OpenRaiser/Intern-Atlas huggingface dataset:
Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory
Venue: arXiv cs.CR 2018
Year: 2018

In Internet of Vehicles (IoV), data sharing among vehicles is essential to improve driving safety and enhance vehicular services. To ensure data sharing security and traceability, highefficiency Delegated Proof-of-Stake consensus scheme as a hard security solution is utilized to establish blockchain-enabled IoV (BIoV). However, as miners are selected from miner candidates by stake-based voting, it is difficult to defend against voting collusion between the candidates and compromised high-stake vehicles, which introduces serious security challenges to the BIoV. To address such challenges, we propose a soft security enhancement solution including two stages: (i) miner selection and (ii) block verification. In the first stage, a reputation-based voting scheme for the blockchain is proposed to ensure secure miner selection. This scheme evaluates candidates' reputation by using both historical interactions and recommended opinions from other vehicles. The candidates with high reputation are selected to be active miners and standby miners. In the second stage, to prevent internal collusion among the active miners, a newly generated block is further verified and audited by the standby miners. To incentivize the standby miners to participate in block verification, we formulate interactions between the active miners and the standby miners by using contract theory, which takes block verification security and delay into consideration. Numerical results based on a real-world dataset indicate that our schemes are secure and efficient for data sharing in BIoV.
Pachinko Prediction: A Bayesian method for event prediction from social media data
Venue: arXiv cs.CY 2018
Year: 2018

The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee the appropriate use of such methods. Here we develop a Bayesian method for predicting social unrest events in Australia using social media data. This method uses machine learning methods to classify individual postings to social media as being relevant, and an empirical Bayesian approach to calculate posterior event probabilities. We use the method to predict events in Australian cities over a period in 2017/18.
Minimum Number of Test Paths for Prime Path and other Structural Coverage Criteria
Venue: arXiv cs.SE 2018
Year: 2018

The software system under test can be modeled as a graph comprising of a set of vertices, (V) and a set of edges, (E). Test Cases are Test Paths over the graph meeting a particular test criterion. In this paper, we present a method to achieve the minimum number of Test Paths needed to cover different structural coverage criteria. Our method can accommodate Prime Path, Edge-Pair, Simple & Complete Round Trip, Edge and Node coverage criteria. Our method obtains the optimal solution by transforming the graph into a flow graph and solving the minimum flow problem. We present an algorithm for the minimum flow problem that matches the best known solution complexity of $O(|V| |E|)$. Our method is evaluated through two sets of tests. In the first, we test against graphs representing actual software. In the second test, we create random graphs of varying complexity. In each test we measure the number of Test Paths, the length of Test Paths, the lower bound on minimum number of Test Paths and the execution time.
Optimizing a Generalized Gini Index in Stable Marriage Problems: NP-Hardness, Approximation and a Polynomial Time Special Case
Venue: arXiv cs.CC 2018
Year: 2018

This paper deals with fairness in stable marriage problems. The idea studied here is to achieve fairness thanks to a Generalized Gini Index (GGI), a well-known criterion in inequality measurement, that includes both the egalitarian and utilitarian criteria as special cases. We show that determining a stable marriage optimizing a GGI criterion of agents' disutilities is an NP-hard problem. We then provide a polynomial time 2-approximation algorithm in the general case, as well as an exact algorithm which is polynomial time in the case of a constant number of non-zero weights parametrizing the GGI criterion.
Spectrum and Energy Efficient Multiple Access for Detection in Wireless Sensor Networks
Venue: arXiv cs.IT 2018
Year: 2018

We consider a binary hypothesis testing problem using Wireless Sensor Networks (WSNs). The decision is made by a fusion center and is based on received data from the sensors. We focus on a spectrum and energy efficient transmission scheme used to reduce the spectrum usage and energy consumption during the detection task. We propose a Spectrum and Energy Efficient Multiple Access (SEEMA) transmission protocol that performs a censoring-type transmission based on the density of observations using multiple access channels (MAC). Specifically, in SEEMA, only sensors with highly informative observations transmit their data in each data collection. The sensors transmit a common shaping waveform and the fusion center receives a superposition of the analog transmitted signals. SEEMA has important advantages for detection tasks in WSNs. First, it is highly energy and bandwidth efficient due to transmission savings and narrowband transmission over MAC. Second, it can be implemented by simple dumb sensors (oblivious to observation statistics, and local data processing is not required) which simplifies the implementation as compared to existing MAC transmission schemes for detection in WSNs. We establish a finite sample analysis and an asymptotic analysis of the error probability with respect to the network size and provide system design conditions to obtain the exponential decay of the error. Specific performance analysis is developed for common non-i.i.d. observation scenarios, including local i.i.d. observations, and Markovian correlated observations. Numerical examples demonstrate SEEMA performance.
Understanding Tor Usage with Privacy-Preserving Measurement
Venue: arXiv cs.CR 2018
Year: 2018

The Tor anonymity network is difficult to measure because, if not done carefully, measurements could risk the privacy (and potentially the safety) of the network's users. Recent work has proposed the use of differential privacy and secure aggregation techniques to safely measure Tor, and preliminary proof-of-concept prototype tools have been developed in order to demonstrate the utility of these techniques. In this work, we significantly enhance two such tools--PrivCount and Private Set-Union Cardinality--in order to support the safe exploration of new types of Tor usage behavior that have never before been measured. Using the enhanced tools, we conduct a detailed measurement study of Tor covering three major aspects of Tor usage: how many users connect to Tor and from where do they connect, with which destinations do users most frequently communicate, and how many onion services exist and how are they used. Our findings include that Tor has ~8 million daily users (a factor of four more than previously believed) while Tor user IPs turn over almost twice in a 4 day period. We also find that ~40% of the sites accessed over Tor have a torproject.org domain name, ~10% of the sites have an amazon.com domain name, and ~80% of the sites have a domain name that is included in the Alexa top 1 million sites list. Finally, we find that ~90% of lookups for onion addresses are invalid, and more than 90% of attempted connections to onion services fail.
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
Venue: arXiv cs.CV 2018
Year: 2018

Earlier work demonstrates the promise of deep-learning-based approaches for point cloud segmentation; however, these approaches need to be improved to be practically useful. To this end, we introduce a new model SqueezeSegV2 that is more robust to dropout noise in LiDAR point clouds. With improved model structure, training loss, batch normalization and additional input channel, SqueezeSegV2 achieves significant accuracy improvement when trained on real data. Training models for point cloud segmentation requires large amounts of labeled point-cloud data, which is expensive to obtain. To sidestep the cost of collection and annotation, simulators such as GTA-V can be used to create unlimited amounts of labeled, synthetic data. However, due to domain shift, models trained on synthetic data often do not generalize well to the real world. We address this problem with a domain-adaptation training pipeline consisting of three major components: 1) learned intensity rendering, 2) geodesic correlation alignment, and 3) progressive domain calibration. When trained on real data, our new model exhibits segmentation accuracy improvements of 6.0-8.6% over the original SqueezeSeg. When training our new model on synthetic data using the proposed domain adaptation pipeline, we nearly double test accuracy on real-world data, from 29.0% to 57.4%. Our source code and synthetic dataset will be open-sourced.
A Train Status Assistant for Indian Railways
Venue: arXiv cs.AI 2018
Year: 2018

Trains are part-and-parcel of every day lives in countries with large, diverse, multi-lingual population like India. Consequently, an assistant which can accurately predict and explain train delays will help people and businesses alike. We present a novel conversation agent which can engage with people about train status and inform them about its delay at in-line stations. It is trained on past delay data from a subset of trains and generalizes to others.
Regrasping by Fixtureless Fixturing
Venue: arXiv cs.RO 2018
Year: 2018

This paper presents a fixturing strategy for regrasping that does not require a physical fixture. To regrasp an object in a gripper, a robot pushes the object against external contact/s in the environment such that the external contact keeps the object stationary while the fingers slide over the object. We call this manipulation technique fixtureless fixturing. Exploiting the mechanics of pushing, we characterize a convex polyhedral set of pushes that results in fixtureless fixturing. These pushes are robust against uncertainty in the object inertia, grasping force, and the friction at the contacts. We propose a sampling-based planner that uses the sets of robust pushes to rapidly build a tree of reachable grasps. A path in this tree is a pushing strategy, possibly involving pushes from different sides, to regrasp the object. We demonstrate the experimental validity and robustness of the proposed manipulation technique with different regrasp examples on a manipulation platform. Such a fast and flexible regrasp planner facilitates versatile and flexible automation solutions.
Learning to Read by Spelling: Towards Unsupervised Text Recognition
Venue: arXiv cs.CV 2018
Year: 2018

This work presents a method for visual text recognition without using any paired supervisory data. We formulate the text recognition task as one of aligning the conditional distribution of strings predicted from given text images, with lexically valid strings sampled from target corpora. This enables fully automated, and unsupervised learning from just line-level text-images, and unpaired text-string samples, obviating the need for large aligned datasets. We present detailed analysis for various aspects of the proposed method, namely - (1) impact of the length of training sequences on convergence, (2) relation between character frequencies and the order in which they are learnt, (3) generalisation ability of our recognition network to inputs of arbitrary lengths, and (4) impact of varying the text corpus on recognition accuracy. Finally, we demonstrate excellent text recognition accuracy on both synthetically generated text images, and scanned images of real printed books, using no labelled training examples.
On the Behavior of the Expectation-Maximization Algorithm for Mixture Models
Venue: arXiv cs.LG 2018
Year: 2018

Finite mixture models are among the most popular statistical models used in different data science disciplines. Despite their broad applicability, inference under these models typically leads to computationally challenging non-convex problems. While the Expectation-Maximization (EM) algorithm is the most popular approach for solving these non-convex problems, the behavior of this algorithm is not well understood. In this work, we focus on the case of mixture of Laplacian (or Gaussian) distribution. We start by analyzing a simple equally weighted mixture of two single dimensional Laplacian distributions and show that every local optimum of the population maximum likelihood estimation problem is globally optimal. Then, we prove that the EM algorithm converges to the ground truth parameters almost surely with random initialization. Our result extends the existing results for Gaussian distribution to Laplacian distribution. Then we numerically study the behavior of mixture models with more than two components. Motivated by our extensive numerical experiments, we propose a novel stochastic method for estimating the mean of components of a mixture model. Our numerical experiments show that our algorithm outperforms the Naive EM algorithm in almost all scenarios.
A Survey of Conventional and Artificial Intelligence / Learning based Resource Allocation and Interference Mitigation Schemes in D2D Enabled Networks
Venue: arXiv cs.AI 2018
Year: 2018

5th generation networks are envisioned to provide seamless and ubiquitous connection to 1000-fold more devices and is believed to provide ultra-low latency and higher data rates up to tens of Gbps. Different technologies enabling these requirements are being developed including mmWave communications, Massive MIMO and beamforming, Device to Device (D2D) communications and Heterogeneous Networks. D2D communication is a promising technology to enable applications requiring high bandwidth such as online streaming and online gaming etc. It can also provide ultra- low latencies required for applications like vehicle to vehicle communication for autonomous driving. D2D communication can provide higher data rates with high energy efficiency and spectral efficiency compared to conventional communication. The performance benefits of D2D communication can be best achieved when D2D users reuses the spectrum being utilized by the conventional cellular users. This spectrum sharing in a multi-tier heterogeneous network will introduce complex interference among D2D users and cellular users which needs to be resolved. Motivated by limited number of surveys for interference mitigation and resource allocation in D2D enabled heterogeneous networks, we have surveyed different conventional and artificial intelligence based interference mitigation and resource allocation schemes developed in recent years. Our contribution lies in the analysis of conventional interference mitigation techniques and their shortcomings. Finally, the strengths of AI based techniques are determined and open research challenges deduced from the recent research are presented.
Learning to Detect Fake Face Images in the Wild
Venue: arXiv cs.MM 2018
Year: 2018

Although Generative Adversarial Network (GAN) can be used to generate the realistic image, improper use of these technologies brings hidden concerns. For example, GAN can be used to generate a tampered video for specific people and inappropriate events, creating images that are detrimental to a particular person, and may even affect that personal safety. In this paper, we will develop a deep forgery discriminator (DeepFD) to efficiently and effectively detect the computer-generated images. Directly learning a binary classifier is relatively tricky since it is hard to find the common discriminative features for judging the fake images generated from different GANs. To address this shortcoming, we adopt contrastive loss in seeking the typical features of the synthesized images generated by different GANs and follow by concatenating a classifier to detect such computer-generated images. Experimental results demonstrate that the proposed DeepFD successfully detected 94.7% fake images generated by several state-of-the-art GANs.
Optimal Multicast of Tiled 360 VR Video
Venue: arXiv cs.IT 2018
Year: 2018

In this letter, we study optimal multicast of tiled 360 virtual reality (VR) video from one server (base station or access point) to multiple users. We consider random viewing directions and random channel conditions, and adopt time division multiple access (TDMA). For given video quality, we optimize the transmission time and power allocation to minimize the average transmission energy. For given transmission energy budget, we optimize the transmission time and power allocation as well as the encoding rate of each tile to maximize the received video quality. These two optimization problems are challenging non-convex problems. We obtain globally optimal closed-form solutions of the two non-convex problems, which reveal important design insights for multicast of tiled 360 VR video. Finally, numerical results demonstrate the advantage of the proposed solutions.
Social Navigation Planning Based on People's Awareness of Robots
Venue: arXiv cs.RO 2018
Year: 2018

When mobile robots maneuver near people, they run the risk of rudely blocking their paths; but not all people behave the same around robots. People that have not noticed the robot are the most difficult to predict. This paper investigates how mobile robots can generate acceptable paths in dynamic environments by predicting human behavior. Here, human behavior may include both physical and mental behavior, we focus on the latter. We introduce a simple safe interaction model: when a human seems unaware of the robot, it should avoid going too close. In this study, people around robots are detected and tracked using sensor fusion and filtering techniques. To handle uncertainties in the dynamic environment, a Partially-Observable Markov Decision Process Model (POMDP) is used to formulate a navigation planning problem in the shared environment. People's awareness of robots is inferred and included as a state and reward model in the POMDP. The proposed planner enables a robot to change its navigation plan based on its perception of each person's robot-awareness. As far as we can tell, this is a new capability. We conduct simulation and experiments using the Toyota Human Support Robot (HSR) to validate our approach. We demonstrate that the proposed framework is capable of running in real-time.
Analytical Derivation of the Impulse Response for the Bounded 2-D Diffusion Channel
Venue: arXiv cs.ET 2018
Year: 2018

This paper focuses on the derivation of the distribution of diffused particles absorbed by an agent in a bounded environment. In particular, we analogously consider to derive the impulse response of a molecular communication channel in 2-D and 3-D environment. In 2-D, the channel involves a point transmitter that releases molecules to a circular absorbing receiver that absorbs incoming molecules in an environment surrounded by a circular reflecting boundary. Considering this setup, the joint distribution of the molecules on the circular absorbing receiver with respect to time and angle is derived. Using this distribution, the channel characteristics are examined. Furthermore, we also extend this channel model to 3-D using a cylindrical receiver and investigate the channel properties. We also propose how to obtain an analytical solution for the unbounded 2-D channel from our derived solutions, as no analytical derivation for this channel is present in the literature.
An Architecture for Person-Following using Active Target Search
Venue: arXiv cs.RO 2018
Year: 2018

This paper addresses a novel architecture for person-following robots using active search. The proposed system can be applied in real-time to general mobile robots for learning features of a human, detecting and tracking, and finally navigating towards that person. To succeed at person-following, perception, planning, and robot behavior need to be integrated properly. Toward this end, an active target searching capability, including prediction and navigation toward vantage locations for finding human targets, is proposed. The proposed capability aims at improving the robustness and efficiency for tracking and following people under dynamic conditions such as crowded environments. A multi-modal sensor information approach including fusing an RGB-D sensor and a laser scanner, is pursued to robustly track and identify human targets. Bayesian filtering for keeping track of human and a regression algorithm to predict the trajectory of people are investigated. In order to make the robot autonomous, the proposed framework relies on a behavior-tree structure. Using Toyota Human Support Robot (HSR), real-time experiments demonstrate that the proposed architecture can generate fast, efficient person-following behaviors.
Die-Stacked DRAM: Memory, Cache, or MemCache?
Venue: arXiv cs.AR 2018
Year: 2018

Die-stacked DRAM is a promising solution for satisfying the ever-increasing memory bandwidth requirements of multi-core processors. Manufacturing technology has enabled stacking several gigabytes of DRAM modules on the active die, thereby providing orders of magnitude higher bandwidth as compared to the conventional DIMM-based DDR memories. Nevertheless, die-stacked DRAM, due to its limited capacity, cannot accommodate entire datasets of modern big-data applications. Therefore, prior proposals use it either as a sizable memory-side cache or as a part of the software-visible main memory. Cache designs can adapt themselves to the dynamic variations of applications but suffer from the tag storage/latency/bandwidth overhead. On the other hand, memory designs eliminate the need for tags, and hence, provide efficient access to data, but are unable to capture the dynamic behaviors of applications due to their static nature. In this work, we make a case for using the die-stacked DRAM partly as main memory and partly as a cache. We observe that in modern big-data applications there are many hot pages with a large number of accesses. Based on this observation, we propose to use a portion of the die-stacked DRAM as main memory to host hot pages, enabling serving a significant number of the accesses from the high-bandwidth DRAM without the overhead of tag-checking, and manage the rest of the DRAM as a cache, for capturing the dynamic behavior of applications. In this proposal, a software procedure pre-processes the application and determines hot pages, then asks the OS to map them to the memory portion of the die-stacked DRAM. The cache portion of the die-stacked DRAM is managed by hardware, caching data allocated in the off-chip memory.
Dense neural networks as sparse graphs and the lightning initialization
Venue: arXiv cs.NE 2018
Year: 2018

Even though dense networks have lost importance today, they are still used as final logic elements. It could be shown that these dense networks can be simplified by the sparse graph interpretation. This in turn shows that the information flow between input and output is not optimal with an initialization common today. The lightning initialization sets the weights so that complete information paths exist between input and output from the start. It turned out that pure dense networks and also more complex networks with additional layers benefit from this initialization. The networks accuracy increases faster. The lightning initialization has two parameters which behaved robustly in the tests carried out. However, especially with more complex networks, an improvement effect only occurs at lower learning rates, which shows that the initialization retains its positive effect over the epochs with learning rate reduction.