Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection

Recent advances in deep learning renewed the research interests in machine learning for Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to sequential learning models, due to their ability to extract the temporal characteristics of network traffic flows (NetFlows), and use them for NIDS tasks. However, the applications of these sequential models often consist of transferring and adapting methodologies directly from other fields, without an in-depth investigation on how to leverage the specific circumstances of cybersecurity scenarios; moreover, there is a lack of comprehensive studies on sequential models that rely on NetFlow data, which presents significant advantages over traditional full packet captures. We tackle this problem in this paper. We propose a detailed methodology to extract temporal sequences of NetFlows that denote patterns of malicious activities. Then, we apply this methodology to compare the efficacy of sequential learning models against traditional static learning models. In particular, we perform a fair comparison of a ‘sequential’ Long Short-Term Memory (LSTM) against a ‘static’ Feedforward Neural Networks (FNN) in distinct environments represented by two well-known datasets for NIDS: the CICIDS2017 and the CTU13. Our results highlight that LSTM achieves comparable performance to FNN in the CICIDS2017 with over 99.5% F1-score; while obtaining superior performance in the CTU13, with 95.7% F1-score against 91.5%. This paper thus paves the way to future applications of sequential learning models for NIDS.
SRIB Submission to Interspeech 2021 DiCOVA Challenge

The COVID-19 pandemic has resulted in more than 125 million infections and more than 2.7 million casualties. In this paper, we attempt to classify covid vs non-covid cough sounds using signal processing and deep learning methods. Air turbulence, the vibration of tissues, movement of fluid through airways, opening, and closure of glottis are some of the causes for the production of the acoustic sound signals during cough. Does the COVID-19 alter the acoustic characteristics of breath, cough, and speech sounds produced through the respiratory system? This is an open question waiting for answers. In this paper, we incorporated novel data augmentation methods for cough sound augmentation and multiple deep neural network architectures and methods along with handcrafted features. Our proposed system gives 14% absolute improvement in area under the curve (AUC). The proposed system is developed as part of Interspeech 2021 special sessions and challenges viz. diagnosing of COVID-19 using acoustics (DiCOVA). Our proposed method secured the 5th position on the leaderboard among 29 participants.
Thompson Sampling for Unimodal Bandits

In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms. To exploit the unimodal structure better, at each step, instead of exploration from the entire decision space, our algorithm makes decision according to posterior distribution only in the neighborhood of the arm that has the highest empirical mean estimate. We theoretically prove that, for Bernoulli rewards, the regret of our algorithm reaches the lower bound of unimodal bandits, thus it is asymptotically optimal. For Gaussian rewards, the regret of our algorithm is $\mathcal{O}(\log T)$, which is far better than standard Thompson Sampling algorithms. Extensive experiments demonstrate the effectiveness of the proposed algorithm on both synthetic data sets and the real-world applications.
Object Detection And Autoencoder-Based 6d Pose Estimation For Highly Cluttered Bin Picking

Bin picking is a core problem in industrial environments and robotics, with its main module as 6D pose estimation. However, industrial depth sensors have a lack of accuracy when it comes to small objects. Therefore, we propose a framework for pose estimation in highly cluttered scenes with small objects, which mainly relies on RGB data and makes use of depth information only for pose refinement. In this work, we compare synthetic data generation approaches for object detection and pose estimation and introduce a pose filtering algorithm that determines the most accurate estimated poses. We will make our real dataset for object detection available with the paper.
The EMU view of the Large Magellanic Cloud: troubles for sub-TeV WIMPs

We present a radio search for WIMP dark matter in the Large Magellanic Cloud (LMC). We make use of a recent deep image of the LMC obtained from observations of the Australian Square Kilometre Array Pathfinder (ASKAP), and processed as part of the Evolutionary Map of the Universe (EMU) survey. LMC is an extremely promising target for WIMP searches at radio frequencies because of the large J-factor and the presence of a substantial magnetic field. We detect no evidence for emission arising from WIMP annihilations and derive stringent bounds on the annihilation rate as a function of the WIMP mass, for different annihilation channels. This work excludes the thermal cross section for masses below 480 GeV and annihilation into quarks.
Jamming Detection with Subcarrier Blanking for 5G and Beyond in Industry 4.0 Scenarios

Security attacks at the physical layer, in the form of radio jamming for denial of service, are an increasing threat in the Industry 4.0 scenarios. In this paper, we consider the problem of jamming detection in 5G-and-beyond communication systems and propose a defense mechanism based on pseudo-random blanking of subcarriers with orthogonal frequency division multiplexing (OFDM). We then design a detector by applying the generalized likelihood ratio test (GLRT) on those subcarriers. We finally evaluate the performance of the proposed technique against a smart jammer, which is pursuing one of the following objectives: maximize stealthiness, minimize spectral efficiency (SE) with mobile broadband (MBB) type of traffic, and maximize block error rate (BLER) with ultra-reliable low-latency communications (URLLC). Numerical results show that a smart jammer a) needs to compromise between missed detection (MD) probability and SE reduction with MBB and b) can achieve low detectability and high system performance degradation with URLLC only if it has sufficiently high power.
CausalNLP: A Practical Toolkit for Causal Inference with Text

Causal inference is the process of estimating the effect or impact of a treatment on an outcome with other covariates as potential confounders (and mediators) that may need to be controlled. The vast majority of existing methods and systems for causal inference assume that all variables under consideration are categorical or numerical ( e.g., gender, price, enrollment). In this paper, we present CausalNLP , a toolkit for inferring causality with observational data that includes text in addition to traditional numerical and categorical variables. CausalNLP employs the use of meta-learners for treatment effect estimation and supports using raw text and its linguistic properties as a treatment, an outcome, or a “controlled-for” variable ( e.g., confounder). The library is open-source and available at https://github.com/amaiya/causalnlp .
A Clinically Inspired Approach for Melanoma classification

Melanoma is a leading cause of deaths due to skin cancer deaths and hence, early and effective diagnosis of melanoma is of interest. Current approaches for automated diagnosis of melanoma either use pattern recognition or analytical recognition like ABCDE (asymmetry, border, color, diameter and evolving) criterion. In practice however, a differential approach wherein outliers (ugly duckling) are detected and used to evaluate nevi/lesions. Incorporation of differential recognition in Computer Aided Diagnosis (CAD) systems has not been explored but can be beneficial as it can provide a clinical justification for the derived decision. We present a method for identifying and quantifying ugly ducklings by performing Intra-Patient Comparative Analysis (IPCA) of neighboring nevi. This is then incorporated in a CAD system design for melanoma detection. This design ensures flexibility to handle cases where IPCA is not possible. Our experiments on a public dataset show that the outlier information helps boost the sensitivity of detection by at least 4.1 % and specificity by 4.0 % to 8.9 %, depending on the use of a strong (EfficientNet) or moderately strong (VGG or ResNet) classifier.
Galactic traversability: a new concept for extragalactic SETI

Interstellar travel in the Milky Way is commonly thought to be a long and dangerous enterprise, but are all galaxies so hazardous? I introduce the concept of galactic traversability to address this question. Stellar populations are one factor in traversability, with higher stellar densities and velocity dispersions aiding rapid spread across a galaxy. The interstellar medium (ISM) is another factor, as gas, dust grains and cosmic rays all pose hazards to starfarers. I review the current understanding of these components in different types of galaxies, and conclude that red quiescent galaxies without star formation have favourable traversability. Compact elliptical galaxies and globular clusters could be ‘super-traversable’, because stars are packed tightly together and there are minimal ISM hazards. Overall, if the ISM is the major hindrance to interstellar travel, galactic traversability increases with cosmic time as gas fractions and star formation decline. Traversability is a consideration in extragalactic surveys for the Search for Extraterrestrial Intelligence (SETI).
MICo: Learning improved representations via sampling-based state similarity for Markov decision processes

We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an eﬀective means of shaping the learnt representations of deep reinforcement learning agents. While existing notions of state similarity are typically diﬃcult to learn at scale due to high computational cost and lack of sample-based algorithms, our newly-proposed distance addresses both of these issues. In addition to providing detailed theoretical analysis
String Diagrammatic Electrical Circuit Theory

We develop a comprehensive string diagrammatic treatment of electrical circuits. Building on previous, limited case studies, we introduce controlled sources and meters as elements, and the impedance calculus , a powerful toolbox for diagrammatic reasoning on circuit diagrams. We demonstrate the power of our approach by giving comprehensive proofs of several textbook results, including the superposition theorem and Th´evenin’s theorem.
Boson Sampling in a reconfigurable continuously-coupled 3D photonic circuit

Francesco Hoch,1, ∗ Simone Piacentini,2, 3, ∗ Taira Giordani,1 Zhen-Nan Tian,3 Mariagrazia Iuliano,1 Chiara Esposito,1 Anita Camillini,1 Gonzalo Carvacho,1 Francesco Ceccarelli,3 Nicolò Spagnolo,1 Andrea Crespi,2, 3 Fabio Sciarrino,1, † and Roberto Osellame3, 2, ‡ Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 5, I-00185 Roma, Italy Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milano, Italy Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche (IFN-CNR), Piazza Leonardo da Vinci, 32, I-20133 Milano, Italy
Credit Assignment in Neural Networks through Deep Feedback Control

The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output. However, the majority of current attempts at biologically-plausible learning methods are either non-local in time, require highly specific connectivity motives, or have no clear link to any known mathematical optimization method. Here, we introduce Deep Feedback Control (DFC), a new learning method that uses a feedback controller to drive a deep neural network to match a desired output target and whose control signal can be used for credit assignment. The resulting learning rule is fully local in space and time and approximates Gauss-Newton optimization for a wide range of feedback connectivity patterns. To further underline its biological plausibility, we relate DFC to a multi-compartment model of cortical pyramidal neurons with a local voltage-dependent synaptic plasticity rule, consistent with recent theories of dendritic processing. By combining dynamical system theory with mathematical optimization theory, we provide a strong theoretical foundation for DFC that we corroborate with detailed results on toy experiments and standard computer-vision benchmarks.
Effect of Reaction Inhibitors on Synthesized Silver Nanostructures via Solvothermal Method

In this paper, silver nanostructures were synthesized via a solvothermal method by reducing silver nitrate with ethylene glycol in the presence of poly (vinylpyrrolidone) and using NaCl, MgCl2, AlCl3, FeCl3, Fe2(SO4)3 , NaOH, NaBr and Na2S as reaction inhibitor, respectively. Characteristics of the as-prepared silver nanostructures were studied by scanning electron microscopy (SEM), X-ray diffraction (XRD) and UV-visible (UV-vis) absorption spectrum. It is found that the mixture of nanoparticles and nanorods and nanowires can be observed in the AlCl3 and NaOH systems, the nanorods as main products can be obtained in the MgCl2 and NaCl and Fe2(SO4)3 systems, the large scale silver nanowires as main products can be obtained in the FeCl3 and Na2S systems, especially in the Na2S system, silver nanowires are very long and curved. The results indicated the reasons of the effect of the reaction inhibitors on the morphologies of silver nanostructures depend on the solubility product constant of silver salt which formed with silver ion and inhibitor anion ion in the initial stage and the properties of metal cationic ion of inhibitor. The smaller solubility product constant of silver salt or the presence of Fe3+ or low valence metal iongreatly facilitate the formation of one-dimensional silver nanostructures, especially to nanowires.
Modeling memory bandwidth patterns on NUMA machines with performance counters

Computers used for data analytics are often NUMA systems with multiple sockets per machine, multiple cores per socket, and multiple thread contexts per core. To get the peak performance out of these machines requires the correct number of threads to be placed in the correct positions on the machine. One particularly interesting element of the placement of memory and threads is the way it effects the movement of data around the machine, and the increased latency this can introduce to reads and writes. In this paper we describe work on modeling the bandwidth requirements of an application on a NUMA compute node based on the placement of threads. The model is parameterized by sampling performance counters during 2 application runs with carefully chosen thread placements. Evaluating the model with thousands of measurements shows a median difference from predictions of 2.34% of the bandwidth. The results of this modeling can be used in a number of ways varying from: Performance debugging during development where the programmer can be alerted to potentially problematic memory access patterns; To systems such as Pandia which take an application and predict the performance and system load of a proposed thread count and placement; To libraries of data structures such as Parallel Collections and Smart Arrays that can abstract from the user memory placement and thread placement issues when parallelizing code.
Distributed Optimization With Global Constraints Using Noisy Measurements

We propose a new distributed optimization algorithm for solving a class of constrained optimization problems in which the objective function is separable (i.e., the sum of local objective functions of agents), the optimization variables of distributed agents, which are subject to nontrivial local constraints, are coupled by global constraints, and only noisy observations are available to estimate (the gradients of) local objective functions. In many practical scenarios, agents may not be willing to share their optimization variables with others. For this reason, we propose a distributed algorithm that does not require the agents to share their optimization variables with each other; instead, each agent maintains a local estimate of the global constraint functions and shares the estimate only with its neighbors. These local estimates of constraint functions are updated using a consensus-type algorithm, whereas the local optimization variables of each agent are updated using a first-order method based on noisy estimates of gradient. We prove that, when the agents adopt the proposed algorithm, their optimization variables converge with probability 1 to an optimal point of an approximated problem based on the penalty method.
Identifying Roles, Requirements and Responsibilities in Trustworthy AI Systems

Artificial Intelligence (AI) systems are being deployed around the globe in critical fields such as healthcare and education. In some cases, expert practitioners in these domains are being tasked with introducing or using such systems, but have little or no insight into what data these complex systems are based on, or how they are put together. In this paper, we consider an AI system from the domain practitioner’s perspective and identify key roles that are involved in system deployment. We consider the differing requirements and responsibilities of each role, and identify tensions between transparency and confidentiality that need to be addressed so that domain practitioners are able to intelligently assess whether a particular AI system is appropriate for use in their domain.
Non-asymptotic convergence bounds for Wasserstein approximation using point clouds

Several issues in machine learning and inverse problems require to generate discrete data, as if sampled from a model probability distribution. A common way to do so relies on the construction of a uniform probability distribution over a set of $N$ points which minimizes the Wasserstein distance to the model distribution. This minimization problem, where the unknowns are the positions of the atoms, is non-convex. Yet, in most cases, a suitably adjusted version of Lloyd's algorithm -- in which Voronoi cells are replaced by Power cells -- leads to configurations with small Wasserstein error. This is surprising because, again, of the non-convex nature of the problem, as well as the existence of spurious critical points. We provide explicit upper bounds for the convergence speed of this Lloyd-type algorithm, starting from a cloud of points sufficiently far from each other. This already works after one step of the iteration procedure, and similar bounds can be deduced, for the corresponding gradient descent. These bounds naturally lead to a modified Poliak-Lojasiewicz inequality for the Wasserstein distance cost, with an error term depending on the distances between Dirac masses in the discrete distribution.
Inducing and optimizing Markovian Mpemba effect with stochastic reset

A hot Markovian system can cool down faster than a colder one: this is known as the Mpemba effect. Here, we show that a non-equilibrium driving via stochastic reset can induce this phenomenon, when absent. Moreover, we derive an optimal driving protocol simultaneously optimizing the appearance time of the Mpemba effect, and the total energy dissipation into the environment, revealing the existence of a Pareto front. Building upon previous experimental results, our findings open up the avenue of possible experimental realizations of optimal cooling protocols in Markovian systems.
Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning

With the Deep Neural Networks (DNNs) as a powerful function approxima-tor, Deep Reinforcement Learning (DRL) has been excellently demonstrated on robotic control tasks. Compared to DNNs with vanilla artiﬁcial neurons, the biologically plausible Spiking Neural Network (SNN) contains a diverse population of spiking neurons, making it naturally powerful on state representation with spatial and temporal information. Based on a hybrid learning framework, where a spike actor-network infers actions from states and a deep critic network evaluates the actor, we propose a Population-coding and Dynamic-neurons improved Spiking Actor Network (PDSAN) for eﬃcient state representation from two diﬀerent scales: input coding and neuronal coding. For input coding, we apply population coding with dynamically receptive ﬁelds to directly encode each input state component. For neuronal coding, we propose diﬀerent types of dynamic-neurons (containing 1st-order and 2nd-order neuronal dynamics) to describe much more complex neuronal dynamics. Finally, the PDSAN is trained in conjunction with deep critic networks using the Twin Delayed Deep Deterministic policy gradient algorithm (TD3-PDSAN). Extensive experimental results show that our TD3-PDSAN model achieves better performance than state-of-the-art models on four OpenAI gym benchmark tasks. It is an im∗
PhyMask: Robust Sensing of Brain Activity and Physiological Signals During Sleep with an All-textile Eye Mask

Clinical-grade wearable sleep monitoring is a challenging problem since it requires concurrently monitoring brain activity, eye movement, muscle activity, cardio-respiratory features, and gross body movements. This requires multiple sensors to be worn at different locations as well as uncomfortable adhesives and discrete electronic components to be placed on the head. As a result, existing wearables either compromise comfort or compromise accuracy in tracking sleep variables. We propose PhyMask, an all-textile sleep monitoring solution that is practical and comfortable for continuous use and that acquires all signals of interest to sleep solely using comfortable textile sensors placed on the head. We show that PhyMask can be used to accurately measure all the signals required for precise sleep stage tracking and to extract advanced sleep markers such as spindles and K-complexes robustly in the real-world setting. We validate PhyMask against polysomnography (PSG) and show that it significantly outperforms two commercially-available sleep tracking wearables—Fitbit and Oura Ring.
Voting for the right answer: Adversarial defense for speaker verification

Automatic speaker verification (ASV) is a well developed technology for biometric identification, and has been ubiquitous implemented in security-critic applications, such as banking and access control. However, previous works have shown that ASV is under the radar of adversarial attacks, which are very similar to their original counterparts from human's perception, yet will manipulate the ASV render wrong prediction. Due to the very late emergence of adversarial attacks for ASV, effective countermeasures against them are limited. Given that the security of ASV is of high priority, in this work, we propose the idea of"voting for the right answer"to prevent risky decisions of ASV in blind spot areas, by employing random sampling and voting. Experimental results show that our proposed method improves the robustness against both the limited-knowledge attackers by pulling the adversarial samples out of the blind spots, and the perfect-knowledge attackers by introducing randomness and increasing the attackers' budgets.