OpenRaiser/Intern-Atlas huggingface dataset:
Embedding a Deterministic BFT Protocol in a Block DAG
Venue: arXiv cs.DC 2021
Year: 2021

This work formalizes the structure and protocols underlying recent distributed systems leveraging block DAGs, which are essentially encoding Lamport's happened-before relations between blocks, as their core network primitives. We then present an embedding of any deterministic Byzantine fault tolerant protocol $\mathcal{P}$ to employ a block DAG for interpreting interactions between servers. Our main theorem proves that this embedding maintains all safety and liveness properties of $\mathcal{P}$. Technically, our theorem is based on the insight that a block DAG merely acts as an efficient reliable point-to-point channel between instances of P while also using $\mathcal{P}$ for efficient message compression.
Control Variate Approximation for DNN Accelerators
Venue: arXiv cs.LG 2021
Year: 2021

In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators. The control variate technique is used in Monte Carlo methods to achieve variance reduction. Our approach significantly decreases the induced error due to approximate multiplications in DNN inference, without requiring time-exhaustive retraining compared to state-of-the-art. Leveraging our control variate method, we use highly approximated multipliers to generate power-optimized DNN accelerators. Our experimental evaluation on six DNNs, for Cifar-10 and Cifar-100 datasets, demonstrates that, compared to the accurate design, our control variate approximation achieves same performance and 24% power reduction for a merely 0.16% accuracy loss.
NATOs Mission-Critical Space Capabilities under Threat: Cybersecurity Gaps in the Military Space Asset Supply Chain
Venue: arXiv cs.CR 2021
Year: 2021

The North Atlantic Treaty Organizations (NATO) public-private Space Asset Supply Chain (SASC) currently exhibits significant cybersecurity gaps. It is well-established that data obtained from space assets is fundamental to NATO, as they allow for the facilitation of its missions, self-defence and effective deterrence of its adversaries. Any hostile cyber operation, suspending control over a space asset, severely impacts both NATO missions and allied Member States national security. This threat is exacerbated by NATOs mostly unregulated cyber SASC. Hence, this thesis answers a twofold research question: a) What are current cybersecurity gaps along NATOs global SASC; and b) How can NATO and its allied Member States gain greater control over such gaps to safeguard the supply of NATO mission-critical information? An ontological field study is carried out by conducting nineteen semi-structured interviews with high-level representatives from relevant public, private and academic organizations. This research was undertaken in collaboration with the NATO Cooperative Cyber Defence Centre of Excellence (CCDCOE) in Tallinn, Estonia. This thesis concludes that current cybersecurity gaps along NATOs SASC are caused by cyber vulnerabilities such as legacy systems or the use of Commercial-Off-the-Shelf (COTS) technology. Inadequate cyber SASC management is caused by hindrances such as misaligned classification levels and significant understaffing. On this basis, NATO should consider two major collaboration initiatives: a) Raising Awareness throughout the whole of the NATO system, and b) Pushing forward the creation of regulation through a standardized security framework on SASC cybersecurity. Doing so would enable NATO and its Member States to recognise cyberthreats to mission-critical data early on along its cyber SASC, and thus increase transparency, responsibility, and liability.
Algorithm for Cross-shard Cross-EE Atomic User-level ETH Transfer in Ethereum
Venue: arXiv cs.DC 2021
Year: 2021

Sharding is a way to address scalability problem in blockchain technologies. Ethereum, a prominent blockchain technology, has included sharding in its roadmap to increase its throughput. The plan is also to include multiple execution environments. We address the problem of atomic cross shard value transfer in the presence of multiple execution environments. We leverage on the proposed Ethereum architecture, more specificially on Beacon chain and crosslinks, and propose a solution on top of the netted-balance approach that was proposed for EE-level atomic ðtransfers. We split a cross-shard transfer into two transactions: a debit and a credit. First, the debit transaction is processed at the source shard. The corresponding credit transaction is processed at the destination shard in a subsequent block. We use {\em netted} shard states as channels to communicate pending credits and pending reverts. We discuss various scenarios of debit failures and credit failures, and show our approach ensures atomicity even in the presence of a Byzantine Block proposer. The benefits of our approach are that we do not use any locks nor impose any constraints on the Block Proposer to select specific transactions. However we inherit the limitation of an expensive operation from the netted-balance approach of querying partial states from all other shards. We also show a bound on the size of such inter-shard state reads.
A Compiler Infrastructure for Accelerator Generators
Venue: arXiv cs.PL 2021
Year: 2021

We present Calyx, a new intermediate language (IL) for compiling high-level programs into hardware designs. Calyx combines a hardware-like structural language with a software-like control flow representation with loops and conditionals. This split representation enables a new class of hardware-focused optimizations that require both structural and control flow information which are crucial for high-level programming models for hardware design. The Calyx compiler lowers control flow constructs using finite-state machines and generates synthesizable hardware descriptions. We have implemented Calyx in an optimizing compiler that translates high-level programs to hardware. We demonstrate Calyx using two DSL-to-RTL compilers, a systolic array generator and one for a recent imperative accelerator language, and compare them to equivalent designs generated using high-level synthesis (HLS). The systolic arrays are $4.6\times$ faster and $1.1\times$ larger on average than HLS implementations, and the HLS-like imperative language compiler is within a few factors of a highly optimized commercial HLS toolchain. We also describe three optimizations implemented in the Calyx compiler.
On a Variational Definition for the Jensen-Shannon Symmetrization of Distances based on the Information Radius
Venue: arXiv cs.IT 2021
Year: 2021

We generalize the Jensen-Shannon divergence by considering a variational definition with respect to a generic mean extending thereby the notion of Sibson's information radius. The variational definition applies to any arbitrary distance and yields another way to define a Jensen-Shannon symmetrization of distances. When the variational optimization is further constrained to belong to prescribed probability measure families, we get relative Jensen-Shannon divergences and symmetrizations which generalize the concept of information projections. Finally, we discuss applications of these variational Jensen-Shannon divergences and diversity indices to clustering and quantization tasks of probability measures including statistical mixtures.
Decentralized Deterministic Multi-Agent Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

[Zhang, ICML 2018] provided the first decentralized actor-critic algorithm for multi-agent reinforcement learning (MARL) that offers convergence guarantees. In that work, policies are stochastic and are defined on finite action spaces. We extend those results to offer a provably-convergent decentralized actor-critic algorithm for learning deterministic policies on continuous action spaces. Deterministic policies are important in real-world settings. To handle the lack of exploration inherent in deterministic policies, we consider both off-policy and on-policy settings. We provide the expression of a local deterministic policy gradient, decentralized deterministic actor-critic algorithms and convergence guarantees for linearly-approximated value functions. This work will help enable decentralized MARL in high-dimensional action spaces and pave the way for more widespread use of MARL.
Introducing an experimental distortion-tolerant speech encryption scheme for secure voice communication
Venue: arXiv cs.CR 2021
Year: 2021

The current increasing need for privacy-preserving voice communications is leading to new ideas for securing voice transmission. This paper refers to a relatively new concept of sending encrypted speech as pseudo-speech in the audio domain over digital voice communication infrastructures, like 3G cellular network and VoIP. This work presents a novel distortion-tolerant speech encryption scheme for secure voice communications over voice channels that combines the robustness of analog speech scrambling and elevated security offered by digital ciphers like AES-CTR. The system scrambles vocal parameters of a speech signal (loudness, pitch, timbre) using distance-preserving pseudo-random translations and rotations on a hypersphere of parameters. Next, scrambled parameters are encoded to a pseudo-speech signal adapted to transmission over digital voice channels equipped with voice activity detection. Upon reception of this pseudo-speech signal, the legitimate receiver restores distorted copies of the initial vocal parameters. Despite some deciphering errors, an integrated neural-based vocoder based on the LPCNet architecture reconstructs an intelligible speech. The experimental implementation of this speech encryption scheme has been tested by simulations and sending an encrypted signal over FaceTime between two iPhones 6 connected to the same WiFi network. Moreover, speech excerpts restored from encrypted signals were evaluated by a speech quality assessment on a group of about 40 participants. The experiments demonstrated that the proposed scheme produces intelligible speech with a gracefully progressive quality degradation depending on the channel noise. Finally, the preliminary computational analysis suggested that the presented setting may operate on high-end portable devices in nearly real-time.
Tail Modulo Cons
Venue: arXiv cs.PL 2021
Year: 2021

OCaml function calls consume space on the system stack. Operating systems set default limits on the stack space which are much lower than the available memory. If a program runs out of stack space, they get the dreaded "Stack Overflow" exception -- they crash. As a result, OCaml programmers have to be careful, when they write recursive functions, to remain in the so-called _tail-recursive_ fragment, using _tail_ calls that do not consume stack space. This discipline is a source of difficulties for both beginners and experts. Beginners have to be taught recursion, and then tail-recursion. Experts disagree on the "right" way to write `List.map`. The direct version is beautiful but not tail-recursive, so it crashes on larger inputs. The naive tail-recursive transformation is (slightly) slower than the direct version, and experts may want to avoid that cost. Some libraries propose horrible implementations, unrolling code by hand, to compensate for this performance loss. In general, tail-recursion requires the programmer to manually perform sophisticated program transformations. In this work we propose an implementation of "Tail Modulo Cons" (TMC) for OCaml. TMC is a program transformation for a fragment of non-tail-recursive functions, that rewrites them in _destination-passing style_. The supported fragment is smaller than other approaches such as continuation-passing-style, but the performance of the transformed code is on par with the direct, non-tail-recursive version. Many useful functions that traverse a recursive datastructure and rebuild another recursive structure are in the TMC fragment, in particular `List.map` (and `List.filter`, `List.append`, etc.). Finally those functions can be written in a way that is beautiful, correct on all inputs, and efficient.
Architectural Decay as Predictor of Issue- and Change-Proneness
Venue: arXiv cs.SE 2021
Year: 2021

Architectural decay imposes real costs in terms of developer effort, system correctness, and performance. Over time, those problems are likely to be revealed as explicit implementation issues (defects, feature changes, etc.). Recent empirical studies have demonstrated that there is a significant correlation between architectural "smells" -- manifestations of architectural decay -- and implementation issues. In this paper, we take a step further in exploring this phenomenon. We analyze the available development data from 10 open-source software systems and show that information regarding current architectural decay in these systems can be used to build models that accurately predict future issue-proneness and change-proneness of the systems' implementations. As a less intuitive result, we also show that, in cases where historical data for a system is unavailable, such data from other, unrelated systems can provide reasonably accurate issue- and change-proneness prediction capabilities.
Key Technologies for Networked Virtual Environments
Venue: arXiv cs.NI 2021
Year: 2021

Thanks to the improvements experienced in technology in the last few years, most especially in virtual reality systems, the number and potential of networked virtual environments or NVEs and their users are increasing. NVEs aim to give distributed users a feeling of immersion in a virtual world and the possibility of interacting with other users or with virtual objects inside it, like when they interact in the real world. Being able to provide that feeling and natural interactions when the users are geographically separated is one of the goals of these systems. Nevertheless, this goal is especially sensitive to different issues, such as different connections with heterogeneous throughput or different network latencies, which can lead to consistency and synchronization problems and, thus, to a worsening of the users' quality of experience or QoE. With the purpose of solving these issues, researchers have proposed and evaluated numerous technical solutions, in fields like network architectures, data distribution and filtering, resource balancing, computing models, predictive modeling and synchronization in NVEs. This paper gathers and classifies them, summarizing their advantages and disadvantages, using a new way of classification. With the current increase of the number of NVEs and the multiple solutions proposed so far, this work aims to become a useful tool and a starting point not only for future researchers in this field but also for those who are new in NVEs development, in which guaranteeing a good users' QoE is essential.
Differential Privacy-based Permissioned Blockchain for Private Data Sharing in Industrial IoT
Venue: arXiv cs.CR 2021
Year: 2021

Permissioned blockchain such as Hyperledger fabric enables a secure supply chain model in Industrial Internet of Things (IIoT) through multichannel and private data collection mechanisms. Sharing of Industrial data including private data exchange at every stage between supply chain partners helps to improve product quality, enable future forecast, and enhance management activities. However, the existing data sharing and querying mechanism in Hyperledger fabric is not suitable for supply chain environment in IIoT because the queries are evaluated on actual data stored on ledger which consists of sensitive information such as business secrets, and special discounts offered to retailers and individuals. To solve this problem, we propose a differential privacy-based permissioned blockchain using Hyperledger fabric to enable private data sharing in supply chain in IIoT (DH-IIoT). We integrate differential privacy into the chaindcode (smart contract) of Hyperledger fabric to achieve privacy preservation. As a result, the query response consists of perturbed data which protects the sensitive information in the ledger. The proposed work (DH-IIoT) is evaluated by simulating a permissioned blockchain using Hyperledger fabric. We compare our differential privacy integrated chaincode of Hyperledger fabric with the default chaincode setting of Hyperledger fabric for supply chain scenario. The results confirm that the proposed work maintains 96.15% of accuracy in the shared data while guarantees the protection of sensitive ledger's data.
Network Coding with Myopic Adversaries
Venue: arXiv cs.IT 2021
Year: 2021

We consider the problem of reliable communication over a network containing a hidden {\it myopic} adversary who can eavesdrop on some $z_{ro}$ links, jam some $z_{wo}$ links, and do both on some $z_{rw}$ links. We provide the first information-theoretically tight characterization of the optimal rate of communication possible under all possible settings of the tuple $(z_{ro},z_{wo},z_{rw})$ by providing a novel coding scheme/analysis for a subset of parameter regimes. In particular, our vanishing-error schemes bypass the Network Singleton Bound (which requires a zero-error recovery criteria) in a certain parameter regime where the capacity had been heretofore open. As a direct corollary we also obtain the capacity of the corresponding problem where information-theoretic secrecy against eavesdropping is required in addition to reliable communication.
The DMT of Real and Quaternionic Lattice Codes and DMT Classification of Division Algebra Codes
Venue: arXiv cs.IT 2021
Year: 2021

In this paper we consider the diversity-multiplexing gain tradeoff (DMT) of so-called minimum delay asymmetric space-time codes. Such codes are less than full dimensional lattices in their natural ambient space. Apart from the multiple input single output (MISO) channel there exist very few methods to analyze the DMT of such codes. Further, apart from the MISO case, no DMT optimal asymmetric codes are known. We first discuss previous criteria used to analyze the DMT of space-time codes and comment on why these methods fail when applied to asymmetric codes. We then consider two special classes of asymmetric codes where the code-words are restricted to either real or quaternion matrices. We prove two separate diversity-multiplexing gain trade-off (DMT) upper bounds for such codes and provide a criterion for a lattice code to achieve these upper bounds. We also show that lattice codes based on Q-central division algebras satisfy this optimality criterion. As a corollary this result provides a DMT classification for all Q-central division algebra codes that are based on standard embeddings. While the Q-central division algebra based codes achieve the largest possible DMT of a code restricted to either real or quaternion space, they still fall short of the optimal DMT apart from the MISO case.
Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections
Venue: arXiv cs.LG 2021
Year: 2021

Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data. Dynamic mode decomposition (DMD) is a powerful tool for extracting coherent patterns, but the original DMD and most of its variants do not consider label information, which is often available as side information of spatio-temporal data. In this work, we propose a new method for extracting distinctive coherent patterns from labeled spatio-temporal data collections, such that they contribute to major differences in a labeled set of dynamics. We achieve such pattern extraction by incorporating discriminant analysis into DMD. To this end, we define a kernel function on subspaces spanned by sets of dynamic modes and develop an objective to take both reconstruction goodness as DMD and class-separation goodness as discriminant analysis into account. We illustrate our method using a synthetic dataset and several real-world datasets. The proposed method can be a useful tool for exploratory data analysis for understanding spatio-temporal data.
Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data
Venue: arXiv cs.LG 2021
Year: 2021

The presence of Super-Apps have changed the way we think about the interactions between users and commerce. It then comes as no surprise that it is also redefining the way banking is done. The paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior. To this end, two experiments with different graph-based methodologies are proposed, the first uses graph based features as input in a classification model and the second uses graph neural networks. Our results show that variables of centrality, behavior of neighboring users and transactionality of a user constituted new forms of knowledge that enhance statistical and financial performance of credit risk models. Furthermore, opportunities are identified for Super-Apps to redefine the definition of credit risk by contemplating all the environment that their platforms entail, leading to a more inclusive financial system.
Curvy: An Interactive Design Tool for Varying Density Support Structures
Venue: arXiv cs.GR 2021
Year: 2021

We introduce Curvy-an interactive design tool to generate varying density support structures for 3D printing. Support structures are essential for printing models with extreme overhangs. Yet, they often cause defects on contact areas, resulting in poor surface quality. Low-level design of support structures may alleviate such negative effects. However, it is tedious and unintuitive for novice users as it is hard to predict the impact of changes to the support structure on the final printed part. Curvy allows users to define their high-level preferences on the surface quality directly on the target object rather than explicitly designing the supports. These preferences are then automatically translated into low-level design parameters to generate the support structure. Underlying novel curvy zigzag toolpathing algorithm uses these instructions to generate varying density supports by altering the spacing between individual paths in order to achieve prescribed quality. Combined with the build orientation optimization, Curvy provides a practical solution to the design of support structures with minimal perceptual or functional impact on the target part to be printed.
DyNetKAT: An Algebra of Dynamic Networks
Venue: arXiv cs.NI 2021
Year: 2021

We introduce a formal language for specifying dynamic updates for Software Defined Networks. Our language builds upon Network Kleene Algebra with Tests (NetKAT) and adds constructs for synchronisations and multi-packet behaviour to capture the interaction between the control- and data-plane in dynamic updates. We provide a sound and ground-complete axiomatisation of our language. We exploit the equational theory to provide an efficient reasoning method about safety properties for dynamic networks. We implement our equational theory in DyNetiKAT -- a tool prototype, based on the Maude Rewriting Logic and the NetKAT tool, and apply it to a case study. We show that we can analyse the case study for networks with hundreds of switches using our initial tool prototype.