OpenRaiser/Intern-Atlas huggingface dataset:
A novel health risk model based on intraday physical activity time series collected by smartphones
Venue: arXiv cs.HC 2018
Year: 2018

We compiled a demo application and collected a motion database of more than 10,000 smartphone users to produce a health risk model trained on physical activity streams. We turned to adversarial domain adaptation and employed the UK Biobank dataset of motion data, augmented by a rich set of clinical information as the source domain to train the model using a deep residual convolutional neuron network (ResNet). The model risk score is a biomarker of ageing, since it was predictive of lifespan and healthspan (as defined by the onset of specified diseases), and was elevated in groups associated with life-shortening lifestyles, such as smoking. We ascertained the target domain performance in a smaller cohort of the mobile application that included users who were willing to share answers to a short questionnaire related to their disease and smoking status. We thus conclude that the proposed pipeline combining deep convolutional and Domain Adversarial neuron networks (DANN) is a powerful tool for disease risk and lifestyle-associated hazard assessment from mobile motion sensors that are transferable across devices and populations.
Representing pictures with emotions
Venue: arXiv cs.IR 2018
Year: 2018

Modern research in content-based image retrieval systems (CIBR) has become progressively more focused on the richness of human semantics. Several approaches may be used to reduced the 'semantic gap' between the high-level human experience and the low level visual features of pictures. Object ontology, among others, is one of the methods. In this paper we investigate the use of a codified emotion ontology over global color features of images to annotate the images at a high semantic level. In order to speed up the annotation process the images are sampled so that each digital image is represented by a random subset of its content. We test within controlled conditions how this random subset may represent the adequate high level emotional concept presented in the image. We monitor this information reducing process with entropy measures, showing that controlled random sampling can capture with significant relevance high level concepts for picture representation.
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
Venue: arXiv cs.CV 2018
Year: 2018

An unsupervised human action modeling framework can provide useful pose-sequence representation, which can be utilized in a variety of pose analysis applications. In this work we propose a novel temporal pose-sequence modeling framework, which can embed the dynamics of 3D human-skeleton joints to a continuous latent space in an efficient manner. In contrast to end-to-end framework explored by previous works, we disentangle the task of individual pose representation learning from the task of learning actions as a trajectory in pose embedding space. In order to realize a continuous pose embedding manifold with improved reconstructions, we propose an unsupervised, manifold learning procedure named Encoder GAN, (or EnGAN). Further, we use the pose embeddings generated by EnGAN to model human actions using a bidirectional RNN auto-encoder architecture, PoseRNN. We introduce first-order gradient loss to explicitly enforce temporal regularity in the predicted motion sequence. A hierarchical feature fusion technique is also investigated for simultaneous modeling of local skeleton joints along with global pose variations. We demonstrate state-of-the-art transfer-ability of the learned representation against other supervisedly and unsupervisedly learned motion embeddings for the task of fine-grained action recognition on SBU interaction dataset. Further, we show the qualitative strengths of the proposed framework by visualizing skeleton pose reconstructions and interpolations in pose-embedding space, and low dimensional principal component projections of the reconstructed pose trajectories.
$β$-VAEs can retain label information even at high compression
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we investigate the degree to which the encoding of a $β$-VAE captures label information across multiple architectures on Binary Static MNIST and Omniglot. Even though they are trained in a completely unsupervised manner, we demonstrate that a $β$-VAE can retain a large amount of label information, even when asked to learn a highly compressed representation.
On Configurable Defense against Adversarial Example Attacks
Venue: arXiv cs.LG 2018
Year: 2018

Machine learning systems based on deep neural networks (DNNs) have gained mainstream adoption in many applications. Recently, however, DNNs are shown to be vulnerable to adversarial example attacks with slight perturbations on the inputs. Existing defense mechanisms against such attacks try to improve the overall robustness of the system, but they do not differentiate different targeted attacks even though the corresponding impacts may vary significantly. To tackle this problem, we propose a novel configurable defense mechanism in this work, wherein we are able to flexibly tune the robustness of the system against different targeted attacks to satisfy application requirements. This is achieved by refining the DNN loss function with an attack sensitive matrix to represent the impacts of different targeted attacks. Experimental results on CIFAR-10 and GTSRB data sets demonstrate the efficacy of the proposed solution.
A dataset of continuous affect annotations and physiological signals for emotion analysis
Venue: arXiv cs.HC 2018
Year: 2018

From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, direct, real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as it focusses on real-time continuous annotation of emotions, as experienced by the participants, while watching various videos. For this purpose, a novel, intuitive joystick-based annotation interface was developed, that allowed for simultaneous reporting of valence and arousal, that are instead often annotated independently. In parallel, eight high quality, synchronized physiological recordings (1000 Hz, 16-bit ADC) were made of ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature. The dataset consists of the physiological and annotation data from 30 participants, 15 male and 15 female, who watched several validated video-stimuli. The validity of the emotion induction, as exemplified by the annotation and physiological data, is also presented.
Neural Image Decompression: Learning to Render Better Image Previews
Venue: arXiv cs.CV 2018
Year: 2018

A rapidly increasing portion of Internet traffic is dominated by requests from mobile devices with limited- and metered-bandwidth constraints. To satisfy these requests, it has become standard practice for websites to transmit small and extremely compressed image previews as part of the initial page-load process. Recent work, based on an adaptive triangulation of the target image, has shown the ability to generate thumbnails of full images at extreme compression rates: 200 bytes or less with impressive gains (in terms of PSNR and SSIM) over both JPEG and WebP standards. However, qualitative assessments and preservation of semantic content can be less favorable. We present a novel method to significantly improve the reconstruction quality of the original image with no changes to the encoded information. Our neural-based decoding not only achieves higher PSNR and SSIM scores than the original methods, but also yields a substantial increase in semantic-level content preservation. In addition, by keeping the same encoding stream, our solution is completely inter-operable with the original decoder. The end result is suitable for a range of small-device deployments, as it involves only a single forward-pass through a small, scalable network.
Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection
Venue: arXiv cs.LG 2018
Year: 2018

Purpose: Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. Methods: To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. Results: We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. Conclusions: This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.
PriMe: Per-Flow Network Measurement by Combining SRAM with DRAM
Venue: arXiv cs.NI 2018
Year: 2018

Network measurement is necessary to obtain an understanding of the network traffic and keep the network healthy. Flow-level measurement is widely used because it provides rich enough information while being resource efficient, in contrast to the traffic-level solutions like SNMP and packet-level solutions like tcpdump. Flow-level measurement requires to maintain a TFR for each active flow, and update the corresponding TFR when a packet arrives. In the scenario of high-speed networks, the processing time for each packet is only several or tens of nanoseconds, and there may be up to millions of concurrent active flows. Two requirements have to be met to be able to do flow-level measurement in this scenario, i.e., the memory size have to be large enough to maintain a TFR for each flow, and the access time of the memory have to be small enough to be able to update the TFR for each packet. Unfortunately, SRAM, which is fast enough, is expensive and there are limited amount available for the measurement tasks, while DRAM, which is large in capacity, is slow and cannot support the per-packet access. In this paper, we proposed a solution, PriMe, by combining SRAM with DRAM, overcoming the resource limitation by exporting TFRs to the DRAM when necessary, while enjoying the fast access of SRAM. We designed a peculiar algorithm to reduce the frequency of exporting TFRs to DRAM, as well as an algorithm to reduce the accesses of DRAM in aggregating the TFRs exported from SRAM.
Non-crossing geometric spanning trees with bounded degree and monochromatic leaves on bicolored point sets
Venue: arXiv cs.DM 2018
Year: 2018

Let $R$ and $B$ be a set of red points and a set of blue points in the plane, respectively, such that $R\cup B$ is in general position, and let $f:R \to \{2,3,4, \ldots \}$ be a function. We show that if $2\le |B|\le \sum_{x\in R}(f(x)-2) + 2$, then there exists a non-crossing geometric spanning tree $T$ on $R\cup B$ such that $2\le \operatorname{deg}_T(x)\le f(x)$ for every $x\in R$ and the set of leaves of $T$ is $B$, where every edge of $T$ is a straight-line segment.
Gated Attentive-Autoencoder for Content-Aware Recommendation
Venue: arXiv cs.IR 2018
Year: 2018

The rapid growth of Internet services and mobile devices provides an excellent opportunity to satisfy the strong demand for the personalized item or product recommendation. However, with the tremendous increase of users and items, personalized recommender systems still face several challenging problems: (1) the hardness of exploiting sparse implicit feedback; (2) the difficulty of combining heterogeneous data. To cope with these challenges, we propose a gated attentive-autoencoder (GATE) model, which is capable of learning fused hidden representations of items' contents and binary ratings, through a neural gating structure. Based on the fused representations, our model exploits neighboring relations between items to help infer users' preferences. In particular, a word-level and a neighbor-level attention module are integrated with the autoencoder. The word-level attention learns the item hidden representations from items' word sequences, while favoring informative words by assigning larger attention weights. The neighbor-level attention learns the hidden representation of an item's neighborhood by considering its neighbors in a weighted manner. We extensively evaluate our model with several state-of-the-art methods and different validation metrics on four real-world datasets. The experimental results not only demonstrate the effectiveness of our model on top-N recommendation but also provide interpretable results attributed to the attention modules.
Towards economic NMPC for multi-stage AC optimal power flow
Venue: arXiv cs.SY 2018
Year: 2018

Recently there has been considerable progress on the analysis of stability and performance properties of so-called economic Nonlinear Model Predictive Control (NMPC) schemes; i.e. NMPC schemes employing stage costs that are not directly related to distance measures of pre-computed setpoints. At the same time, with respect to the energy transition, the use of NMPC schemes is proposed and investigated in a plethora of papers in different contexts. For example receding-horizon approaches to generator dispatch problems, which is also known as multi-stage Optimal Power Flow (OPF), naturally lead to economic NMPC schemes based on non-convex discrete-time Optimal Control Problems (OCP). The present paper investigates the transfer of analytic results available for general economic NMPC schemes to receding-horizon multistage OPF. We propose a blueprint formulation of multi-stage opf including AC power flow equations. Based on this formulation we present results on the dissipativity and recursive feasibility properties of the underlying OCP. Finally, we draw upon simulations using a 5 bus system and a 118 bus system to illustrate our findings.
ShuffleNASNets: Efficient CNN models through modified Efficient Neural Architecture Search
Venue: arXiv cs.LG 2018
Year: 2018

Neural network architectures found by sophistic search algorithms achieve strikingly good test performance, surpassing most human-crafted network models by significant margins. Although computationally efficient, their design is often very complex, impairing execution speed. Additionally, finding models outside of the search space is not possible by design. While our space is still limited, we implement undiscoverable expert knowledge into the economic search algorithm Efficient Neural Architecture Search (ENAS), guided by the design principles and architecture of ShuffleNet V2. While maintaining baseline-like 2.85% test error on CIFAR-10, our ShuffleNASNets are significantly less complex, require fewer parameters, and are two times faster than the ENAS baseline in a classification task. These models also scale well to a low parameter space, achieving less than 5% test error with little regularization and only 236K parameters.
Power Allocation in Multi-user Cellular Networks With Deep Q Learning Approach
Venue: arXiv cs.IT 2018
Year: 2018

The model-driven power allocation (PA) algorithms in the wireless cellular networks with interfering multiple-access channel (IMAC) have been investigated for decades. Nowadays, the data-driven model-free machine learning-based approaches are rapidly developed in this field, and among them the deep reinforcement learning (DRL) is proved to be of great promising potential. Different from supervised learning, the DRL takes advantages of exploration and exploitation to maximize the objective function under certain constraints. In our paper, we propose a two-step training framework. First, with the off-line learning in simulated environment, a deep Q network (DQN) is trained with deep Q learning (DQL) algorithm, which is well-designed to be in consistent with this PA issue. Second, the DQN will be further fine-tuned with real data in on-line training procedure. The simulation results show that the proposed DQN achieves the highest averaged sum-rate, comparing to the ones with present DQL training. With different user densities, our DQN outperforms benchmark algorithms and thus a good generalization ability is verified.
Back to square one: probabilistic trajectory forecasting without bells and whistles
Venue: arXiv cs.LG 2018
Year: 2018

We introduce a spatio-temporal convolutional neural network model for trajectory forecasting from visual sources. Applied in an auto-regressive way it provides an explicit probability distribution over continuations of a given initial trajectory segment. We discuss it in relation to (more complicated) existing work and report on experiments on two standard datasets for trajectory forecasting: MNISTseq and Stanford Drones, achieving results on-par with or better than previous methods.
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
Venue: arXiv cs.RO 2018
Year: 2018

Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle. We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough. We propose exposing the learner to synthesized data in the form of perturbations to the expert's driving, which creates interesting situations such as collisions and/or going off the road. Rather than purely imitating all data, we augment the imitation loss with additional losses that penalize undesirable events and encourage progress -- the perturbations then provide an important signal for these losses and lead to robustness of the learned model. We show that the ChauffeurNet model can handle complex situations in simulation, and present ablation experiments that emphasize the importance of each of our proposed changes and show that the model is responding to the appropriate causal factors. Finally, we demonstrate the model driving a car in the real world.
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach
Venue: arXiv cs.LG 2018
Year: 2018

Machine Learning models are vulnerable to adversarial attacks that rely on perturbing the input data. This work proposes a novel strategy using Autoencoder Deep Neural Networks to defend a machine learning model against two gradient-based attacks: The Fast Gradient Sign attack and Fast Gradient attack. First we use an autoencoder to denoise the test data, which is trained with both clean and corrupted data. Then, we reduce the dimension of the denoised data using the hidden layer representation of another autoencoder. We perform this experiment for multiple values of the bound of adversarial perturbations, and consider different numbers of reduced dimensions. When the test data is preprocessed using this cascaded pipeline, the tested deep neural network classifier yields a much higher accuracy, thus mitigating the effect of the adversarial perturbation.
Taking the Scenic Route: Automatic Exploration for Videogames
Venue: arXiv cs.HC 2018
Year: 2018

Machine playtesting tools and game moment search engines require exposure to the diversity of a game's state space if they are to report on or index the most interesting moments of possible play. Meanwhile, mobile app distribution services would like to quickly determine if a freshly-uploaded game is fit to be published. Having access to a semantic map of reachable states in the game would enable efficient inference in these applications. However, human gameplay data is expensive to acquire relative to the coverage of a game that it provides. We show that off-the-shelf automatic exploration strategies can explore with an effectiveness comparable to human gameplay on the same timescale. We contribute generic methods for quantifying exploration quality as a function of time and demonstrate our metric on several elementary techniques and human players on a collection of commercial games sampled from multiple game platforms (from Atari 2600 to Nintendo 64). Emphasizing the diversity of states reached and the semantic map extracted, this work makes productive contrast with the focus on finding a behavior policy or optimizing game score used in most automatic game playing research.
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks
Venue: arXiv cs.CR 2018
Year: 2018

In the world today computer networks have a very important position and most of the urban and national infrastructure as well as organizations are managed by computer networks, therefore, the security of these systems against the planned attacks is of great importance. Therefore, researchers have been trying to find these vulnerabilities so that after identifying ways to penetrate the system, they will provide system protection through preventive or countermeasures. SVM is one of the major algorithms for intrusion detection. In this research, we studied a variety of malware and methods of intrusion detection, provide an efficient method for detecting attacks and utilizing dimension reduction.Thus, we will be able to detect attacks by carefully combining these two algorithms and pre-processes that are performed before the two on the input data. The main question raised is how we can identify attacks on computer networks with the above-mentioned method. In anomalies diagnostic method, by identifying behavior as a normal behavior for the user, the host, or the whole system, any deviation from this behavior is considered as an abnormal behavior, which can be a potential occurrence of an attack. The network intrusion detection system is used by anomaly detection method that uses the SVM algorithm for classification and SVD to reduce the size. Steps of the proposed method include pre-processing of the data set, feature selection, support vector machine, and evaluation.The NSL-KDD data set has been used to teach and test the proposed model. In this study, we inferred the intrusion detection using the SVM algorithm for classification and SVD for diminishing dimensions with no classification algorithm.Also the KNN algorithm has been compared in situations with and without diminishing dimensions,the results have shown that the proposed method has a better performance than comparable methods.
MPTCP Linux Kernel Congestion Controls
Venue: arXiv cs.NI 2018
Year: 2018

MultiPath TCP (MPTCP) is a promising protocol which brings new light to the TCP/IP protocol stack ossification problem by means of an impactful innovation of the transport layer. A MPTCP connection consists of a set of one or more subflows, where each subflow offers an alternative path to reach a target remote end-system. However, simply applying the standard TCP congestion control on each subflow would give an unfair resource sharing. Various subflows of a connection would dispute bottleneck links with regular single-path TCP connections, leading them to starvation conditions. To deal with this concern, a multipath congestion control algorithm adjusts the sending operation of all subflows in a coupled fashion in order to achieve various objectives, e.g., friendliness, responsiveness, throughput improvement, and congestion balance. In this report, we describe the four coupled congestion control algorithms deployed in the MPTCP Linux kernel implementation, namely: LIA, OLIA, BALIA, and wVegas. We provide a concise material with technical details of each algorithm, while summarizing all of them together from a single notation.