Reviewerly/exHarmony huggingface dataset:
Learning Invariant Representations with Missing Data

Spurious correlations allow flexible models to predict well during training but poorly on related test populations. Recent work has shown that models that satisfy particular independencies involving correlation-inducing \textit{nuisance} variables have guarantees on their test performance. Enforcing such independencies requires nuisances to be observed during training. However, nuisances, such as demographics or image background labels, are often missing. Enforcing independence on just the observed data does not imply independence on the entire population. Here we derive \acrshort{mmd} estimators used for invariance objectives under missing nuisances. On simulations and clinical data, optimizing through these estimates achieves test performance similar to using estimators that make use of the full data.
Correction to: Differential-Linear Cryptanalysis of the Lightweight Cryptographic Algorithm KNOT

In an older version of this paper, there was an orthographical error in the title. This has been corrected.
Shared Model of Sense-making for Human-Machine Collaboration

We present a model of sense-making that greatly facilitates the collaboration between an intelligent analyst and a knowledge-based agent. It is a general model grounded in the science of evidence and the scientific method of hypothesis generation and testing, where sense-making hypotheses that explain an observation are generated, relevant evidence is then discovered, and the hypotheses are tested based on the discovered evidence. We illustrate how the model enables an analyst to directly instruct the agent to understand situations involving the possible production of weapons (e.g., chemical warfare agents) and how the agent becomes increasingly more competent in understanding other situations from that domain (e.g., possible production of centrifuge-enriched uranium or of stealth fighter aircraft).
Quantum Limits to Classically Spoofing an Electromagnetic Signal

Spoofing an electromagnetic signal involves measuring its properties and preparing a spoof signal that is a close enough copy to fool a receiver. A classic application of spoofing is in radar where an airborne target attempts to avoid being tracked by a ground-based radar by emitting pulses indicating a false range or velocity. In certain scenarios it has been shown that a sensor can exploit quantum mechanics to detect spoofing at the single-photon level. Here we analyze an idealized spoofing scenario where a transmitter-receiver pair, seeking to detect spoofing, utilizes a signal chosen randomly from a set of non-orthogonal, coherent states. We show that a spoofer optimally employing classical information on the state of the transmitted signal (i.e. the best measure-and-prepare strategy allowed by quantum mechanics) inevitably emits imperfect spoofs that can be exploited by the receiver to reveal the presence of the spoofer, or to discriminate between true reflections and spoofs. Importantly, we show that the quantum limitations on classical spoofing remain significant even in the large mean-photon-number regime.
APT Attack Heuristic Induction Honeypot Platform Based on Snort and OpenFlow

The honeypot can record attacker’s aggressive behavior and analyze methods of attack in order to develop more intelligent protection policies in the system. While traditional honeypot technology has evolved from static configuration over to the dynamic deployment and greatly reduces the possibility of an attacker identifying a honeypot. But most of the prior technology, there are passive listening, high maintenance costs, controllable weak, low monitoring coverage, easy to identify and other issues. In this paper, T-Pot Multi-platform honeypot based Snort and OpenFlow technology, we proposed the attack traffic into the Multi-platform honeypot to prevent further harm to the system. In order to reduce system load and improve system performance, perform feature extraction and modeling analysis on the attack data set, and Based on the ATT&CK model, a multi-honeypot platform for APT attack recognition is implemented.
Conformal transformation of uniform domains under weights that depend on distance to the boundary

The sphericalization procedure converts a Euclidean space into a compact sphere. In this note we propose a variant of this procedure for locally compact, rectifiably path-connected, non-complete, unbounded metric spaces by using conformal deformations that depend only on the distance to the boundary of the metric space. This deformation is locally bi-Lipschitz to the original domain near its boundary, but transforms the space into a bounded domain. We will show that if the original metric space is a uniform domain with respect to its completion, then the transformed space is also a uniform domain.
KORSAL: Key-point Detection based Online Real-Time Spatio-Temporal Action Localization

Real-time and online action localization in a video is a critical yet highly challenging problem. Accurate action localization requires the utilization of both temporal and spatial information. Recent attempts achieve this by using computationally intensive 3D CNN architectures or highly redundant two-stream architectures with optical flow, making them both unsuitable for real-time, online applications. To accomplish activity localization under highly challenging real-time constraints, we propose utilizing fast and efficient key-point based bounding box prediction to spatially localize actions. We then introduce a tube-linking algorithm that maintains the continuity of action tubes temporally in the presence of occlusions. Further, we eliminate the need for a two-stream architecture by combining temporal and spatial information into a cascaded input to a single network, allowing the network to learn from both types of information. Temporal information is efficiently extracted using a structural similarity index map as opposed to computationally intensive optical flow. Despite the simplicity of our approach, our lightweight end-to-end architecture achieves state-of-the-art frame-mAP of 74.7% on the challenging UCF101-24 dataset, demonstrating a performance gain of 6.4% over the previous best online methods. We also achieve state-of-the-art video-mAP results compared to both online and offline methods. Moreover, our model achieves a frame rate of 41.8 FPS, which is a 10.7% improvement over contemporary real-time methods.
Accelerated carrier invoice factoring using predictive freight transport events

Invoice factoring is an invoice financing process where business organizations sell their invoices to banks or financial institutions at a discount to gain faster access to the invoice amount. In global trade, ocean and land carriers exercise invoice factoring to gain quick access to the money they get paid for the shipment of consignments by shippers. Shippers typically clear the invoice payment within 60-90 days of goods getting delivered. In order to get early access to capital, carriers initiate invoice factoring after the completion of goods delivery to the shipper. In this work, we provide an approach to enable accelerated carrier invoice factoring even before the delivery of goods. Carrier invoice value for a given shipment depends on the actual values of shipment tracking events. We predict the carrier invoice value at different freight transport milestone events as the goods transportation progresses from supplier to shipper using smart contracts on a blockchain network that is operated by the global trade logistics participants. The prediction at a given stage is based on past shipment tracking events and predicted future shipment tracking events. Accurate prediction of invoice value for ongoing shipment enables the carrier organization to initiate invoice factoring on the trade finance network before the completion of goods delivery to the shipper. Further, based on the past accuracy of prediction models, the financial institutions may choose to release the invoice amount in installments at different freight transportation milestone events.
Energy landscape transformation of Ising problem with invariant eigenvalues for quantum annealing

Quantum annealing tends to be more difficult as the energy landscape of the problem becomes complicated with many local minima. We have found a transformation for changing the energy landscape that swaps the eigenvalues and paired states without changing the eigenvalues of the instance at all. The transformation is basically a partial recombination of the two-spin interaction coefficient Jij and the longitudinal magnetic field interaction coefficient hi. The Hamming distance corresponding to a barrier between the states changes by the transformation, which in turn affects the ground state convergence. In the quantum annealing simulation results of a small number of spin instances, the annealing time was shortened by several orders of magnitude by applying the transformation. In addition, we also obtained a result using a D-Wave quantum annealer, which also showed a big improvement in the ground state convergence.
We’ve always been antagonistic: Algorithmic resistances and dissidences beyond the Global North

In this article we suggest that otherwise unacknowledged histories of technological antagonism can help us (artists, activists, and researchers) to more deeply appreciate the foundations on which we develop activist resistances to contemporary computing. Departing from the case of Brazil, our goal is to bridge historical and contemporary perspectives by: 1) discussing the everyday practices of technological dissidence in the country, and how appropriation has been used to resist unequal power structures; 2) presenting how particular tactical ruptures in the history of art and media activism have sought to contaminate and re-envision networked technologies; 3) exploring the particular notions of algorithmic antagonism that two contemporary projects (PretaLab/Olabi and Silo/Caipiratech) advance, and how they relate to their historical counterparts. In sum, these different threads remind us that we’ve always been antagonistic, and that recognizing a longer genealogy of technological dissidences and ruptures can strengthen current practices against algorithmic oppressions.
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review

Due to their massive success in various domains, deep learning techniques are increasingly used to design network intrusion detection solutions that detect and mitigate unknown and known attacks with high accuracy detection rates and minimal feature engineering. However, it has been found that deep learning models are vulnerable to data instances that can mislead the model to make incorrect classification decisions so-called (adversarial examples). Such vulnerability allows attackers to target NIDSs by adding small crafty perturbations to the malicious traffic to evade detection and disrupt the system's critical functionalities. The problem of deep adversarial learning has been extensively studied in the computer vision domain; however, it is still an area of open research in network security applications. Therefore, this survey explores the researches that employ different aspects of adversarial machine learning in the area of network intrusion detection in order to provide directions for potential solutions. First, the surveyed studies are categorized based on their contribution to generating adversarial examples, evaluating the robustness of ML-based NIDs towards adversarial examples, and defending these models against such attacks. Second, we highlight the characteristics identified in the surveyed research. Furthermore, we discuss the applicability of the existing generic adversarial attacks for the NIDS domain, the feasibility of launching the proposed attacks in real-world scenarios, and the limitations of the existing mitigation solutions.
Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis

Due to the constraints of the imaging device and high cost in operation time, computer tomography (CT) scans are usually acquired with low intra-slice resolution. Improving the intra-slice resolution is beneficial to the disease diagnosis for both human experts and computer-aided systems. To this end, this paper builds a novel medical slice synthesis to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy to accomplish this task in the self-supervised learning manner. Specifically, we model this problem from three different views: slice-wise interpolation from axial view and pixel-wise interpolation from coronal and sagittal views. Under this circumstance, the models learned from different views can distill valuable knowledge to guide the learning processes of each other. We can repeat this process to make the models synthesize intermediate slice data with increasing inter-slice resolution. To demonstrate the effectiveness of the proposed approach, we conduct comprehensive experiments on a large-scale CT dataset. Quantitative and qualitative comparison results show that our method outperforms state-of-the-art algorithms by clear margins.
Critical configurations for three projective views

The problem of structure from motion is concerned with recovering the 3-dimensional structure of an object from a set of 2-dimensional images. Generally, all information can be uniquely recovered if enough images and image points are provided, yet there are certain cases where unique recovery is impossible; these are called critical configurations. In this paper we use an algebraic approach to study the critical configurations for three projective cameras. We show that all critical configurations lie on the intersection of quadric surfaces, and classify exactly which intersections constitute a critical configuration.
Preemptive Image Robustification for Protecting Users against  Man-in-the-Middle Adversarial Attacks

Deep neural networks have become the driving force of modern image recognition systems. However, the vulnerability of neural networks against adversarial attacks poses a serious threat to the people affected by these systems. In this paper, we focus on a real-world threat model where a Man-in-the-Middle adversary maliciously intercepts and perturbs images web users upload online. This type of attack can raise severe ethical concerns on top of simple performance degradation. To prevent this attack, we devise a novel bi-level optimization algorithm that finds points in the vicinity of natural images that are robust to adversarial perturbations. Experiments on CIFAR-10 and ImageNet show our method can effectively robustify natural images within the given modification budget. We also show the proposed method can improve robustness when jointly used with randomized smoothing.
BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer

Memes are prevalent on the internet and continue to grow and evolve alongside our culture. An automatic understanding of memes propagating on the internet can shed light on the general sentiment and cultural attitudes of people. In this work, we present team BLUE's solution for the second edition of the MEMOTION shared task. We showcase two approaches for meme classification (i.e. sentiment, humour, offensive, sarcasm and motivation levels) using a text-only method using BERT, and a Multi-Modal-Multi-Task transformer network that operates on both the meme image and its caption to output the final scores. In both approaches, we leverage state-of-the-art pretrained models for text (BERT, Sentence Transformer) and image processing (EfficientNetV4, CLIP). Through our efforts, we obtain first place in task A, second place in task B and third place in task C. In addition, our team obtained the highest average score for all three tasks.
USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation

The vast majority of evaluation metrics for machine translation are supervised, i.e., (i) are trained on human scores, (ii) assume the existence of reference translations, or (iii) leverage parallel data. This hinders their applicability to cases where such supervision signals are not available. In this work, we develop fully unsupervised evaluation metrics. To do so, we leverage similarities and synergies between evaluation metric induction, parallel corpus mining, and MT systems. In particular, we use an unsupervised evaluation metric to mine pseudo-parallel data, which we use to remap deficient underlying vector spaces (in an iterative manner) and to induce an unsupervised MT system, which then provides pseudo-references as an additional component in the metric. Finally, we also induce unsupervised multilingual sentence embeddings from pseudo-parallel data. We show that our fully unsupervised metrics are effective, i.e., they beat supervised competitors on 4 out of our 5 evaluation datasets. We make our code publicly available.
Multimeasurement Generative Models

We formally map the problem of sampling from an unknown distribution with a density in $\mathbb{R}^d$ to the problem of learning and sampling a smoother density in $\mathbb{R}^{Md}$ obtained by convolution with a fixed factorial kernel: the new density is referred to as M-density and the kernel as multimeasurement noise model (MNM). The M-density in $\mathbb{R}^{Md}$ is smoother than the original density in $\mathbb{R}^d$, easier to learn and sample from, yet for large $M$ the two problems are mathematically equivalent since clean data can be estimated exactly given a multimeasurement noisy observation using the Bayes estimator. To formulate the problem, we derive the Bayes estimator for Poisson and Gaussian MNMs in closed form in terms of the unnormalized M-density. This leads to a simple least-squares objective for learning parametric energy and score functions. We present various parametrization schemes of interest including one in which studying Gaussian M-densities directly leads to multidenoising autoencoders--this is the first theoretical connection made between denoising autoencoders and empirical Bayes in the literature. Samples in $\mathbb{R}^d$ are obtained by walk-jump sampling (Saremi & Hyvarinen, 2019) via underdamped Langevin MCMC (walk) to sample from M-density and the multimeasurement Bayes estimation (jump). We study permutation invariant Gaussian M-densities on MNIST, CIFAR-10, and FFHQ-256 datasets, and demonstrate the effectiveness of this framework for realizing fast-mixing stable Markov chains in high dimensions.
Residual Aligner Network

Image registration is important for medical imaging, the estimation of the spatial transformation between different images. Many previous studies have used learning-based methods for coarse-to-fine registration to efficiently perform 3D image registration. The coarse-to-fine approach, however, is limited when dealing with the different motions of nearby objects. Here we propose a novel Motion-Aware (MA) structure that captures the different motions in a region. The MA structure incorporates a novel Residual Aligner (RA) module which predicts the multi-head displacement field used to disentangle the different motions of multiple neighbouring objects. Compared with other deep learning methods, the network based on the MA structure and RA module achieve one of the most accurate unsupervised inter-subject registration on the 9 organs of assorted sizes in abdominal CT scans, with the highest-ranked registration of the veins (Dice Similarity Coefficient / Average surface distance: 62\%/4.9mm for the vena cava and 34\%/7.9mm for the portal and splenic vein), with a half-sized structure and more efficient computation. Applied to the segmentation of lungs in chest CT scans, the new network achieves results which were indistinguishable from the best-ranked networks (94\%/3.0mm). Additionally, the theorem on predicted motion pattern and the design of MA structure are validated by further analysis.
Complex-Valued Autoencoders for Object Discovery

Object-centric representations form the basis of human perception, and enable us to reason about the world and to systematically generalize to new settings. Currently, most works on unsupervised object discovery focus on slot-based approaches, which explicitly separate the latent representations of individual objects. While the result is easily interpretable, it usually requires the design of involved architectures. In contrast to this, we propose a comparatively simple approach - the Complex AutoEncoder (CAE) - that creates distributed object-centric representations. Following a coding scheme theorized to underlie object representations in biological neurons, its complex-valued activations represent two messages: their magnitudes express the presence of a feature, while the relative phase differences between neurons express which features should be bound together to create joint object representations. In contrast to previous approaches using complex-valued activations for object discovery, we present a fully unsupervised approach that is trained end-to-end - resulting in significant improvements in performance and efficiency. Further, we show that the CAE achieves competitive or better unsupervised object discovery performance on simple multi-object datasets compared to a state-of-the-art slot-based approach while being up to 100 times faster to train.
Do Developers Refactor Data Access Code? An Empirical Study

Developers often refactor code to improve the maintainability and comprehension of the software. There are many studies on refactoring activities in traditional software systems. However, refactoring in data-intensive systems is not well explored. Understanding the refactoring practices of developers is important to develop efficient tool support.We conducted a longitudinal study of refactoring activities in data access classes using 12 data-intensive subject systems. We investigated the prevalence and evolution of refactorings and the association of refactorings with data access smells. We also conducted a manual analysis of over 378 samples of data access refactoring instances to identify the functionalities of the code that are targeted by such refactorings. Our results show that (1) data access refactorings are prevalent and different in type. \textit{Rename variable} is the most prevalent data access refactoring. (2) The prevalence and type of refactorings vary as systems evolve in time. (3) Most data access refactorings target codes that implement data fetching and insertion. (4) Data access refactorings do not generally touch SQL queries. Overall, the results show that data access refactorings focus on improving the code quality but not the underlying data access operations. Hence, more work is needed from the research community on providing awareness and support to practitioners on the benefits of addressing data access smells with refactorings.
Design Challenges for a Multi-Perspective Search Engine

Many users turn to document retrieval systems (e.g.search engines) to seek answers to controversial or open-ended questions.However, classical document retrieval systems fall short at delivering users a set of direct and diverse responses in such cases, which requires identifying responses within web documents in the context of the query, and aggregating the responses based on their different perspectives.The goal of this work is to survey and study the user information needs for building a multi-perspective search engine of such.We examine the challenges of synthesizing such language understanding objectives with document retrieval, and study a new multiperspective document retrieval paradigm.We discuss and assess the inherent natural language understanding challenges one needs to address in order to achieve the goal.Following the design challenges and principles, we propose and evaluate a practical prototype pipeline system.We use the prototype system to conduct a user survey in order to assess the utility of our paradigm, as well as understanding the user information needs when issuing controversial and open-ended queries to a search engine.