Miracle-dz/UniFAR-FTUs huggingface dataset:
D-Box: DMA-enabled Compartmentalization for Embedded Applications

Embedded and Internet-of-Things (IoT) devices have seen an increase in adoption in many domains. The security of these devices is of great importance as they are often used to control critical infrastructure, medical devices, and vehicles. Existing solutions to isolate microcontroller (MCU) resources in order to increase their security face significant challenges such as specific hardware unavailability, Memory Protection Unit (MPU) limitations and a significant lack of Direct Memory Access (DMA) support. Nevertheless, DMA is fundamental for the power and performance requirements of embedded applications. In this paper, we present D-Box, a systematic approach to enable secure DMA operations for compartmentalization solutions of embedded applications using real-time operating systems (RTOS). D-Box defines a reference architecture and a workflow to protect DMA operations holistically. It provides practical methods to harden the kernel and define capability-based security policies for easy definition of DMA operations with strong security properties. We implemented a D-Box prototype for the Cortex-M3/M4 on top of the popular FreeRTOS-MPU (F-MPU). The D-Box procedures and a stricter security model enabled DMA operations, yet it exposed 41 times less ROP (return-orienting-programming) gadgets when compared with the standard F-MPU. D-Box adds only a 2% processor overhead while reducing the power consumption of peripheral operation benchmarks by 18.2%. The security properties and performance of D-Box were tested and confirmed on a real-world case study of a Programmable Logic Controller (PLC) application.
The Implicit Regularization of Momentum Gradient Descent with Early Stopping

The study on the implicit regularization induced by gradient-based optimization is a longstanding pursuit. In the present paper, we characterize the implicit regularization of momentum gradient descent (MGD) with early stopping by comparing with the explicit $\ell_2$-regularization (ridge). In details, we study MGD in the continuous-time view, so-called momentum gradient flow (MGF), and show that its tendency is closer to ridge than the gradient descent (GD) [Ali et al., 2019] for least squares regression. Moreover, we prove that, under the calibration $t=\sqrt{2/\lambda}$, where $t$ is the time parameter in MGF and $\lambda$ is the tuning parameter in ridge regression, the risk of MGF is no more than 1.54 times that of ridge. In particular, the relative Bayes risk of MGF to ridge is between 1 and 1.035 under the optimal tuning. The numerical experiments support our theoretical results strongly.
Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)

Motivated by scenarios of information diffusion and advertising in social media, we study an influence maximization problem in which little is assumed to be known about the diffusion network or about the model that determines how information may propagate. In such a highly uncertain environment, one can focus on multi-round diffusion campaigns, with the objective to maximize the number of distinct users that are influenced or activated, starting from a known base of few influential nodes. During a campaign, spread seeds are selected sequentially at consecutive rounds, and feedback is collected in the form of the activated nodes at each round. A round's impact (reward) is then quantified as the number of newly activated nodes. Overall, one must maximize the campaign's total spread, as the sum of rounds' rewards. In this setting, an explore-exploit approach could be used to learn the key underlying diffusion parameters, while running the campaign. We describe and compare two methods of contextual multi-armed bandits, with upper-confidence bounds on the remaining potential of influencers, one using a generalized linear model and the Good-Turing estimator for remaining potential (GLM-GT-UCB), and another one that directly adapts the LinUCB algorithm to our setting (LogNorm-LinUCB). We show that they outperform baseline methods using state-of-the-art ideas, on synthetic and real-world data, while at the same time exhibiting different and complementary behavior, depending on the scenarios in which they are deployed.
Structure Enhanced Graph Neural Networks for Link Prediction

Graph Neural Networks (GNNs) have shown promising results in various tasks, among which link prediction is an important one. GNN models usually follow a node-centric message passing procedure that aggregates the neighborhood information to the central node recursively. Following this paradigm, features of nodes are passed through edges without caring about where the nodes are located and which role they played. However, the neglected topological information is shown to be valuable for link prediction tasks. In this paper, we propose Structure Enhanced Graph neural network (SEG) for link prediction. SEG introduces the path labeling method to capture surrounding topological information of target nodes and then incorporates the structure into an ordinary GNN model. By jointly training the structure encoder and deep GNN model, SEG fuses topological structures and node features to take full advantage of graph information. Experiments on the OGB link prediction datasets demonstrate that SEG achieves state-of-the-art results among all three public datasets.
Working Paper: NLP in Human Rights Research - Extracting Knowledge Graphs About Police and Army Units and Their Commanders

In this working paper we explore the use of an NLP system to assist the work of Security Force Monitor (SFM). SFM creates data about the organizational structure, command personnel and operations of police, army and other security forces, which assists human rights researchers, journalists and litigators in their work to help identify and bring to account speciﬁc units and personnel alleged to have committed abuses of human rights and international criminal law. This working paper presents an NLP system that extracts from English language news reports the names of security force units and the biographical details of their personnel, and infers the formal relationship between them. Published alongside this working paper are the system’s code and training dataset. We ﬁnd that the experimental NLP system performs the task at a fair to good level. Its performance is suﬃcient to justify further development into a live workﬂow that will give insight into whether its performance translates into savings in time and resource that would make it an eﬀective technical intervention.
Test-Measured Rényi Divergences

One possibility of defining a quantum Rényi <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-divergence of two quantum states is to optimize the classical Rényi <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-divergence of their post-measurement probability distributions over all possible measurements (measured Rényi divergence), and maybe regularize these quantities over multiple copies of the two states (regularized measured Rényi <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-divergence). A key observation behind the theorem for the strong converse exponent of asymptotic binary quantum state discrimination is that the regularized measured Rényi <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-divergence coincides with the sandwiched Rényi <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-divergence when <inline-formula> <tex-math notation="LaTeX">$\alpha >1$ </tex-math></inline-formula>. Moreover, it also follows from the same theorem that to achieve this, it is sufficient to consider 2-outcome measurements (tests) for any number of copies (this is somewhat surprising, as achieving the measured Rényi <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-divergence for <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> copies might require a number of measurement outcomes that diverges in <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, in general). In view of this, it seems natural to expect the same when <inline-formula> <tex-math notation="LaTeX">$\alpha < 1$ </tex-math></inline-formula>; however, we show that this is not the case. In fact, we show that even for commuting states (classical case) the regularized quantity attainable using 2-outcome measurements is in general strictly smaller than the Rényi <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-divergence (which is unique in the classical case). In the general quantum case this shows that the above “regularized test-measured” Rényi <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-divergence is not even a quantum extension of the classical Rényi divergence when <inline-formula> <tex-math notation="LaTeX">$\alpha < 1$ </tex-math></inline-formula>, in sharp contrast to the <inline-formula> <tex-math notation="LaTeX">$\alpha >1$ </tex-math></inline-formula> case.
A uniform Linnik basic lemma and entropy bounds

We prove a version of Linnik’s basic lemma uniformly over the base field using θ-series and geometric invariant theory in the spirit of Khayutin’s approach (Duke Math. J., 168(12), 2019). As an application, we establish entropy bounds for weak-limits of invariant measures on homogeneous toral sets in GL4 of biquadratic, cyclic, or dihedral type.
Model-Based Framework for exploiting sensors of IoT devices using a Botnet: A case study with Android

Botnets have become a serious security threat not only to the Internet but also to the devices connected to it. Factors like the exponential growth of IoT, the COVID-19 pandemic that's sweeping the planet, and the ever-larger number of cyber-criminals who now have access to or have developed increasingly more sophisticated tools are incentivizing the growth of botnets in this domain. The recent outbreak of botnets like Dark Nexus (derived from Qbot and Mirai), Mukashi, LeetHozer, Hoaxcalls, etc. shows the alarming rate at which this threat is converging. The botnets have attributes that make them an excellent platform for malicious activities in IoT devices. These IoT devices are used by organizations that need to both innovate and safeguard the personal and confidential data of their customers, employees, and business partners. The IoT devices have built-in sensors or actuators which can be exploited to monitor or control the physical environment of the entities connected to them thereby violating the fundamental concept of privacy-by-design of these devices. In this paper, we design and describe a modular botnet framework for IoT. Our framework is communication channel independent because it utilizes various available communication channels for command and control of an IoT device. The framework uses an enhanced centralized architecture associated with a novel Domain Fluxing Technique. The proposed framework will provide insights into how privacy in IoT devices can be incorporated at design time to check the sensors and actuators in these devices against malicious exploitation consequently preserving privacy. This paper includes design considerations, command and control structures, characteristics, capabilities, intrusion, and other related work. Furthermore, proof of concept Botnet is implemented and explained using the developed framework.
Application of Multi-Armed Bandits to Model-assisted designs for Dose-Finding Clinical Trials

We consider applying multi-armed bandits to model-assisted designs for dose-finding clinical trials. Multi-armed bandits are very simple and powerful methods to determine actions to maximize a reward in a limited number of trials. Among the multi-armed bandits, we first consider the use of Thompson sampling which determines actions based on random samples from a posterior distribution. In the small sample size, as shown in dose-finding trials, because the tails of posterior distribution are heavier and random samples are too much variability, we also consider an application of regularized Thompson sampling and greedy algorithm. The greedy algorithm determines a dose based on a posterior mean. In addition, we also propose a method to determine a dose based on a posterior median. We evaluate the performance of our proposed designs for six scenarios via simulation studies.
Monte Carlo Simulation of Dust Particles in a Protoplanetary Disk: Crystalline to Amorphous Silicate Ratio in Comets

The observationally inferred crystalline abundance in silicates in comets, which should have been formed in the outer region of a protoplanetary disk, is relatively high (∼10%–60%), although crystalline silicates would be formed by the annealing of amorphous precursors in the inner disk region. In order to quantitatively address this puzzle, we performed a Monte Carlo simulation of the advection/diffusion of silicate particles in a turbulent disk in a setting based on the pebble accretion model: pebbles consisting of many small amorphous silicates embedded in an icy mantle are formed in the outer disk region, silicate particles are released at the snow line, crystalline silicate particles are produced at the annealing line, silicate particles diffuse beyond the snow line, and they eventually stick to drifting pebbles to return to the snow line. In the simple case without sticking and with steady pebble flux, we show through the simulations and analytical arguments that the crystalline components in silicate materials beyond the snow line are robustly and uniformly ≃5%. On the other hand, in a more realistic case with sticking and with a decaying pebble flux, the crystalline abundance increases to ∼20%–25%, depending on the ratio of the decay to diffusion timescales. This abundance is consistent with the observations. In this investigation, we assume a simple steady-accretion disk. The simulations coupled with the disk evolution are needed for a more detailed comparison with observed data.
Time reversal of spinal processes for linear and non-linear branching processes near stationarity

We consider a stochastic individual-based population model with competition, trait-structure affecting reproduction and survival, and changing environment. The changes of traits are described by jump processes, and the dynamics can be approximated in large population by a non-linear PDE with a non-local mutation operator. Using the fact that this PDE admits a non-trivial stationary solution, we can approximate the non-linear stochastic population process by a linear birth-death process where the interactions are frozen, as long as the population remains close to this equilibrium. This allows us to derive, when the population is large, the equation satisfied by the ancestral lineage of an individual uniformly sampled at a fixed time $T$, which is the path constituted of the traits of the ancestors of this individual in past times $t\leq T$. This process is a time inhomogeneous Markov process, but we show that the time reversal of this process possesses a very simple structure (e.g. time-homogeneous and independent of $T$). This extends recent results where the authors studied a similar model with a Laplacian operator but where the methods essentially relied on the Gaussian nature of the mutations.
Energy-efficient workflow scheduling based on workflow structures under deadline and budget constraints in the cloud

The utilization of cloud environments to deploy scientific workflow applications is an emerging trend in scientific community. In this area, the main issue is the scheduling of workflows, which is known as an NP-complete problem. Apart from respecting user-defined deadline and budget, energy consumption is a major concern for cloud providers in implementing the scheduling strategy. The types and the number of virtual machines (VMs) used are determinant to handle those issues, and their determination is highly influenced by the structure of the workflow. In this paper, we propose two workflow scheduling algorithms that take advantage of the structural properties of the workflows. The first algorithm is called Structure-based Multi-objective Workflow Scheduling with an Optimal instance type (SMWSO). It introduces a new approach to determine the optimal instance type along with the optimal number of VMs to be provisioned. We also consider the use of heterogeneous VMs in the Structure-based Multi-objective Workflow Scheduling with Heterogeneous instance types (SMWSH), to highlight the algorithm's strength within the heterogeneous environment. The simulation results show that our proposal produces better energy-efficiency in 80% of workflow/workload scenarios, and save more than 50% overall energy compared to a recent state-of-the-art algorithm.
Multi-Narrative Semantic Overlap Task: Evaluation and Benchmark

In this paper, we introduce an important yet relatively unexplored NLP task called Multi-Narrative Semantic Overlap (MNSO), which entails generating a Semantic Overlap of multiple alternate narratives. As no benchmark dataset is readily available for this task, we created one by crawling 2,925 narrative pairs from the web and then, went through the tedious process of manually creating 411 different ground-truth semantic overlaps by engaging human annotators. As a way to evaluate this novel task, we first conducted a systematic study by borrowing the popular ROUGE metric from text-summarization literature and discovered that ROUGE is not suitable for our task. Subsequently, we conducted further human annotations/validations to create 200 document-level and 1,518 sentence-level ground-truth labels which helped us formulate a new precision-recall style evaluation metric, called SEM-F1 (semantic F1). Experimental results show that the proposed SEM-F1 metric yields higher correlation with human judgement as well as higher inter-rater-agreement compared to ROUGE metric.
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering

Outside-knowledge visual question answering (OK-VQA) requires the agent to comprehend the image, make use of relevant knowledge from the entire web, and digest all the information to answer the question. Most previous works address the problem by first fusing the image and question in the multi-modal space, which is inflexible for further fusion with a vast amount of external knowledge. In this paper, we call for a paradigm shift for the OK-VQA task, which transforms the image into plain text, so that we can enable knowledge passage retrieval, and generative question-answering in the natural language space. This paradigm takes advantage of the sheer volume of gigantic knowledge bases and the richness of pre-trained language models. A Transform-Retrieve-Generate framework (TRiG) framework is proposed, which can be plug-and-played with alternative image-to-text models and textual knowledge bases. Experimental results show that our TRiG framework outperforms all state-of-the-art supervised methods by at least 11.1% absolute margin.
This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign

Understanding collective decision making at a large-scale, and elucidating how community organization and community dynamics shape collective behavior are at the heart of social science research. In this work we study the behavior of thousands of communities with millions of active members. We define a novel task: predicting which community will undertake an unexpected, large-scale, distributed campaign. To this end, we develop a hybrid model, combining textual cues, community meta-data, and structural properties. We show how this multi-faceted model can accurately predict large-scale collective decision-making in a distributed environment. We demonstrate the applicability of our model through Reddit’s r/place – a large-scale online experiment in which millions of users, self-organized in thousands of communities, clashed and collaborated in an effort to realize their agenda. Our hybrid model achieves a high F1 prediction score of 0.826. We find that coarse meta-features are as important for prediction accuracy as fine-grained textual cues, while explicit structural features play a smaller role. Interpreting our model, we provide and support various social insights about the unique characteristics of the communities that participated in the r/place experiment. Our results and analysis shed light on the complex social dynamics that drive collective behavior, and on the factors that propel user coordination. The scale and the unique conditions of the r/place experiment suggest that our findings may apply in broader contexts, such as online activism, (countering) the spread of hate speech and reducing political polarization. The broader applicability of the model is demonstrated through an extensive analysis of the WallStreetBets community, their role in r/place and four years later, in the GameStop short squeeze campaign of 2021.
Applications of Grassmannian and graph flows to coagulation systems

We demonstrate how many classes of Smoluchowski-type coagulation models can be realised as Grassmannian or nonlinear graph flows and are therefore linearisable, and/or integrable in this sense. We first prove that a general Smoluchowski-type equation with a constant frequency kernel, that encompasses a large class of such models, is realisable as a multiplicative Grassmannian flow, and then go on to establish that several other related constant kernel models can be realised as such. These include: the Gallay–Mielke coarsening model; the Derrida–Retaux depinning transition model and a general mutliple merger coagulation model. We then introduce and explore the notion of nonlinear graph flows, which are related to the notion of characteristics for partial differential equations. These generalise flows on a Grassmann manifold from sets of graphs of linear maps to sets of graphs of nonlinear maps. We demonstrate that Smoluchowski’s coagulation equation in the additive and multiplicative frequency kernel cases, are realisable as nonlinear graph flows, and are thus integrable provided we can uniquely retrace the initial data map along characteristics. The additive and multiplicative frequency kernel cases correspond to inviscid Burgers flow. We explore further applications of such nonlinear graph flows, for example, to the stochastic viscous Burgers equation. Lastly we consider an example stochastic partial differential equation with a nonlocal nonlinearity that generalises the convolution form associated with nonlinear coagulation interaction, and demonstrate it can be realised as an infinite dimensional Grassmannian flow. In our companion paper, Doikou et al. [23], we consider the application of such infinite dimensional Grassmannian flows to classical noncommutative integrable systems such as the Korteweg–de Vries and nonlinear Schrödinger equations.