OpenRaiser/Intern-Atlas huggingface dataset:
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
Venue: arXiv cs.LG 2017
Year: 2017

Consider two data providers, each maintaining private records of different feature sets about common entities. They aim to learn a linear model jointly in a federated setting, namely, data is local and a shared model is trained from locally computed updates. In contrast with most work on distributed learning, in this scenario (i) data is split vertically, i.e. by features, (ii) only one data provider knows the target variable and (iii) entities are not linked across the data providers. Hence, to the challenge of private learning, we add the potentially negative consequences of mistakes in entity resolution. Our contribution is twofold. First, we describe a three-party end-to-end solution in two phases ---privacy-preserving entity resolution and federated logistic regression over messages encrypted with an additively homomorphic scheme---, secure against a honest-but-curious adversary. The system allows learning without either exposing data in the clear or sharing which entities the data providers have in common. Our implementation is as accurate as a naive non-private solution that brings all data in one place, and scales to problems with millions of entities with hundreds of features. Second, we provide what is to our knowledge the first formal analysis of the impact of entity resolution's mistakes on learning, with results on how optimal classifiers, empirical losses, margins and generalisation abilities are affected. Our results bring a clear and strong support for federated learning: under reasonable assumptions on the number and magnitude of entity resolution's mistakes, it can be extremely beneficial to carry out federated learning in the setting where each peer's data provides a significant uplift to the other.
Road Extraction by Deep Residual U-Net
Venue: arXiv cs.CV 2017
Year: 2017

Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network which combines the strengths of residual learning and U-Net is proposed for road area extraction. The network is built with residual units and has similar architecture to that of U-Net. The benefits of this model is two-fold: first, residual units ease training of deep networks. Second, the rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters however better performance. We test our network on a public road dataset and compare it with U-Net and other two state of the art deep learning based road extraction methods. The proposed approach outperforms all the comparing methods, which demonstrates its superiority over recently developed state of the arts.
Backscatter Multiplicative Multiple-Access Systems: Fundamental Limits and Practical Design
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we consider a novel ambient backscatter multiple-access system, where a receiver (Rx) simultaneously detects the signals transmitted from an active transmitter (Tx) and a backscatter Tag. Specifically, the information-carrying signal sent by the Tx arrives at the Rx through two wireless channels: the direct channel from the Tx to the Rx, and the backscatter channel from the Tx to the Tag and then to the Rx. The received signal from the backscatter channel also carries the Tag's information because of the multiplicative backscatter operation at the Tag. This multiple-access system introduces a new channel model, referred to as multiplicative multiple-access channel (M-MAC). We analyze the achievable rate region of the M-MAC, and prove that its region is strictly larger than that of the conventional time-division multiple-access scheme in many cases, including, e.g., the high SNR regime and the case when the direct channel is much stronger than the backscatter channel. Hence, the multiplicative multiple-access scheme is an attractive technique to improve the throughput for ambient backscatter communication systems. Moreover, we analyze the detection error rates for coherent and noncoherent modulation schemes adopted by the Tx and the Tag, respectively, in both synchronous and asynchronous scenarios, which further bring interesting insights for practical system design.
A critical analysis of string APIs: The case of Pharo
Venue: arXiv cs.PL 2017
Year: 2017

Most programming languages, besides C, provide a native abstraction for character strings, but string APIs vary widely in size, expressiveness, and subjective convenience across languages. In Pharo, while at first glance the API of the String class seems rich, it often feels cumbersome in practice; to improve its usability, we faced the challenge of assessing its design. However, we found hardly any guideline about design forces and how they structure the design space, and no comprehensive analysis of the expected string operations and their different variations. In this article, we first analyse the Pharo 4 String library, then contrast it with its Haskell, Java, Python, Ruby, and Rust counterparts. We harvest criteria to describe a string API, and reflect on features and design tensions. This analysis should help language designers in understanding the design space of strings, and will serve as a basis for a future redesign of the string library in Pharo.
Behavior of Wireless Body-to-Body Networks Routing Strategies for Public Protection and Disaster Relief
Venue: arXiv cs.NI 2017
Year: 2017

Critical and public safety operations require real-time data transfer from the incident area(s) to the distant operations command center going through the evacuation and medical support areas. Any delay in communication may cause significant loss. In some cases, it is anticipated that the existing communication infrastructures can be damaged or out-of-service. It is thus required to deploy tactical ad-hoc networks to cover the operation zones. Routing data over the deployed network is a significant challenge with consideration to the operations conditions. In this paper we evaluate the performance of mutli-hop routing protocols while using different wireless technologies in an urban critical and emergency scenario. Using a realistic mobility model, Mobile Ad hoc, geographic based and data-centric routing protocols are evaluated with different communication technologies (i.e. WiFi IEEE 802.11; WSN IEEE 802.15.4; WBAN IEEE 802.15.6). It is concluded that, WiFi IEEE 802.11 is the best wireless technology with consideration to the packet reception rate and the energy consumption. Whereas, in terms of delay, WBAN IEEE 802.15.6 is the most efficient. With regards to the routing protocols, assuming that the location information is available, geographical based routing protocol with WiFi IEEE 802.11 performed much better compared to the others routing protocols. In case where the location information is unavailable, gradient based routing protocol with WBAN IEEE 802.15.6 seems the best combination.
Data Dissemination Strategies for Emerging Wireless Body-to-Body Networks based Internet of Humans
Venue: arXiv cs.DC 2017
Year: 2017

With the recent advent of Internet of Humans (IoH), wireless body-to-body networks (WBBNs) are emerging as the fundamental part of this new paradigm. In particular with reference to newly emerging applications, the research trends on data routing and dissemination strategies have gained a great interest in WBBN. In this paper, we present the performance evaluation of the clustered and distributed data dissemination approaches in tactical WBBN. We used a realistic radio-link and biomechanical mobility model for on-body motions, and group mobility model for WBBN to effectively realize rescue and emergency management application scenario. In this regard, we are using the newly proposed IEEE 802.15.6 standard targeted for body area networks. Extensive (IEEE 802.15.6 standard compliance) network level, packet oriented simulations are conducted in WSNet simulator. During the simulations, various payloads, frequencies (narrow-band) and modulation techniques are exploited. We based our performance evaluation on relevant metrics according to the operational requirements for tactical networks such as packet reception ratio, latency, energy consumption and hop count. The results showed a trade-offs between clustered-based and distributed-based dissemination approaches. With regards to packet delay, distributed approach provided the best performance. However, in terms of average packet reception ratio (PRR), clustered-based approach achieves up to 97% reception and remained the best strategy. Whereas, the results of the hop count and energy consumption are almost comparable in both schemes.
InterPSS: A New Generation Power System Simulation Engine
Venue: arXiv cs.SE 2017
Year: 2017

This paper presents the design of InterPSS simulation engine, including its object model, open software architecture, and software development process. Several advanced applications, including an integrated transmission and distribution co-simulation, an electromagnetic transient and phasor domain hybrid simulation, and InterPSS integration with a market simulator, have been developed by either extending InterPSS simulation engine or integrating it with other programs and/or platforms. These advanced applications show that the open architecture combined with the comprehensive modeling and simulation capabilities make InterPSS a very attractive option for the research and the future new power system simulation application development.
On S-packing edge-colorings of cubic graphs
Venue: arXiv cs.DM 2017
Year: 2017

Given a non-decreasing sequence S = (s 1,s 2,. .. ,s k) of positive integers, an S-packing edge-coloring of a graph G is a partition of the edge set of G into k subsets {X 1 ,X 2,. .. ,X k } such that for each 1 $\le$ i $\le$ k, the distance between two distinct edges e, e ' $\in$ X i is at least s i + 1. This paper studies S-packing edge-colorings of cubic graphs. Among other results, we prove that cubic graphs having a 2-factor are (1,1,1,3,3)-packing edge-colorable, (1,1,1,4,4,4,4,4)-packing edge-colorable and (1,1,2,2,2,2,2)-packing edge-colorable. We determine sharper results for cubic graphs of bounded oddness and 3-edge-colorable cubic graphs and we propose many open problems.
(Biased) Majority Rule Cellular Automata
Venue: arXiv cs.FL 2017
Year: 2017

Consider a graph $G=(V,E)$ and a random initial vertex-coloring, where each vertex is blue independently with probability $p_{b}$, and red with probability $p_r=1-p_b$. In each step, all vertices change their current color synchronously to the most frequent color in their neighborhood and in case of a tie, a vertex conserves its current color; this model is called majority model. If in case of a tie a vertex always chooses blue color, it is called biased majority model. We are interested in the behavior of these deterministic processes, especially in a two-dimensional torus (i.e., cellular automaton with (biased) majority rule). In the present paper, as a main result we prove both majority and biased majority cellular automata exhibit a threshold behavior with two phase transitions. More precisely, it is shown that for a two-dimensional torus $T_{n,n}$, there are two thresholds $0\leq p_1, p_2\leq 1$ such that $p_b \ll p_1$, $p_1 \ll p_b \ll p_2$, and $p_2 \ll p_b$ result in monochromatic configuration by red, stable coexistence of both colors, and monochromatic configuration by blue, respectively in $\mathcal{O}(n^2)$ number of steps
Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning
Venue: arXiv cs.SY 2017
Year: 2017

The combination of Artificial Intelligence (AI) and Internet-of-Things (IoT), which is denoted as AI-powered Internet-of-Things (AIoT), is capable of processing huge amount of data generated from a large number of devices and handling complex problems in social infrastructures. As AI and IoT technologies are becoming mature, in this paper, we propose to apply AIoT technologies for traffic light control, which is an essential component for intelligent transportation system, to improve the efficiency of smart city's road system. Specifically, various sensors such as surveillance cameras provide real-time information for intelligent traffic light control system to observe the states of both motorized traffic and non-motorized traffic. In this paper, we propose an intelligent traffic light control solution by using distributed multi-agent Q learning, considering the traffic information at the neighboring intersections as well as local motorized and non-motorized traffic, to improve the overall performance of the entire control system. By using the proposed multi-agent Q learning algorithm, our solution is targeting to optimize both the motorized and non-motorized traffic. In addition, we considered many constraints/rules for traffic light control in the real world, and integrate these constraints in the learning algorithm, which can facilitate the proposed solution to be deployed in real operational scenarios. We conducted numerical simulations for a real-world map with real-world traffic data. The simulation results show that our proposed solution outperforms existing solutions in terms of vehicle and pedestrian queue lengths, waiting time at intersections, and many other key performance metrics.
A Centralized Reputation Management Scheme for Isolating Malicious Controller(s) in Distributed Software-Defined Networks
Venue: arXiv cs.NI 2017
Year: 2017

Software-Defined Networks have seen an increasing in their deployment because they offer better network manageability compared to traditional networks. Despite their immense success and popularity, various security issues in SDN remain open problems for research. Particularly, the problem of securing the controllers in distributed environment is still short of any solutions. This paper proposes a scheme to identify any rogue/malicious controller(s) in a distributed environment. Our scheme is based on trust and reputation system which is centrally managed. As such, our scheme identifies any controllers acting maliciously by comparing the state of installed flows/policies with policies that should be installed. Controllers rate each other on this basis and report the results to a central entity, which reports it to the network administrator.
Deep Learning for identifying radiogenomic associations in breast cancer
Venue: arXiv cs.CV 2017
Year: 2017

Purpose: To determine whether deep learning models can distinguish between breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and methods: In this institutional review board-approved single-center study, we analyzed DCE-MR images of 270 patients at our institution. Lesions of interest were identified by radiologists. The task was to automatically determine whether the tumor is of the Luminal A subtype or of another subtype based on the MR image patches representing the tumor. Three different deep learning approaches were used to classify the tumor according to their molecular subtypes: learning from scratch where only tumor patches were used for training, transfer learning where networks pre-trained on natural images were fine-tuned using tumor patches, and off-the-shelf deep features where the features extracted by neural networks trained on natural images were used for classification with a support vector machine. Network architectures utilized in our experiments were GoogleNet, VGG, and CIFAR. We used 10-fold crossvalidation method for validation and area under the receiver operating characteristic (AUC) as the measure of performance. Results: The best AUC performance for distinguishing molecular subtypes was 0.65 (95% CI:[0.57,0.71]) and was achieved by the off-the-shelf deep features approach. The highest AUC performance for training from scratch was 0.58 (95% CI:[0.51,0.64]) and the best AUC performance for transfer learning was 0.60 (95% CI:[0.52,0.65]) respectively. For the off-the-shelf approach, the features extracted from the fully connected layer performed the best. Conclusion: Deep learning may play a role in discovering radiogenomic associations in breast cancer.
Task Replication for Deadline-Constrained Vehicular Cloud Computing: Optimal Policy, Performance Analysis and Implications on Road Traffic
Venue: arXiv cs.IT 2017
Year: 2017

In vehicular cloud computing (VCC) systems, the computational resources of moving vehicles are exploited and managed by infrastructures, e.g., roadside units, to provide computational services. The offloading of computational tasks and collection of results rely on successful transmissions between vehicles and infrastructures during encounters. In this paper, we investigate how to provide timely computational services in VCC systems. In particular, we seek to minimize the deadline violation probability given a set of tasks to be executed in vehicular clouds. Due to the uncertainty of vehicle movements, the task replication methodology is leveraged which allows one task to be executed by several vehicles, and thus trading computational resources for delay reduction. The optimal task replication policy is of key interest. We first formulate the problem as a finite-horizon sampled-time Markov decision problem and obtain the optimal policy by value iterations. To conquer the complexity issue, we propose the balanced-task-assignment (BETA) policy which is proved optimal and has a clear structure: it always assigns the task with the minimum number of replicas. Moreover, a tight closed-form performance upper bound for the BETA policy is derived, which indicates that the deadline violation probability follows the Rayleigh distribution approximately. Applying the vehicle speed-density relationship in the traffic flow theory, we find that vehicle mobility benefits VCC systems more compared with road traffic systems, by showing that the optimum vehicle speed to minimize the deadline violation probability is larger than the critical vehicle speed in traffic theory which maximizes traffic flow efficiency.
Predicting and Explaining Human Semantic Search in a Cognitive Model
Venue: arXiv cs.CL 2017
Year: 2017

Recent work has attempted to characterize the structure of semantic memory and the search algorithms which, together, best approximate human patterns of search revealed in a semantic fluency task. There are a number of models that seek to capture semantic search processes over networks, but they vary in the cognitive plausibility of their implementation. Existing work has also neglected to consider the constraints that the incremental process of language acquisition must place on the structure of semantic memory. Here we present a model that incrementally updates a semantic network, with limited computational steps, and replicates many patterns found in human semantic fluency using a simple random walk. We also perform thorough analyses showing that a combination of both structural and semantic features are correlated with human performance patterns.
Time Domain Neural Audio Style Transfer
Venue: arXiv cs.SD 2017
Year: 2017

A recently published method for audio style transfer has shown how to extend the process of image style transfer to audio. This method synthesizes audio "content" and "style" independently using the magnitudes of a short time Fourier transform, shallow convolutional networks with randomly initialized filters, and iterative phase reconstruction with Griffin-Lim. In this work, we explore whether it is possible to directly optimize a time domain audio signal, removing the process of phase reconstruction and opening up possibilities for real-time applications and higher quality syntheses. We explore a variety of style transfer processes on neural networks that operate directly on time domain audio signals and demonstrate one such network capable of audio stylization.
Safe Exploration for Identifying Linear Systems via Robust Optimization
Venue: arXiv cs.LG 2017
Year: 2017

Safely exploring an unknown dynamical system is critical to the deployment of reinforcement learning (RL) in physical systems where failures may have catastrophic consequences. In scenarios where one knows little about the dynamics, diverse transition data covering relevant regions of state-action space is needed to apply either model-based or model-free RL. Motivated by the cooling of Google's data centers, we study how one can safely identify the parameters of a system model with a desired accuracy and confidence level. In particular, we focus on learning an unknown linear system with Gaussian noise assuming only that, initially, a nominal safe action is known. Define safety as satisfying specific linear constraints on the state space (e.g., requirements on process variable) that must hold over the span of an entire trajectory, and given a Probably Approximately Correct (PAC) style bound on the estimation error of model parameters, we show how to compute safe regions of action space by gradually growing a ball around the nominal safe action. One can apply any exploration strategy where actions are chosen from such safe regions. Experiments on a stylized model of data center cooling dynamics show how computing proper safe regions can increase the sample efficiency of safe exploration.
Emerging basic, clinical and translational research fronts in dental biomaterials R&D
Venue: arXiv cs.DL 2017
Year: 2017

The current (2007-2007) structure and content of dental materials research has been investigated by identifying and describing the emergent research fronts which can be related to basic, translational and clinical observation research. By a combination of network analysis and text mining of the literature on dental materials indexed in the Web of Science, we have identified eleven emerging research fronts. These fronts are related to different dental materials applications which are at different levels in the knowledge translation and biomedical innovation process. We identified fronts related to dominant designs like titanium implants, competing technologies like ceramics and composites applications to prothesis and restauration, and disruptive technologies like nanomaterials and mineral trioxide aggregates. Our results suggest the possible relation between the technological complexity of the dental materials and the level of advance in terms of knowledge translation. This is the first time the structure and content of research on dental materials research is analyzed.
Session Types for Orchestrated Interactions
Venue: arXiv cs.LO 2017
Year: 2017

In the setting of the pi-calculus with binary sessions, we aim at relaxing the notion of duality of session types by the concept of retractable compliance developed in contract theory. This leads to extending session types with a new type operator of "speculative selection" including choices not necessarily offered by a compliant partner. We address the problem of selecting successful communicating branches by means of an operational semantics based on orchestrators, which has been shown to be equivalent to the retractable semantics of contracts, but clearly more feasible. A type system, sound with respect to such a semantics, is hence provided.