OpenRaiser/Intern-Atlas huggingface dataset:
Parallel Structure from Motion for UAV Images via Weighted Connected Dominating Set
Venue: arXiv cs.CV 2022
Year: 2022

Incremental Structure from Motion (ISfM) has been widely used for UAV image orientation. Its efficiency, however, decreases dramatically due to the sequential constraint. Although the divide-and-conquer strategy has been utilized for efficiency improvement, cluster merging becomes difficult or depends on seriously designed overlap structures. This paper proposes an algorithm to extract the global model for cluster merging and designs a parallel SfM solution to achieve efficient and accurate UAV image orientation. First, based on vocabulary tree retrieval, match pairs are selected to construct an undirected weighted match graph, whose edge weights are calculated by considering both the number and distribution of feature matches. Second, an algorithm, termed weighted connected dominating set (WCDS), is designed to achieve the simplification of the match graph and build the global model, which incorporates the edge weight in the graph node selection and enables the successful reconstruction of the global model. Third, the match graph is simultaneously divided into compact and non-overlapped clusters. After the parallel reconstruction, cluster merging is conducted with the aid of common 3D points between the global and cluster models. Finally, by using three UAV datasets that are captured by classical oblique and recent optimized views photogrammetry, the validation of the proposed solution is verified through comprehensive analysis and comparison. The experimental results demonstrate that the proposed parallel SfM can achieve 17.4 times efficiency improvement and comparative orientation accuracy. In absolute BA, the geo-referencing accuracy is approximately 2.0 and 3.0 times the GSD (Ground Sampling Distance) value in the horizontal and vertical directions, respectively. For parallel SfM, the proposed solution is a more reliable alternative.
Reducing the Error Floor of the Sign-Preserving Min-Sum LDPC Decoder via Message Weighting of Low-Degree Variable Nodes
Venue: arXiv cs.IT 2022
Year: 2022

Some low-complexity LDPC decoders suffer from error floors. We apply iteration-dependent weights to the degree-3 variable nodes to solve this problem. When the 802.3ca EPON LDPC code is considered, an error floor decrease of more than 3 orders of magnitude is achieved.
INTERPLAY: An Intelligent Model for Predicting Performance Degradation due to Multi-cache Way-disabling
Venue: arXiv cs.PF 2022
Year: 2022

Modern and future processors need to remain functionally correct in the presence of permanent faults to sustain scaling benefits and limit field returns. This paper presents a combined analytical and microarchitectural simulation-based framework called INTERPLAY, that can rapidly predict, at design-time, the performance degradation expected from a processor employing way-disabling to handle permanent faults in caches while in-the-field. The proposed model can predict a program's performance with an accuracy of up to 98.40% for a processor with a two-level cache hierarchy, when multiple caches suffer from faults and need to disable one or more of their ways. INTERPLAY is 9.2x faster than an exhaustive simulation approach since it only needs the training simulation runs for the single-cache way-disabling configurations to predict the performance for any multi-cache way-disabling configuration.
Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations
Venue: arXiv cs.LG 2022
Year: 2022

Predictive maintenance (PdM) is the task of scheduling maintenance operations based on a statistical analysis of the system's condition. We propose a human-in-the-loop PdM approach in which a machine learning system predicts future problems in sets of workstations (computers, laptops, and servers). Our system interacts with domain experts to improve predictions and elicit their knowledge. In our approach, domain experts are included in the loop not only as providers of correct labels, as in traditional active learning, but as a source of explicit decision rule feedback. The system is automated and designed to be easily extended to novel domains, such as maintaining workstations of several organizations. In addition, we develop a simulator for reproducible experiments in a controlled environment and deploy the system in a large-scale case of real-life workstations PdM with thousands of workstations for dozens of companies.
MAGIC: A Method for Assessing Cyber Incidents Occurrence
Venue: arXiv cs.CR 2022
Year: 2022

The assessment of cyber risk plays a crucial role for cybersecurity management, and has become a compulsory task for certain types of companies and organizations. This makes the demand for reliable cyber risk assessment tools continuously increasing, especially concerning quantitative tools based on statistical approaches. Probabilistic cyber risk assessment methods, however, follow the general paradigm of probabilistic risk assessment, which requires the magnitude and the likelihood of incidents as inputs. Unfortunately, for cyber incidents, the likelihood of occurrence is hard to estimate based on historical and publicly available data; so, expert evaluations are commonly used, which however leave space to subjectivity. In this paper, we propose a novel probabilistic model, called MAGIC (Method for AssessinG cyber Incidents oCcurrence), to compute the likelihood of occurrence of a cyber incident, based on the evaluation of the cyber posture of the target organization. This allows deriving tailor-made inputs for probabilistic risk assessment methods, like HTMA (How To Measure Anything in cybersecurity risk), FAIR (Factor Analysis of Information Risk) and others, thus considerably reducing the margin of subjectivity in the assessment of cyber risk. We corroborate our approach through a qualitative and a quantitative comparison with several classical methods.
Learning To Generate Scene Graph from Head to Tail
Venue: arXiv cs.CV 2022
Year: 2022

Scene Graph Generation (SGG) represents objects and their interactions with a graph structure. Recently, many works are devoted to solving the imbalanced problem in SGG. However, underestimating the head predicates in the whole training process, they wreck the features of head predicates that provide general features for tail ones. Besides, assigning excessive attention to the tail predicates leads to semantic deviation. Based on this, we propose a novel SGG framework, learning to generate scene graphs from Head to Tail (SGG-HT), containing Curriculum Re-weight Mechanism (CRM) and Semantic Context Module (SCM). CRM learns head/easy samples firstly for robust features of head predicates and then gradually focuses on tail/hard ones. SCM is proposed to relieve semantic deviation by ensuring the semantic consistency between the generated scene graph and the ground truth in global and local representations. Experiments show that SGG-HT significantly alleviates the biased problem and chieves state-of-the-art performances on Visual Genome.
A Decentralized Framework with Dynamic and Event-Driven Container Orchestration at the Edge
Venue: arXiv cs.DC 2022
Year: 2022

Virtualization provides an abstraction layer for the Internet of Things technology to tackle the heterogeneity of the edge networks. It enables the deployment of an application on devices with different architectures to achieve uniformity. This study lays down the fundamentals of a framework for dynamic and event-driven orchestration towards a fully decentralized edge. It provides a blockchain-based delivery platform for containerized applications registered with their resource requirements through a registry on a distributed file system, namely InterPlanetary File System (IPFS). The decentralized resource manager running on the metrics scraped from the host and the virtualization platform, i.e., Docker in our implementation, dynamically optimizes the resources allocated to each container. The framework ensures that variable workloads of a heterogeneous environment can co-exist on multiple edge devices. An event-driven architecture is built over a lightweight messaging protocol, MQTT, capitalizing on the asynchronous and distributed nature of the publish/subscribe pattern to achieve a truly distributed system.
Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models
Venue: arXiv cs.CL 2022
Year: 2022

NLP models trained on text have been shown to reproduce human stereotypes, which can magnify harms to marginalized groups when systems are deployed at scale. We adapt the Agency-Belief-Communion (ABC) stereotype model of Koch et al. (2016) from social psychology as a framework for the systematic study and discovery of stereotypic group-trait associations in language models (LMs). We introduce the sensitivity test (SeT) for measuring stereotypical associations from language models. To evaluate SeT and other measures using the ABC model, we collect group-trait judgments from U.S.-based subjects to compare with English LM stereotypes. Finally, we extend this framework to measure LM stereotyping of intersectional identities.
Towards a Model Theory of Ordered Logics: Expressivity and Interpolation (Extended version)
Venue: arXiv cs.LO 2022
Year: 2022

We consider the family of guarded and unguarded ordered logics, that constitute a recently rediscovered family of decidable fragments of first-order logic (FO), in which the order of quantification of variables coincides with the order in which those variables appear as arguments of predicates. While the complexities of their satisfiability problems are now well-established, their model theory, however, is poorly understood. Our paper aims to provide some insight into it. We start by providing suitable notions of bisimulation for ordered logics. We next employ bisimulations to compare the relative expressive power of ordered logics, and to characterise our logics as bisimulation-invariant fragments of FO a la van Benthem. Afterwards, we study the Craig Interpolation Property~(CIP). We refute yet another claim from the infamous work by Purdy, by showing that the fluted and forward fragments do not enjoy CIP. We complement this result by showing that the ordered fragment and the guarded ordered logics enjoy CIP. These positive results rely on novel and quite intricate model constructions, which take full advantage of the "forwardness" of our logics.
What makes you, you? Analyzing Recognition by Swapping Face Parts
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning advanced face recognition to an unprecedented accuracy. However, understanding how local parts of the face affect the overall recognition performance is still mostly unclear. Among others, face swap has been experimented to this end, but just for the entire face. In this paper, we propose to swap facial parts as a way to disentangle the recognition relevance of different face parts, like eyes, nose and mouth. In our method, swapping parts from a source face to a target one is performed by fitting a 3D prior, which establishes dense pixels correspondence between parts, while also handling pose differences. Seamless cloning is then used to obtain smooth transitions between the mapped source regions and the shape and skin tone of the target face. We devised an experimental protocol that allowed us to draw some preliminary conclusions when the swapped images are classified by deep networks, indicating a prominence of the eyes and eyebrows region. Code available at https://github.com/clferrari/FacePartsSwap
Algorithms for 2-connected network design and flexible Steiner trees with a constant number of terminals
Venue: arXiv cs.DS 2022
Year: 2022

The $k$-Steiner-2NCS problem is as follows: Given a constant $k$, and an undirected connected graph $G = (V,E)$, non-negative costs $c$ on $E$, and a partition $(T, V-T)$ of $V$ into a set of terminals, $T$, and a set of non-terminals (or, Steiner nodes), where $|T|=k$, find a minimum-cost two-node connected subgraph that contains the terminals. We present a randomized polynomial-time algorithm for the unweighted problem, and a randomized PTAS for the weighted problem. We obtain similar results for the $k$-Steiner-2ECS problem, where the input is the same, and the algorithmic goal is to find a minimum-cost two-edge connected subgraph that contains the terminals. Our methods build on results by Björklund, Husfeldt, and Taslaman (ACM-SIAM SODA 2012) that give a randomized polynomial-time algorithm for the unweighted $k$-Steiner-cycle problem; this problem has the same inputs as the unweighted $k$-Steiner-2NCS problem, and the algorithmic goal is to find a minimum-size simple cycle $C$ that contains the terminals ($C$ may contain any number of Steiner nodes).
Context-based Virtual Adversarial Training for Text Classification with Noisy Labels
Venue: arXiv cs.CL 2022
Year: 2022

Deep neural networks (DNNs) have a high capacity to completely memorize noisy labels given sufficient training time, and its memorization, unfortunately, leads to performance degradation. Recently, virtual adversarial training (VAT) attracts attention as it could further improve the generalization of DNNs in semi-supervised learning. The driving force behind VAT is to prevent the models from overfitting data points by enforcing consistency between the inputs and the perturbed inputs. This strategy could be helpful in learning from noisy labels if it prevents neural models from learning noisy samples while encouraging the models to generalize clean samples. In this paper, we propose context-based virtual adversarial training (ConVAT) to prevent a text classifier from overfitting to noisy labels. Unlike the previous works, the proposed method performs the adversarial training at the context level rather than the inputs. It makes the classifier not only learn its label but also its contextual neighbors, which alleviates the learning from noisy labels by preserving contextual semantics on each data point. We conduct extensive experiments on four text classification datasets with two types of label noises. Comprehensive experimental results clearly show that the proposed method works quite well even with extremely noisy settings.
Lifelong Learning Natural Language Processing Approach for Multilingual Data Classification
Venue: arXiv cs.CL 2022
Year: 2022

The abundance of information in digital media, which in today's world is the main source of knowledge about current events for the masses, makes it possible to spread disinformation on a larger scale than ever before. Consequently, there is a need to develop novel fake news detection approaches capable of adapting to changing factual contexts and generalizing previously or concurrently acquired knowledge. To deal with this problem, we propose a lifelong learning-inspired approach, which allows for fake news detection in multiple languages and the mutual transfer of knowledge acquired in each of them. Both classical feature extractors, such as Term frequency-inverse document frequency or Latent Dirichlet Allocation, and integrated deep NLP (Natural Language Processing) BERT (Bidirectional Encoder Representations from Transformers) models paired with MLP (Multilayer Perceptron) classifier, were employed. The results of experiments conducted on two datasets dedicated to the fake news classification task (in English and Spanish, respectively), supported by statistical analysis, confirmed that utilization of additional languages could improve performance for traditional methods. Also, in some cases supplementing the deep learning method with classical ones can positively impact obtained results. The ability of models to generalize the knowledge acquired between the analyzed languages was also observed.
LRPC codes with multiple syndromes: near ideal-size KEMs without ideals
Venue: arXiv cs.CR 2022
Year: 2022

We introduce a new rank-based key encapsulation mechanism (KEM) with public key and ciphertext sizes around 3.5 Kbytes each, for 128 bits of security, without using ideal structures. Such structures allow to compress objects, but give reductions to specific problems whose security is potentially weaker than for unstructured problems. To the best of our knowledge, our scheme improves in size all the existing unstructured post-quantum lattice or code-based algorithms such as FrodoKEM or Classic McEliece. Our technique, whose efficiency relies on properties of rank metric, is to build upon existing Low Rank Parity Check (LRPC) code-based KEMs and to send multiple syndromes in one ciphertext, allowing to reduce the parameters and still obtain an acceptable decoding failure rate. Our system relies on the hardness of the Rank Support Learning problem, a well-known variant of the Rank Syndrome Decoding problem. The gain on parameters is enough to significantly close the gap between ideal and non-ideal constructions. It enables to choose an error weight close to the rank Gilbert-Varshamov bound, which is a relatively harder zone for algebraic attacks. We also give a version of our KEM that keeps an ideal structure and permits to roughly divide the bandwidth by two compared to previous versions of LRPC KEMs submitted to the NIST with a Decoding Failure Rate (DFR) of $2^{-128}$.
Looking for related discussions on GitHub Discussions
Venue: arXiv cs.SE 2022
Year: 2022

Software teams are increasingly adopting different tools and communication channels to aid the software collaborative development model and coordinate tasks. Among such resources, Programming Community-based Question Answering (PCQA) forums have become widely used by developers. Such environments enable developers to get and share technical information. Interested in supporting the development and management of Open Source Software (OSS) projects, GitHub announced GitHub Discussions - a native forum to facilitate collaborative discussions between users and members of communities hosted on the platform. As GitHub Discussions resembles PCQA forums, it faces challenges similar to those faced by such environments, which include the occurrence of related discussions (duplicates or near-duplicated posts). While duplicate posts have the same content - and may be exact copies - near-duplicates share similar topics and information. Both can introduce noise to the platform and compromise project knowledge sharing. In this paper, we address the problem of detecting related posts in GitHub Discussions. To do so, we propose an approach based on a Sentence-BERT pre-trained model: the RD-Detector. We evaluated RD-Detector using data from different OSS communities. OSS maintainers and Software Engineering (SE) researchers manually evaluated the RD-Detector results, which achieved 75% to 100% in terms of precision. In addition, maintainers pointed out practical applications of the approach, such as merging the discussions' threads and making discussions as comments on one another. OSS maintainers can benefit from RD-Detector to address the labor-intensive task of manually detecting related discussions and answering the same question multiple times.
Knowledge Distillation via Weighted Ensemble of Teaching Assistants
Venue: arXiv cs.LG 2022
Year: 2022

Knowledge distillation in machine learning is the process of transferring knowledge from a large model called the teacher to a smaller model called the student. Knowledge distillation is one of the techniques to compress the large network (teacher) to a smaller network (student) that can be deployed in small devices such as mobile phones. When the network size gap between the teacher and student increases, the performance of the student network decreases. To solve this problem, an intermediate model is employed between the teacher model and the student model known as the teaching assistant model, which in turn bridges the gap between the teacher and the student. In this research, we have shown that using multiple teaching assistant models, the student model (the smaller model) can be further improved. We combined these multiple teaching assistant models using weighted ensemble learning where we have used a differential evaluation optimization algorithm to generate the weight values.
When Satellites Work as Eavesdroppers
Venue: arXiv cs.IT 2022
Year: 2022

This paper considers satellite eavesdroppers in uplink satellite communication systems where the eavesdroppers are randomly distributed at arbitrary altitudes according to homogeneous binomial point processes and attempt to overhear signals that a ground terminal transmits to a serving satellite. Non-colluding eavesdropping satellites are assumed, i.e., they do not cooperate with each other, so that their received signals are not combined but are decoded individually. Directional beamforming with two types of antennas: fixed- and steerable-beam antennas, is adopted at the eavesdropping satellites. The possible distribution cases for the eavesdropping satellites and the distributions of the distances between the terminal and the satellites are analyzed. The distributions of the signal-to-noise ratios (SNRs) at both the serving satellite and the most detrimental eavesdropping satellite are derived as closed-form expressions. The ergodic and outage secrecy capacities of the systems are derived with the secrecy outage probability using the SNR distributions. Simpler approximate expressions for the secrecy performance are obtained based on the Poisson limit theorem, and asymptotic analyses are also carried out in the high-SNR regime. Monte-Carlo simulations verify the analytical results for the secrecy performance. The analytical results are expected to be used to evaluate the secrecy performance and design secure satellite constellations by considering the impact of potential threats from malicious satellite eavesdroppers.
DialogID: A Dialogic Instruction Dataset for Improving Teaching Effectiveness in Online Environments
Venue: arXiv cs.CL 2022
Year: 2022

Online dialogic instructions are a set of pedagogical instructions used in real-world online educational contexts to motivate students, help understand learning materials, and build effective study habits. In spite of the popularity and advantages of online learning, the education technology and educational data mining communities still suffer from the lack of large-scale, high-quality, and well-annotated teaching instruction datasets to study computational approaches to automatically detect online dialogic instructions and further improve the online teaching effectiveness. Therefore, in this paper, we present a dataset of online dialogic instruction detection, \textsc{DialogID}, which contains 30,431 effective dialogic instructions. These teaching instructions are well annotated into 8 categories. Furthermore, we utilize the prevalent pre-trained language models (PLMs) and propose a simple yet effective adversarial training learning paradigm to improve the quality and generalization of dialogic instruction detection. Extensive experiments demonstrate that our approach outperforms a wide range of baseline methods. The data and our code are available for research purposes from: https://github.com/ai4ed/DialogID.
Protecting President Zelenskyy against Deep Fakes
Venue: arXiv cs.CV 2022
Year: 2022

The 2022 Russian invasion of Ukraine is being fought on two fronts: a brutal ground war and a duplicitous disinformation campaign designed to conceal and justify Russia's actions. This campaign includes at least one example of a deep-fake video purportedly showing Ukrainian President Zelenskyy admitting defeat and surrendering. In anticipation of future attacks of this form, we describe a facial and gestural behavioral model that captures distinctive characteristics of Zelenskyy's speaking style. Trained on over eight hours of authentic video from four different settings, we show that this behavioral model can distinguish Zelenskyy from deep-fake imposters.This model can play an important role -- particularly during the fog of war -- in distinguishing the real from the fake.