OpenRaiser/Intern-Atlas huggingface dataset:
White-Box Target Attack for EEG-Based BCI Regression Problems
Venue: arXiv cs.CR 2019
Year: 2019

Machine learning has achieved great success in many applications, including electroencephalogram (EEG) based brain-computer interfaces (BCIs). Unfortunately, many machine learning models are vulnerable to adversarial examples, which are crafted by adding deliberately designed perturbations to the original inputs. Many adversarial attack approaches for classification problems have been proposed, but few have considered target adversarial attacks for regression problems. This paper proposes two such approaches. More specifically, we consider white-box target attacks for regression problems, where we know all information about the regression model to be attacked, and want to design small perturbations to change the regression output by a pre-determined amount. Experiments on two BCI regression problems verified that both approaches are effective. Moreover, adversarial examples generated from both approaches are also transferable, which means that we can use adversarial examples generated from one known regression model to attack an unknown regression model, i.e., to perform black-box attacks. To our knowledge, this is the first study on adversarial attacks for EEG-based BCI regression problems, which calls for more attention on the security of BCI systems.
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Venue: arXiv cs.CV 2019
Year: 2019

Few-shot learners aim to recognize new object classes based on a small number of labeled training examples. To prevent overfitting, state-of-the-art few-shot learners use meta-learning on convolutional-network features and perform classification using a nearest-neighbor classifier. This paper studies the accuracy of nearest-neighbor baselines without meta-learning. Surprisingly, we find simple feature transformations suffice to obtain competitive few-shot learning accuracies. For example, we find that a nearest-neighbor classifier used in combination with mean-subtraction and L2-normalization outperforms prior results in three out of five settings on the miniImageNet dataset.
On the Capacity of Massive MIMO With 1-Bit ADCs and DACs at the Receiver and at the Transmitter
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we investigate the capacity of massive multiple-input multiple-output (MIMO) systems corrupted by complex-valued additive white Gaussian noise (AWGN) when both the transmitter and the receiver employ 1-bit digital-to-analog converters (DACs) and 1-bit analog-to-digital-converters (ADCs). As a result of 1-bit DACs and ADCs, the transmitted and received symbols, as well as the transmit- and receive-side channel state information (CSI), are assumed to be quantized to 1-bit of information. The derived results show that the capacity of the considered massive MIMO system is $2N$ and $2M$ bits per channel use when $N$ is fixed and $M\to\infty$ and when $M$ is fixed and $N\to\infty$, respectively, where $M$ and $N$ denote the number of transmit and receive antennas, respectively. These coincide with the respective capacities with full CSI at both the transmitter and the receiver. In both cases, we showed that the derived capacities can be achieved with noisy 1-bit CSI at the transmitter-end or at the receiver-end, and without any CSI at the other end. Moreover, we showed that the capacity can be achieved in one channel use without employing channel coding, which results in a latency of one channel use.
PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network
Venue: arXiv cs.SD 2019
Year: 2019

Time-frequency (T-F) domain masking is a mainstream approach for single-channel speech enhancement. Recently, focuses have been put to phase prediction in addition to amplitude prediction. In this paper, we propose a phase-and-harmonics-aware deep neural network (DNN), named PHASEN, for this task. Unlike previous methods that directly use a complex ideal ratio mask to supervise the DNN learning, we design a two-stream network, where amplitude stream and phase stream are dedicated to amplitude and phase prediction. We discover that the two streams should communicate with each other, and this is crucial to phase prediction. In addition, we propose frequency transformation blocks to catch long-range correlations along the frequency axis. The visualization shows that the learned transformation matrix spontaneously captures the harmonic correlation, which has been proven to be helpful for T-F spectrogram reconstruction. With these two innovations, PHASEN acquires the ability to handle detailed phase patterns and to utilize harmonic patterns, getting 1.76dB SDR improvement on AVSpeech + AudioSet dataset. It also achieves significant gains over Google's network on this dataset. On Voice Bank + DEMAND dataset, PHASEN outperforms previous methods by a large margin on four metrics.
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data
Venue: arXiv cs.LG 2019
Year: 2019

Advances in deep generative and density models have shown impressive capacity to model complex probability density functions in lower-dimensional space. Also, applying such models to high-dimensional image data to model the PDF has shown poor generalization, with out-of-distribution data being assigned equal or higher likelihood than in-sample data. Methods to deal with this have been proposed that deviate from a fully unsupervised approach, requiring large ensembles or additional knowledge about the data, not commonly available in the real-world. In this work, the previously offered reasoning behind these issues is challenged empirically, and it is shown that data-sets such as MNIST fashion/digits and CIFAR10/SVHN are trivially separable and have no overlap on their respective data manifolds that explains the higher OoD likelihood. Models like masked autoregressive flows and block neural autoregressive flows are shown to not suffer from OoD likelihood issues to the extent of GLOW, PixelCNN++, and real NVP. A new avenue is also explored which involves a change of basis to a new space of the same dimension with an orthonormal unitary basis of eigenvectors before modeling. In the test data-sets and models, this aids in pushing down the relative likelihood of the contrastive OoD data set and improve discrimination results. The significance of the density of the original space is maintained, while invertibility remains tractable. Finally, a look to the previous generation of generative models in the form of probabilistic principal component analysis is inspired, and revisited for the same data-sets and shown to work really well for discriminating anomalies based on likelihood in a fully unsupervised fashion compared with pixelCNN++, GLOW, and real NVP with less complexity and faster training. Also, dimensionality reduction using PCA is shown to improve anomaly detection in generative models.
A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data
Venue: arXiv cs.CL 2019
Year: 2019

Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories. Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights. Our model can utilize persona-sparse dialogues in a unified manner during the training process, and can also control the amount of persona-related features to exhibit during the inference process. Both automatic and manual evaluation demonstrates that the proposed model outperforms state-of-the-art methods for generating more coherent and persona consistent responses with persona-sparse data.
ConnectionChain: Secure Interworking of Blockchains
Venue: arXiv cs.CR 2019
Year: 2019

Blockchain is a core technology to manage the value of cryptocurrencies, or to record trails of important business trades. The Smart Contract on blockchain is expected to improve security on blockchain system with automated operation, but it cannot be the solution when the application service required to operate tightly related blockchain ledgers as service business logic. This paper proposed the method to extend the functionality of traditional Smart Contract on blockchain, and introduced the prototype system, named 'ConnectionChain'.
Aplib: Tactical Programming of Intelligent Agents
Venue: arXiv cs.AI 2019
Year: 2019

This paper presents aplib, a Java library for programming intelligent agents, featuring BDI and multi agency, but adding on top of it a novel layer of tactical programming inspired by the domain of theorem proving. Aplib is also implemented in such a way to provide the fluency of a Domain Specific Language (DSL). Compared to dedicated BDI agent programming languages such as JASON, 2APL, or GOAL,aplib's embedded DSL approach does mean that \aplib\ programmers will still be limited by Java syntax, but on other hand they get all the advantages that Java programmers get: rich language features (object orientation, static type checking, $λ$-expression, libraries, etc), a whole array of development tools, integration with other technologies, large community, etc.
Effects of data ambiguity and cognitive biases on the interpretability of machine learning models in humanitarian decision making
Venue: arXiv cs.LG 2019
Year: 2019

The effectiveness of machine learning algorithms depends on the quality and amount of data and the operationalization and interpretation by the human analyst. In humanitarian response, data is often lacking or overburdening, thus ambiguous, and the time-scarce, volatile, insecure environments of humanitarian activities are likely to inflict cognitive biases. This paper proposes to research the effects of data ambiguity and cognitive biases on the interpretability of machine learning algorithms in humanitarian decision making.
On Policy Gradients
Venue: arXiv cs.LG 2019
Year: 2019

The goal of policy gradient approaches is to find a policy in a given class of policies which maximizes the expected return. Given a differentiable model of the policy, we want to apply a gradient-ascent technique to reach a local optimum. We mainly use gradient ascent, because it is theoretically well researched. The main issue is that the policy gradient with respect to the expected return is not available, thus we need to estimate it. As policy gradient algorithms also tend to require on-policy data for the gradient estimate, their biggest weakness is sample efficiency. For this reason, most research is focused on finding algorithms with improved sample efficiency. This paper provides a formal introduction to policy gradient that shows the development of policy gradient approaches, and should enable the reader to follow current research on the topic.
Efficient Inverse-Free Algorithms for Extreme Learning Machine Based on the Recursive Matrix Inverse and the Inverse LDL' Factorization
Venue: arXiv cs.LG 2019
Year: 2019

The inverse-free extreme learning machine (ELM) algorithm proposed in [4] was based on an inverse-free algorithm to compute the regularized pseudo-inverse, which was deduced from an inverse-free recursive algorithm to update the inverse of a Hermitian matrix. Before that recursive algorithm was applied in [4], its improved version had been utilized in previous literatures [9], [10]. Accordingly from the improved recursive algorithm [9], [10], we deduce a more efficient inverse-free algorithm to update the regularized pseudo-inverse, from which we develop the proposed inverse-free ELM algorithm 1. Moreover, the proposed ELM algorithm 2 further reduces the computational complexity, which computes the output weights directly from the updated inverse, and avoids computing the regularized pseudoinverse. Lastly, instead of updating the inverse, the proposed ELM algorithm 3 updates the LDLT factor of the inverse by the inverse LDLT factorization [11], to avoid numerical instabilities after a very large number of iterations [12]. With respect to the existing ELM algorithm, the proposed ELM algorithms 1, 2 and 3 are expected to require only (8+3)/M , (8+1)/M and (8+1)/M of complexities, respectively, where M is the output node number. In the numerical experiments, the standard ELM, the existing inverse-free ELM algorithm and the proposed ELM algorithms 1, 2 and 3 achieve the same performance in regression and classification, while all the 3 proposed algorithms significantly accelerate the existing inverse-free ELM algorithm
HMTNet:3D Hand Pose Estimation from Single Depth Image Based on Hand Morphological Topology
Venue: arXiv cs.RO 2019
Year: 2019

Thanks to the rapid development of CNNs and depth sensors, great progress has been made in 3D hand pose estimation. Nevertheless, it is still far from being solved for its cluttered circumstance and severe self-occlusion of hand. In this paper, we propose a method that takes advantage of human hand morphological topology (HMT) structure to improve the pose estimation performance. The main contributions of our work can be listed as below. Firstly, in order to extract more powerful features, we concatenate original and last layer of initial feature extraction module to preserve hand information better. Next, regression module inspired from hand morphological topology is proposed. In this submodule, we design a tree-like network structure according to hand joints distribution to make use of high order dependency of hand joints. Lastly, we conducted sufficient ablation experiments to verify our proposed method on each dataset. Experimental results on three popular hand pose dataset show superior performance of our method compared with the state-of-the-art methods. On ICVL and NYU dataset, our method outperforms great improvement over 2D state-of-the-art methods. On MSRA dataset, our method achieves comparable accuracy with the state-of-the-art methods. To summarize, our method is the most efficient method which can run at 220:7 fps on a single GPU compared with approximate accurate methods at present. The code will be available at.
Accelerating Training in Pommerman with Imitation and Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

The Pommerman simulation was recently developed to mimic the classic Japanese game Bomberman, and focuses on competitive gameplay in a multi-agent setting. We focus on the 2$\times$2 team version of Pommerman, developed for a competition at NeurIPS 2018. Our methodology involves training an agent initially through imitation learning on a noisy expert policy, followed by a proximal-policy optimization (PPO) reinforcement learning algorithm. The basic PPO approach is modified for stable transition from the imitation learning phase through reward shaping, action filters based on heuristics, and curriculum learning. The proposed methodology is able to beat heuristic and pure reinforcement learning baselines with a combined 100,000 training games, significantly faster than other non-tree-search methods in literature. We present results against multiple agents provided by the developers of the simulation, including some that we have enhanced. We include a sensitivity analysis over different parameters, and highlight undesirable effects of some strategies that initially appear promising. Since Pommerman is a complex multi-agent competitive environment, the strategies developed here provide insights into several real-world problems with characteristics such as partial observability, decentralized execution (without communication), and very sparse and delayed rewards.
Iteratively Training Look-Up Tables for Network Quantization
Venue: arXiv cs.LG 2019
Year: 2019

Operating deep neural networks (DNNs) on devices with limited resources requires the reduction of their memory as well as computational footprint. Popular reduction methods are network quantization or pruning, which either reduce the word length of the network parameters or remove weights from the network if they are not needed. In this article we discuss a general framework for network reduction which we call `Look-Up Table Quantization` (LUT-Q). For each layer, we learn a value dictionary and an assignment matrix to represent the network weights. We propose a special solver which combines gradient descent and a one-step k-means update to learn both the value dictionaries and assignment matrices iteratively. This method is very flexible: by constraining the value dictionary, many different reduction problems such as non-uniform network quantization, training of multiplierless networks, network pruning or simultaneous quantization and pruning can be implemented without changing the solver. This flexibility of the LUT-Q method allows us to use the same method to train networks for different hardware capabilities.
Bayesian Graph Convolutional Neural Networks using Node Copying
Venue: arXiv cs.LG 2019
Year: 2019

Graph convolutional neural networks (GCNN) have numerous applications in different graph based learning tasks. Although the techniques obtain impressive results, they often fall short in accounting for the uncertainty associated with the underlying graph structure. In the recently proposed Bayesian GCNN (BGCN) framework, this issue is tackled by viewing the observed graph as a sample from a parametric random graph model and targeting joint inference of the graph and the GCNN weights. In this paper, we introduce an alternative generative model for graphs based on copying nodes and incorporate it within the BGCN framework. Our approach has the benefit that it uses information provided by the node features and training labels in the graph topology inference. Experiments show that the proposed algorithm compares favorably to the state-of-the-art in benchmark node classification tasks.
Some Considerations and a Benchmark Related to the CNF Property of the Koczy-Hirota Fuzzy Rule Interpolation
Venue: arXiv cs.AI 2019
Year: 2019

The goal of this paper is twofold. Once to highlight some basic problematic properties of the KH Fuzzy Rule Interpolation through examples, secondly to set up a brief Benchmark set of Examples, which is suitable for testing other Fuzzy Rule Interpolation (FRI) methods against these ill conditions. Fuzzy Rule Interpolation methods were originally proposed to handle the situation of missing fuzzy rules (sparse rule-bases) and to reduce the decision complexity. Fuzzy Rule Interpolation is an important technique for implementing inference with sparse fuzzy rule-bases. Even if a given observation has no overlap with the antecedent of any rule from the rule-base, FRI may still conclude a conclusion. The first FRI method was the Koczy and Hirota proposed "Linear Interpolation", which was later renamed to "KH Fuzzy Interpolation" by the followers. There are several conditions and criteria have been suggested for unifying the common requirements an FRI methods have to satisfy. One of the most common one is the demand for a convex and normal fuzzy (CNF) conclusion, if all the rule antecedents and consequents are CNF sets. The KH FRI is the one, which cannot fulfill this condition. This paper is focusing on the conditions, where the KH FRI fails the demand for the CNF conclusion. By setting up some CNF rule examples, the paper also defines a Benchmark, in which other FRI methods can be tested if they can produce CNF conclusion where the KH FRI fails.
Time-Aware Prospective Modeling of Users for Online Display Advertising
Venue: arXiv cs.LG 2019
Year: 2019

Prospective display advertising poses a great challenge for large advertising platforms as the strongest predictive signals of users are not eligible to be used in the conversion prediction systems. To that end efforts are made to collect as much information as possible about each user from various data sources and to design powerful models that can capture weaker signals ultimately obtaining good quality of conversion prediction probability estimates. In this study we propose a novel time-aware approach to model heterogeneous sequences of users' activities and capture implicit signals of users' conversion intents. On two real-world datasets we show that our approach outperforms other, previously proposed approaches, while providing interpretability of signal impact to conversion probability.
Coordinated Management of DVFS and Cache Partitioning under QoS Constraints to Save Energy in Multi-Core Systems
Venue: arXiv cs.AR 2019
Year: 2019

Reducing the energy expended to carry out a computational task is important. In this work, we explore the prospects of meeting Quality-of-Service requirements of tasks on a multi-core system while adjusting resources to expend a minimum of energy. This paper considers, for the first time, a QoS-driven coordinated resource management algorithm (RMA) that dynamically adjusts the size of the per-core last-level cache partitions and the per-core voltage-frequency settings to save energy while respecting QoS requirements of every application in multi-programmed workloads run on multi-core systems. It does so by doing configuration-space exploration across the spectrum of LLC partition sizes and Dynamic Voltage Frequency Scaling (DVFS) settings at runtime at negligible overhead. We show that the energy of 4-core and 8-core systems can be reduced by up to 18% and 14%, respectively, compared to a baseline with even distribution of cache resources and a fixed mid-range core voltage-frequency setting. The energy savings can potentially reach 29% if the QoS targets are relaxed to 40% longer execution time.
Coordinated Management of Processor Configuration and Cache Partitioning to Optimize Energy under QoS Constraints
Venue: arXiv cs.AR 2019
Year: 2019

An effective way to improve energy efficiency is to throttle hardware resources to meet a certain performance target, specified as a QoS constraint, associated with all applications running on a multicore system. Prior art has proposed resource management (RM) frameworks in which the share of the last-level cache (LLC) assigned to each processor and the voltage-frequency (VF) setting for each processor is managed in a coordinated fashion to reduce energy. A drawback of such a scheme is that, while one core gives up LLC resources for another core, the performance drop must be compensated by a higher VF setting which leads to a quadratic increase in energy consumption. By allowing each core to be adapted to exploit instruction and memory-level parallelism (ILP/MLP), substantially higher energy savings are enabled. This paper proposes a coordinated RM for LLC partitioning, processor adaptation, and per-core VF scaling. A first contribution is a systematic study of the resource trade-offs enabled when trading between the three classes of resources in a coordinated fashion. A second contribution is a new RM framework that utilizes these trade-offs to save more energy. Finally, a challenge to accurately model the impact of resource throttling on performance is to predict the amount of MLP with high accuracy. To this end, the paper contributes with a mechanism that estimates the effect of MLP over different processor configurations and LLC allocations. Overall, we show that up to 18% of energy, and on average 10%, can be saved using the proposed scheme.