OpenRaiser/Intern-Atlas huggingface dataset:
An Energy-Efficient Reconfigurable DTLS Cryptographic Engine for End-to-End Security in IoT Applications
Venue: arXiv cs.CR 2019
Year: 2019

This paper presents a reconfigurable cryptographic engine that implements the DTLS protocol to enable end-to-end security for IoT. This implementation of the DTLS engine demonstrates 10x reduction in code size and 438x improvement in energy-efficiency over software. Our ECC primitive is 237x and 9x more energy-efficient compared to software and state-of-the-art hardware respectively. Pairing the DTLS engine with an on-chip RISC-V allows us to demonstrate applications beyond DTLS with up to 2 orders of magnitude energy savings.
A New Microscopic Traffic Model Using a Spring-Mass-Damper-Clutch System
Venue: arXiv cs.SY 2019
Year: 2019

Microscopic traffic models describe how cars interact with their neighbors in an uninterrupted traffic flow and are frequently used for reference in advanced vehicle control design. In this paper, we propose a novel mechanical system inspired microscopic traffic model using a mass-spring-damper-clutch system. This model naturally captures the ego vehicle's resistance to large relative speed and deviation from a (driver and speed dependent) desired relative distance when following the lead vehicle. Comparing to existing car following (CF) models, this model offers physically interpretable insights on the underlying CF dynamics, and is able to characterize the impact of the ego vehicle on the lead vehicle, which is neglected in existing CF models. Thanks to the nonlinear wave propagation analysis techniques for mechanical systems, the proposed model therefore has great scalability so that multiple mass-spring-damper-clutch system can be chained to study the macroscopic traffic flow. We investigate the stability of the proposed model on the system parameters and the time delay using spectral element method. We also develop a parallel recursive least square with inverse QR decomposition (PRLS-IQR) algorithm to identify the model parameters online. These real-time estimated parameters can be used to predict the driving trajectory that can be incorporated in advanced vehicle longitudinal control systems for improved safety and fuel efficiency. The PRLS-IQR is computationally efficient and numerically stable so it is suitable for online implementation. The traffic model and the parameter identification algorithm are validated on both simulations and naturalistic driving data from multiple drivers. Promising performance is demonstrated.
Optimal Time Allocation in VANETs Advertising: A Price-based Approach using Stackelberg Game
Venue: arXiv cs.GT 2019
Year: 2019

Vehicular ad-hoc networks (VANETs) have recently attracted a lot of attention due to their immense potentials and applications. Wide range of coverage and accessibility to end users make VANETs a good target for commercial companies. In this paper, we consider a scenario in which advertising companies aim to disseminate their advertisements in different areas of a city by utilizing VANETs infrastructure. These companies compete for renting the VANETs infrastructure to spread their advertisements. We partition the city map into different blocks, and consider a manager for all the blocks who is in charge of splitting the time between interested advertising companies. Each advertising company (AdC) is charged proportional to the allocated time. In order to find the best time splitting between AdCs, we propose a Stackelberg game scheme in which the block manager assigns the companies to the blocks and imposes the renting prices to different companies in order to maximize its own profit. Based on this, AdCs request the amount of time they desire to rent the infrastructure in order to maximize their utilities. To obtain the Stackelberg equilibrium of the game, a mixed integer nonlinear optimization problem is solved using the proposed optimal and sub-optimal algorithms. The simulation results demonstrate that the sub-optimal algorithm approaches the optimal one in performance with lower complexity.
Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our algorithmic trading model. We tested our model separately between 2007-2012 and 2012-2017 for representing different market conditions. The results indicate that the model was able to outperform Buy and Hold strategy, especially in trendless or bear markets. Since this is a preliminary study and probably one of the first attempts using such an unconventional approach, there is always potential for improvement. Overall, the results are promising and the model might be integrated as part of an ensemble trading model combined with different strategies.
Generating and Sampling Orbits for Lifted Probabilistic Inference
Venue: arXiv cs.AI 2019
Year: 2019

A key goal in the design of probabilistic inference algorithms is identifying and exploiting properties of the distribution that make inference tractable. Lifted inference algorithms identify symmetry as a property that enables efficient inference and seek to scale with the degree of symmetry of a probability model. A limitation of existing exact lifted inference techniques is that they do not apply to non-relational representations like factor graphs. In this work we provide the first example of an exact lifted inference algorithm for arbitrary discrete factor graphs. In addition we describe a lifted Markov-Chain Monte-Carlo algorithm that provably mixes rapidly in the degree of symmetry of the distribution.
Estimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphs
Venue: arXiv cs.SY 2019
Year: 2019

Transportation networks are unprecedentedly complex with heterogeneous vehicular flow. Conventionally, vehicle classes are considered by vehicle classifications (such as standard passenger cars and trucks). However, vehicle flow heterogeneity stems from many other aspects in general, e.g., ride-sourcing vehicles versus personal vehicles, human driven vehicles versus connected and automated vehicles. Provided with some observations of vehicular flow for each class in a large-scale transportation network, how to estimate the multi-class spatio-temporal vehicular flow, in terms of time-varying Origin-Destination (OD) demand and path/link flow, remains a big challenge. This paper presents a solution framework for multi-class dynamic OD demand estimation (MCDODE) in large-scale networks. The proposed framework is built on a computational graph with tensor representations of spatio-temporal flow and all intermediate features involved in the MCDODE formulation. A forward-backward algorithm is proposed to efficiently solve the MCDODE formulation on computational graphs. In addition, we propose a novel concept of tree-based cumulative curves to estimate the gradient of OD demand. A Growing Tree algorithm is developed to construct tree-based cumulative curves. The proposed framework is examined on a small network as well as a real-world large-scale network. The experiment results indicate that the proposed framework is compelling, satisfactory and computationally plausible.
Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning
Venue: arXiv cs.LG 2019
Year: 2019

Bayesian optimization is popular for optimizing time-consuming black-box objectives. Nonetheless, for hyperparameter tuning in deep neural networks, the time required to evaluate the validation error for even a few hyperparameter settings remains a bottleneck. Multi-fidelity optimization promises relief using cheaper proxies to such objectives --- for example, validation error for a network trained using a subset of the training points or fewer iterations than required for convergence. We propose a highly flexible and practical approach to multi-fidelity Bayesian optimization, focused on efficiently optimizing hyperparameters for iteratively trained supervised learning models. We introduce a new acquisition function, the trace-aware knowledge-gradient, which efficiently leverages both multiple continuous fidelity controls and trace observations --- values of the objective at a sequence of fidelities, available when varying fidelity using training iterations. We provide a provably convergent method for optimizing our acquisition function and show it outperforms state-of-the-art alternatives for hyperparameter tuning of deep neural networks and large-scale kernel learning.
Progressive Generative Adversarial Binary Networks for Music Generation
Venue: arXiv cs.SD 2019
Year: 2019

Recent improvements in generative adversarial network (GAN) training techniques prove that progressively training a GAN drastically stabilizes the training and improves the quality of outputs produced. Adding layers after the previous ones have converged has proven to help in better overall convergence and stability of the model as well as reducing the training time by a sufficient amount. Thus we use this training technique to train the model progressively in the time and pitch domain i.e. starting from a very small time value and pitch range we gradually expand the matrix sizes until the end result is a completely trained model giving outputs having tensor sizes [4 (bar) x 96 (time steps) x 84 (pitch values) x 8 (tracks)]. As proven in previously proposed models deterministic binary neurons also help in improving the results. Thus we make use of a layer of deterministic binary neurons at the end of the generator to get binary valued outputs instead of fractional values existing between 0 and 1.
Open-Set Recognition Using Intra-Class Splitting
Venue: arXiv cs.LG 2019
Year: 2019

This paper proposes a method to use deep neural networks as end-to-end open-set classifiers. It is based on intra-class data splitting. In open-set recognition, only samples from a limited number of known classes are available for training. During inference, an open-set classifier must reject samples from unknown classes while correctly classifying samples from known classes. The proposed method splits given data into typical and atypical normal subsets by using a closed-set classifier. This enables to model the abnormal classes by atypical normal samples. Accordingly, the open-set recognition problem is reformulated into a traditional classification problem. In addition, a closed-set regularization is proposed to guarantee a high closed-set classification performance. Intensive experiments on five well-known image datasets showed the effectiveness of the proposed method which outperformed the baselines and achieved a distinct improvement over the state-of-the-art methods.
Online Human Activity Recognition Employing Hierarchical Hidden Markov Models
Venue: arXiv cs.LG 2019
Year: 2019

In the last few years there has been a growing interest in Human Activity Recognition~(HAR) topic. Sensor-based HAR approaches, in particular, has been gaining more popularity owing to their privacy preserving nature. Furthermore, due to the widespread accessibility of the internet, a broad range of streaming-based applications such as online HAR, has emerged over the past decades. However, proposing sufficiently robust online activity recognition approach in smart environment setting is still considered as a remarkable challenge. This paper presents a novel online application of Hierarchical Hidden Markov Model in order to detect the current activity on the live streaming of sensor events. Our method consists of two phases. In the first phase, data stream is segmented based on the beginning and ending of the activity patterns. Also, on-going activity is reported with every receiving observation. This phase is implemented using Hierarchical Hidden Markov models. The second phase is devoted to the correction of the provided label for the segmented data stream based on statistical features. The proposed model can also discover the activities that happen during another activity - so-called interrupted activities. After detecting the activity pane, the predicted label will be corrected utilizing statistical features such as time of day at which the activity happened and the duration of the activity. We validated our proposed method by testing it against two different smart home datasets and demonstrated its effectiveness, which is competing with the state-of-the-art methods.
What sets Verified Users apart? Insights, Analysis and Prediction of Verified Users on Twitter
Venue: arXiv cs.SI 2019
Year: 2019

Social network and publishing platforms, such as Twitter, support the concept of a secret proprietary verification process, for handles they deem worthy of platform-wide public interest. In line with significant prior work which suggests that possessing such a status symbolizes enhanced credibility in the eyes of the platform audience, a verified badge is clearly coveted among public figures and brands. What are less obvious are the inner workings of the verification process and what being verified represents. This lack of clarity, coupled with the flak that Twitter received by extending aforementioned status to political extremists in 2017, backed Twitter into publicly admitting that the process and what the status represented needed to be rethought. With this in mind, we seek to unravel the aspects of a user's profile which likely engender or preclude verification. The aim of the paper is two-fold: First, we test if discerning the verification status of a handle from profile metadata and content features is feasible. Second, we unravel the features which have the greatest bearing on a handle's verification status. We collected a dataset consisting of profile metadata of all 231,235 verified English-speaking users (as of July 2018), a control sample of 175,930 non-verified English-speaking users and all their 494 million tweets over a one year collection period. Our proposed models are able to reliably identify verification status (Area under curve AUC > 99%). We show that number of public list memberships, presence of neutral sentiment in tweets and an authoritative language style are the most pertinent predictors of verification status. To the best of our knowledge, this work represents the first attempt at discerning and classifying verification worthy users on Twitter.
A Distributed Hierarchical SGD Algorithm with Sparse Global Reduction
Venue: arXiv cs.LG 2019
Year: 2019

Reducing communication in training large-scale machine learning applications on distributed platform is still a big challenge. To address this issue, we propose a distributed hierarchical averaging stochastic gradient descent (Hier-AVG) algorithm with infrequent global reduction by introducing local reduction. As a general type of parallel SGD, Hier-AVG can reproduce several popular synchronous parallel SGD variants by adjusting its parameters. We show that Hier-AVG with infrequent global reduction can still achieve standard convergence rate for non-convex optimization problems. In addition, we show that more frequent local averaging with more participants involved can lead to faster training convergence. By comparing Hier-AVG with another popular distributed training algorithm K-AVG, we show that through deploying local averaging with fewer number of global averaging, Hier-AVG can still achieve comparable training speed while frequently get better test accuracy. This indicates that local averaging can serve as an alternative remedy to effectively reduce communication overhead when the number of learners is large. Experimental results of Hier-AVG with several state-of-the-art deep neural nets on CIFAR-10 and IMAGENET-1K are presented to validate our analysis and show its superiority.
BenchPress: Analyzing Android App Vulnerability Benchmark Suites
Venue: arXiv cs.SE 2019
Year: 2019

In recent years, various benchmark suites have been developed to evaluate the efficacy of Android security analysis tools. The choice of such benchmark suites used in tool evaluations is often based on the availability and popularity of suites and not on their characteristics and relevance. One of the reasons for such choices is the lack of information about the characteristics and relevance of benchmarks suites. In this context, we empirically evaluated four Android specific benchmark suites: DroidBench, Ghera, IccBench, and UBCBench. For each benchmark suite, we identified the APIs used by the suite that were discussed on Stack Overflow in the context of Android app development and measured the usage of these APIs in a sample of 227K real world apps (coverage). We also compared each pair of benchmark suites to identify the differences between them in terms of API usage. Finally, we identified security-related APIs used in real-world apps but not in any of the above benchmark suites to assess the opportunities to extend benchmark suites (gaps). The findings in this paper can help 1) Android security analysis tool developers choose benchmark suites that are best suited to evaluate their tools (informed by coverage and pairwise comparison) and 2) Android app vulnerability benchmark creators develop and extend benchmark suites (informed by gaps).
Richness of Deep Echo State Network Dynamics
Venue: arXiv cs.LG 2019
Year: 2019

Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs). Recently, the advantages of the RC approach have been extended to the context of multi-layered RNNs, with the introduction of the Deep Echo State Network (DeepESN) model. In this paper, we study the quality of state dynamics in progressively higher layers of DeepESNs, using tools from the areas of information theory and numerical analysis. Our experimental results on RC benchmark datasets reveal the fundamental role played by the strength of inter-reservoir connections to increasingly enrich the representations developed in higher layers. Our analysis also gives interesting insights into the possibility of effective exploitation of training algorithms based on stochastic gradient descent in the RC field.
Learning Gaussian Policies from Corrective Human Feedback
Venue: arXiv cs.LG 2019
Year: 2019

Learning from human feedback is a viable alternative to control design that does not require modelling or control expertise. Particularly, learning from corrective advice garners advantages over evaluative feedback as it is a more intuitive and scalable format. The current state-of-the-art in this field, COACH, has proven to be a effective approach for confined problems. However, it parameterizes the policy with Radial Basis Function networks, which require meticulous feature space engineering for higher order systems. We introduce Gaussian Process Coach (GPC), where feature space engineering is avoided by employing Gaussian Processes. In addition, we use the available policy uncertainty to 1) inquire feedback samples of maximal utility and 2) to adapt the learning rate to the teacher's learning phase. We demonstrate that the novel algorithm outperforms the current state-of-the-art in final performance, convergence rate and robustness to erroneous feedback in OpenAI Gym continuous control benchmarks, both for simulated and real human teachers.
Improving the quality of healthcare through Internet of Things
Venue: arXiv cs.CY 2019
Year: 2019

This paper attempts to outline how the adoption of Internet of Things (IoT) in healthcare can create real economic value and improve the patient experience. Thus, getting the maximum benefits requires understanding both the IoT paradigm and the enabling technologies, and how IoT can be applied in the field of healthcare. We will mention some open challenging issues to be addressed by the research community, and not only. Besides the real barriers in adopting the Internet of Things, there are some advantages regard collecting and processing patient data, and monitoring the daily health states of individuals, just to name a few. These aspects could revolutionize the healthcare industry.
MDS codes over finite fields
Venue: arXiv cs.IT 2019
Year: 2019

The mds (maximum distance separable) conjecture claims that a nontrivial linear mds $[n,k]$ code over the finite field $GF(q)$ satisfies $n \leq (q + 1)$, except when $q$ is even and $k = 3$ or $k = q- 1$ in which case it satisfies $n \leq (q + 2)$. For given field $GF(q)$ and any given $k$, series of mds $[q+1,k]$ codes are constructed. Any $[n,3]$ mds or $[n,n-3]$ mds code over $GF(q)$ must satisfy $n\leq (q+1)$ for $q$ odd and $n\leq (q+2)$ for $q$ even. For even $q$, mds $[q+2,3]$ and mds $[q+2, q-1]$ codes are constructed over $GF(q)$. The codes constructed have efficient encoding and decoding algorithms.
Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy
Venue: arXiv cs.LG 2019
Year: 2019

Due to the high variance of policy gradients, on-policy optimization algorithms are plagued with low sample efficiency. In this work, we propose Augment-Reinforce-Merge (ARM) policy gradient estimator as an unbiased low-variance alternative to previous baseline estimators on tasks with binary action space, inspired by the recent ARM gradient estimator for discrete random variable models. We show that the ARM policy gradient estimator achieves variance reduction with theoretical guarantees, and leads to significantly more stable and faster convergence of policies parameterized by neural networks.
DeepCount: Crowd Counting with WiFi via Deep Learning
Venue: arXiv cs.LG 2019
Year: 2019

Recently, the research of wireless sensing has achieved more intelligent results, and the intelligent sensing of human location and activity can be realized by means of WiFi devices. However, most of the current human environment perception work is limited to a single person's environment, because the environment in which multiple people exist is more complicated than the environment in which a single person exists. In order to solve the problem of human behavior perception in a multi-human environment, we first proposed a solution to achieve crowd counting (inferred population) using deep learning in a closed environment with WIFI signals - DeepCout, which is the first in a multi-human environment. step. Since the use of WiFi to directly count the crowd is too complicated, we use deep learning to solve this problem, use Convolutional Neural Network(CNN) to automatically extract the relationship between the number of people and the channel, and use Long Short Term Memory(LSTM) to resolve the dependencies of number of people and Channel State Information(CSI) . To overcome the massive labelled data required by deep learning method, we add an online learning mechanism to determine whether or not someone is entering/leaving the room by activity recognition model, so as to correct the deep learning model in the fine-tune stage, which, in turn, reduces the required training data and make our method evolving over time. The system of DeepCount is performed and evaluated on the commercial WiFi devices. By massive training samples, our end-to-end learning approach can achieve an average of 86.4% prediction accuracy in an environment of up to 5 people. Meanwhile, by the amendment mechanism of the activity recognition model to judge door switch to get the variance of crowd to amend deep learning predicted results, the accuracy is up to 90%.