Miracle-dz/UniFAR-FTUs huggingface dataset:
MLSNet: A Policy Complying Multilevel Security Framework for Software Defined Networking

Ensuring that information flowing through a network is secure from manipulation and eavesdropping by unauthorized parties is an important task for network administrators. Many cyber attacks rely on a lack of network-level information flow controls to successfully compromise a victim network. Once an adversary exploits an initial entry point, they can eavesdrop and move laterally within the network (e.g., scan and penetrate internal nodes) to further their malicious goals. In this article, we propose a novel multilevel security (MLS) framework to enforce a secure inter-node information flow policy within the network and therein vastly reduce the attack surface available to an adversary who has penetrated it. In contrast to prior work on multilevel security in computer networks which relied on enforcing the policy at network endpoints, we leverage the centralization of software-defined networks (SDNs) by moving the task to the controller and providing this service transparently to all network nodes. Our framework, MLSNet, formalizes the generation of a policy compliant network configuration (i.e., set of flow rules on the SDN switches) as network optimization problems, with the objectives of (1) maximizing the number of flows satisfying all security constraints and (2) minimizing the security cost of routing any remaining flows to guarantee availability. We demonstrate that MLSNet can securely and efficiently route flows that satisfy the security constraints and route the remaining flows with a minimal security cost (e.g., route >85% of flows, where the heuristic achieves 89% and 87% of the optimal solutions for the optimization problems).
PDE-Constrained Optimization Models and Pseudospectral Methods for Multiscale Particle Dynamics

We derive novel algorithms for optimization problems constrained by partial differential equations describing multiscale particle dynamics, including non-local integral terms representing interactions between particles. In particular, we investigate problems where the control acts as an advection 'flow' vector or a source term of the partial differential equation, and the constraint is equipped with boundary conditions of Dirichlet or no-flux type. After deriving continuous first-order optimality conditions for such problems, we solve the resulting systems by developing a link with computational methods for statistical mechanics, deriving pseudospectral methods in both space and time variables, and utilizing variants of existing fixed point methods. Numerical experiments indicate the effectiveness of our approach for a range of problem set-ups, boundary conditions, as well as regularization and model parameters.
Experimental Review of Neural-Based Approaches for Network Intrusion Management

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has taken a prominent role in the network security management field, due to the substantial number of sophisticated attacks that often pass undetected through classic IDSs. These are typically aimed at recognizing attacks based on a specific signature, or at detecting anomalous events. However, deterministic, rule-based methods often fail to differentiate particular (rarer) network conditions (as in peak traffic during specific network situations) from actual cyber attacks. In this article we provide an experimental-based review of neural-based methods applied to intrusion detection issues. Specifically, we i) offer a complete view of the most prominent neural-based techniques relevant to intrusion detection, including deep-based approaches or weightless neural networks, which feature surprising outcomes; ii) evaluate novel datasets (updated w.r.t. the obsolete KDD99 set) through a designed-from-scratch Python-based routine; iii) perform experimental analyses including time complexity and performance (accuracy and F-measure), considering both single-class and multi-class problems, and identifying trade-offs between resource consumption and performance. Our evaluation quantifies the value of neural networks, particularly when state-of-the-art datasets are used to train the models. This leads to interesting guidelines for security managers and computer network practitioners who are looking at the incorporation of neural-based ML into IDS.
Achieving Proportionality up to the Maximin Item with Indivisible Goods

We study the problem of fairly allocating indivisible goods and focus on the classic fairness notion of proportionality. The indivisibility of the goods is long known to pose highly non-trivial obstacles to achieving fairness, and a very vibrant line of research has aimed to circumvent them using appropriate notions of approximate fairness. Recent work has established that even approximate versions of proportionality (PROPx) may be impossible to achieve even for small instances, while the best known achievable approximations (PROP1) are much weaker. We introduce the notion of proportionality up to the maximin item (PROPm) and show how to reach an allocation satisfying this notion for any instance involving up to five agents with additive valuations. PROPm provides a well-motivated middle-ground between PROP1 and PROPx, while also capturing some elements of the well-studied maximin share (MMS) benchmark: another relaxation of proportionality that has attracted a lot of attention.
Emotional Musical Prosody for the Enhancement of Trust in Robotic Arm Communication

As robotic arms become prevalent in industry it is crucial to improve levels of trust from human collaborators. Low levels of trust in human-robot interaction can reduce overall performance and prevent full robot utilization. We investigated the potential benefits of using emotional musical prosody to allow the robot to respond emotionally to the user's actions. We tested participants' responses to interacting with a virtual robot arm that acted as a decision agent, helping participants select the next number in a sequence. We compared results from three versions of the application in a between-group experiment, where the robot had different emotional reactions to the user's input depending on whether the user agreed with the robot and whether the user's choice was correct. In all versions, the robot reacted with emotional gestures. One version used prosody-based emotional audio phrases selected from our dataset of singer improvisations, the second version used audio consisting of a single pitch randomly assigned to each emotion, and the final version used no audio, only gestures. Our results showed no significant difference for the percentage of times users from each group agreed with the robot, and no difference between user's agreement with the robot after it made a mistake. However, participants also took a trust survey following the interaction, and we found that the reported trust ratings of the musical prosody group were significantly higher than both the single-pitch and no audio groups.
Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context

Simulating online recommender system performance is notoriously difficult and the discrepancy between the online and offline behaviors is typically not accounted for in offline evaluations. Recommender systems research tends to evaluate model performance on randomly sampled targets, yet the same systems are later used to predict user behavior sequentially from a fixed point in time. This disparity permits weaknesses to go unnoticed until the model is deployed in a production setting. We first demonstrate how omitting temporal context when evaluating recommender system performance leads to false confidence. To overcome this, we propose an offline evaluation protocol modeling the real-life use-case that simultaneously accounts for temporal context. Next, we propose a training procedure to further embed the temporal context in existing models: we introduce it in a multi-objective approach to traditionally time-unaware recommender systems. We confirm the advantage of adding a temporal objective via the proposed evaluation protocol. Finally, we validate that the Pareto Fronts obtained with the added objective dominate those produced by state-of-the-art models that are only optimized for accuracy on three real-world publicly available datasets. The results show that including our temporal objective can improve recall@20 by up to 20%.
COPOD: Copula-Based Outlier Detection

Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm called COPOD, which is inspired by copulas for modeling multivariate data distribution. COPOD first constructs an empirical copula, and then uses it to predict tail probabilities of each given data point to determine its level of “extremeness”. Intuitively, we think of this as calculating an anomalous p-value. This makes COPOD both parameter-free, highly interpretable, and computationally efficient. In this work, we make three key contributions, 1) propose a novel, parameter-free outlier detection algorithm with both great performance and interpretability, 2) perform extensive experiments on 30 benchmark datasets to show that COPOD outperforms in most cases and is also one of the fastest algorithms, and 3) release an easy-to-use Python implementation for reproducibility.
Dichotomy of Baryons as Quantum Hall Droplets and Skyrmions In Compact-Star Matter

We discuss the "sheet structure" of compressed baryonic matter possibly present in massive compact stars in terms of quantum Hall droplets and skyrmions for baryons in medium. The theoretical framework is anchored on a generalized scale symmetric hidden local symmetry that encompasses standard nuclear effective field theory ($s$EFT) and can access the density regimes relevant to massive compact stars. It hints at a basically different, hitherto unexplored structure of the {\it densest} baryonic matter stable against collapse to black hole. Hidden scale symmetry and hidden local symmetry together in nuclear effective field theory are seen to play a potentially crucial role in providing the hadron-quark duality in compressed baryonic matter.
MIRAGE: Mitigating Conflict-Based Cache Attacks with a Practical Fully-Associative Design

Shared caches in modern processors are vulnerable to conflict-based attacks, whereby an attacker monitors the access pattern of a victim by engineering cache-set conflicts. Recent mitigations propose a randomized mapping of addresses to cache locations to obfuscate addresses that can conflict with a target address. Unfortunately, such designs continue to select eviction candidates from a small subset of the resident cache lines, which makes such designs vulnerable to algorithms that can quickly identify the conflicting addresses. This paper presents Mirage, a practical design for a fully associative cache, wherein eviction candidates are selected randomly from among all the lines resident in the cache, to be immune to set-conflicts. A key challenge in naively adopting such designs for large shared caches (containing tens of thousands of lines) is the complexity of cache-lookup, as that can require searching through all the lines resident in the cache in such designs. Mirage practically enables a fully-associative design, while maintaining the access latency similar to a traditional set-associative cache using: (1) Pointer-based indirection from the tag-store to the data-store, which allows a newly installed address to evict data of any resident line, (2) Skewed-associative tag-store with extra invalid tags, wherein incoming addresses can be installed without set-conflicts, and (3) Load-aware placement that maximizes the availability of sets with invalid tags, to eliminate set-conflicts. Our analysis shows Mirage provides the global-eviction property of a fully-associative cache throughout the system lifetime (violations of full-associativity, i.e set-conflicts, occur less than once in 10^4 to 10^17 years), offering a principled defense against set-conflict based attacks. Mirage incurs negligible slowdown (0.3%) and 12-15% extra storage compared to the recently proposed Scatter-Cache.
Analysing and measuring the performance of memristive integrating amplifiers

Recording reliably extracellular neural activities is an essential prerequisite for the development of bioelectronics and neuroprosthetic applications. Recently, a fully differential, two‐stage, integrating pre‐amplifier was proposed for amplifying and then digitising neural signals. The amplifier featured a finely tuneable offset that was used as a variable threshold detector. Given that the amplifier is integrating, the DC operating point keeps changing during integration, rendering traditional analysis (AC/DC) unsuitable. In this work, we analyse the operation of this circuit and propose alternative definitions for validating the necessary key performance metrics, including gain, bandwidth, offset tuning range and offset sensitivity with respect to the memory states of the employed memristors. The amplification process is analysed largely through investigating the transient behaviour during the integration phase. This benchmarking approach is finally leveraged for providing useful insights and design trade‐offs of the memristor‐based integrating amplifier.
3D Modeling and WebVR Implementation using Azure Kinect, Open3D, and Three.js

This paper proposes a method of extracting an RGB-D image using Azure Kinect, a depth camera, creating a fragment, i.e., 6D images (RGBXYZ), using Open3D, creating it as a point cloud object, and implementing webVR using three.js. Furthermore, it presents limitations and potentials for development.
Unexpected thermo-elastic effects in liquid glycerol by mechanical deformation

It is commonly accepted that shear waves do not propagate in a liquid medium. The shear wave energy is supposed to dissipate nearly instantaneously. This statement originates from the difficulty to access static shear stress in macroscopic liquids. In this paper, we take a different approach. We focus on the stability of the thermal equilibrium while the liquid (glycerol) is submitted to a sudden shear strain at sub-millimetre scale. A thermal response of the deformed liquid is unveiled. The liquid exhibits simultaneous and opposite bands of about +0.04 °C and -0.04 °C temperature variations. The sudden thermal changes exclude the possibility of heat transfer and highlight the ability of the liquid to store the shear energy in non-uniform thermodynamic states. The thermal effects depend nearly linearly on the amplitude of the deformation supporting the hypothesis of a shear wave propagation (elastic correlations) extending up to several hundreds microns. This new physical effect can be explained in terms of the underlying phonon physics of confined liquids, which unveils a hidden solid-like response with many similarities to glassy systems.
State-of-Charge Estimation of a Li-Ion Battery using Deep Forward Neural Networks

This article presents two Deep Forward Networks with two and four hidden layers, respectively, that model the drive cycle of a Panasonic 18650PF lithium-ion (Li-ion) battery at a given temperature using the K-fold cross-validation method, in order to estimate the State of Charge (SOC) of the cell. The drive cycle power profile is calculated for an electric truck with a 35kWh battery pack scaled for a single 18650PF cell. We propose a machine learning workflow which is able to fight overfitting when developing deep learning models for SOC estimation. The contribution of this work is to present a methodology of building a Deep Forward Network for a lithium-ion battery and its performance assessment, which follows the best practices in machine learning.
Measurement in AI Policy: Opportunities and Challenges

As artificial intelligence increasingly influences our world, it becomes crucial to assess its technical progress and societal impact. This paper surveys problems and opportunities in the measurement of AI systems and their impact, based on a workshop held at Stanford University in the fall of 2019. We identify six summary challenges inherent to measuring the progress and impact of AI, and summarize over 40 presentations and associated discussions from the workshop. We hope this can inspire research agendas in this crucial area.
Parameterized tests of general relativity with gravitational wave generation and propagation

Any modification on gravity would affect not only gravitational wave (GW) generation but also GW propagation. Therefore, tests of general relativity (GR) with only GW generation or GW propagation will lead to an overestimate for deviations. Here we try to use one set of parameters to parameterize the modifications on both GW generation and GW propagation and then test GR with GW150914. In our simplest case, we find that graviton mass $\mu<6.3\times10^{-23}{\rm eV/c^2}$ at $90\%$ C.L. and there are no deviations from GR at $90\%$ C.L.
Prime-localized Weinstein subdomains

For any high-dimensional Weinstein domain and finite collection of primes, we construct a Weinstein subdomain whose wrapped Fukaya category is a localization of the original wrapped Fukaya category away from the given primes. When the original domain is a cotangent bundle, these subdomains form a decreasing lattice whose order cannot be reversed. Furthermore, we classify the possible wrapped Fukaya categories of Weinstein subdomains of a cotangent bundle of a simply connected, spin manifold, showing that they all coincide with one of these prime localizations. In the process, we describe which twisted complexes in the wrapped Fukaya category of a cotangent bundle of a sphere are isomorphic to genuine Lagrangians.
Deliberate Self-Attention Network with Uncertainty Estimation for Multi-Aspect Review Rating Prediction

In recent years, several online platforms have seen a rapid increase in the number of review systems that request users to provide aspect-level feedback. Multi-Aspect Rating Prediction (MARP), where the goal is to predict the ratings from a review at an individual aspect level, has become a challenging and an imminent problem. To tackle this challenge, we propose a deliberate self-attention deep neural network model, named as FEDAR, for the MARP problem, which can achieve competitive performance while also being able to interpret the predictions made. As opposed to the previous studies, which make use of hand-crafted keywords to determine aspects in sentiment predictions, our model does not suffer from human bias issues since aspect keywords are automatically detected through a self-attention mechanism. FEDAR is equipped with a highway word embedding layer to transfer knowledge from pre-trained word embeddings, an RNN encoder layer with output features enriched by pooling and factorization techniques, and a deliberate self-attention layer. In addition, we also propose an Attention-driven Keywords Ranking (AKR) method, which can automatically extract aspect-level sentiment-related keywords from the review corpus based on the attention weights. Since crowdsourcing annotation can be an alternate way to recover missing ratings of reviews, we propose a LEcture-AuDience (LEAD) strategy to estimate model uncertainty in the context of multi-task learning, so that valuable human resources can focus on the most uncertain predictions. Our extensive set of experiments on different DMSC datasets demonstrate the superiority of the proposed FEDAR and LEAD models. Visualization of aspect-level sentiment keywords demonstrate the interpretability of our model and effectiveness of our AKR method.
Optimizing Away JavaScript Obfuscation

JavaScript is a popular attack vector for releasing malicious payloads on unsuspecting Internet users. Authors of this malicious JavaScript often employ numerous obfuscation techniques in order to prevent the automatic detection by antivirus and hinder manual analysis by professional malware analysts. Consequently, this paper presents SAFE-DEOBS, a JavaScript deobfuscation tool that we have built. The aim of SAFE-DEOBS is to automatically deobfuscate JavaScript malware such that an analyst can more rapidly determine the malicious script’s intent. This is achieved through a number of static analyses, inspired by techniques from compiler theory. We demonstrate the utility of SAFE-DEOBS through a case study on real-world JavaScript malware, and show that it is a useful addition to a malware analyst’s toolset.
Dark matter as a solution to muonic puzzles

We propose a simple model in which dark matter particle exchanges mediate a new quantum force between muons and nucleons, resolving the proton charge radius puzzle. At the same time, the discrepancy between the measured anomalous magnetic moment of the muon and the Standard Model prediction can be accommodated, and thermal relic abundance of the dark matter candidate is consistent with observations. The dark matter particle mass is in the MeV range. We show that the model is consistent with a variety of experimental and observational constraints.
On the Threat of npm Vulnerable Dependencies in Node.js Applications

Software vulnerabilities have a large negative impact on the software systems that we depend on daily. Reports on software vulnerabilities always paint a grim picture, with some reports showing that 83% of organizations depend on vulnerable software. However, our experience leads us to believe that, in the grand scheme of things, these software vulnerabilities may have less impact than what is reported. Therefore, we perform a study to better understand the threat of npm vulnerable packages used in Node.js applications. We define three threat levels for vulnerabilities in packages, based on their lifecycle, where a package vulnerability is assigned a low threat level if it was hidden or still unknown at the time it was used in the dependent application (t), medium threat level if the vulnerability was reported but not yet published at t, and high if it was publicly announced at t. Then, we perform an empirical study involving 6,673 real-world, active, and mature open source Node.js applications. Our findings show that although 67.93% of the examined applications depend on at least one vulnerable package, 94.91% of the vulnerable packages in those affected applications are classified as having low threat. Moreover, we find that in the case of vulnerable packages classified as having high threat, it is the application's lack of updating that makes them vulnerable, i.e., it is not the existence of the vulnerability that is the real problem. Furthermore, we verify our findings at different stages of the application's lifetime and find that our findings still hold. Our study argues that when it comes to software vulnerabilities, things may not be as bad as they seem and that considering vulnerability threat is key.