OpenRaiser/Intern-Atlas huggingface dataset:
SpectreRewind: Leaking Secrets to Past Instructions
Venue: arXiv cs.CR 2020
Year: 2020

Transient execution attacks utilize micro-architectural covert channels to leak secrets that should not have been accessible during logical program execution. Commonly used micro-architectural covert channels are those that leave lasting footprints in the microarchitectural state, for example, a cache state change, from which the secret is recovered after the transient execution is completed. In this paper, we present SpectreRewind, a new approach to create contention based covert channels for transient execution attacks. In our approach, a covert channel is established by issuing the necessary instructions logically before the transiently executed victim code. Unlike prior contention based covert channels, which require simultaneous multi-threading (SMT), SpectreRewind supports single hardware thread based covert channels, making it viable on systems where attacker cannot utilize SMT. We show that contention on the floating point division unit on commodity processors can be used to create a high-performance (~100 KB/s), low-noise covert channel for transient execution attacks instead of commonly used flush+reload based cache covert channels. We implement a Meltdown attack utilizing the proposed covert channel showing competitive performance compared to the stateof-the-art cache based covert channel implementation. We also show that the covert channel works in the JavaScript engine of a Chrome browser.
Incorporating Expert Prior in Bayesian Optimisation via Space Warping
Venue: arXiv cs.LG 2020
Year: 2020

Bayesian optimisation is a well-known sample-efficient method for the optimisation of expensive black-box functions. However when dealing with big search spaces the algorithm goes through several low function value regions before reaching the optimum of the function. Since the function evaluations are expensive in terms of both money and time, it may be desirable to alleviate this problem. One approach to subside this cold start phase is to use prior knowledge that can accelerate the optimisation. In its standard form, Bayesian optimisation assumes the likelihood of any point in the search space being the optimum is equal. Therefore any prior knowledge that can provide information about the optimum of the function would elevate the optimisation performance. In this paper, we represent the prior knowledge about the function optimum through a prior distribution. The prior distribution is then used to warp the search space in such a way that space gets expanded around the high probability region of function optimum and shrinks around low probability region of optimum. We incorporate this prior directly in function model (Gaussian process), by redefining the kernel matrix, which allows this method to work with any acquisition function, i.e. acquisition agnostic approach. We show the superiority of our method over standard Bayesian optimisation method through optimisation of several benchmark functions and hyperparameter tuning of two algorithms: Support Vector Machine (SVM) and Random forest.
Unsupervised Cross-Modal Audio Representation Learning from Unstructured Multilingual Text
Venue: arXiv cs.MM 2020
Year: 2020

We present an approach to unsupervised audio representation learning. Based on a triplet neural network architecture, we harnesses semantically related cross-modal information to estimate audio track-relatedness. By applying Latent Semantic Indexing (LSI) we embed corresponding textual information into a latent vector space from which we derive track relatedness for online triplet selection. This LSI topic modelling facilitates fine-grained selection of similar and dissimilar audio-track pairs to learn the audio representation using a Convolution Recurrent Neural Network (CRNN). By this we directly project the semantic context of the unstructured text modality onto the learned representation space of the audio modality without deriving structured ground-truth annotations from it. We evaluate our approach on the Europeana Sounds collection and show how to improve search in digital audio libraries by harnessing the multilingual meta-data provided by numerous European digital libraries. We show that our approach is invariant to the variety of annotation styles as well as to the different languages of this collection. The learned representations perform comparable to the baseline of handcrafted features, respectively exceeding this baseline in similarity retrieval precision at higher cut-offs with only 15\% of the baseline's feature vector length.
Rational Agent-Based Decision Algorithm for Strategic Converged Network Migration Planning
Venue: arXiv cs.NI 2020
Year: 2020

To keep up with constantly growing user demands for services with higher quality and bandwidth requirements, telecommunication operators are forced to upgrade their networks. This upgrade, or migration of the network to a new technology, is a complex strategic network planning problem that involves techno-economic evaluations over multiple periods of time. The state-of-the-art approaches consider migrations to a concrete architecture and do not take uncertainties, such as user churn, into account. This results in migration cost underestimations and profitability over-estimations. In this paper, we propose a generic migration algorithm derived from a search based rational agent decision process that can deal with uncertainties and provides the migration path using a maximized utility function. The algorithm maximizes the migration project profitability, measured as accumulated Net Present Value (NPV). This flexible and generic methodology has been evaluated on the example of migration from existing copper networks to the future-proof Passive Optical Network (PON) architectures. Our proposed flexible migration algorithm is validated over pure residential and converged scenarios in a fully reproducible case study. The results yield that the migration flexibility is a key to the profit maximization.
Mobile phone data and COVID-19: Missing an opportunity?
Venue: arXiv cs.CY 2020
Year: 2020

This paper describes how mobile phone data can guide government and public health authorities in determining the best course of action to control the COVID-19 pandemic and in assessing the effectiveness of control measures such as physical distancing. It identifies key gaps and reasons why this kind of data is only scarcely used, although their value in similar epidemics has proven in a number of use cases. It presents ways to overcome these gaps and key recommendations for urgent action, most notably the establishment of mixed expert groups on national and regional level, and the inclusion and support of governments and public authorities early on. It is authored by a group of experienced data scientists, epidemiologists, demographers and representatives of mobile network operators who jointly put their work at the service of the global effort to combat the COVID-19 pandemic.
Democratic Value and Money for Decentralized Digital Society
Venue: arXiv cs.CY 2020
Year: 2020

Classical monetary systems regularly subject the most vulnerable majority of the world's population to debilitating financial shocks, and have manifestly allowed uncontrolled global inequality over the long term. Given these basic failures, how can we avoid asking whether mainstream macroeconomic principles are actually compatible with democratic principles such as equality or the protection of human rights and dignity? This idea paper takes a constructive look at this question, by exploring how alternate monetary principles might result in a form of money more compatible with democratic principles -- dare we call it "democratic money"? In this alternative macroeconomic philosophy, both the supply of and the demand for money must be rooted in people, so as to give all people both equal opportunities for economic participation. Money must be designed around equality, not only across all people alive at a given moment, but also across past and future generations of people, guaranteeing that our descendants cannot be enslaved by their ancestors' economic luck or misfortune. Democratic money must reliably give all people a means to enable everyday commerce, investment, and value creation in good times and bad, and must impose hard limits on financial inequality. Democratic money must itself be governed democratically, and must economically facilitate the needs of citizens in a democracy for trustworthy and unbiased information with which to make wise collective decisions. An intriguing approach to implementing and deploying democratic money is via a cryptocurrency built on a proof-of-personhood foundation, giving each opt-in human participant one equal unit of stake. Such a cryptocurrency would have both interesting similarities to, and important differences from, a Universal Basic Income (UBI) denominated in an existing currency.
A New Gene Selection Algorithm using Fuzzy-Rough Set Theory for Tumor Classification
Venue: arXiv cs.LG 2020
Year: 2020

In statistics and machine learning, feature selection is the process of picking a subset of relevant attributes for utilizing in a predictive model. Recently, rough set-based feature selection techniques, that employ feature dependency to perform selection process, have been drawn attention. Classification of tumors based on gene expression is utilized to diagnose proper treatment and prognosis of the disease in bioinformatics applications. Microarray gene expression data includes superfluous feature genes of high dimensionality and smaller training instances. Since exact supervised classification of gene expression instances in such high-dimensional problems is very complex, the selection of appropriate genes is a crucial task for tumor classification. In this study, we present a new technique for gene selection using a discernibility matrix of fuzzy-rough sets. The proposed technique takes into account the similarity of those instances that have the same and different class labels to improve the gene selection results, while the state-of-the art previous approaches only address the similarity of instances with different class labels. To meet that requirement, we extend the Johnson reducer technique into the fuzzy case. Experimental results demonstrate that this technique provides better efficiency compared to the state-of-the-art approaches.
Resilient Cyber-Physical Systems: Using NFV Orchestration
Venue: arXiv cs.CR 2020
Year: 2020

Cyber-Physical Systems (CPSs) are increasingly important in critical areas of our society such as intelligent power grids, next generation mobile devices, and smart buildings. CPS operation has characteristics including considerable heterogeneity, variable dynamics, and high complexity. These systems have also scarce resources in order to satisfy their entire load demand, which can be divided into data processing and service execution. These new characteristics of CPSs need to be managed with novel strategies to ensure their resilient operation. Towards this goal, we propose an SDN-based solution enhanced by distributed Network Function Virtualization (NFV) modules located at the top-most level of our solution architecture. These NFV agents will take orchestrated management decisions among themselves to ensure a resilient CPS configuration against threats, and an optimum operation of the CPS. For this, we study and compare two distinct incentive mechanisms to enforce cooperation among NFVs. Thus, we aim to offer novel perspectives into the management of resilient CPSs, embedding IoT devices, modeled by Game Theory (GT), using the latest software and virtualization platforms.
Learning Implicit Surface Light Fields
Venue: arXiv cs.CV 2020
Year: 2020

Implicit representations of 3D objects have recently achieved impressive results on learning-based 3D reconstruction tasks. While existing works use simple texture models to represent object appearance, photo-realistic image synthesis requires reasoning about the complex interplay of light, geometry and surface properties. In this work, we propose a novel implicit representation for capturing the visual appearance of an object in terms of its surface light field. In contrast to existing representations, our implicit model represents surface light fields in a continuous fashion and independent of the geometry. Moreover, we condition the surface light field with respect to the location and color of a small light source. Compared to traditional surface light field models, this allows us to manipulate the light source and relight the object using environment maps. We further demonstrate the capabilities of our model to predict the visual appearance of an unseen object from a single real RGB image and corresponding 3D shape information. As evidenced by our experiments, our model is able to infer rich visual appearance including shadows and specular reflections. Finally, we show that the proposed representation can be embedded into a variational auto-encoder for generating novel appearances that conform to the specified illumination conditions.
Towards Cognitive Routing based on Deep Reinforcement Learning
Venue: arXiv cs.NI 2020
Year: 2020

Routing is one of the key functions for stable operation of network infrastructure. Nowadays, the rapid growth of network traffic volume and changing of service requirements call for more intelligent routing methods than before. Towards this end, we propose a definition of cognitive routing and an implementation approach based on Deep Reinforcement Learning (DRL). To facilitate the research of DRL-based cognitive routing, we introduce a simulator named RL4Net for DRL-based routing algorithm development and simulation. Then, we design and implement a DDPG-based routing algorithm. The simulation results on an example network topology show that the DDPG-based routing algorithm achieves better performance than OSPF and random weight algorithms. It demonstrate the preliminary feasibility and potential advantage of cognitive routing for future network.
Evolutionary Bin Packing for Memory-Efficient Dataflow Inference Acceleration on FPGA
Venue: arXiv cs.DC 2020
Year: 2020

Convolutional neural network (CNN) dataflow inference accelerators implemented in Field Programmable Gate Arrays (FPGAs) have demonstrated increased energy efficiency and lower latency compared to CNN execution on CPUs or GPUs. However, the complex shapes of CNN parameter memories do not typically map well to FPGA on-chip memories (OCM), which results in poor OCM utilization and ultimately limits the size and types of CNNs which can be effectively accelerated on FPGAs. In this work, we present a design methodology that improves the mapping efficiency of CNN parameters to FPGA OCM. We frame the mapping as a bin packing problem and determine that traditional bin packing algorithms are not well suited to solve the problem within FPGA- and CNN-specific constraints. We hybridize genetic algorithms and simulated annealing with traditional bin packing heuristics to create flexible mappers capable of grouping parameter memories such that each group optimally fits FPGA on-chip memories. We evaluate these algorithms on a variety of FPGA inference accelerators. Our hybrid mappers converge to optimal solutions in a matter of seconds for all CNN use-cases, achieve an increase of up to 65% in OCM utilization efficiency for deep CNNs, and are up to 200$\times$ faster than current state-of-the-art simulated annealing approaches.
FLIC: A Distributed Fog Cache for City-Scale Applications
Venue: arXiv cs.DC 2020
Year: 2020

We present FLIC, a distributed software data caching framework for fogs that reduces network traffic and latency. FLICis targeted toward city-scale deployments of cooperative IoT devices in which each node gathers and shares data with surrounding devices. As machine learning and other data processing techniques that require large volumes of training data are ported to low-cost and low-power IoT systems, we expect that data analysis will be moved away from the cloud. Separation from the cloud will reduce reliance on power-hungry centralized cloud-based infrastructure. However, city-scale deployments of cooperative IoT devices often connect to the Internet with cellular service, in which service charges are proportional to network usage. IoT system architects must be clever in order to keep costs down in these scenarios. To reduce the network bandwidth required to operate city-scale deployments of cooperative IoT systems, FLIC implements a distributed cache on the IoT nodes in the fog. FLIC allows the IoT network to share its data without repetitively interacting with a simple cloud storage service reducing calls out to a backing store. Our results displayed a less than 2% miss rate on reads. Thus, allowing for only 5% of requests needing the backing store. We were also able to achieve more than 50% reduction in bytes transmitted per second.
Hardware Fingerprinting for the ARINC 429 Avionic Bus
Venue: arXiv cs.CR 2020
Year: 2020

ARINC 429 is the most common data bus in use today in civil avionics. However, the protocol lacks any form of source authentication. A technician with physical access to the bus is able to replace a transmitter by a rogue device, and the receivers will accept its malicious data as they have no method of verifying the authenticity of messages. Updating the protocol would close off security loopholes in new aircraft but would require thousands of airplanes to be modified. For the interim, until the protocol is replaced, we propose the first intrusion detection system that utilizes a hardware fingerprinting approach for sender identification for the ARINC 429 data bus. Our approach relies on the observation that changes in hardware, such as replacing a transmitter or a receiver with a rogue one, modify the electric signal of the transmission. Because we rely on the analog properties, and not on the digital content of the transmissions, we are able to detect a hardware switch as soon as it occurs, even if the data that is being transmitted is completely normal. Thus, we are able to preempt the attack before any damage is caused. In this paper we describe the design of our intrusion detection system and evaluate its performance against different adversary models. Our analysis includes both a theoretical Markov-chain model and an extensive empirical evaluation. For this purpose, we collected a data corpus of ARINC 429 data traces, which may be of independent interest since, to the best of our knowledge, no public corpus is available. We find that our intrusion detection system is quite realistic: e.g., it achieves near-zero false alarms per second, while detecting a rogue transmitter in under 50ms, and detecting a rogue receiver in under 3 seconds. In other words, technician attacks can be reliably detected during the pre-flight checks, well before the aircraft takes off.
AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance
Venue: arXiv cs.DC 2020
Year: 2020

The ever-growing availability of computing power and the sustained development of advanced computational methods have contributed much to recent scientific progress. These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial. In recent years, we have been developing AiiDA (http://www.aiida.net), a robust open-source high-throughput infrastructure addressing the challenges arising from the needs of automated workflow management and data provenance recording. Here, we introduce developments and capabilities required to reach sustained performance, with AiiDA supporting throughputs of tens of thousands processes/hour, while automatically preserving and storing the full data provenance in a relational database making it queryable and traversable, thus enabling high-performance data analytics. AiiDA's workflow language provides advanced automation, error handling features and a flexible plugin model to allow interfacing with any simulation software. The associated plugin registry enables seamless sharing of extensions, empowering a vibrant user community dedicated to making simulations more robust, user-friendly and reproducible.
Non-linearity of the Carlet-Feng function, and repartition of Gauss sums
Venue: arXiv cs.IT 2020
Year: 2020

The search for Boolean functions that can withstand the main crypyographic attacks is essential. In 2008, Carlet and Feng studied a class of functions which have optimal cryptographic properties with the exception of nonlinearity for which they give a good but not optimal bound. Carlet and some people who have also worked on this problem of nonlinearity have asked for a new answer to this problem. We provide a new solution to improve the evaluation of the nonlinearity of the Carlet-Feng function, by means of the estimation of the distribution of Gauss sums. This work is in progress and we give some suggestions to improve this work.
Maneuver-based Driving for Intervention in Autonomous Cars
Venue: arXiv cs.HC 2020
Year: 2020

The way we communicate with autonomous cars will fundamentally change as soon as manual input is no longer required as back-up for the autonomous system. Maneuver-based driving is a potential way to allow still the user to intervene with the autonomous car to communicate requests such as stopping at the next parking lot. In this work, we highlight different research questions that still need to be explored to gain insights into how such control can be realized in the future.
Identification of Choquet capacity in multicriteria sorting problems through stochastic inverse analysis
Venue: arXiv cs.AI 2020
Year: 2020

In multicriteria decision aiding (MCDA), the Choquet integral has been used as an aggregation operator to deal with the case of interacting decision criteria. While the application of the Choquet integral for ranking problems have been receiving most of the attention, this paper rather focuses on multicriteria sorting problems (MCSP). In the Choquet integral context, a practical problem that arises is related to the elicitation of parameters known as the Choquet capacities. We address the problem of Choquet capacity identification for MCSP by applying the Stochastic Acceptability Multicriteri Analysis (SMAA), proposing the SMAA-S-Choquet method. The proposed method is also able to model uncertain data that may be present in both decision matrix and limiting profiles, the latter a parameter associated with the sorting problematic. We also introduce two new descriptive measures in order to conduct reverse analysis regarding the capacities: the Scenario Acceptability Index and the Scenario Central Capacity vector.
A Security and Performance Driven Architecture for Cloud Data Centers
Venue: arXiv cs.CR 2020
Year: 2020

With the growing cyber-security threats, ensuring the security of data in Cloud data centers is a challenging task. A prominent type of attack on Cloud data centers is data tampering attack that can jeopardize the confidentiality and the integrity of data. In this article, we present a security and performance driven architecture for these centers that incorporates an intrusion management system for multi-tenant distributed transactional databases. The proposed architecture uses a novel data partitioning and placement scheme based on damage containment and communication cost of distributed transactions. In addition, we present a benchmarking framework for evaluating the performance of the proposed architecture. The results illustrate a trade-off between security and performance goals for Cloud data centers.