OpenRaiser/Intern-Atlas huggingface dataset:
Achieving Multi-Port Memory Performance on Single-Port Memory with Coding Techniques
Venue: arXiv cs.AR 2020
Year: 2020

Many performance critical systems today must rely on performance enhancements, such as multi-port memories, to keep up with the increasing demand of memory-access capacity. However, the large area footprints and complexity of existing multi-port memory designs limit their applicability. This paper explores a coding theoretic framework to address this problem. In particular, this paper introduces a framework to encode data across multiple single-port memory banks in order to {\em algorithmically} realize the functionality of multi-port memory. This paper proposes three code designs with significantly less storage overhead compared to the existing replication based emulations of multi-port memories. To further improve performance, we also demonstrate a memory controller design that utilizes redundancy across coded memory banks to more efficiently schedule read and write requests sent across multiple cores. Furthermore, guided by DRAM traces, the paper explores {\em dynamic coding} techniques to improve the efficiency of the coding based memory design. We then show significant performance improvements in critical word read and write latency in the proposed coded-memory design when compared to a traditional uncoded-memory design.
Operationally-based Program Equivalence Proofs using LCTRSs
Venue: arXiv cs.LO 2020
Year: 2020

We propose an operationally-based deductive proof method for program equivalence. It is based on encoding the language semantics as logically constrained term rewriting systems (LCTRSs) and the two programs as terms. The main feature of our method is its flexibility. We illustrate this flexibility in two applications, which are novel. For the first application, we show how to encode low-level details such as stack size in the language semantics and how to prove equivalence between two programs operating at different levels of abstraction. For our running example, we show how our method can prove equivalence between a recursive function operating with an unbounded stack and its tail-recursive optimized version operating with a bounded stack. This type of equivalence checking can be used to ensure that new, undesirable behavior is not introduced by a more concrete level of abstraction. For the second application, we show how to formalize read-sets and write-sets of symbolic expressions and statements by extending the operational semantics in a conservative way. This enables the relational verification of program schemas, which we exploit to prove correctness of compiler optimizations, some of which cannot be proven by existing tools. Our method requires an extension of standard LCTRSs with axiomatized symbols. We also present a prototype implementation that proves the feasibility of both applications that we propose.
The α-divergences associated with a pair of strictly comparable quasi-arithmetic means
Venue: arXiv cs.IT 2020
Year: 2020

We generalize the family of $α$-divergences using a pair of strictly comparable weighted means. In particular, we obtain the $1$-divergence in the limit case $α\rightarrow 1$ (a generalization of the Kullback-Leibler divergence) and the $0$-divergence in the limit case $α\rightarrow 0$ (a generalization of the reverse Kullback-Leibler divergence). We state the condition for a pair of quasi-arithmetic means to be strictly comparable, and report the formula for the quasi-arithmetic $α$-divergences and its subfamily of bipower homogeneous $α$-divergences which belong to the Csisár's $f$-divergences. Finally, we show that these generalized quasi-arithmetic $1$-divergences and $0$-divergences can be decomposed as the sum of generalized cross-entropies minus entropies, and rewritten as conformal Bregman divergences using monotone embeddings.
Distributed systems and trusted execution environments: Trade-offs and challenges
Venue: arXiv cs.CR 2020
Year: 2020

Security and privacy concerns in computer systems have grown in importance with the ubiquity of connected devices. TEEs provide security guarantees based on cryptographic constructs built in hardware. Intel software guard extensions (SGX), in particular, implements powerful mechanisms that can shield sensitive data even from privileged users with full control of system software. In this work, we essentially explore some of the challenges of designing secure distributed systems by using Intel SGX as cornerstone. We do so by designing and experimentally evaluating several elementary systems ranging from communication and processing middleware to a peer-to-peer privacy-preserving solution. We start with support systems that naturally fit cloud deployment scenarios, namely content-based routing, batching and stream processing frameworks. We implement prototypes and use them to analyse the manifested memory usage issues intrinsic to SGX. Next, we aim at protecting very sensitive data: cryptographic keys. By leveraging TEEs, we design protocols for group data sharing that have lower computational complexity than legacy methods. As a bonus, our proposals allow large savings on metadata volume and processing time of cryptographic operations, all with equivalent security guarantees. Finally, we propose privacy-preserving systems against established services like web-search engines. Our evaluation shows that we propose the most robust system in comparison to existing solutions with regard to user re-identification rates and results accuracy in a scalable way. Overall, this thesis proposes new mechanisms that take advantage of TEEs for distributed system architectures. We show through an empirical approach on top of Intel SGX what are the trade-offs of distinct designs applied to distributed communication and processing, cryptographic protocols and private web search.
Towards Secure Composition of Integrated Circuits and Electronic Systems: On the Role of EDA
Venue: arXiv cs.CR 2020
Year: 2020

Modern electronic systems become evermore complex, yet remain modular, with integrated circuits (ICs) acting as versatile hardware components at their heart. Electronic design automation (EDA) for ICs has focused traditionally on power, performance, and area. However, given the rise of hardware-centric security threats, we believe that EDA must also adopt related notions like secure by design and secure composition of hardware. Despite various promising studies, we argue that some aspects still require more efforts, for example: effective means for compilation of assumptions and constraints for security schemes, all the way from the system level down to the "bare metal"; modeling, evaluation, and consideration of security-relevant metrics; or automated and holistic synthesis of various countermeasures, without inducing negative cross-effects. In this paper, we first introduce hardware security for the EDA community. Next we review prior (academic) art for EDA-driven security evaluation and implementation of countermeasures. We then discuss strategies and challenges for advancing research and development toward secure composition of circuits and systems.
Solving Maxwell's Eigenvalue Problem via Isogeometric Boundary Elements and a Contour Integral Method
Venue: arXiv cs.CE 2020
Year: 2020

We solve Maxwell's eigenvalue problem via isogeometric boundary elements and a contour integral method. We discuss the analytic properties of the discretisation, outline the implementation, and showcase numerical examples.
DP-CGAN: Differentially Private Synthetic Data and Label Generation
Venue: arXiv cs.LG 2020
Year: 2020

Generative Adversarial Networks (GANs) are one of the well-known models to generate synthetic data including images, especially for research communities that cannot use original sensitive datasets because they are not publicly accessible. One of the main challenges in this area is to preserve the privacy of individuals who participate in the training of the GAN models. To address this challenge, we introduce a Differentially Private Conditional GAN (DP-CGAN) training framework based on a new clipping and perturbation strategy, which improves the performance of the model while preserving privacy of the training dataset. DP-CGAN generates both synthetic data and corresponding labels and leverages the recently introduced Renyi differential privacy accountant to track the spent privacy budget. The experimental results show that DP-CGAN can generate visually and empirically promising results on the MNIST dataset with a single-digit epsilon parameter in differential privacy.
Design of the Ouroboros packet network
Venue: arXiv cs.NI 2020
Year: 2020

The 5-layer TCP and 7-layer OSI models are taught as high-level frameworks in which the various protocols that are used in computer networks operate. These models provide valid insights in the organization of network functionalities and protocols; however, the difficulties to fit some crucial technologies within them hints that they don't provide a complete model for the organization of -- and relationships between -- different mechanisms in a computer network. Recently, a recursive model for computer networks was proposed, which organizes networks in layers that conceptually provide the same mechanisms through a common interface. Instead of defined by function, these layers are distinguished by scope. We report our research on a model for computer networks. Following a rigorous regime alternating design with the evaluation of its implications in an implementation, we converged on a recursive architecture, named Ouroboros. One of our main main objectives was to disentangle the fundamental mechanisms that are found in computer networks as much as possible. Its distinguishing feature is the separation of unicast and broadcast as different mechanisms, giving rise to two different types of layers. These unicast and broadcast layers can easily be spotted in today's networks. This article presents the concepts underpinning Ouroboros, details its organization and interfaces, and introduces the free software prototype. We hope the insights it provides can guide future network design and implementation.
Understanding Our People at Scale
Venue: arXiv cs.CY 2020
Year: 2020

Human psychology plays an important role in organizational performance. However, understanding our employees is a difficult task due to issues such as psychological complexities, unpredictable dynamics, and the lack of data. Leveraging evidence-based psychology knowledge, this paper proposes a hybrid machine learning plus ontology-based reasoning system for detecting human psychological artifacts at scale. This unique architecture provides a balance between system's processing speed and explain-ability. System outputs can be further consumed by graph science and/or model management system for optimizing business processes, understanding team dynamics, predicting insider threats, managing talents, and beyond.
Fairness and Decision-making in Collaborative Shift Scheduling Systems
Venue: arXiv cs.CY 2020
Year: 2020

The strains associated with shift work decrease healthcare workers' well-being. However, shift schedules adapted to their individual needs can partially mitigate these problems. From a computing perspective, shift scheduling was so far mainly treated as an optimization problem with little attention given to the preferences, thoughts, and feelings of the healthcare workers involved. In the present study, we explore fairness as a central, human-oriented attribute of shift schedules as well as the scheduling process. Three in-depth qualitative interviews and a validating vignette study revealed that while on an abstract level healthcare workers agree on equality as the guiding norm for a fair schedule, specific scheduling conflicts should foremost be resolved by negotiating the importance of individual needs. We discuss elements of organizational fairness, including transparency and team spirit. Finally, we present a sketch for fair scheduling systems, summarizing key findings for designers in a readily usable way.
Towards organizational guidelines for the responsible use of AI
Venue: arXiv cs.CY 2020
Year: 2020

In the past few years, several large companies have published ethical principles of Artificial Intelligence (AI). National governments, the European Commission, and inter-governmental organizations have come up with requirements to ensure the good use of AI. However, individual organizations that want to join this effort, are faced with many unsolved questions. This paper proposes guidelines for organizations committed to the responsible use of AI, but lack the required knowledge and experience. The guidelines consist of two parts: i) helping organizations to decide what principles to adopt, and ii) a methodology for implementing the principles in organizational processes. In case of future AI regulation, organizations following this approach will be well-prepared.
Running on Raygun
Venue: arXiv cs.GR 2020
Year: 2020

With the introduction of Nvidia RTX hardware, ray tracing is now viable as a general real time rendering technique for complex 3D scenes. Leveraging this new technology, we present Raygun, an open source rendering, simulation, and game engine focusing on simplicity, expandability, and the topic of ray tracing realized through Nvidia's Vulkan ray tracing extension.
Uncertainty-based Modulation for Lifelong Learning
Venue: arXiv cs.LG 2020
Year: 2020

The creation of machine learning algorithms for intelligent agents capable of continuous, lifelong learning is a critical objective for algorithms being deployed on real-life systems in dynamic environments. Here we present an algorithm inspired by neuromodulatory mechanisms in the human brain that integrates and expands upon Stephen Grossbergś ground-breaking Adaptive Resonance Theory proposals. Specifically, it builds on the concept of uncertainty, and employs a series of neuromodulatory mechanisms to enable continuous learning, including self-supervised and one-shot learning. Algorithm components were evaluated in a series of benchmark experiments that demonstrate stable learning without catastrophic forgetting. We also demonstrate the critical role of developing these systems in a closed-loop manner where the environment and the agentś behaviors constrain and guide the learning process. To this end, we integrated the algorithm into an embodied simulated drone agent. The experiments show that the algorithm is capable of continuous learning of new tasks and under changed conditions with high classification accuracy (greater than 94 percent) in a virtual environment, without catastrophic forgetting. The algorithm accepts high dimensional inputs from any state-of-the-art detection and feature extraction algorithms, making it a flexible addition to existing systems. We also describe future development efforts focused on imbuing the algorithm with mechanisms to seek out new knowledge as well as employ a broader range of neuromodulatory processes.
Verifying Software Vulnerabilities in IoT Cryptographic Protocols
Venue: arXiv cs.CR 2020
Year: 2020

Internet of Things (IoT) is a system that consists of a large number of smart devices connected through a network. The number of these devices is increasing rapidly, which creates a massive and complex network with a vast amount of data communicated over that network. One way to protect this data in transit, i.e., to achieve data confidentiality, is to use lightweight encryption algorithms for IoT protocols. However, the design and implementation of such protocols is an error-prone task; flaws in the implementation can lead to devastating security vulnerabilities. These vulnerabilities can be exploited by an attacker and affect users' privacy. There exist various techniques to verify software and detect vulnerabilities. Bounded Model Checking (BMC) and Fuzzing are useful techniques to check the correctness of a software system concerning its specifications. Here we describe a framework called Encryption-BMC and Fuzzing (EBF) using combined BMC and fuzzing techniques. We evaluate the application of EBF verification framework on a case study, i.e., the S-MQTT protocol, to check security vulnerabilities in cryptographic protocols for IoT.
Optimal Sampling and Scheduling for Timely Status Updates in Multi-source Networks
Venue: arXiv cs.IT 2020
Year: 2020

We consider a joint sampling and scheduling problem for optimizing data freshness in multi-source systems. Data freshness is measured by a non-decreasing penalty function of \emph{age of information}, where all sources have the same age-penalty function. Sources take turns to generate update packets, and forward them to their destinations one-by-one through a shared channel with random delay. There is a scheduler, that chooses the update order of the sources, and a sampler, that determines when a source should generate a new packet in its turn. We aim to find the optimal scheduler-sampler pairs that minimize the total-average age-penalty at delivery times (Ta-APD) and the total-average age-penalty (Ta-AP). We prove that the Maximum Age First (MAF) scheduler and the zero-wait sampler are jointly optimal for minimizing the Ta-APD. Meanwhile, the MAF scheduler and a relative value iteration with reduced complexity (RVI-RC) sampler are jointly optimal for minimizing the Ta-AP. The RVI-RC sampler is based on a relative value iteration algorithm whose complexity is reduced by exploiting a threshold property in the optimal sampler. Finally, a low-complexity threshold-type sampler is devised via an approximate analysis of Bellman's equation. This threshold-type sampler reduces to a simple water-filling sampler for a linear age-penalty function.
The Apiza Corpus: API Usage Dialogues with a Simulated Virtual Assistant
Venue: arXiv cs.SE 2020
Year: 2020

Virtual assistant technology has the potential to make a significant impact in the field of software engineering. However, few SE-related datasets exist that would be suitable for the design or training of a virtual assistant. To help lay the groundwork for a hypothetical virtual assistant for API usage, we designed and conducted a Wizard-of-Oz study to gather this crucial data. We hired 30 professional programmers to complete a series of programming tasks by interacting with a simulated virtual assistant. Unbeknownst to the programmers, the virtual assistant was actually operated by another human expert. In this report, we describe our experimental methodology and summarize the results of the study.
Manipulating Deformable Objects by Interleaving Prediction, Planning, and Control
Venue: arXiv cs.RO 2020
Year: 2020

We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or simulation. The key question we address is when should we use planning and when should we use control to achieve the task? Planners are designed to find paths through complex configuration spaces, but for highly underactuated systems, such as deformable objects, achieving a specific configuration is very difficult even with high-fidelity models. Conversely, controllers can be designed to achieve specific configurations, but they can be trapped in undesirable local minima due to obstacles. Our approach consists of three components: (1) A global motion planner to generate gross motion of the deformable object; (2) A local controller for refinement of the configuration of the deformable object; and (3) A novel deadlock prediction algorithm to determine when to use planning versus control. By separating planning from control we are able to use different representations of the deformable object, reducing overall complexity and enabling efficient computation of motion. We provide a detailed proof of probabilistic completeness for our planner, which is valid despite the fact that our system is underactuated and we do not have a steering function. We then demonstrate that our framework is able to successfully perform several manipulation tasks with rope and cloth in simulation which cannot be performed using either our controller or planner alone. These experiments suggest that our planner can generate paths efficiently, taking under a second on average to find a feasible path in three out of four scenarios. We also show that our framework is effective on a 16 DoF physical robot, where reachability and dual-arm constraints make the planning more difficult.
The Effects of Gender Signals and Performance in Online Product Reviews
Venue: arXiv cs.CY 2020
Year: 2020

This work quantifies the effects of signaling and performing gender on the success of reviews written on the popular amazon shopping platform. Highly rated reviews play an important role in e-commerce since they are prominently displayed below products. Differences in how gender-signaling and gender-performing review authors are received can lead to important biases in what content and perspectives are represented among top reviews. To investigate this, we extract signals of author gender from user names, distinguishing reviews where the author's likely gender can be inferred. Using reviews authored by these gender-signaling authors, we train a deep-learning classifier to quantify the gendered writing style or gendered performance of reviews written by authors who do not send clear gender signals via their user name. We contrast the effects of gender signaling and performance on review success using matching experiments. While we find no general trend that gendered signals or performances influence overall review success, we find strong context-specific effects. For example, reviews in product categories such as Electronics or Computers are perceived as less helpful when authors signal that they are likely woman, but are received as more helpful in categories such as Beauty or Clothing. In addition to these interesting findings, our work provides a general chain of tools for studying gender-specific effects across various social media platforms.
Designing a Socially Assistive Robot for Long-Term In-Home Use for Children with Autism Spectrum Disorders
Venue: arXiv cs.RO 2020
Year: 2020

Socially assistive robotics (SAR) research has shown great potential for supplementing and augmenting therapy for children with autism spectrum disorders (ASD). However, the vast majority of SAR research has been limited to short-term studies in highly controlled environments. The design and development of a SAR system capable of interacting autonomously {\it in situ} for long periods of time involves many engineering and computing challenges. This paper presents the design of a fully autonomous SAR system for long-term, in-home use with children with ASD. We address design decisions based on robustness and adaptability needs, discuss the development of the robot's character and interactions, and provide insights from the month-long, in-home data collections with children with ASD. This work contributes to a larger research program that is exploring how SAR can be used for enhancing the social and cognitive development of children with ASD.
Accelerating Transient Fault Injection Campaigns by using Dynamic HDL Slicing
Venue: arXiv cs.AR 2020
Year: 2020

Along with the complexity of electronic systems for safety-critical applications, the cost of safety mechanisms evaluation by fault injection simulation is rapidly going up. To reduce these efforts, we propose a fault injection methodology where Hardware Description Language (HDL) code slicing is exploited to accelerate transient fault injection campaigns by pruning fault lists and reducing the number of the injections. In particular, the dynamic HDL slicing technique provides for a critical fault list and allows avoiding injections at non-critical time-steps. Experimental results on an industrial core show that the proposed methodology can successfully reduce the number of injections by up to 10 percent and speed-up the fault injection campaigns.