Reviewerly/exHarmony huggingface dataset:
Generative Methods for Out-of-distribution Prediction and Applications for Threat Detection and Analysis: A Short Review

In recent times, Machine Learning has played an important role in developing novel advanced tools for threat detection and mitigation. Intrusion Detection, Misinformation, Malware, and Fraud Detection are just some examples of cybersecurity fields in which Machine Learning techniques are used to reveal the presence of malicious behaviors. However, Out-of-Distribution, i.e., the potential distribution gap between training and test set, can heavily affect the performances of the traditional Machine Learning based methods. Indeed, they could fail in identifying out-of-samples as possible threats, therefore devising robust approaches to cope with this issue is a crucial and relevant challenge to mitigate the risk of undetected attacks. Moreover, a recent emerging line proposes to use generative models to yield synthetic likely examples to feed the learning algorithms. In this work, we first survey recent Machine Learning and Deep Learning based solutions to face both the problems, i.e., outlier detection and generation; then we illustrate the main cybersecurity application scenarios in which these approaches have been adopted successfully.
Doubly adaptive zero-knowledge proofs

In [TCC 2009 and JoC 2011] Lindell and Zarosim defined adaptive-corruption zero knowledge giving to the environment the power to perform post-execution corruption only. Moreover, their zero-knowledge proof system does not enjoy adaptive-input selection since it crucially relies on fixing the inputs already at the onset of the protocol. In this paper, we propose a new definition of adaptive-corruption zero-knowledge proofs that more naturally fits the setting that benefits from adaptive-input selection. We allow the non-rewindable environment to corrupt the prover adaptively based on its visibility of the messages exchanged so far. Then we show a zero-knowledge proof system for NP that is doubly adaptive (i.e., it enjoys adaptive-corruption under our stronger notion and adaptive-input selection) matching the same assumptions and round complexity of classical zero-knowledge proofs.
TrackFuse: improving tracker performance by late stage fusion

Multiple object tracking (MOT) is a common computer vision problem that focuses on detecting objects and maintaining their identities through a sequence of image frames. Until now, there have been three main approaches to improve MOT performance: 1) improving the detector’s quality, 2) improving the tracker’s quality, or 3) creating novel approaches to jointly model detection and tracking. In this work, we argue that there is a fourth, simpler way to improve MOT performance, by fusing multiple multiple object trackers together. In this paper, we introduce a novel approach, TrackFuse, that aims to fuse the final tracks from two different models into a single output, similar to classification ensembling or weighted box fusion for object detection. The fundamental assumption of TrackFuse is that multiple trackers will fail uniquely, and similarly, multiple detectors will fail uniquely too. Thus, by fusing the output of multiple approaches to MOT, we can improve tracking performance. We test our approach on combinations of several high performing approaches to tracking and show state-of-the-art results on the MOTA metric on a held out validation set of the MOT17 dataset, compared to individual tracking models. Furthermore, we consistently show that fusing multiple object trackers provides a performance boost on multiple metrics compared to results of individual model outputs sent for fusion. Our code will be released soon.
BugRadar: Bug localization by knowledge graph link prediction

: Information Retrieval-based Bug Localization (IRBL) aims to design automatic systems that find buggy files according to bug reports, which can reduce the time consumption to fix bugs for programmers. There has been extensive research on IRBL techniques in recent years. However, these methods cannot make full use of the structure information in bug reports and source files. : In this paper, we propose a novel scheme BugRadar. It combines text features and structure features from bug reports and source files for bug localization. Especially, BugRadar leverages a knowledge graph to make use of structure features. : We originally propose a knowledge graph named TriGraph based on structure features and apply hyperbolic attention embedding to get the link prediction scores. For text features, we propose Partial Text Similarity which improves traditional Text Similarity and Method Level Text Similarity. We also propose Word Collaborative Filtering Score which leverages historical bug reports with more attention on important terms. Finally, we calculate the final suspicious scores based on the structure features, text features, and fixing time information from bug fixing history with a neural network. : We apply our scheme to four projects (Tomcat, SWT, JDT, and Birt) in a popular dataset and get approving results. BugRadar gets better results than other state-of-the-art methods on three projects out of the four. It achieves a relative improvement of 8.8% in SWT and 9.8% in JDT for Mean Average Precision compared to the previous best scheme KGBugLocator and 11.4% in Birt compared to Adaptive Regression. : BugRadar can achieve approving performance on large-scale projects with enough historical bug reports. It verifies that knowledge graphs are capable of representing the structure features for bug localization. The novel Partial Text Similarity and Word Collaborative Filtering Score are both effective improvements for using text features.
Skeleton-based human action recognition with a physics-augmented encoder-decoder network

Human action recognition is important for many applications such as surveillance monitoring, safety, and healthcare. As 3D body skeletons can accurately characterize body actions and are robust to camera views, we propose a 3D skeleton-based human action method. Different from the existing skeleton-based methods that use only geometric features for action recognition, we propose a physics-augmented encoder and decoder model that produces physically plausible geometric features for human action recognition. Specifically, given the input skeleton sequence, the encoder performs a spatiotemporal graph convolution to produce spatiotemporal features for both predicting human actions and estimating the generalized positions and forces of body joints. The decoder, implemented as an ODE solver, takes the joint forces and solves the Euler-Lagrangian equation to reconstruct the skeletons in the next frame. By training the model to simultaneously minimize the action classification and the 3D skeleton reconstruction errors, the encoder is ensured to produce features that are consistent with both body skeletons and the underlying body dynamics as well as being discriminative. The physics-augmented spatiotemporal features are used for human action classification. We evaluate the proposed method on NTU-RGB+D, a large-scale dataset for skeleton-based action recognition. Compared with existing methods, our method achieves higher accuracy and better generalization ability.
Book review

In this work we study a Hebbian neural network, where neurons are arranged according to a hierarchical architecture such that their couplings scale with their reciprocal distance. As a full statistical mechanics solution is not yet available, after a streamlined introduction to the state of the art via that route, the problem is consistently approached through signal-to-noise technique and extensive numerical simulations. Focusing on the low-storage regime, where the amount of stored patterns grows at most logarithmical with the system size, we prove that these non-mean-field Hopfield-like networks display a richer phase diagram than their classical counterparts. In particular, these networks are able to perform serial processing (i.e. retrieve one pattern at a time through a complete rearrangement of the whole ensemble of neurons) as well as parallel processing (i.e. retrieve several patterns simultaneously, delegating the management of different patterns to diverse communities that build network). The tune between the two regimes is given by the rate of the coupling decay and by the level of noise affecting the system.The price to pay for those remarkable capabilities lies in a network’s capacity smaller than the mean field counterpart, thus yielding a new budget principle: the wider the multitasking capabilities, the lower the network load and vice versa. This may have important implications in our understanding of biological complexity.
Attractiveness Computing in Image Media

Our research group has been working on attractiveness prediction, reasoning, and even enhancement for multimedia content, which we call “attractiveness computing.” Attractiveness includes impressiveness, instagrammability, memorability, clickability, and so on. Analyzing such attractiveness was usually done by experienced professionals but we have experimentally revealed that artificial intelligence (AI) based on big multimedia data can imitate or reproduce professionals' skills in some cases. In this paper, we introduce some of the representative works and possible real-life applications of our attractiveness computing for image media.
Detecting review manipulation from behavior deviation: A deep learning approach

This research considers the problem of review manipulation by exploring rating distribution deviations and proposing a detection framework that captures review bias. We characterize the severity of review manipulation based on the degree to which the corresponding rating deviates from a normal distribution. Then we utilize deep learning approaches to learn hidden textual representations of unmanipulated reviews for biased review detection. Experiments conducted on 1,396,132 movie reviews demonstrate that highly/low biased reviews yield significant difference through our proposed model. Moreover, sentiment and topic analysis on experimental results reveal that biased reviews differentiate from authentic reviews in covering less movie-related topics and embedding stronger and more inflammatory emotions. The findings provide managerial insights for business owners in detecting review manipulation and suggest practical signals for online consumers to distinguish misleading information online.
Normalized determinant pooling layer in CNNs for multi-label classification

Convolutional neural networks (CNNs) are a widely researched neural network architecture that has demonstrated exemplary performance in image processing tasks and applications compared to other popular deep learning and machine learning methods resulting in state-of-the-art performance in many image processing tasks such as image classification and segmentation. CNNs operate on the principle of automated learning of filters or kernels in contrast with hand-crafted digital filters to extrapolate features from images effectively. This paper aims to investigate whether a matrix's determinant can be used to preserve information in CNN convolutional layers. Geometrically the absolute value of the determinant is defined as a scaling factor of the linear transformation resulting from matrix multiplication. When an image's size is reduced into a feature space through a convolutional layer of a CNN, some information is lost. The intuition is that the scaling factor that results from the determinant of the pooling layer matrix can enhance the feature space introducing scaling as a piece of information in the feature space as well as lost relations between adjacent pixels.
Decentralized approaches disrupt multi-stakeholder activities

Decentralized approaches play an increasingly critical role in multi-stakeholder activities which require (a) shared situational awareness, (b) shared coordination points, and (c) historical record of activities, in disperse and distributed activities, such as manufacturing supply chains, space, and national security. These activities require stakeholders to mutually coordinate and cooperate by sharing information with each other. The common modes of information sharing are bi-lateral exchanges, and ‘walled garden’ sharing among a subset of stakeholders. Problems arise due to incomplete information sharing and lack of trust among stakeholders. The origins of mistrust are: (a) stakeholders may not trust (political, technical, risk) a single stakeholder to be fully in control of information flow and storage; (b) risk of central-point-of-failure is too high; (c) uncertain data integrity between stakeholders where all stakeholders do not trust each other. Multi-stakeholder data sharing challenges can be addressed with decentralized approaches and associated decentralized data technology such as ledgers and blockchain. Decentralized data technology includes decentralized identity (e.g., W3C DID), decentralized file storage, and blockchain/ledgers to provide cryptographically provable data integrity properties (e.g., tamper evident), as well as increased resilience to accident and attack. Fortunately, the decentralized data technology sector is maturing, and entering a phase of wider adoption in industry, pursuing decentralized data technologies at an increasing rate. This paper starts with a brief review of decentralized data technology, then describes multi-stakeholder examples in space sensor tasking and manufacturing supply chain where decentralized approaches and data technologies can improve coordination and cooperation.
Secure federated machine learning with flexible topology and distributed privacy controls

Federated machine learning (FML) for training of deep neural network models is a useful technique where insufficient sample data is available at a local level. In applications where data privacy must be preserved, such as in health care, financial services, and defense contexts, it is important that there is no exchange of data between constituents of the distributed network. It may also be desirable to protect the integrity and secrecy of the algorithms and trained models deployed within the network. Demonstrating the privacy-enhancing technology of Confidential Computing, we present a novel solution for FML implementation that supports extensible graph-based network topology configuration under federated, distributed, or centralized training regimes. The presented solution provides for policy-based control of model training and automated monitoring of model convergence and network performance. Owners of private datasets can retain independent control of their data through local encryption, while global data anonymization policies can be applied over the sample data. Full auditability of the model training process is provided to distributed data owners and the model owner using hardware-based cryptographic secrets that underpin zero-trust implementation of the training network. Operation of the proposed secure FML solution is discussed in the context of model training over distributed radiological image data for weakly-supervised learning and classification of common thorax diseases. Cross-domain adaptation of the proposed solution and integrated model integrity protection against adversarial attacks reflects a breakthrough technology for data science teams working with distributed datasets.
Double-edged sword of large language models: mitigating security risks of AI-generated code

With the increasing reliance on collaborative and cloud-based systems, there is a drastic increase in attack surfaces and code vulnerabilities. Automation is key for fielding and defending software systems at scale. Researchers in Symbolic AI have had considerable success in finding flaws in human-created code. Also, run-time testing methods such as fuzzing do uncover numerous bugs. However, the major deficiency of both approaches is the inability of the methods to fix the discovered errors. They also do not scale and defy automation. Static analysis methods also suffer from the false positive problem – an overwhelming number of reported flaws are not real bugs. This brings up an interesting conundrum: Symbolic approaches actually have a detrimental impact on programmer productivity, and therefore do not necessarily contribute to improved code quality. What is needed is a combination of automation of code generation using large language models (LLMs), with scalable defect elimination methods using symbolic AI, to create an environment for the automated generation of defect-free code.
Evaluation of an autonomous intelligent cyberdefense agent at NATO cyber coalition exercise 2022

In this paper the authors present the results of an experiment conducted at the 2022 NATO Cyber Coalition Exercise in Tallinn, Estonia wherein a prototype Autonomous Intelligent Cyberdefense Agent (AICA) was evaluated for its contributions to cyber operator efficacy. Six teams were given list of objectives to accomplish including preventing other teams from accomplishing the same. This included monitoring the operations of a simulated power micro-grid supporting each team’s network. Half of the teams (assigned at random) were given a AICA prototype. Evaluation of efficacy included self-reports from participating teams and measurement of total system uptime. Testing highlighted areas where future development can further enhance the prototype to improve automated responses and interactions with operators, as well as enhancements for future exercises of this type.
Optical wavefront engineering using reconfigurable metaphotonic structures

A new hybrid Material platform for dynamically reconfigurable metaphotonic devices with control over amplitude, phase, polarization, and spectral properties of an optical wavefront will be demonstrated. The integration of dielectric, plasmonic, and phase-change materials in this platform enables the engineering of the electromagnetic modes in different states of the device with a wide range of properties for realization of reconfigurable metaphotonic devices. Design, fabrication, and application of these platforms for state-of-the-art applications will be covered.
Pixel-level transformations in domain adaptive object detection for thermal imagery

Object detection is a common task for defense and intelligence applications in the visible spectrum. Deep learning models achieve state-of-the-art performance for this task. Besides, thermal cameras are more robust against real-world computer vision problems such as illumination changes. Thus, they are commonly used in military applications and security surveillance tasks. Recently, significant improvements have been made by exploiting numerous data for object detection. However, collecting and labeling a vast number of samples is challenging, especially for applications on the thermal spectrum. Since deep networks are sensitive to domain shift, a deep model trained on visible spectrum data may fail to generalize thermal spectrum data. Therefore, developing a method to adapt models to different domains, e.g., visible-to-thermal, is crucial. Feature-level unsupervised domain adaptation methods train a model that maps both domains into a common feature space without requiring image pairs. Such methods reduce the domain gap between two different domains. Besides feature-level adaptation, we propose applying pixel-level transformations on the source domain, e.g., visible spectrum images, to reduce further the domain gap between the visible and thermal spectrum. This results in a significant improvement in the performance of domain adaptive object detection methods. We propose to apply gray-scale conversion, histogram matching, histogram equalization, gamma correction, adaptive gamma correction, and Fourier domain adaptation as pixel-level transformations on visible spectrum images. We conduct experiments on real-world datasets. The evaluations use Cityscapes as the visible spectrum dataset and FLIR ADAS as the thermal spectrum dataset.
Improved List Decoding of Folded Reed-Solomon and Multiplicity Codes

We show new and improved list decoding properties of folded Reed–Solomon (RS) codes and multiplicity codes. Both of these families of codes are based on polynomials over finite fields, and both have been the source of recent advances in coding theory: folded RS codes were the first known explicit construction of capacity-achieving list decodable codes [V. Guruswami and A. Rudra, IEEE Trans. Inform. Theory, 54 (2008), pp. 135–150], and multiplicity codes were the first construction of high-rate locally decodable codes [S. Kopparty, S. Saraf, and S. Yekhanin, J. ACM, 61 (2014), 28]. In this work, we show that folded RS codes and multiplicity codes are in fact better than previously known in the context of list decoding and local list decoding. Our first main result shows that folded RS codes achieve list decoding capacity with constant list sizes, independent of the block length. Prior work with constant list sizes first obtained list sizes that are polynomial in the block length and relied on pre-encoding with subspace evasive sets to reduce the list sizes to a constant [V. Guruswami and C. Wang, IEEE Trans. Inform. Theory, 59 (2013), pp. 3257–3268], [Z. Dvir and S. Lovett, Proc. 44th STOC, ACM, 2012, 351–358]. The list size we obtain is where is the gap to capacity, which matches the list size obtained by pre-encoding with subspace evasive sets. For our second main result, we observe that univariate multiplicity codes exhibit similar behavior, and we use this, together with additional ideas, to show that multivariate multiplicity codes are locally list decodable up to their minimum distance. By known reductions, this gives, in turn, capacity-achieving locally list decodable codes with query complexity . This improves on the tensor-based construction of [B. Hemenway, N. Ron-Zewi, and M. Wootters, SIAM J. Comput., 49 (2019), pp. 157–195], which gave capacity-achieving locally list decodable codes of query complexity , and is close to the best known query complexity of for high-rate locally (uniquely) decodable codes [S. Kopparty et al., J. ACM, 64 (2017), 11].
Review and assessment of prior work on and future directions for gradient descent-trained expert systems

This paper reviews prior work demonstrating the efficacy of a new artificial intelligence technique which is based on optimizing expert systems’ rule-fact networks. Systems of this type can learn from presented data and operations; however, they cannot learn any changes that ‘jump out of’ the human-created or validated pathways, ensuring that they don’t learn invalid or non-causal associations. This paper presents a review and assessment of the functionality provided by the base gradient descent-trained expert system, the functionality provided by an enhancement that facilitates automated network development, and several other enhancements. The benefits of each system variant are discussed.