OpenRaiser/Intern-Atlas huggingface dataset:
What can be certified compactly?
Venue: arXiv cs.DC 2022
Year: 2022

Local certification consists in assigning labels (called \emph{certificates}) to the nodes of a network to certify a property of the network or the correctness of a data structure distributed on the network. The verification of this certification must be local: a node typically sees only its neighbors in the network. The main measure of performance of a certification is the size of its certificates. In 2011, Göös and Suomela identified $Θ(\log n)$ as a special certificate size: below this threshold little is possible, and several key properties do have certifications of this type. A certification with such small certificates is now called a \emph{compact local certification}, and it has become the gold standard of the area, similarly to polynomial time for centralized computing. A major question is then to understand which properties have $O(\log n)$ certificates, or in other words: what is the power of compact local certification? Recently, a series of papers have proved that several well-known network properties have compact local certifications: planarity, bounded-genus, etc. But one would like to have more general results, \emph{i.e.} meta-theorems. In the analogue setting of polynomial-time centralized algorithms, a very fruitful approach has been to prove that restricted types of problems can be solved in polynomial time in graphs with restricted structures. These problems are typically those that can be expressed in some logic, and the graph structures are whose with bounded width or depth parameters. We take a similar approach and prove the first meta-theorems for local certification. (See the abstract of the pdf for more details.)
Assessment of E-Learning Readiness of Faculty Members and Students in the Government and Private Higher Education Institutions in the Philippines
Venue: arXiv cs.SI 2022
Year: 2022

This study seeks to determine the level of readiness of selected private and government-managed colleges and universities in the Philippines. The study also aims to determine if there's a significant difference in the level of readiness between the private and government HEI's. In line with this, a descriptive-comparative method was employed. A questionnaire from the works of Aklaslan and Merca was utilized to determine the faculty members' and students' status of their e-learning readiness, acceptance, training, technological infrastructure, and tool awareness on the implementation of an e-learning program. Weighted mean was used to determine the level of readiness while a t-test was employed to determine if there is a significant difference between results.
Online V2X Scheduling for Raw-Level Cooperative Perception
Venue: arXiv cs.IT 2022
Year: 2022

Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence. While raw-level cooperative perception preserves most information to guarantee accuracy, it is demanding in communication bandwidth and computation power. Therefore, it is important to schedule the most beneficial vehicle to share its sensor in terms of supplementary view and stable network connection. In this paper, we present a model of raw-level cooperative perception and formulate the energy minimization problem of sensor sharing scheduling as a variant of the Multi-Armed Bandit (MAB) problem. Specifically, volatility of the neighboring vehicles, heterogeneity of V2X channels, and the time-varying traffic context are taken into consideration. Then we propose an online learning-based algorithm with logarithmic performance loss, achieving a decent trade-off between exploration and exploitation. Simulation results under different scenarios indicate that the proposed algorithm quickly learns to schedule the optimal cooperative vehicle and saves more energy as compared to baseline algorithms.
NeuVV: Neural Volumetric Videos with Immersive Rendering and Editing
Venue: arXiv cs.CV 2022
Year: 2022

Some of the most exciting experiences that Metaverse promises to offer, for instance, live interactions with virtual characters in virtual environments, require real-time photo-realistic rendering. 3D reconstruction approaches to rendering, active or passive, still require extensive cleanup work to fix the meshes or point clouds. In this paper, we present a neural volumography technique called neural volumetric video or NeuVV to support immersive, interactive, and spatial-temporal rendering of volumetric video contents with photo-realism and in real-time. The core of NeuVV is to efficiently encode a dynamic neural radiance field (NeRF) into renderable and editable primitives. We introduce two types of factorization schemes: a hyper-spherical harmonics (HH) decomposition for modeling smooth color variations over space and time and a learnable basis representation for modeling abrupt density and color changes caused by motion. NeuVV factorization can be integrated into a Video Octree (VOctree) analogous to PlenOctree to significantly accelerate training while reducing memory overhead. Real-time NeuVV rendering further enables a class of immersive content editing tools. Specifically, NeuVV treats each VOctree as a primitive and implements volume-based depth ordering and alpha blending to realize spatial-temporal compositions for content re-purposing. For example, we demonstrate positioning varied manifestations of the same performance at different 3D locations with different timing, adjusting color/texture of the performer's clothing, casting spotlight shadows and synthesizing distance falloff lighting, etc, all at an interactive speed. We further develop a hybrid neural-rasterization rendering framework to support consumer-level VR headsets so that the aforementioned volumetric video viewing and editing, for the first time, can be conducted immersively in virtual 3D space.
Kirigami, the Verifiable Art of Network Cutting
Venue: arXiv cs.NI 2022
Year: 2022

We introduce a modular verification approach to network control plane verification, where we cut a network into smaller fragments to improve the scalability of SMT solving. Users provide an annotated cut which describes how to generate these fragments from the monolithic network, and we verify each fragment independently, using the annotations to define assumptions and guarantees over fragments akin to assume-guarantee reasoning. We prove this modular network verification procedure is sound and complete with respect to verification over the monolithic network. We implement this procedure as Kirigami, an extension of NV - a network verification language and tool - and evaluate it on industrial topologies with synthesized policies. We observe a 2-8x improvement in end-to-end NV verification time, with SMT solve time improving by up to 6 orders of magnitude.
Mitigating the Effects of Ransomware Attacks on Healthcare Systems
Venue: arXiv cs.CR 2022
Year: 2022

Healthcare information systems deal with a large amount of Personally Identifiable Information related to patients like dates of birth and social security numbers, patients health information and history, and financial information like credit card details and bank accounts. Most healthcare institutions purchase information systems from commercial vendors and have minimal inhouse expertise required to maintain these systems. Most institutions lack the expertise required to research evolving threats and maintain a tough security posture. We propose a risk transference based system architecture that moves sensitive data outside the system boundary, into data stores that are managed with stringent and efficient security protocols.
Semantic-Oriented Unlabeled Priming for Large-Scale Language Models
Venue: arXiv cs.CL 2022
Year: 2022

Due to the high costs associated with finetuning large language models, various recent works propose to adapt them to specific tasks without any parameter updates through in-context learning. Unfortunately, for in-context learning there is currently no way to leverage unlabeled data, which is often much easier to obtain in large quantities than labeled examples. In this work, we therefore investigate ways to make use of unlabeled examples to improve the zero-shot performance of pretrained language models without any finetuning: We introduce Semantic-Oriented Unlabeled Priming (SOUP), a method that classifies examples by retrieving semantically similar unlabeled examples, assigning labels to them in a zero-shot fashion, and then using them for in-context learning. We also propose bag-of-contexts priming, a new priming strategy that is more suitable for our setting and enables the usage of more examples than fit into the context window.
Ticketing System: A Descriptive Research on the Use of Ticketing System for Project Management and Issue Tracking in IT Companies
Venue: arXiv cs.SE 2022
Year: 2022

IT companies are popular in the present time as technology arises in the entire world. Incidents and Requests for either clients or company internal are hard to manually track as it is a day-to-day transaction. To keep all the logs, IT Companies are using a ticketing system to track all the records they are gathering. One of the most popular ticketing systems is the JIRA. Either the support team or company internal is benefitting from the use of JIRA as it easily documents all the different transactions and keeps track of different incidents and requests. One of the cons of JIRA is its limited features. The researchers conducted research where it aims to determine the effectiveness of JIRA as a ticketing system despite its limited features. The researchers used a descriptive method to analyze the pros and cons of this ticketing system. It is concluded that this JIRA Ticketing system is helpful for both support groups and internal members of the company as they track all the incidents and request tickets for their clients despite the limited features of JIRA. The researchers strongly recommend to venture out and try other analytic models and start with the ones mentioned in this study for a good foundation and idea. This is can provide the research a new set of results that can be useful to both established and startup companies.
Graph-adaptive Rectified Linear Unit for Graph Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

Graph Neural Networks (GNNs) have achieved remarkable success by extending traditional convolution to learning on non-Euclidean data. The key to the GNNs is adopting the neural message-passing paradigm with two stages: aggregation and update. The current design of GNNs considers the topology information in the aggregation stage. However, in the updating stage, all nodes share the same updating function. The identical updating function treats each node embedding as i.i.d. random variables and thus ignores the implicit relationships between neighborhoods, which limits the capacity of the GNNs. The updating function is usually implemented with a linear transformation followed by a non-linear activation function. To make the updating function topology-aware, we inject the topological information into the non-linear activation function and propose Graph-adaptive Rectified Linear Unit (GReLU), which is a new parametric activation function incorporating the neighborhood information in a novel and efficient way. The parameters of GReLU are obtained from a hyperfunction based on both node features and the corresponding adjacent matrix. To reduce the risk of overfitting and the computational cost, we decompose the hyperfunction as two independent components for nodes and features respectively. We conduct comprehensive experiments to show that our plug-and-play GReLU method is efficient and effective given different GNN backbones and various downstream tasks.
A macro modelling continuum approach with embedded discontinuities for the assessment of masonry arch bridges under earthquake loading
Venue: arXiv cs.CE 2022
Year: 2022

The paper presents an effective macro-modelling approach, utilising an anisotropic material model with embedded discontinuities, for masonry arches and bridges under cyclic loading, including dynamic actions induced by earthquakes. Realistic numerical simulations of masonry arch bridges under static and dynamic loading require accurate models representing the anionotropic nature of masonry and material nonlinearity due to opening and closure of tensile cracks and shear sliding along mortar joints. The proposed 3D modelling approach allows for masonry bond via simple calibration, and enables the representation of tensile cracking, crushing and shear damage in the brickwork. A two-scale representation is adopted, where 3D continuum elements at the structural scale are linked to embedded nonlinear interfaces representing the meso-structure of the material. The potential and accuracy of the proposed approach are shown in numerical examples and comparisons against physical experiments on masonry arches and bridges under static and dynamic loading.
An Analysis of Variations in the Effectiveness of Query Performance Prediction
Venue: arXiv cs.IR 2022
Year: 2022

A query performance predictor estimates the retrieval effectiveness of an IR system for a given query. An important characteristic of QPP evaluation is that, since the ground truth retrieval effectiveness for QPP evaluation can be measured with different metrics, the ground truth itself is not absolute, which is in contrast to other retrieval tasks, such as that of ad-hoc retrieval. Motivated by this argument, the objective of this paper is to investigate how such variances in the ground truth for QPP evaluation can affect the outcomes of QPP experiments. We consider this not only in terms of the absolute values of the evaluation metrics being reported (e.g. Pearson's $r$, Kendall's $τ$), but also with respect to the changes in the ranks of different QPP systems when ordered by the QPP metric scores. Our experiments reveal that the observed QPP outcomes can vary considerably, both in terms of the absolute evaluation metric values and also in terms of the relative system ranks. Through our analysis, we report the optimal combinations of QPP evaluation metric and experimental settings that are likely to lead to smaller variations in the observed results.
Progressive Backdoor Erasing via connecting Backdoor and Adversarial Attacks
Venue: arXiv cs.CV 2022
Year: 2022

Deep neural networks (DNNs) are known to be vulnerable to both backdoor attacks as well as adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, in this paper we find an intriguing connection between them: for a model planted with backdoors, we observe that its adversarial examples have similar behaviors as its triggered images, i.e., both activate the same subset of DNN neurons. It indicates that planting a backdoor into a model will significantly affect the model's adversarial examples. Based on these observations, a novel Progressive Backdoor Erasing (PBE) algorithm is proposed to progressively purify the infected model by leveraging untargeted adversarial attacks. Different from previous backdoor defense methods, one significant advantage of our approach is that it can erase backdoor even when the clean extra dataset is unavailable. We empirically show that, against 5 state-of-the-art backdoor attacks, our PBE can effectively erase the backdoor without obvious performance degradation on clean samples and significantly outperforms existing defense methods.
Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars
Venue: arXiv cs.CV 2022
Year: 2022

We present EXE-GAN, a novel exemplar-guided facial inpainting framework using generative adversarial networks. Our approach can not only preserve the quality of the input facial image but also complete the image with exemplar-like facial attributes. We achieve this by simultaneously leveraging the global style of the input image, the stochastic style generated from the random latent code, and the exemplar style of exemplar image. We introduce a novel attribute similarity metric to encourage networks to learn the style of facial attributes from the exemplar in a self-supervised way. To guarantee the natural transition across the boundaries of inpainted regions, we introduce a novel spatial variant gradient backpropagation technique to adjust the loss gradients based on the spatial location. Extensive evaluations and practical applications on public CelebA-HQ and FFHQ datasets validate the superiority of EXE-GAN in terms of the visual quality in facial inpainting.
Surgical Scheduling via Optimization and Machine Learning with Long-Tailed Data
Venue: arXiv cs.LG 2022
Year: 2022

Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to reduce recovery unit congestion. We estimate the LOS and its probability distribution using machine learning models, schedule procedures on a rolling basis using a variety of optimization models, and estimate performance with simulation. The machine learning models achieved only modest LOS prediction accuracy, despite access to a very rich set of patient characteristics. Compared to the current paper-based system used in the hospital, most optimization models failed to reduce congestion without increasing wait times for surgery. A conservative stochastic optimization with sufficient sampling to capture the long tail of the LOS distribution outperformed the current manual process and other stochastic and robust optimization approaches. These results highlight the perils of using oversimplified distributional models of LOS for scheduling procedures and the importance of using optimization methods well-suited to dealing with long-tailed behavior.
Energy Conscious Dynamic Window Scheduling of Chip Multiprocessors
Venue: arXiv cs.DC 2022
Year: 2022

The need to develop systems that exploit multi and many-core architectures to reduce wasteful heat generation is of utmost importance in compute-intensive applications. We propose an energy-conscious approach to multicore scheduling known as non-preemptive dynamic window (NPDW) scheduling that achieves effective load and temperature balancing over chip multiprocessors. NPDW utilizes the concept of dynamic time windows to accumulate tasks and find an optimal stable matching between accumulated tasks and available processor cores using a modified Gale-Shapely algorithm. The metrics of window and matching performance are defined to create a dynamic window heuristic to determine the next time window size based on the current and previous window sizes. Based on derived formulation and experimental results, we show that our NPDW scheduler is able to distribute the computational and thermal load throughout the processors in a multicore environment better than baseline schedulers. We believe that within multicore compute applications requiring temperature and energy-conscious system design, our scheduler may be employed to effectively disperse system load and prevent excess core heating.
Work in progress: Identifying Two-Factor Authentication Support in Banking Sites
Venue: arXiv cs.CR 2022
Year: 2022

Two-factor authentication (2FA) offers several security benefits that security-conscious users might expect from high-value services such as online banks. In this work, we present our preliminary study to develop a scoring scheme to automatically recognize when bank sites mention support for two-factor authentication. We extract information related to security features (primarily 2FA) offered by 379 bank domains from 93 countries. We use a subset of these sites to refine our scoring scheme to include several heuristics for identifying whether sites offer 2FA. For each bank domain in our dataset, we use our algorithm based on text-analysis to calculate whether the domain offers 2FA to the users of the domain's online banking platform. Our preliminary findings suggest that 2FA is yet to be widely adopted by banking domains.
Tightly Coupled Learning Strategy for Weakly Supervised Hierarchical Place Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Visual place recognition (VPR) is a key issue for robotics and autonomous systems. For the trade-off between time and performance, most of methods use the coarse-to-fine hierarchical architecture, which consists of retrieving top-N candidates using global features, and re-ranking top-N with local features. However, since the two types of features are usually processed independently, re-ranking may harm global retrieval, termed re-ranking confusion. Moreover, re-ranking is limited by global retrieval. In this paper, we propose a tightly coupled learning (TCL) strategy to train triplet models. Different from original triplet learning (OTL) strategy, it combines global and local descriptors for joint optimization. In addition, a bidirectional search dynamic time warping (BS-DTW) algorithm is also proposed to mine locally spatial information tailored to VPR in re-ranking. The experimental results on public benchmarks show that the models using TCL outperform the models using OTL, and TCL can be used as a general strategy to improve performance for weakly supervised ranking tasks. Further, our lightweight unified model is better than several state-of-the-art methods and has over an order of magnitude of computational efficiency to meet the real-time requirements of robots.
Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles
Venue: arXiv cs.CR 2022
Year: 2022

Recent developments of advanced Human-Vehicle Interactions rely on the concept Internet-of-Vehicles (IoV), to achieve large-scale communications and synchronizations of data in practice. The concept of IoV is highly similar to a distributed system, where each vehicle is considered as a node and all nodes are grouped with a centralized server. In this manner, the concerns of data privacy are significant since all vehicles collect, process and share personal statistics (e.g. multi-modal, driving statuses and etc.). Therefore, it's important to understand how modern privacy-preserving techniques suit for IoV. We present the most comprehensive study to characterize modern privacy-preserving techniques for IoV to date. We focus on Differential Privacy (DP), a representative set of mathematically-guaranteed mechanisms for both privacy-preserving processing and sharing on sensitive data. The purpose of our study is to demystify the tradeoffs of deploying DP techniques, in terms of service quality. We first characterize representative privacy-preserving processing mechanisms, enabled by advanced DP approaches. Then we perform a detailed study of an emerging in-vehicle, Deep-Neural-Network-driven application, and study the upsides and downsides of DP for diverse types of data streams. Our study obtains 11 key findings and we highlight FIVE most significant observations from our detailed characterizations. We conclude that there are a large volume of challenges and opportunities for future studies, by enabling privacy-preserving IoV with low overheads for service quality.
Homogenous and Heterogenous Parallel Clustering: An Overview
Venue: arXiv cs.DC 2022
Year: 2022

Recent advances in computer architecture and networking opened the opportunity for parallelizing the clustering algorithms. This divide-and-conquer strategy often results in better results to centralized clustering with a much-improved time performance. This paper reviews key parallel clustering and provides insight into their strategy. The review brings together disparate attempts in parallel clustering to provide a comprehensive account of advances in this emerging field