OpenRaiser/Intern-Atlas huggingface dataset:
A Novel Load Balancing Scheme for Mobile Edge Computing
Venue: arXiv cs.DC 2021
Year: 2021

To overcome long propagation delays for data exchange between the remote cloud data center and end devices in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC) is merging to push mobile computing, network control and storage to the network edges. A cloudlet in MEC is a mobility-enhanced small-scale cloud, which contains several MEC servers located in close proximity to mobile devices. The main purpose of a cloudlet is to stably provide services to mobile devices with low latency. When a cloudlet offers hundreds kinds of services to millions of mobile devices, it is critical to balance the loads so as to improve performance. In this paper, we propose a three-layer mobile hybrid hierarchical P2P (MHP2P) model as a cloudlet. MHP2P performs satisfactory service lookup efficiency and system scalability as well as high stability. More importantly, a load balance scheme is provided so that the loads of MHP2P can be well balanced with the increasing of MEC servers and query load. A large number of simulation experiments indicate that the proposed scheme is efficient for enhancing the load balancing performance in MHP2P based MEC systems.
An Architecture for Exploiting Native User-Land Checkpoint-Restart to Improve Fuzzing
Venue: arXiv cs.CR 2021
Year: 2021

Fuzzing is one of the most popular and widely used techniques to find vulnerabilities in any application. Fuzzers are fast enough, but they still spend a good portion of time to restart a crashed application and then fuzz it from the beginning. Fuzzing an application from a point deeper in the execution is also important. To do this, a user needs to take a snapshot of the program while fuzzing it on top of an emulator, virtual machine, or by utilizing a special kernel module to enable checkpointing. Even with this ability, it can be difficult to attach a fuzzer after restoring a checkpoint. As a result, most fuzzers leverage a form of fork-server design. We propose a novel testing architecture that allows users to attach a fuzzer after the program has started running. We do this by natively checkpointing the target application at a point of interest, and attaching the fuzzer after restoring the checkpoint. A fork-server may even be engaged at the point of restoration. This not only improves the throughput of the fuzzing campaign by minimizing startup time, but opens up a new way to fuzz applications. With this architecture, a user can take a series of checkpoints at points of interest, and run parallel tests to reduce the overall state-complexity of an individual test. Checkpoints allow us to begin fuzzing from a deeper point in the execution path, omitting prior execution from the required coverage path. This and other checkpointing techniques are described in the paper to help improve fuzzing.
Control of a Hexapod Robot Considering Terrain Interaction
Venue: arXiv cs.RO 2021
Year: 2021

Bio-inspired walking hexapod robots are a relatively young branch in robotics in both state of the art and applications. Despite their high degree of flexibility and adaptability derived by their redundant design, the research field that compliments their abilities is still very lacking. In this paper will be proposed state-of-the-art hexapod robot specific control architecture that allows for full control over robot speed, body orientation and walk gait type to employ. Furthermore terrain interaction will be deeply investigated, leading to the development of a terrain-adapting control algorithm that will allow the robot to react swiftly to terrain shape and asperities such as non-linearities and non-continuity within the workspace. It will be presented a dynamic model derived from the interpretation of the hexapod movement to be comparable to these of the base-platform PKM machines, and said model will be validated through Matlab SimMechanicsTM physics simulation. A feed-back control system able to recognize leg-terrain touch and react accordingly to assure movement stability will then be developed. Finally results coming from an experimental campaign based of the PhantomX AX Metal Hexapod Mark II robotic platform by Trossen RoboticsTM is reported.
Mobility-Aware Performance in Hybrid RF and Terahertz Wireless Networks
Venue: arXiv cs.IT 2021
Year: 2021

Using tools from stochastic geometry, this paper develops a tractable framework to analyze the performance of a mobile user in a two-tier wireless network operating on sub-6GHz and terahertz (THz) transmission frequencies. Specifically, using an equivalence distance approach, we characterize the overall handoff (HO) probability in terms of the horizontal and vertical HO and mobility-aware coverage probability. In addition, we characterize novel coverage probability expressions for THz network in the presence of molecular absorption noise and highlight its significant impact on the users' performance. Specifically, we derive a novel closed-form expression for the Laplace Transform of the cumulative molecular noise and interference observed by a mobile user in a hybrid RF-THz network. Furthermore, we provide a novel approximation to derive the conditional distance distributions of a typical user in a hybrid RF-THz network. Finally, using the overall HO probability and coverage probability expressions, the mobility-aware probability of coverage has been derived in a hybrid RF-THz network. Our mathematical results validate the correctness of the derived expressions using Monte-Carlo simulations. The results offer insights into the adverse impact of users' mobility and molecular noise in THz transmissions on the probability of coverage of mobile users. Our results demonstrate that a small increase in the intensity of terahertz base-stations (TBSs) (about 5 times) can increase the HO probability much more compared to the case when the intensity of RF BSs (RBSs) is increased by 100 times. Furthermore, we note that high molecular absorption can be beneficial (in terms of minimizing interference and molecular noise) for specific deployment intensity of TBSs and the benefits can outweigh the drawbacks of signal degradation due to molecular absorption.
Deep Surrogate for Direct Time Fluid Dynamics
Venue: arXiv cs.LG 2021
Year: 2021

The ubiquity of fluids in the physical world explains the need to accurately simulate their dynamics for many scientific and engineering applications. Traditionally, well established but resource intensive CFD solvers provide such simulations. The recent years have seen a surge of deep learning surrogate models substituting these solvers to alleviate the simulation process. Some approaches to build data-driven surrogates mimic the solver iterative process. They infer the next state of the fluid given its previous one. Others directly infer the state from time input. Approaches also differ in their management of the spatial information. Graph Neural Networks (GNN) can address the specificity of the irregular meshes commonly used in CFD simulations. In this article, we present our ongoing work to design a novel direct time GNN architecture for irregular meshes. It consists of a succession of graphs of increasing size connected by spline convolutions. We test our architecture on the Von K{á}rm{á}n's vortex street benchmark. It achieves small generalization errors while mitigating error accumulation along the trajectory.
Performance analysis of SDN controllers: POX, Floodlight and Opendaylight
Venue: arXiv cs.NI 2021
Year: 2021

The IP network is time-consuming for configuration and troubleshooting because it requires access to every device command line interface (CLI) or Graphical User Interface (GUI). In other words, the control plane (gathering information to forward data) and data plane (data forwarding plane) are run on every intermediary device. To solve this problem, the software defined network emerged and separated the control plane (done by software) from the data plane (done by hardware) [1]. In addition, the control plane is operated in the central point named the controller. There are many controller software programs in simulation and production network environments. In this paper, we compare three open source controllers called POX, Floodlight and Opendaylight (ODL) in simulation network created by MININET SDN emulation program in terms of TCP and UDP throughput and average Round Trip Time (RTT) of the first packet of the flow in the mesh and tree topology.
Certified Federated Adversarial Training
Venue: arXiv cs.LG 2021
Year: 2021

In federated learning (FL), robust aggregation schemes have been developed to protect against malicious clients. Many robust aggregation schemes rely on certain numbers of benign clients being present in a quorum of workers. This can be hard to guarantee when clients can join at will, or join based on factors such as idle system status, and connected to power and WiFi. We tackle the scenario of securing FL systems conducting adversarial training when a quorum of workers could be completely malicious. We model an attacker who poisons the model to insert a weakness into the adversarial training such that the model displays apparent adversarial robustness, while the attacker can exploit the inserted weakness to bypass the adversarial training and force the model to misclassify adversarial examples. We use abstract interpretation techniques to detect such stealthy attacks and block the corrupted model updates. We show that this defence can preserve adversarial robustness even against an adaptive attacker.
Ultra-Reliable and Low-Latency Short-Packet Communications for Multihop MIMO Relaying
Venue: arXiv cs.IT 2021
Year: 2021

This work considers the multihop multiple-input multiple-output relay network under short-packet communications to facilitate not only ultra-reliability but also low-latency communications. We assume that the transmit antenna selection (TAS) scheme is utilized at the transmit side, whereas either selection combining (SC) or maximum ratio combining (MRC) is leveraged at the receive side to achieve diversity gains. For quasi-static Rayleigh fading channels and the finite-blocklength regime, we derive the approximate closed-form expressions of the end-to-end (e2e) block error rate (BLER) for both the TAS/MRC and TAS/SC schemes. The asymptotic performance in the high signal-to-noise ratio regime is derived, from which the comparison of TAS/MRC and TAS/SC schemes in terms of the diversity order, e2e BLER loss, and SNR gap is provided. The e2e latency and throughputs are also analyzed for the considered schemes. The correctness of our analysis is confirmed via Monte Carlo simulations.
Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages
Venue: arXiv cs.CL 2021
Year: 2021

Large pretrained masked language models have become state-of-the-art solutions for many NLP problems. While studies have shown that monolingual models produce better results than multilingual models, the training datasets must be sufficiently large. We trained a trilingual LitLat BERT-like model for Lithuanian, Latvian, and English, and a monolingual Est-RoBERTa model for Estonian. We evaluate their performance on four downstream tasks: named entity recognition, dependency parsing, part-of-speech tagging, and word analogy. To analyze the importance of focusing on a single language and the importance of a large training set, we compare created models with existing monolingual and multilingual BERT models for Estonian, Latvian, and Lithuanian. The results show that the newly created LitLat BERT and Est-RoBERTa models improve the results of existing models on all tested tasks in most situations.
NFTGAN: Non-Fungible Token Art Generation Using Generative Adversarial Networks
Venue: arXiv cs.LG 2021
Year: 2021

Digital arts have gained an unprecedented level of popularity with the emergence of non-fungible tokens (NFTs). NFTs are cryptographic assets that are stored on blockchain networks and represent a digital certificate of ownership that cannot be forged. NFTs can be incorporated into a smart contract which allows the owner to benefit from a future sale percentage. While digital art producers can benefit immensely with NFTs, their production is time consuming. Therefore, this paper explores the possibility of using generative adversarial networks (GANs) for automatic generation of digital arts. GANs are deep learning architectures that are widely and effectively used for synthesis of audio, images, and video contents. However, their application to NFT arts have been limited. In this paper, a GAN-based architecture is implemented and evaluated for novel NFT-style digital arts generation. Results from the qualitative case study indicate that the generated artworks are comparable to the real samples in terms of being interesting and inspiring and they were judged to be more innovative than real samples.
A Method for Hiding the Increased Non-Volatile Cache Read Latency
Venue: arXiv cs.AR 2021
Year: 2021

The increased memory demands of workloads is putting high pressure on Last Level Caches (LLCs). Unfortunately, there is limited opportunity to increase the capacity of LLCs due to the area and power requirements of the underlying SRAM technology. Interestingly, emerging Non-Volatile Memory (NVM) technologies promise a feasible alternative to SRAM for LLCs due to their higher area density. However, NVMs have substantially higher read and write latencies, which offset their area density benefit. Although researchers have proposed methods to tolerate NVM's increased write latency, little emphasis has been placed on reducing the critical NVM read latency. To address this problem, this paper proposes Cloak. Cloak exploits data reuse in the LLC at the page level, to hide NVM read latency. Specifically, on certain L1 TLB misses to a page, Cloak transfers LLC-resident data belonging to the page from the LLC NVM array to a set of small SRAM Page Buffers that will service subsequent requests to this page. Further, to enable the high-bandwidth, low-latency transfer of lines of a page to the page buffers, Cloak uses an LLC layout that accelerates the discovery of LLC-resident cache lines from the page. We evaluate Cloak with full-system simulations of a 4-core processor across 14 workloads. We find that, on average, Cloak outperforms an SRAM LLC by 23.8% and an NVM-only LLC by 8.9% -- in both cases, with negligible additional area. Further, Cloak's ED^2 is 39.9% and 17.5% lower, respectively, than these designs.
Bayesian neural network priors for edge-preserving inversion
Venue: arXiv cs.LG 2021
Year: 2021

We consider Bayesian inverse problems wherein the unknown state is assumed to be a function with discontinuous structure a priori. A class of prior distributions based on the output of neural networks with heavy-tailed weights is introduced, motivated by existing results concerning the infinite-width limit of such networks. We show theoretically that samples from such priors have desirable discontinuous-like properties even when the network width is finite, making them appropriate for edge-preserving inversion. Numerically we consider deconvolution problems defined on one- and two-dimensional spatial domains to illustrate the effectiveness of these priors; MAP estimation, dimension-robust MCMC sampling and ensemble-based approximations are utilized to probe the posterior distribution. The accuracy of point estimates is shown to exceed those obtained from non-heavy tailed priors, and uncertainty estimates are shown to provide more useful qualitative information.
Lite Vision Transformer with Enhanced Self-Attention
Venue: arXiv cs.CV 2021
Year: 2021

Despite the impressive representation capacity of vision transformer models, current light-weight vision transformer models still suffer from inconsistent and incorrect dense predictions at local regions. We suspect that the power of their self-attention mechanism is limited in shallower and thinner networks. We propose Lite Vision Transformer (LVT), a novel light-weight transformer network with two enhanced self-attention mechanisms to improve the model performances for mobile deployment. For the low-level features, we introduce Convolutional Self-Attention (CSA). Unlike previous approaches of merging convolution and self-attention, CSA introduces local self-attention into the convolution within a kernel of size 3x3 to enrich low-level features in the first stage of LVT. For the high-level features, we propose Recursive Atrous Self-Attention (RASA), which utilizes the multi-scale context when calculating the similarity map and a recursive mechanism to increase the representation capability with marginal extra parameter cost. The superiority of LVT is demonstrated on ImageNet recognition, ADE20K semantic segmentation, and COCO panoptic segmentation. The code is made publicly available.
The PUEVA Inventory: A Toolkit to Evaluate the Personality, Usability and Enjoyability of Voice Agents
Venue: arXiv cs.HC 2021
Year: 2021

The proliferation of voice agents in consumer devices requires new tools for evaluating these systems beyond their technical functionality. This paper presents a toolkit for the evaluation of Voice User Interfaces (VUIs) with the intention of measuring the crucial factors of subjective enjoyment in the user experience. The PUEVA toolkit was constructed using a meta-analysis of existing literature, structured N=20 and semi-structured N=18 interviews and a within subjects lab study. The resulting questionnaire contains 35 items that represent 12 scales in three categories: (1) Personality (2) Usablity and (3) Enjoyability. The PUEVA Toolkit moves us towards the capacity to evaluate and compare subjective, joyful experiences in between-subject as well as within-subject research designs.
AGPNet -- Autonomous Grading Policy Network
Venue: arXiv cs.RO 2021
Year: 2021

In this work, we establish heuristics and learning strategies for the autonomous control of a dozer grading an uneven area studded with sand piles. We formalize the problem as a Markov Decision Process, design a simulation which demonstrates agent-environment interactions and finally compare our simulator to a real dozer prototype. We use methods from reinforcement learning, behavior cloning and contrastive learning to train a hybrid policy. Our trained agent, AGPNet, reaches human-level performance and outperforms current state-of-the-art machine learning methods for the autonomous grading task. In addition, our agent is capable of generalizing from random scenarios to unseen real world problems.
DB-BERT: a Database Tuning Tool that "Reads the Manual"
Venue: arXiv cs.DB 2021
Year: 2021

DB-BERT is a database tuning tool that exploits information gained via natural language analysis of manuals and other relevant text documents. It uses text to identify database system parameters to tune as well as recommended parameter values. DB-BERT applies large, pre-trained language models (specifically, the BERT model) for text analysis. During an initial training phase, it fine-tunes model weights in order to translate natural language hints into recommended settings. At run time, DB-BERT learns to aggregate, adapt, and prioritize hints to achieve optimal performance for a specific database system and benchmark. Both phases are iterative and use reinforcement learning to guide the selection of tuning settings to evaluate (penalizing settings that the database system rejects while rewarding settings that improve performance). In our experiments, we leverage hundreds of text documents about database tuning as input for DB-BERT. We compare DB-BERT against various baselines, considering different benchmarks (TPC-C and TPC-H), metrics (throughput and run time), as well as database systems (Postgres and MySQL). In all cases, DB-BERT finds the best parameter settings among all compared methods. The code of DB-BERT is available online at https://itrummer.github.io/dbbert/.
Reinforcement Learning based Sequential Batch-sampling for Bayesian Optimal Experimental Design
Venue: arXiv cs.LG 2021
Year: 2021

Engineering problems that are modeled using sophisticated mathematical methods or are characterized by expensive-to-conduct tests or experiments, are encumbered with limited budget or finite computational resources. Moreover, practical scenarios in the industry, impose restrictions, based on logistics and preference, on the manner in which the experiments can be conducted. For example, material supply may enable only a handful of experiments in a single-shot or in the case of computational models one may face significant wait-time based on shared computational resources. In such scenarios, one usually resorts to performing experiments in a manner that allows for maximizing one's state-of-knowledge while satisfying the above mentioned practical constraints. Sequential design of experiments (SDOE) is a popular suite of methods, that has yielded promising results in recent years across different engineering and practical problems. A common strategy, that leverages Bayesian formalism is the Bayesian SDOE, which usually works best in the one-step-ahead or myopic scenario of selecting a single experiment at each step of a sequence of experiments. In this work, we aim to extend the SDOE strategy, to query the experiment or computer code at a batch of inputs. To this end, we leverage deep reinforcement learning (RL) based policy gradient methods, to propose batches of queries that are selected taking into account entire budget in hand. The algorithm retains the sequential nature, inherent in the SDOE, while incorporating elements of reward based on task from the domain of deep RL. A unique capability of the proposed methodology is its ability to be applied to multiple tasks, for example optimization of a function, once its trained. We demonstrate the performance of the proposed algorithm on a synthetic problem, and a challenging high-dimensional engineering problem.
Counting Simplices in Hypergraph Streams
Venue: arXiv cs.DS 2021
Year: 2021

We consider the problem of space-efficiently estimating the number of simplices in a hypergraph stream. This is the most natural hypergraph generalization of the highly-studied problem of estimating the number of triangles in a graph stream. Our input is a $k$-uniform hypergraph $H$ with $n$ vertices and $m$ hyperedges. A $k$-simplex in $H$ is a subhypergraph on $k+1$ vertices $X$ such that all $k+1$ possible hyperedges among $X$ exist in $H$. The goal is to process a stream of hyperedges of $H$ and compute a good estimate of $T_k(H)$, the number of $k$-simplices in $H$. We design a suite of algorithms for this problem. Under a promise that $T_k(H) \ge T$, our algorithms use at most four passes and together imply a space bound of $O( ε^{-2} \logδ^{-1} \text{polylog} n \cdot \min\{ m^{1+1/k}/T, m/T^{2/(k+1)} \} )$ for each fixed $k \ge 3$, in order to guarantee an estimate within $(1\pmε)T_k(H)$ with probability at least $1-δ$. We also give a simpler $1$-pass algorithm that achieves $O(ε^{-2} \logδ^{-1} \log n\cdot (m/T) ( Δ_E + Δ_V^{1-1/k} ))$ space, where $Δ_E$ (respectively, $Δ_V$) denotes the maximum number of $k$-simplices that share a hyperedge (respectively, a vertex). We complement these algorithmic results with space lower bounds of the form $Ω(ε^{-2})$, $Ω(m^{1+1/k}/T)$, $Ω(m/T^{1-1/k})$ and $Ω(mΔ_V^{1/k}/T)$ for multi-pass algorithms and $Ω(mΔ_E/T)$ for $1$-pass algorithms, which show that some of the dependencies on parameters in our upper bounds are nearly tight. Our techniques extend and generalize several different ideas previously developed for triangle counting in graphs, using appropriate innovations to handle the more complicated combinatorics of hypergraphs.