OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Generation of Adaptive Network Models based on Similarity to the Desired Complex Network
Venue: arXiv cs.SI 2018
Year: 2018

Complex networks have become powerful mechanisms for studying a variety of realworld systems. Consequently, many human-designed network models are proposed that reproduce nontrivial properties of complex networks, such as long-tail degree distribution or high clustering coefficient. Therefore, we may utilize network models in order to generate graphs similar to desired networks. However, a desired network structure may deviate from emerging structure of any generative model, because no selected single model may support all the needed properties of the target graph and instead, each network model reflects a subset of the required features. In contrast to the classical approach of network modeling, an appropriate modern network model should adapt the desired features of the target network. In this paper, we propose an automatic approach for constructing network models that are adapted to the desired network features. We employ Genetic Algorithms in order to evolve network models based on the characteristics of the target networks. The experimental evaluations show that our proposed framework, called NetMix, results network models that outperform baseline models according to the compliance with the desired features of the target networks.
LIT: Block-wise Intermediate Representation Training for Model Compression
Venue: arXiv cs.LG 2018
Year: 2018

Knowledge distillation (KD) is a popular method for reducing the computational overhead of deep network inference, in which the output of a teacher model is used to train a smaller, faster student model. Hint training (i.e., FitNets) extends KD by regressing a student model's intermediate representation to a teacher model's intermediate representation. In this work, we introduce bLock-wise Intermediate representation Training (LIT), a novel model compression technique that extends the use of intermediate representations in deep network compression, outperforming KD and hint training. LIT has two key ideas: 1) LIT trains a student of the same width (but shallower depth) as the teacher by directly comparing the intermediate representations, and 2) LIT uses the intermediate representation from the previous block in the teacher model as an input to the current student block during training, avoiding unstable intermediate representations in the student network. We show that LIT provides substantial reductions in network depth without loss in accuracy -- for example, LIT can compress a ResNeXt-110 to a ResNeXt-20 (5.5x) on CIFAR10 and a VDCNN-29 to a VDCNN-9 (3.2x) on Amazon Reviews without loss in accuracy, outperforming KD and hint training in network size for a given accuracy. We also show that applying LIT to identical student/teacher architectures increases the accuracy of the student model above the teacher model, outperforming the recently-proposed Born Again Networks procedure on ResNet, ResNeXt, and VDCNN. Finally, we show that LIT can effectively compress GAN generators, which are not supported in the KD framework because GANs output pixels as opposed to probabilities.
The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight
Venue: arXiv cs.CV 2018
Year: 2018

The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale, aggressive indoor flight dataset collected using a custom-built quadrotor platform for use in evaluation of agile perception.Inspired by the potential of future high-speed fully-autonomous drone racing, the Blackbird dataset contains over 10 hours of flight data from 168 flights over 17 flight trajectories and 5 environments at velocities up to $7.0ms^-1$. Each flight includes sensor data from 120Hz stereo and downward-facing photorealistic virtual cameras, 100Hz IMU, $\sim190Hz$ motor speed sensors, and 360Hz millimeter-accurate motion capture ground truth. Camera images for each flight were photorealistically rendered using FlightGoggles across a variety of environments to facilitate easy experimentation of high performance perception algorithms. The dataset is available for download at http://blackbird-dataset.mit.edu/
Balancing Efficiency and Coverage in Human-Robot Dialogue Collection
Venue: arXiv cs.RO 2018
Year: 2018

We describe a multi-phased Wizard-of-Oz approach to collecting human-robot dialogue in a collaborative search and navigation task. The data is being used to train an initial automated robot dialogue system to support collaborative exploration tasks. In the first phase, a wizard freely typed robot utterances to human participants. For the second phase, this data was used to design a GUI that includes buttons for the most common communications, and templates for communications with varying parameters. Comparison of the data gathered in these phases show that the GUI enabled a faster pace of dialogue while still maintaining high coverage of suitable responses, enabling more efficient targeted data collection, and improvements in natural language understanding using GUI-collected data. As a promising first step towards interactive learning, this work shows that our approach enables the collection of useful training data for navigation-based HRI tasks.
Transferring Physical Motion Between Domains for Neural Inertial Tracking
Venue: arXiv cs.LG 2018
Year: 2018

Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. However, they are affected greatly by changes in sensor placement/orientation or motion dynamics, and it is infeasible to collect labelled data from every domain. To overcome the challenges of domain adaptation on long sensory sequences, we propose a novel framework that extracts domain-invariant features of raw sequences from arbitrary domains, and transforms to new domains without any paired data. Through the experiments, we demonstrate that it is able to efficiently and effectively convert the raw sequence from a new unlabelled target domain into an accurate inertial trajectory, benefiting from the physical motion knowledge transferred from the labelled source domain. We also conduct real-world experiments to show our framework can reconstruct physically meaningful trajectories from raw IMU measurements obtained with a standard mobile phone in various attachments.
Shakedown: compiler-based moving target protection for Return Oriented Programing attacks on an industrial IoT device
Venue: arXiv cs.CR 2018
Year: 2018

Cybercriminals use Return Oriented Programming techniques to attack systems and IoT devices. While defenses have been developed, not all of them are applicable to constrained devices. We present Shakedown, which is a compile-time randomizing build tool which creates several versions of the binary, each with a distinct memory layout. An attack developed against one device will not work on another device which has a different memory layout. We tested Shakedown on an industrial IoT device and shown that its normal functionality remained intact while an exploit was blocked.
Monte Carlo Dependency Estimation
Venue: arXiv cs.LG 2018
Year: 2018

Estimating the dependency of variables is a fundamental task in data analysis. Identifying the relevant attributes in databases leads to better data understanding and also improves the performance of learning algorithms, both in terms of runtime and quality. In data streams, dependency monitoring provides key insights into the underlying process, but is challenging. In this paper, we propose Monte Carlo Dependency Estimation (MCDE), a theoretical framework to estimate multivariate dependency in static and dynamic data. MCDE quantifies dependency as the average discrepancy between marginal and conditional distributions via Monte Carlo simulations. Based on this framework, we present Mann-Whitney P (MWP), a novel dependency estimator. We show that MWP satisfies a number of desirable properties and can accommodate any kind of numerical data. We demonstrate the superiority of our estimator by comparing it to the state-of-the-art multivariate dependency measures.
A Single Approach to Decide Chase Termination on Linear Existential Rules
Venue: arXiv cs.LO 2018
Year: 2018

Existential rules, long known as tuple-generating dependencies in database theory, have been intensively studied in the last decade as a powerful formalism to represent ontological knowledge in the context of ontology-based query answering. A knowledge base is then composed of an instance that contains incomplete data and a set of existential rules, and answers to queries are logically entailed from the knowledge base. This brought again to light the fundamental chase tool, and its different variants that have been proposed in the literature. It is well-known that the problem of determining, given a chase variant and a set of existential rules, whether the chase will halt on any instance, is undecidable. Hence, a crucial issue is whether it becomes decidable for known subclasses of existential rules. In this work, we consider linear existential rules, a simple yet important subclass of existential rules that generalizes inclusion dependencies. We show the decidability of the all instance chase termination problem on linear rules for three main chase variants, namely semi-oblivious, restricted and core chase. To obtain these results, we introduce a novel approach based on so-called derivation trees and a single notion of forbidden pattern. Besides the theoretical interest of a unified approach and new proofs, we provide the first positive decidability results concerning the termination of the restricted chase, proving that chase termination on linear existential rules is decidable for both versions of the problem: Does every fair chase sequence terminate? Does some fair chase sequence terminate?
COMIT - Cryptographically-secure Off-chain Multi-asset Instant Transaction Network
Venue: arXiv cs.DC 2018
Year: 2018

Since the creation of Bitcoin in 2009 we have seen a great push towards public and private blockchains. In order to avoid fragmentation, a global network connecting all these blockchains is envisioned. Just like the Internet facilitates communication and the transfer of information, we propose a system, similar in size and reach for payments and transactions: A cryptographically-secure off-chain multi-asset instant transaction network (COMIT) can connect and exchange any asset on any blockchain to any other blockchain using a cross-chain routing protocol (CRP). COMIT is a super blockchain network that allows for instant transactions which are enforced using off-chain smart contracts. It leverages Payment Channels and Hashed Timelock Contracts (HTLC) across chains to solve the problem of double spending attacks without requiring a settlement onto the underlying blockchains. COMIT's connectivity is provided by Liquidity Providers (LP), who operate on one or more blockchains, acting as payment hubs and nodes on a single chain and market makers in a decentralized network for cross-chain asset conversions. This paper lays out how COMIT works, the benefits for Users, Liquidity Providers and Businesses; and how this does not only accelerate the adoption of blockchain technology, but furthermore allows for an integration with the traditional banking system.
Privacy-Preserving Multiparty Learning For Logistic Regression
Venue: arXiv cs.CR 2018
Year: 2018

In recent years, machine learning techniques are widely used in numerous applications, such as weather forecast, financial data analysis, spam filtering, and medical prediction. In the meantime, massive data generated from multiple sources further improve the performance of machine learning tools. However, data sharing from multiple sources brings privacy issues for those sources since sensitive information may be leaked in this process. In this paper, we propose a framework enabling multiple parties to collaboratively and accurately train a learning model over distributed datasets while guaranteeing the privacy of data sources. Specifically, we consider logistic regression model for data training and propose two approaches for perturbing the objective function to preserve ε-differential privacy. The proposed solutions are tested on real datasets, including Bank Marketing and Credit Card Default prediction. Experimental results demonstrate that the proposed multiparty learning framework is highly efficient and accurate.
Tight Information Theoretic Converse Results for some Pliable Index Coding Problems
Venue: arXiv cs.IT 2018
Year: 2018

This paper studies the Pliable Index CODing problem (PICOD), which models content-type distribution networks. In the PICOD$(t)$ problem there are $m$ messages, $n$ users and each user has a distinct message side information set, as in the classical Index Coding problem (IC). Differently from IC, where each user has a pre-specified set of messages to decode, in the PICOD$(t)$ a user is "pliable" and is satisfied if it can decode any $t$ messages that are not in its side information set. The goal is to find a code with the shortest length that satisfies all the users. This flexibility in determining the desired message sets makes the PICOD$(t)$ behave quite differently compared to the IC, and its analysis challenging. This paper mainly focuses on the \emph{complete--$S$} PICOD$(t)$ with $m$ messages, where the set $S\subset[m]$ contains the sizes of the side information sets, and the number of users is $n=\sum_{s\in S}\binom{m}{s}$, with no two users having the same side information set. Capacity results are shown for: (i) the \emph{consecutive} complete--$S$ PICOD$(t)$, where $S=[s_{\min}:s_{\max}]$ for some $0 \leq s_{\min} \leq s_{\max} \leq m-t$, and (ii) the \emph{complement-consecutive} complete--$S$ PICOD$(t)$, where $S=[0:m-t]\backslash[s_{\min}:s_{\max}]$, for some $0 < s_{\min} \leq s_{\max} < m-t$. The novel converse proof is inspired by combinatorial design techniques and the key insight is to consider all messages that a user can eventually decode successfully, even those in excess of the $t$ required ones. This allows one to circumvent the need to consider all possible desired message set assignments at the users in order to find the one that leads to the shortest code length. In addition, tight converse results are also shown for those PICOD$(1)$ with circular-arc network topology hypergraph.
The Looming Threat of China: An Analysis of Chinese Influence on Bitcoin
Venue: arXiv cs.CR 2018
Year: 2018

As Bitcoin's popularity has grown over the decade since its creation, it has become an increasingly attractive target for adversaries of all kinds. One of the most powerful potential adversaries is the country of China, which has expressed adversarial positions regarding the cryptocurrency and demonstrated powerful capabilities to influence it. In this paper, we explore how China threatens the security, stability, and viability of Bitcoin through its dominant position in the Bitcoin ecosystem, political and economic control over domestic activity, and control over its domestic Internet infrastructure. We explore the relationship between China and Bitcoin, document China's motivation to undermine Bitcoin, and present a case study to demonstrate the strong influence that China has over Bitcoin. Finally, we systematize the class of attacks that China can deploy against Bitcoin to better understand the threat China poses. We conclude that China has mature capabilities and strong motives for performing a variety of attacks against Bitcoin.
Global Types for Open Systems
Venue: arXiv cs.LO 2018
Year: 2018

Global-type formalisms enable to describe the overall behaviour of distributed systems and at the same time to enforce safety properties for communications between system components. Our goal is that of amending a weakness of such formalisms: the difficulty in describing open systems, i.e. systems which can be connected and interact with other open systems. We parametrically extend, with the notion of interface role and interface connection, the syntax of global-type formalisms. Semantically, global types with interface roles denote open systems of communicating finite state machines connected by means of gateways obtained from compatible interfaces. We show that safety properties are preserved when open systems are connected that way.
CAC and Traffic Modeling for Integrated Macrocell/Femtocell Networks
Venue: arXiv cs.NI 2018
Year: 2018

Dense femtocells and the integration of these femtocells with the macrocell are the ultimate goal of the femtocellular network deployment. Integrated macrocell/femtocell networks surely able to provide high data rate for the indoor users as well as able to offload huge traffic from the macrocellular networks to femtocellular networks. Efficient handling of handover calls is the key for the successful macrocell/femtocell integration. An appropriate traffic model for the integrated macrocell/femtocell networks is also needed for the performance analysis measurement. In this paper we presented a call admission control process and a traffic model for the integrated macrocell/femtocell networks. The numerical and simulation results show the important of the integrated macrocell/femtocell network and the performance improvement of the proposed schemes.
Intelligent Interference Management Based on On-Demand Service Connectivity for Femtocellular Networks
Venue: arXiv cs.NI 2018
Year: 2018

The femto-access-point (FAP), a low power small cellular base station provides better signal quality for the indoor users as to provide high data-rate communications with improved coverage, access network capacity and quality of service. Highly dense femtocellular deployments-the ultimate goal of the femtocellular technology-will require significant degree of selforganization in lieu of manual configuration as to mitigate unwanted handovers and interference effects. The deployment of femtocells with our proposed on-demand scheme in the multistoried building or home environment can solve the low level signal-to-noise plus interference ratio and throughput problems. In this paper, we propose new application of the femtocell technology with a new idea of femto-idle-mode and femto-active-mode system. The signal-to-noise plus interference ratio level and throughput are analyzed. The simulation results show that the proposed on-demand scheme in femtocell deployment significantly enhances the signal-to-noise plus interference ratio level and throughput in the multistoried building or home environment.
Interference Declination for Dynamic Channel Borrowing Scheme in Wireless Network
Venue: arXiv cs.NI 2018
Year: 2018

In modern days, users in the wireless networks are increasing drastically. It has become the major concern for researchers to manage the maximum users with limited radio resource. Interference is one of the biggest hindrances to reach the goal. In this paper, being deep apprehension of the issue, an efficient dynamic channel borrowing scheme is proposed that ensures better Quality of Service (QoS) with interference declination. We propose that if channels are borrowed from adjacent cells, cell bifurcation will be introduced that ensures interference declination when the borrowed channels have same frequency band. We also propose a scheme that inactivates the unoccupied interfering channels of adjacent cells, instead of cell bifurcation for interference declination. The simulation outcomes show acceptable performances in terms of SINR level, system capacity, and outage probability compared to conventional scheme without interference declination that may attract the considerable interest for the users.
On Demand Cell Sectoring Based Fractional Frequency Reuse in Wireless Networks
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, a dynamic channel assigning along with dynamic cell sectoring model has been proposed that focuses on the Fractional Frequency Reuse (FFR) not only for interference mitigation but also for enhancement of overall system capacity in wireless networks. We partition the cells in a cluster into two part named centre user part (CUP) and edge user part (EUP). Instead of huge traffic, there may be unoccupied channels in the EUPs of the cells. These unoccupied channels of the EUPs can assist the excessive number of users if these channels are assigned with proper interference management. If the number of traffic of a cell surpasses the number of channels of the EUP, then the cell assigns the channels from the EUP of other cells in the cluster. To alleviate the interference, we propose a dynamic cell sectoring scheme. The scheme sectors the EUP of the cell which assigns channels that the assigned channels are provided to the sectored part where these channels receive negligible interference. The performance analysis illustrates reduced call blocking probability as well as better signal to interference plus noise ratio (SINR) without sacrificing bandwidth utilization. Besides, the proposed model ensures lower outage probability.
A New Guard-Band Call Admission Control Policy Based on Acceptance Factor for Wireless Cellular Networks
Venue: arXiv cs.NI 2018
Year: 2018

To ensure the maximum utilization of the limited bandwidth resources and improved quality of service (QoS) is the key issue for wireless communication networks. Excessive call blocking is a constraint to attain the desired QoS. In cellular network, as the traffic arrival rate increases, call blocking probability (CBP) increases considerably. Paying profound concern, we proposed a scheme that reduces the call blocking probability with approximately steady call dropping probability (CDP). Our proposed scheme also introduces the acceptance factor in specific guard channel where originating calls get access according to the acceptance factor. The analytical performance proves better performance than the conventional new-call bounding scheme in case of higher and lower traffic arrival rate.
Service Aware Fuzzy Logic Based Handover Decision in Heterogeneous Wireless Networks
Venue: arXiv cs.NI 2018
Year: 2018

The ubiquitous services of wireless communication networks are growing rapidly by the development of wireless communication technologies. While a user is roaming from one cell to another cell, an intelligent decision mechanism and network selection is extremely crucial to maintain the quality of service (QoS) during handover. Handover decision must be made precisely to avoid any unnecessary phenomenon like ping-pong, corner effect, call blocking, and call dropping etc. This work considered service types like voice, video, and data and their QoS requirements for handover decision using fuzzy logic in heterogeneous network environment. Service is an important factor for the users and particular service requires respective QoS. This paper provides all the cases of handover decisions between macrocell and femtocell networks considering service type. The proposed system models regarding these handover decisions using fuzzy logic considering several input parameters e.g. received signal strength indicator (RSSI), data rate, user's velocity, and interference level (signal-to-noise plus interference ratio) to make handover from femtocell to macrocell, macrocell to femtocell or femtocell to femtocell. The performance of different parameters are shown based on service type are analyzed.