Miracle-dz/UniFAR-FTUs huggingface dataset:
A review of Federated Learning in Intrusion Detection Systems for IoT

Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. The development of deep learning technologies opened the door to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Internet of Things devices. Current approaches rely on powerful centralized servers that receive data from all their parties -- violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communication overheads. To mitigate these issues, Federated Learning emerged as a promising approach where different agents collaboratively train a shared model, neither exposing training data to others nor requiring a compute-intensive centralized infrastructure. This paper focuses on the application of Federated Learning approaches in the field of Intrusion Detection. Both technologies are described in detail and current scientific progress is reviewed and categorized. Finally, the paper highlights the limitations present in recent works and presents some future directions for this technology.
SceneTrilogy: On Scene Sketches and its Relationship with Text and Photo

We for the first time extend multi-modal scene understanding to include that of free-hand scene sketches. This uniquely results in a trilogy of scene data modalities (sketch, text, and photo), where each offers unique perspectives for scene understanding, and together enable a series of novel scene-specific applications across discriminative (retrieval) and generative (captioning) tasks. Our key objective is to learn a common three-way embedding space that enables many-to-many modality interactions (e.g, sketch+text → photo retrieval). We importantly leverage the information bottleneck theory to achieve this goal, where we (i) decouple intra-modality information by minimising the mutual information between modality-specific and modality-agnostic components via a conditional invertible neural network, and (ii) align cross-modalities information by maximising the mutual information between their modality-agnostic components using InfoNCE, with a specific multihead attention mechanism to allow many-to-many modality interactions. We spell out a few insights on the complementarity of each modality for scene understanding, and study for the first time a series of scene-specific applications like joint sketch- and text-based image retrieval, sketch captioning.
Raman Spectroscopy and Machine Learning-based Optical Sensor for Rapid Tuberculosis Diagnosis via Sputum

Tuberculosis (TB) is a contagious disease that causes 1.5 million deaths per year globally. Early diagnosis of TB patients is critical to control its spread. However, standard TB diagnostic tests such as sputum culture take days to weeks to produce results. Here, we demonstrate a quick, portable, easy-to-use, and non-invasive optical sensor based on sputum samples for TB detection. The probe uses Raman spectroscopy to detect TB in a patient's sputum supernatant. We deploy a machine-learning algorithm, principal component analysis (PCA), on the acquired Raman data to enhance the detection sensitivity and specificity. On testing 112 potential TB patients, our results show that the developed probe's accuracy is 100% for true-positive and 93.4% for true-negative. Moreover, the probe correctly identifies patients on TB medication. We anticipate that our work will lead to a viable and rapid TB diagnostic platform.
Flow-Adapter Architecture for Unsupervised Machine Translation

In this work, we propose a flow-adapter architecture for unsupervised NMT. It leverages normalizing flows to explicitly model the distributions of sentence-level latent representations, which are subsequently used in conjunction with the attention mechanism for the translation task. The primary novelties of our model are: (a) capturing language-specific sentence representations separately for each language using normalizing flows and (b) using a simple transformation of these latent representations for translating from one language to another. This architecture allows for unsupervised training of each language independently. While there is prior work on latent variables for supervised MT, to the best of our knowledge, this is the first work that uses latent variables and normalizing flows for unsupervised MT. We obtain competitive results on several unsupervised MT benchmarks.
Mass renormalization in lattice simulations of false vacuum decay

False vacuum decay, a quantum mechanical first-order phase transition in scalar field theories, is an important phenomenon in early universe cosmology. Recently, real-time semi-classical techniques based on ensembles of lattice simulations were applied to the problem of false vacuum decay. In this context, or any other lattice simulation, the effective potential experienced by long-wavelength modes is not the same as the bare potential. To make quantitative predictions using the real-time semi-classical techniques, it is therefore necessary to understand the redefinition of model parameters and the corresponding deformation of the vacuum state, as well as stochastic contributions that require modeling of unresolved subgrid modes. In this work, we focus on the former corrections and compute the expected modification of the true and false vacuum effective mass, which manifests as a modified dispersion relationship for linear fluctuations about the vacuum. We compare these theoretical predictions to numerical simulations and find excellent agreement. Motivated by this, we use the effective masses to fix the shape of a parameterized effective potential, and explore the modeling uncertainty associated with non-linear corrections. We compute the decay rates in both the Euclidean and real-time formalisms, finding qualitative agreement in the dependence on the UV cutoff. These calculations further demonstrate that a quantitative understanding of the rates requires additional corrections.
Frequency Hopping Joint Radar-Communications With Hybrid Sub-Pulse Frequency and Duration Modulation

Frequency-hopping (FH) joint radar-communications (JRC) can offer excellent security for integrated sensing and communication systems. However, existing JRC schemes mainly embed information using only the sub-pulse frequencies and hence the data rate is limited. In this letter, we propose to use both sub-pulse frequencies and durations for information modulation, leading to higher communication data rates. For information demodulation, we propose a novel scheme by using the time-frequency analysis (TFA) technique and a ‘you only look once’ (YOLO)-based detection system. As such, our system does not require channel estimation, simplifying the transmission signal frame design. Simulation results demonstrate the effectiveness of our scheme, and show that it is robust against the Doppler shift and timing offset between the transceiver and the communication receiver.
GMM is Inadmissible Under Weak Identification

We consider estimation in moment condition models and show that under any bound on identification strength, asymptotically admissible (i.e. undominated) estimators in a wide class of estimation problems must be uniformly continuous in the sample moment function. GMM estimators are in general discontinuous in the sample moments, and are thus inadmissible. We show, by contrast, that bagged, or bootstrap aggregated, GMM estimators as well as quasi-Bayes posterior means have superior continuity properties, while results in the literature imply that they are equivalent to GMM when identification is strong. In simulations calibrated to published instrumental variables specifications, we find that these alternatives often outperform GMM.
Pursuit-Evasion in Graphs: Zombies, Lazy Zombies and a Survivor

We study zombies and survivor, a variant of the game of cops and robber on graphs. In this variant, the single survivor plays the role of the robber and attempts to escape from the zombies that play the role of the cops. The zombies are restricted, on their turn, to always follow an edge of a shortest path towards the survivor. Let $z(G)$ be the smallest number of zombies required to catch the survivor on a graph $G$ with $n$ vertices. We show that there exist outerplanar graphs and visibility graphs of simple polygons such that $z(G) = \Theta(n)$. We also show that there exist maximum-degree-$3$ outerplanar graphs such that $z(G) = \Omega\left(n/\log(n)\right)$. Let $z_L(G)$ be the smallest number of lazy zombies (zombies that can stay still on their turn) required to catch the survivor on a graph $G$. We establish that lazy zombies are more powerful than normal zombies but less powerful than cops. We prove that $z_L(G) = 2$ for connected outerplanar graphs. We show that $z_L(G)\leq k$ for connected graphs with treedepth $k$. This result implies that $z_L(G)$ is at most $(k+1)\log n$ for connected graphs with treewidth $k$, $O(\sqrt{n})$ for connected planar graphs, $O(\sqrt{gn})$ for connected graphs with genus $g$ and $O(h\sqrt{hn})$ for connected graphs with any excluded $h$-vertex minor. Our results on lazy zombies still hold when an adversary chooses the initial positions of the zombies.
On the viability of determining galaxy properties from observations I: Star formation rates and kinematics

We explore how observations relate to the physical properties of the emitting galaxies by post-processing a pair of merging z ∼ 2 galaxies from the cosmological, hydrodynamical simulation NewHorizon using lcars (Light from Cloudy Added to RAMSES) to encode the physical properties of the simulated galaxy into Hα emission line. By carrying out mock observations and analysis on these data cubes we ascertain which physical properties of the galaxy will be recoverable with the HARMONI spectrograph on the European Extremely Large Telescope (ELT). We are able to estimate the galaxy’s star formation rate and dynamical mass to a reasonable degree of accuracy, with values within a factor of 1.81 and 1.38 of the true value. The kinematic structure of the galaxy is also recovered in mock observations. Furthermore, we are able to recover radial profiles of the velocity dispersion and are therefore able to calculate how the dynamical ratio varies as a function of distance from the galaxy centre. Finally, we show that when calculated on galaxy scales the dynamical ratio does not always provide a reliable measure of a galaxy’s stability against gravity or act as an indicator of a minor merger.
CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets

During the first two years of the COVID-19 pandemic, large volumes of biomedical information concerning this new disease have been published on social media. Some of this information can pose a real danger, particularly when false information is shared, for instance recommendations how to treat diseases without professional medical advice. Therefore, automatic fact-checking resources and systems developed specifically for medical domain are crucial. While existing fact-checking resources cover COVID-19 related information in news or quantify the amount of misinformation in tweets, there is no dataset providing fact-checked COVID-19 related Twitter posts with detailed annotations for biomedical entities, relations and relevant evidence. We contribute CoVERT, a fact-checked corpus of tweets with a focus on the domain of biomedicine and COVID-19 related (mis)information. The corpus consists of 300 tweets, each annotated with named entities and relations. We employ a novel crowdsourcing methodology to annotate all tweets with fact-checking labels and supporting evidence, which crowdworkers search for online. This methodology results in substantial inter-annotator agreement. Furthermore, we use the retrieved evidence extracts as part of a fact-checking pipeline, finding that the real-world evidence is more useful than the knowledge directly available in pretrained language models.
Search for Heavy Neutral Leptons in Decays of W Bosons Using a Dilepton Displaced Vertex in sqrt[s]=13  TeV pp Collisions with the ATLAS Detector.

A search for a long-lived, heavy neutral lepton (N) in 139 fb^{-1} of sqrt[s]=13 TeV pp collision data collected by the ATLAS detector at the Large Hadron Collider is reported. The N is produced via W→Nμ or W→Ne and decays into two charged leptons and a neutrino, forming a displaced vertex. The N mass is used to discriminate between signal and background. No signal is observed, and limits are set on the squared mixing parameters of the N with the left-handed neutrino states for the N mass range 3 GeV<m_{N}<15 GeV. For the first time, limits are given for both single-flavor and multiflavor mixing scenarios motivated by neutrino flavor oscillation results for both the normal and inverted neutrino-mass hierarchies.
Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network

Exercise testing has been available for more than a half-century and is a remarkably versatile tool for diagnostic and prognostic information of patients for a range of diseases, especially cardiovascular and pulmonary. With rapid advancements in technology, wearables, and learning algorithm in the last decade, its scope has evolved. Specifically, Cardiopulmonary exercise testing (CPX) is one of the most commonly used laboratory tests for objective evaluation of exercise capacity and performance levels in patients. CPX provides a non-invasive, integrative assessment of the pulmonary, cardiovascular, and skeletal muscle systems involving the measurement of gas exchanges. However, its assessment is challenging, requiring the individual to process multiple time series data points, leading to simplification to peak values and slopes. But this simplification can discard the valuable trend information present in these time series. In this work, we encode the time series as images using the Gramian Angular Field and Markov Transition Field and use it with a convolutional neural network and attention pooling approach for the classification of heart failure and metabolic syndrome patients. Using GradCAMs, we highlight the discriminative features identified by the model. Clinical relevance— The proposed framework can process multivariate exercise testing time-series data and accurately predict cardiovascular diseases. Interpretable Grad-CAMs can be obtained to explain the prediction.
Gate-Level Side-Channel Leakage Assessment with Architecture Correlation Analysis

While side-channel leakage is traditionally evaluated from a fabricated chip, it is more time-efficient and cost-effective to do so during the design phase of the chip. We present a methodology to rank the gates of a design according to their contribution to the side-channel leakage of the chip. The methodology relies on logic synthesis, logic simulation, gate-level power estimation, and gate leakage assessment to compute a ranking. The ranking metric can be defined as a specific test by correlating gate-level activity with a leakage model, or else as a non-specific test by evaluating gate-level activity in response to distinct test vector groups. Our results show that only a minority of the gates in a design contribute most of the side-channel leakage. We demonstrate this property for several designs, including a hardware AES coprocessor and a cryptographic hardware/software interface in a five-stage pipelined RISC processor.
Density anomaly in a minimum model of water-alcohol mixtures

We study the change in the temperature of maximum density (TMD) of a waterlike solvent when small amounts of solute are added to the mixture. The water is modeled as a two length scales potential, which is known to exhibit waterlike characteristic anomalies, while the solute is chosen to have an attractive interaction with the solvent. If the solute is a structure maker the TMD increases with the addition of solute, while if it is a structure breaker the TMD decreases. Both in an exact one dimensional model and using molecular dynamics in three dimensions we show that the necessary ingredient to represent structure makers is a strong solute-solvent attraction.
Fractional Graph Coloring for Functional Compression with Side Information

We describe a rational approach to reduce the computational and communication complexities of lossless point-to-point compression for computation with side information. The traditional method relies on building a characteristic graph with vertices representing the source symbols and with edges that assign a source symbol to a collection of independent sets to be distinguished for the exact recovery of the function. Our approach uses fractional coloring for a b-fold coloring of characteristic graphs to provide a linear programming relaxation to the traditional coloring method and achieves coding at a fine-grained granularity. We derive the fundamental lower bound for compression, given by the fractional characteristic graph entropy, through generalizing the notion of Körner’s graph entropy. We demonstrate the coding gains of fractional coloring over traditional coloring via a computation example. We conjecture that the integrality gap between fractional coloring and traditional coloring approaches the smallest b that attains the fractional chromatic number to losslessly represent the independent sets for a given characteristic graph, up to a linear scaling which is a function of the fractional chromatic number.
Noisy quantum kernel machines

In the noisy intermediate-scale quantum era, an important goal is the conception of implementable algorithms that exploit the rich dynamics of quantum systems and the high dimensionality of the underlying Hilbert spaces to perform tasks while prescinding from noise-proof physical systems. An emerging class of quantum learning machines is that based on the paradigm of quantum kernels. Here, we study how dissipation and decoherence affect their performance. We address this issue by investigating the expressivity and the generalization capacity of these models within the framework of kernel theory. We introduce and study the effective kernel rank, a figure of merit that quantifies the number of independent features a noisy quantum kernel is able to extract from input data. Moreover, we derive an upper bound on the generalization error of the model that involves the average purity of the encoded states. Thereby we show that decoherence and dissipation can be seen as an implicit regularization for the quantum kernel machines. As an illustrative example, we report exact finite-size simulations of machines based on chains of driven-dissipative quantum spins to perform a classification task, where the input data are encoded into the driving fields and the quantum physical system is fixed. We determine how the performance of noisy kernel machines scales with the number of nodes (chain sites) as a function of decoherence and examine the effect of imperfect measurements.
Senatus - A Fast and Accurate Code-to-Code Recommendation Engine

Machine learning on source code (MLOnCode) is a popular research field that has been driven by the availability of large-scale code repositories and the development of powerful probabilistic and deep learning models for mining source code. Code-to-code recommendation is a task in MLOnCode that aims to recommend relevant, diverse and concise code snippets that usefully extend the code currently being written by a developer in their development environment (IDE). Code-to-code recommendation engines hold the promise of increasing developer productivity by reducing context switching from the IDE and increasing code-reuse. Existing code-to-code recommendation engines do not scale gracefully to large codebases, exhibiting a linear growth in query time as the code repository increases in size. In addition, existing code-to-code recommendation engines fail to account for the global statistics of code repositories in the ranking function, such as the distribution of code snippet lengths, leading to sub-optimal retrieval results. We address both of these weaknesses with Senatus, a new code-to-code recommendation engine. At the core of Senatus is De-Skew LSH a new locality sensitive hashing (LSH) algorithm that indexes the data for fast (sub-linear time) retrieval while also counteracting the skewness in the snippet length distribution using novel abstract syntax tree-based feature scoring and selection algorithms. We evaluate Senatus and find the recommendations to be of higher quality than competing baselines, while achieving faster search. For example on the CodeSearchNet dataset Senatus improves performance by 31.21% F1 and 147.9x faster query time compared to Facebook Aroma. Senatus also outperforms standard MinHash LSH by 29.2% F1 and 51.02x faster query time.
An approach to packaging waste reverse logistics: case of Slovenia

Deriving from insights gained in a concrete empirical quantitative modelling research, the aim of the paper is to present the methodology and results of a Packaging Waste (PW) logistics exploratory study that was conducted in cooperation with a leading southeastern European retail chain company. PW Reverse Logistics (RL) is specific, but an integral part of retail operations and can also represent significant logistics and transportation costs concerns. In our research, we have, with a simulation model, been able to reduce the number of tonne-kilometres for 55% which represents significant cost reduction. The study scope focuses on the RL of industrial PW as the handling object of interest in retail, decoupled from the possible returns flow of non-food items and waste generated on premise by shoppers and employees, classified as non-industrial. It introduces an analytical framework, which has been tested and applied to a real case problem. First Published Online: 25 May 2017
Functional activity of RLIM/Rnf12 is regulated by phosphorylation-dependent nucleocytoplasmic shuttling

In mice, the ubiquitin ligase RLIM/Rnf12 is a critical survival factor for mammary milk-producing alveolar cells, but little is known about how its activity is regulated. It is shown here that RLIM shuttles between the nucleus and cytoplasm in a phosphorylation-dependent manner, and shuttling is important for its alveolar survival function.
Design of Compact BPF and Planar Diplexer for UMTS using Embedded-scheme Resonator

A compact planar diplexer utilizing embeddedscheme resonator (ESR) is designed for universal mobile telecommunications system (UMTS). The ESR is formed by embedding interdigital resonators into an open loop resonator. Based on the proposed ESR, a narrowband bandpass filter suitable for diplexer design is proposed, fabricated and measured. The measured results demonstrate that the filter exhibits good transmission properties within band and high frequency selectivity. The rectangular area occupied by the filter has overall dimensions only 0. 086λg by 0. 105λg, promises good potential in wireless communication systems that require compact size and high encapsulation quality. Then, a compact planar diplexer operating at the TX-band of 1920-1980 MHz and the RX-band of 2110-2170 MHz, which is composed of a meander T-junction and two filters initially separately designed, is synthesized, simulated and measured. Both the simulated and measured results indicate that satisfied impedance matching and good isolation between two paths have been achieved.