupervised learning from just unlabeled videos, which can be directly used as a prior for the supervised recognition tasks of actions and objects in images, where our results further show that our features can even surpass a traditional and heavily supervised pre-training plus fine-tunning strategy.
Choreographies for Reactive Programming
Venue: arXiv cs.PL 2018
Year: 2018

Modular programming is a cornerstone in software development, as it allows to build complex systems from the assembly of simpler components, and support reusability and substitution principles. In a distributed setting, component assembly is supported by communication that is often required to follow a prescribed protocol of interaction. In this paper, we present a language for the modular development of distributed systems, where the assembly of components is supported by a choreography that specifies the communication protocol. Our language allows to separate component behaviour, given in terms of reactive data ports, and choreographies, specified as first class entities. This allows us to consider reusability and substitution principles for both components and choreographies. We show how our model can be compiled into a more operational perspective in a provably-correct way, and we present a typing discipline that addresses communication safety and progress of systems, where a notion of substitutability naturally arises.
Outage Performance of Two-Way Relay Non-Orthogonal Multiple Access Systems
Venue: arXiv cs.IT 2018
Year: 2018

This paper investigates a two-way relay nonorthogonal multiple access (TWR-NOMA) system, where two groups of NOMA users exchange messages with the aid of one half-duplex (HD) decode-and-forward (DF) relay. Since the signal-plus-interference-to-noise ratios (SINRs) of NOMA signals mainly depend on effective successive interference cancellation (SIC) schemes, imperfect SIC (ipSIC) and perfect SIC (pSIC) are taken into consideration. To characterize the performance of TWR-NOMA systems, we derive closed-form expressions for both exact and asymptotic outage probabilities of NOMA users' signals with ipSIC/pSIC. Based on the results derived, the diversity order and throughput of the system are examined. Numerical simulations demonstrate that: 1) TWR-NOMA is superior to TWR-OMA in terms of outage probability in low SNR regimes; and 2) Due to the impact of interference signal (IS) at the relay, error floors and throughput ceilings exist in outage probabilities and ergodic rates for TWR-NOMA, respectively.
Multi-optional Many-sorted Past Present Future structures and its description
Venue: arXiv cs.LO 2018
Year: 2018

The cognitive theory of true conditions (CTTC) is a proposal to describe the model-theoretic semantics of symbolic cognitive architectures and design the implementation of cognitive abilities. The CTTC is formulated mathematically using the multi-optional many-sorted past present future(MMPPF) structures. This article defines mathematically the MMPPF structures and the formal languages proposed to describe them by the CTTC.
Knock-Knock: The unbearable lightness of Android Notifications
Venue: arXiv cs.CR 2018
Year: 2018

Android Notifications can be considered as essential parts in Human-Smartphone interaction and inextricable modules of modern mobile applications that can facilitate User Interaction and improve User Experience. This paper presents how this well-crafted and thoroughly documented mechanism, provided by the OS can be exploited by an adversary. More precisely, we present attacks that result either in forging smartphone application notifications to lure the user in disclosing sensitive information, or manipulate Android Notifications to launch a Denial of Service attack to the users' device, locally and remotely, rendering them unusable. This paper concludes by proposing generic countermeasures for the discussed security threats.
Timing Advance Estimation and Beamforming of Random Access Response in Crowded TDD Massive MIMO Systems
Venue: arXiv cs.IT 2018
Year: 2018

Timing advance (TA) estimation at the base station (BS) and reliable decoding of random access response (RAR) at the users are the most important steps in the initial random access (RA) procedure. However, due to the limited availability of physical resources dedicated for RA, successful completion of RA requests would become increasingly difficult in high user density scenarios, due to contention among users requesting RA. In this paper, we propose to use the large antenna array at the massive MIMO BS to jointly group RA requests from different users using the same RA preamble. We then beamform the common RAR of each detected user group onto the same frequency resource, in such a way that most users in the group can reliably decode the RAR. The proposed RAR beamforming therefore automatically resolves the problem of collision between multiple RA requests on the same RA preamble, which reduces the RA latency significantly as compared to LTE. Analysis and simulations also reveal that for a fixed desired SINR of the received RAR, both the required per-user RA preamble transmission power and the total RAR beamforming power can be decreased roughly by 1.5 dB with every doubling in the number of BS antennas.
The Price of Indivisibility in Cake Cutting
Venue: arXiv cs.GT 2018
Year: 2018

We consider the problem of envy-free cake cutting, which is the distribution of a continuous heterogeneous resource among self interested players such that nobody prefers what somebody else receives to what they get. Existing work has focused on two distinct classes of solutions to this problem - allocations which give each player a continuous piece of cake and allocations which give each player arbitrarily many disjoint pieces of cake. Our aim is to investigate allocations between these two extremes by parameterizing the maximum number of disjoint pieces each player may receive. We characterize the Price of Indivisibility (POI) as the gain achieved in social welfare (utilitarian and egalitarian), by moving from allocations which give each player a continuous piece of cake to allocations that may give each player up to $k$ disjoint pieces of cake. Our results contain bounds for the Price of Indivisibility for utilitarian as well as egalitarian social welfare, and for envy-free cake cutting as well as cake cutting without any fairness constraints.
DeepWriting: Making Digital Ink Editable via Deep Generative Modeling
Venue: arXiv cs.HC 2018
Year: 2018

Digital ink promises to combine the flexibility and aesthetics of handwriting and the ability to process, search and edit digital text. Character recognition converts handwritten text into a digital representation, albeit at the cost of losing personalized appearance due to the technical difficulties of separating the interwoven components of content and style. In this paper, we propose a novel generative neural network architecture that is capable of disentangling style from content and thus making digital ink editable. Our model can synthesize arbitrary text, while giving users control over the visual appearance (style). For example, allowing for style transfer without changing the content, editing of digital ink at the word level and other application scenarios such as spell-checking and correction of handwritten text. We furthermore contribute a new dataset of handwritten text with fine-grained annotations at the character level and report results from an initial user evaluation.
Global and Local Consistent Age Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

Age progression/regression is a challenging task due to the complicated and non-linear transformation in human aging process. Many researches have shown that both global and local facial features are essential for face representation, but previous GAN based methods mainly focused on the global feature in age synthesis. To utilize both global and local facial information, we propose a Global and Local Consistent Age Generative Adversarial Network (GLCA-GAN). In our generator, a global network learns the whole facial structure and simulates the aging trend of the whole face, while three crucial facial patches are progressed or regressed by three local networks aiming at imitating subtle changes of crucial facial subregions. To preserve most of the details in age-attribute-irrelevant areas, our generator learns the residual face. Moreover, we employ an identity preserving loss to better preserve the identity information, as well as age preserving loss to enhance the accuracy of age synthesis. A pixel loss is also adopted to preserve detailed facial information of the input face. Our proposed method is evaluated on three face aging datasets, i.e., CACD dataset, Morph dataset and FG-NET dataset. Experimental results show appealing performance of the proposed method by comparing with the state-of-the-art.
C2MSNet: A Novel approach for single image haze removal
Venue: arXiv cs.CV 2018
Year: 2018

Degradation of image quality due to the presence of haze is a very common phenomenon. Existing DehazeNet [3], MSCNN [11] tackled the drawbacks of hand crafted haze relevant features. However, these methods have the problem of color distortion in gloomy (poor illumination) environment. In this paper, a cardinal (red, green and blue) color fusion network for single image haze removal is proposed. In first stage, network fusses color information present in hazy images and generates multi-channel depth maps. The second stage estimates the scene transmission map from generated dark channels using multi channel multi scale convolutional neural network (McMs-CNN) to recover the original scene. To train the proposed network, we have used two standard datasets namely: ImageNet [5] and D-HAZY [1]. Performance evaluation of the proposed approach has been carried out using structural similarity index (SSIM), mean square error (MSE) and peak signal to noise ratio (PSNR). Performance analysis shows that the proposed approach outperforms the existing state-of-the-art methods for single image dehazing.
Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery
Venue: arXiv cs.CV 2018
Year: 2018

The standard architecture of synthetic aperture radar (SAR) automatic target recognition (ATR) consists of three stages: detection, discrimination, and classification. In recent years, convolutional neural networks (CNNs) for SAR ATR have been proposed, but most of them classify target classes from a target chip extracted from SAR imagery, as a classification for the third stage of SAR ATR. In this report, we propose a novel CNN for end-to-end ATR from SAR imagery. The CNN named verification support network (VersNet) performs all three stages of SAR ATR end-to-end. VersNet inputs a SAR image of arbitrary sizes with multiple classes and multiple targets, and outputs a SAR ATR image representing the position, class, and pose of each detected target. This report describes the evaluation results of VersNet which trained to output scores of all 12 classes: 10 target classes, a target front class, and a background class, for each pixel using the moving and stationary target acquisition and recognition (MSTAR) public dataset.
Effective Building Block Design for Deep Convolutional Neural Networks using Search
Venue: arXiv cs.LG 2018
Year: 2018

Deep learning has shown promising results on many machine learning tasks but DL models are often complex networks with large number of neurons and layers, and recently, complex layer structures known as building blocks. Finding the best deep model requires a combination of finding both the right architecture and the correct set of parameters appropriate for that architecture. In addition, this complexity (in terms of layer types, number of neurons, and number of layers) also present problems with generalization since larger networks are easier to overfit to the data. In this paper, we propose a search framework for finding effective architectural building blocks for convolutional neural networks (CNN). Our approach is much faster at finding models that are close to state-of-the-art in performance. In addition, the models discovered by our approach are also smaller than models discovered by similar techniques. We achieve these twin advantages by designing our search space in such a way that it searches over a reduced set of state-of-the-art building blocks for CNNs including residual block, inception block, inception-residual block, ResNeXt block and many others. We apply this technique to generate models for multiple image datasets and show that these models achieve performance comparable to state-of-the-art (and even surpassing the state-of-the-art in one case). We also show that learned models are transferable between datasets.