OpenRaiser/Intern-Atlas huggingface dataset:
Few-shot Classification with Hypersphere Modeling of Prototypes
Venue: arXiv cs.LG 2022
Year: 2022

Metric-based meta-learning is one of the de facto standards in few-shot learning. It composes of representation learning and metrics calculation designs. Previous works construct class representations in different ways, varying from mean output embedding to covariance and distributions. However, using embeddings in space lacks expressivity and cannot capture class information robustly, while statistical complex modeling poses difficulty to metric designs. In this work, we use tensor fields (``areas'') to model classes from the geometrical perspective for few-shot learning. We present a simple and effective method, dubbed hypersphere prototypes (HyperProto), where class information is represented by hyperspheres with dynamic sizes with two sets of learnable parameters: the hypersphere's center and the radius. Extending from points to areas, hyperspheres are much more expressive than embeddings. Moreover, it is more convenient to perform metric-based classification with hypersphere prototypes than statistical modeling, as we only need to calculate the distance from a data point to the surface of the hypersphere. Following this idea, we also develop two variants of prototypes under other measurements. Extensive experiments and analysis on few-shot learning tasks across NLP and CV and comparison with 20+ competitive baselines demonstrate the effectiveness of our approach.
Job Scheduling in Datacenters using Constraint Controlled RL
Venue: arXiv cs.DC 2022
Year: 2022

This paper studies a model for online job scheduling in green datacenters. In green datacenters, resource availability depends on the power supply from the renewables. Intermittent power supply from renewables leads to intermittent resource availability, inducing job delays (and associated costs). Green datacenter operators must intelligently manage their workloads and available power supply to extract maximum benefits. The scheduler's objective is to schedule jobs on a set of resources to maximize the total value (revenue) while minimizing the overall job delay. A trade-off exists between achieving high job value on the one hand and low expected delays on the other. Hence, the aims of achieving high rewards and low costs are in opposition. In addition, datacenter operators often prioritize multiple objectives, including high system utilization and job completion. To accomplish the opposing goals of maximizing total job value and minimizing job delays, we apply the Proportional-Integral-Derivative (PID) Lagrangian methods in Deep Reinforcement Learning to job scheduling problem in the green datacenter environment. Lagrangian methods are widely used algorithms for constrained optimization problems. We adopt a controls perspective to learn the Lagrange multiplier with proportional, integral, and derivative control, achieving favorable learning dynamics. Feedback control defines cost terms for the learning agent, monitors the cost limits during training, and continuously adjusts the learning parameters to achieve stable performance. Our experiments demonstrate improved performance compared to scheduling policies without the PID Lagrangian methods. Experimental results illustrate the effectiveness of the Constraint Controlled Reinforcement Learning (CoCoRL) scheduler that simultaneously satisfies multiple objectives.
Robust Smart Home Face Recognition under Starving Federated Data
Venue: arXiv cs.LG 2022
Year: 2022

Over the past few years, the field of adversarial attack received numerous attention from various researchers with the help of successful attack success rate against well-known deep neural networks that were acknowledged to achieve high classification ability in various tasks. However, majority of the experiments were completed under a single model, which we believe it may not be an ideal case in a real-life situation. In this paper, we introduce a novel federated adversarial training method for smart home face recognition, named FLATS, where we observed some interesting findings that may not be easily noticed in a traditional adversarial attack to federated learning experiments. By applying different variations to the hyperparameters, we have spotted that our method can make the global model to be robust given a starving federated environment. Our code can be found on https://github.com/jcroh0508/FLATS.
Review of Methods for Handling Class-Imbalanced in Classification Problems
Venue: arXiv cs.LG 2022
Year: 2022

Learning classifiers using skewed or imbalanced datasets can occasionally lead to classification issues; this is a serious issue. In some cases, one class contains the majority of examples while the other, which is frequently the more important class, is nevertheless represented by a smaller proportion of examples. Using this kind of data could make many carefully designed machine-learning systems ineffective. High training fidelity was a term used to describe biases vs. all other instances of the class. The best approach to all possible remedies to this issue is typically to gain from the minority class. The article examines the most widely used methods for addressing the problem of learning with a class imbalance, including data-level, algorithm-level, hybrid, cost-sensitive learning, and deep learning, etc. including their advantages and limitations. The efficiency and performance of the classifier are assessed using a myriad of evaluation metrics.
ClassPruning: Speed Up Image Restoration Networks by Dynamic N:M Pruning
Venue: arXiv cs.CV 2022
Year: 2022

Image restoration tasks have achieved tremendous performance improvements with the rapid advancement of deep neural networks. However, most prevalent deep learning models perform inference statically, ignoring that different images have varying restoration difficulties and lightly degraded images can be well restored by slimmer subnetworks. To this end, we propose a new solution pipeline dubbed ClassPruning that utilizes networks with different capabilities to process images with varying restoration difficulties. In particular, we use a lightweight classifier to identify the image restoration difficulty, and then the sparse subnetworks with different capabilities can be sampled based on predicted difficulty by performing dynamic N:M fine-grained structured pruning on base restoration networks. We further propose a novel training strategy along with two additional loss terms to stabilize training and improve performance. Experiments demonstrate that ClassPruning can help existing methods save approximately 40% FLOPs while maintaining performance.
Near-infrared and visible-light periocular recognition with Gabor features using frequency-adaptive automatic eye detection
Venue: arXiv cs.CV 2022
Year: 2022

Periocular recognition has gained attention recently due to demands of increased robustness of face or iris in less controlled scenarios. We present a new system for eye detection based on complex symmetry filters, which has the advantage of not needing training. Also, separability of the filters allows faster detection via one-dimensional convolutions. This system is used as input to a periocular algorithm based on retinotopic sampling grids and Gabor spectrum decomposition. The evaluation framework is composed of six databases acquired both with near-infrared and visible sensors. The experimental setup is complemented with four iris matchers, used for fusion experiments. The eye detection system presented shows very high accuracy with near-infrared data, and a reasonable good accuracy with one visible database. Regarding the periocular system, it exhibits great robustness to small errors in locating the eye centre, as well as to scale changes of the input image. The density of the sampling grid can also be reduced without sacrificing accuracy. Lastly, despite the poorer performance of the iris matchers with visible data, fusion with the periocular system can provide an improvement of more than 20%. The six databases used have been manually annotated, with the annotation made publicly available.
Robust Security Energy Efficiency Optimization for RIS-Aided Cell-Free Networks with Multiple Eavesdroppers
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we investigate the energy efficiency (EE) problem under reconfigurable intelligent surface (RIS)-aided secure cell-free networks, where multiple legitimate users and eavesdroppers (Eves) exist. We formulate a max-min secure EE optimization problem by jointly designing the distributed active beamforming and artificial noise at base stations as well as the passive beamforming at RISs under practical constraints. To deal with it, we first divide the original optimization problem into two sub-ones, and then propose an iterative optimization algorithm to solve each sub-problem based on the fractional programming, constrained convex-convex procedure (CCCP) and semi-definite programming (SDP) techniques. After that, these two sub-problems are alternatively solved until convergence, and the final solutions are obtained. Next, we extend to the imperfect channel state information of the Eves' links, and investigate the robust SEE beamforming optimization problem by bringing the outage probability constraints. Based on this, we first transform the uncertain outage probability constraints into the certain ones by the bernstein-type inequality and sphere boundary techniques, and then propose an alternatively iterative algorithm to obtain the solutions of the original problem based on the S-procedure, successive convex approximation, CCCP and SDP techniques. Finally, the simulation results are conducted to show the effectiveness of the proposed schemes.
Partial Differential Equations Meet Deep Neural Networks: A Survey
Venue: arXiv cs.LG 2022
Year: 2022

Many problems in science and engineering can be represented by a set of partial differential equations (PDEs) through mathematical modeling. Mechanism-based computation following PDEs has long been an essential paradigm for studying topics such as computational fluid dynamics, multiphysics simulation, molecular dynamics, or even dynamical systems. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. At the same time, solving PDEs efficiently has been a long-standing challenge. Generally, except for a few differential equations for which analytical solutions are directly available, many more equations must rely on numerical approaches such as the finite difference method, finite element method, finite volume method, and boundary element method to be solved approximately. These numerical methods usually divide a continuous problem domain into discrete points and then concentrate on solving the system at each of those points. Though the effectiveness of these traditional numerical methods, the vast number of iterative operations accompanying each step forward significantly reduces the efficiency. Recently, another equally important paradigm, data-based computation represented by deep learning, has emerged as an effective means of solving PDEs. Surprisingly, a comprehensive review for this interesting subfield is still lacking. This survey aims to categorize and review the current progress on Deep Neural Networks (DNNs) for PDEs. We discuss the literature published in this subfield over the past decades and present them in a common taxonomy, followed by an overview and classification of applications of these related methods in scientific research and engineering scenarios. The origin, developing history, character, sort, as well as the future trends in each potential direction of this subfield are also introduced.
A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters
Venue: arXiv cs.CR 2022
Year: 2022

Model extraction is a major threat for embedded deep neural network models that leverages an extended attack surface. Indeed, by physically accessing a device, an adversary may exploit side-channel leakages to extract critical information of a model (i.e., its architecture or internal parameters). Different adversarial objectives are possible including a fidelity-based scenario where the architecture and parameters are precisely extracted (model cloning). We focus this work on software implementation of deep neural networks embedded in a high-end 32-bit microcontroller (Cortex-M7) and expose several challenges related to fidelity-based parameters extraction through side-channel analysis, from the basic multiplication operation to the feed-forward connection through the layers. To precisely extract the value of parameters represented in the single-precision floating point IEEE-754 standard, we propose an iterative process that is evaluated with both simulations and traces from a Cortex-M7 target. To our knowledge, this work is the first to target such an high-end 32-bit platform. Importantly, we raise and discuss the remaining challenges for the complete extraction of a deep neural network model, more particularly the critical case of biases.
Quorum Systems in Permissionless Network
Venue: arXiv cs.DC 2022
Year: 2022

Fail-prone systems, and their quorum systems, are useful tools for the design of distributed algorithms. However, fail-prone systems as studied so far require every process to know the full system membership in order to guarantee safety through globally intersecting quorums. Thus, they are of little help in an open, permissionless setting, where such knowledge may not be available. We propose to generalize the theory of fail-prone systems to make it applicable to permissionless systems. We do so by enabling processes not only to make assumptions about failures, but also to make assumptions about the assumptions of other processes. Thus, by transitivity, processes that do not even know of any common process may nevertheless have intersecting quorums and solve, for example, reliable broadcast. Our model generalizes existing models such as the classic fail-prone system model [Malkhi and Reiter, 1998] and the asymmetric fail-prone system model [Cachin and Tackmann, OPODIS 2019]. Moreover, it gives a characterization with standard formalism of the model used by the Stellar blockchain.
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering
Venue: arXiv cs.CL 2022
Year: 2022

Question answering models commonly have access to two sources of "knowledge" during inference time: (1) parametric knowledge - the factual knowledge encoded in the model weights, and (2) contextual knowledge - external knowledge (e.g., a Wikipedia passage) given to the model to generate a grounded answer. Having these two sources of knowledge entangled together is a core issue for generative QA models as it is unclear whether the answer stems from the given non-parametric knowledge or not. This unclarity has implications on issues of trust, interpretability and factuality. In this work, we propose a new paradigm in which QA models are trained to disentangle the two sources of knowledge. Using counterfactual data augmentation, we introduce a model that predicts two answers for a given question: one based on given contextual knowledge and one based on parametric knowledge. Our experiments on the Natural Questions dataset show that this approach improves the performance of QA models by making them more robust to knowledge conflicts between the two knowledge sources, while generating useful disentangled answers.
Finding Critical Nodes in Interdependent Networks with SAT and ILP Solvers
Venue: arXiv cs.NI 2022
Year: 2022

Infrastructure systems, such as power systems, often experience cascading failures. Modeling an infrastructure system as a collection of interdependent networks has recently received attention as a way to explain cascading failures. In this study, we propose an approach to find the set of critical nodes in an interdependent network. For an integer k, we say that a set of k nodes is critical if the initial failures of these k nodes result in the most severe cascading failure among all sets of k nodes. This approach adopts the seminal model of interdependent networks proposed by Buldyrev et al., in which new link failures occur in a network if the connectivity is lost in the paired network. The problem of finding critical nodes is NP-hard; thus the aim of the approach is to accurately solve the problem in feasible time for moderate-size problem instances. The proposed approach consists of two phases. In the first phase, the maximum number of failure propagation stages is computed by repeatedly solving the Boolean satisfiability problem. This number is then used in the second phase, where the set of critical nodes is computed using integer linear programming. The results of applying this approach to a variety of problem instances demonstrate that the approach is feasible for up to at least 30 nodes and can be used as the baseline to compare the performance of heuristic solutions.
FlowDNS: Correlating Netflow and DNS Streams at Scale
Venue: arXiv cs.NI 2022
Year: 2022

Knowing customer's interests, e.g. which Video-On-Demand (VoD) or Social Network services they are using, helps telecommunication companies with better network planning to enhance the performance exactly where the customer's interests lie, and also offer the customers relevant commercial packages. However, with the increasing deployment of CDNs by different services, identification, and attribution of the traffic on network-layer information alone becomes a challenge: If multiple services are using the same CDN provider, they cannot be easily distinguished based on IP prefixes alone. Therefore, it is crucial to go beyond pure network-layer information for traffic attribution. In this work, we leverage real-time DNS responses gathered by the clients' default DNS resolvers. Having these DNS responses and correlating them with network-layer headers, we are able to translate CDN-hosted domains to the actual services they belong to. We design a correlation system for this purpose and deploy it at a large European ISP. With our system, we can correlate an average of 81.7% of the traffic with the corresponding services, without any loss on our live data streams. Our correlation results also show that 0.5% of the daily traffic contains malformatted, spamming, or phishing domain names. Moreover, ISPs can correlate the results with their BGP information to find more details about the origin and destination of the traffic. We plan to publish our correlation software for other researchers or network operators to use.
A new data structure for efficient search on isovists
Venue: arXiv cs.DB 2022
Year: 2022

Spatial data structures allow to make efficient queries on Geographical Information Systems (GIS). Spatial queries involve the geometry of the data, such as points, lines, or polygons. For instance, a spatial query could poll for the nearest restaurants from a given location. Spatial queries can be solved exhaustively by going through the entire data, which is prohibitive as the number of data points increases. In this article, we are interested in making efficient queries on infinitely long geometrical shapes. For instance, angular sectors, defined as the intersection of two half-spaces, are infinitely long. However, regular spatial data structures are not adapted to these geometrical shapes. We propose a new method allowing to make efficient spatial queries on angular sectors (i.e. whether a point is inside an angular sector). It builds a R-tree from the dual space of angular sectors. An extensive evaluation shows our method is faster than using a regular R-tree.
Optimizing the Age of Information in Mixed-Critical Wireless Communication Networks
Venue: arXiv cs.IT 2022
Year: 2022

Beyond fifth generation wireless communication networks (B5G) are applied in many use-cases, such as industrial control systems, smart public transport, and power grids. Those applications require innovative techniques for timely transmission and increased wireless network capacities. Hence, this paper proposes optimizing the data freshness measured by the age of information (AoI) in dense internet of things (IoT) sensor-actuator networks. Given different priorities of data-streams, i.e., different sensitivities to outdated information, mixed-criticality is introduced by analyzing different functions of the age, i.e., we consider linear and exponential aging functions. An intricate non-convex optimization problem managing the physical transmission time and packet outage probability is derived. Such problem is tackled using stochastic reformulations, successive convex approximations, and fractional programming, resulting in an efficient iterative algorithm for AoI optimization. Simulation results validate the proposed scheme's performance in terms of AoI, mixed-criticality, and scalability. The proposed non-orthogonal transmission is shown to outperform an orthogonal access scheme in various deployment cases. Results emphasize the potential gains for dense B5G empowered IoT networks in minimizing the AoI.
Development and Evaluation of the Institutionally Farmed Research On-line Repository and Management System (InFORMs) towards Knowledge-Sharing and Utilization
Venue: arXiv cs.CY 2022
Year: 2022

This paper presents the usability, acceptability and extent of compliance to ISO 25010:2011 of the developed project InFORMS. Key features that aid in the ease of use, access to, and management of the research resource emerged. From the responses, the developed application evidently suggests its usability and compliance to standards from the participants. The application was limited to showcasing all research resources produced in the University, in the last 10 years, after having gone through approval of the research review committee prior inclusion in the database. Maximizing the web application for knowledge-sharing and utilization is commended, advancing instruction and knowledge economy.
Steps towards prompt-based creation of virtual worlds
Venue: arXiv cs.HC 2022
Year: 2022

Large language models trained for code generation can be applied to speaking virtual worlds into existence (creating virtual worlds). In this work we show that prompt-based methods can both accelerate in-VR level editing, as well as can become part of gameplay rather than just part of game development. As an example, we present Codex VR Pong which shows non-deterministic game mechanics using generative processes to not only create static content but also non-trivial interactions between 3D objects. This demonstration naturally leads to an integral discussion on how one would evaluate and benchmark experiences created by generative models - as there are no qualitative or quantitative metrics that apply in these scenarios. We conclude by discussing impending challenges of AI-assisted co-creation in VR.