Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic smallest enclosing ball in high dimensions via subgradient sampling

We study a variant of the median problem for a collection of point sets in high dimensions. This generalizes the geometric median as well as the (probabilistic) smallest enclosing ball (pSEB) problems. Our main objective and motivation is to improve the previously best algorithm for the pSEB problem by reducing its exponential dependence on the dimension to linear. This is achieved via a novel combination of sampling techniques for clustering problems in metric spaces with the framework of stochastic subgradient descent. As a result, the algorithm becomes applicable to shape fitting problems in Hilbert spaces of unbounded dimension via kernel functions. We present an exemplary application by extending the support vector data description (SVDD) shape fitting method to the probabilistic case. This is done by simulating the pSEB algorithm implicitly in the feature space induced by the kernel function.
Designing for Health Chatbots

Building conversational agents have many technical, design and linguistic challenges. Other more complex elements include using emotionally intelligent conversational agent to build trust with the individuals. In this chapter, we introduce the nature of conversational user interfaces (CUIs) for health and describe UX design principles informed by a systematic literature review of relevant research works. We analyze scientific literature in conversational interfaces and chatterbots, providing a survey of major studies and describing UX design principles and interaction patterns.
Extracting Vehicle Sensor Signals from CAN Logs for Driver Re-identification

Data is the new oil for the car industry. Cars generate data about how they are used and who’s behind the wheel which gives rise to a novel way of profiling individuals. Several prior works have successfully demonstrated the feasibility of driver re-identification using the in-vehicle network data captured on the vehicle’s CAN (Controller Area Network) bus. However, all of them used signals (e.g., velocity, brake pedal or accelerator position) that have already been extracted from the CAN log which is itself not a straightforward process. Indeed, car manufacturers intentionally do not reveal the exact signal location within CAN logs. Nevertheless, we show that signals can be efficiently extracted from CAN logs using machine learning techniques. We exploit that signals have several distinguishing statistical features which can be learnt and effectively used to identify them across different vehicles, that is, to quasi ”reverse-engineer” the CAN protocol. We also demonstrate that the extracted signals can be successfully used to re-identify individuals in a dataset of 33 drivers. Therefore, not revealing signal locations in CAN logs per se does not prevent them to be regarded as personal data of drivers.
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models

A growing number of state-of-the-art transfer learning methods employ language models pretrained on large generic corpora. In this paper we present a conceptually simple and effective transfer learning approach that addresses the problem of catastrophic forgetting. Specifically, we combine the task-specific optimization function with an auxiliary language model objective, which is adjusted during the training process. This preserves language regularities captured by language models, while enabling sufficient adaptation for solving the target task. Our method does not require pretraining or finetuning separate components of the network and we train our models end-to-end in a single step. We present results on a variety of challenging affective and text classification tasks, surpassing well established transfer learning methods with greater level of complexity.
Expect More from the Networking: DDoS Mitigation by FITT in Named Data Networking

Distributed Denial of Service (DDoS) attacks have plagued the Internet for decades, but defenses have not fundamentally outpaced attackers. Instead, the size and rate of growth in attacks have actually outpaced carriers' and DDoS mitigation services' growth. In this paper, we comprehensively examine ways in which Named Data Networking (NDN), a proposed data-centric Internet architecture, fundamentally addresses some of the principle weaknesses in today's DDoS defenses in IP networking. We argue that NDN's architectural changes (even when incrementally deployed) can make DDoS attacks fundamentally more difficult to launch and less effective. We present a new DDoS mitigation solution -- Fine-grained Interest Traffic Throttling FITT, to leverage NDN's features to combat DDoS in the Internet of Things (IoT) age. FITT enables the network to detect DDoS directly from feedback from victims, throttle DDoS traffic along its exact path in the network, and perform reinforcement control over the misbehaving entities at their sources. In cases like the Mirai attacks, where smart IoT devices (smart cameras, refrigerators, etc.) were able to cripple high-capacity service providers using diverse DDoS Tactics Techniques and Procedures (TTPs), FITT would be able to precisely squelch the attack traffic at its distributed sources, without disrupting other legitimate application traffic running on the same devices. FITT offers an incrementally deployable solution for service providers to effectuate the application-level remediation at the sources, which remains unattainable in today's DDoS market. Our extensive simulations results show that FITT can effectively throttle attack traffic in a short time and achieve over 99% legitimate traffic.
Mining Objects: Fully Unsupervised Object Discovery and Localization From a Single Image

The goal of our work is to discover dominant objects without using any annotations. We focus on performing unsupervised object discovery and localization in a very general setting where only a single image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e, co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns in an unsupervised manner. Extensive experiments on eleven benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves best results on four benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly.
Solving Power System Differential Algebraic Equations Using Differential Transformation

This paper proposes a novel non-iterative method to solve power system differential algebraic equations (DAEs) using the differential transformation, which is a mathematical tool able to obtain power series coefficients by transformation rules instead of calculating high order derivatives and has proved to be effective in solving state variables of nonlinear differential equations in our previous study. This paper further solves non-state variables, e.g., current injections and bus voltages, directly with a realistic DAE model of power grids. These non-state variables, nonlinearly coupled in network equations, are conventionally solved by numerical methods with time-consuming iterations, but their differential transformations are proved to satisfy formally linear equations in this paper. Thus, a non-iterative algorithm is designed to analytically solve all variables of a power system DAE model with ZIP loads. From test results on a Polish 2383-bus system, the proposed method demonstrates fast and reliable time performance compared to traditional numerical approaches including the implicit trapezoidal rule method and a partitioned scheme using the explicit modified Euler method and Newton Raphson method.
Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification

Traditional clustering algorithms such as K-means rely heavily on the nature of the chosen metric or data representation. To get meaningful clusters, these representations need to be tailored to the downstream task (e.g. cluster photos by object category, cluster faces by identity). Therefore, we frame clustering as a meta-learning task, few-shot clustering, which allows us to specify how to cluster the data at the meta-training level, despite the clustering algorithm itself being unsupervised. We propose Centroid Networks, a simple and efficient few-shot clustering method based on learning representations which are tailored both to the task to solve and to its internal clustering module. We also introduce unsupervised few-shot classification, which is conceptually similar to few-shot clustering, but is strictly harder than supervised* few-shot classification and therefore allows direct comparison with existing supervised few-shot classification methods. On Omniglot and miniImageNet, our method achieves accuracy competitive with popular supervised few-shot classification algorithms, despite using *no labels* from the support set. We also show performance competitive with state-of-the-art learning-to-cluster methods.
Exploiting Population Activity Dynamics to Predict Urban Epidemiological Incidence

Ambulance services worldwide are of vital importance to population health. Timely responding to incidents by dispatching an ambulance vehicle to the location a call came from can offer significant benefits to patient care across a number of medical conditions. Moreover, identifying the reasons that drive ambulance activity at an area not only can improve the operational capacity of emergency services, but can lead to better policy design in healthcare. In this work, we analyse the temporal dynamics of 5.6 million ambulance calls across a region of 7 million residents in the UK. We identify characteristic temporal patterns featuring diurnal and weekly cycles in ambulance call activity. These patterns are stable over time and across geographies. Using a dataset sourced from location intelligence platform Foursquare, we establish a link between the spatio-temporal dynamics of mobile users engaging with urban activities locally and emergency incidents. We use this information to build a novel metric that assesses the health risk of a geographic area in terms of its propensity to yield ambulance calls. Formulating then an online classification task where the goal becomes to identify which regions will need an ambulance at a given time, we demonstrate how semantic information about real world places crowdsourced through online platforms, can become a useful source of information in understanding and predicting regional epidemiological trends.
Efficient classical simulation of Clifford circuits with nonstabilizer input states

We investigate the problem of evaluating the output probabilities of Clifford circuits with nonstabilizer product input states. First, we consider the case when the input state is mixed, and give an efficient classical algorithm to approximate the output probabilities, with respect to the l_{1} norm, of a large fraction of Clifford circuits. The running time of our algorithm decreases as the inputs become more mixed. Second, we consider the case when the input state is a pure nonstabilizer product state, and show that a similar efficient algorithm exists to approximate the output probabilities, when a suitable restriction is placed on the number of qubits measured. This restriction depends on a magic monotone that we call the Pauli rank. We apply our results to give an efficient output probability approximation algorithm for some restricted quantum computation models, such as Clifford circuits with solely magic state inputs, Pauli-based computation, and instantaneous quantum polynomial time circuits. Finally, we discuss the relationship between Pauli rank and stabilizer rank.
Monitorology the art of observing the world

In the age of ever increasing demand for big data and data analytics, a question of collecting the data becomes fundamental. What and how to collect the data is essential as it has direct impact on decision making, system operation and control. Specifically, we focus on the art of observing the world by electronic devices such as sensors and meters that, in general, we call monitors. We define five challenges to ensure effective and efficient monitoring that still need a lot of research. Additionally, we illustrate each challenge by example. Since reliance on big data and data analytics is continuously increasing, these challenges will become ever more relevant to save the world from flood of meaningless, dumb data, leading frequently to false conclusions and wrong decisions whose impact may range from a minor inconvenience to major disasters and even loss of lives.
Adaptive Online Learning with Momentum for Contingency-based Voltage Stability Assessment

Voltage stability refers to the ability of a power system to maintain acceptable voltages among all buses under normal operating conditions and after a disturbance. In this paper, a measurement-based voltage stability assessment (VSA) framework using online deep learning is developed. Since the topology changes induced by transmission contingencies may significantly reduce the voltage stability margin, different network topologies under different operating conditions are involved in our training dataset. To achieve high accuracy in the training process, a gradient-based adaptive learning algorithms is adopted. Numerical results based on the NETS-NYPS 68-bus system demonstrate the effectiveness of the proposed VSA approach. Moreover, with the proximal function modified adaptively, the adaptive algorithm with momentum outperforms traditional non-adaptive algorithms whose learning rate is constant.
Informative Path Planning and Mapping for Active Sensing Under Localization Uncertainty

Robotic platforms are emerging as a timely and cost-efficient tool for exploration and monitoring. However, an open challenge is planning missions for robust, efficient data acquisition in complex environments. To address this issue, we introduce an informative planning framework for active sensing scenarios that accounts for the robot pose uncertainty. Our strategy exploits a Gaussian Process model to capture a target environmental field given the uncertainty on its inputs. This allows us to maintain robust maps, which are used for planning information-rich trajectories in continuous space. A key aspect of our method is a new utility function that couples the localization and field mapping objectives, enabling us to trade-off exploration against exploitation in a principled way. Extensive simulations show that our approach outperforms existing strategies, with reductions of up to 45.1% and 6.3% in mean pose uncertainty and map error. We demonstrate a proof of concept in an indoor temperature mapping scenario.
Circuit Transformations for Quantum Architectures

Quantum computer architectures impose restrictions on qubit interactions. We propose efficient circuit transformations that modify a given quantum circuit to fit an architecture, allowing for any initial and final mapping of circuit qubits to architecture qubits. To achieve this, we first consider the qubit movement subproblem and use the routing via matchings framework to prove tighter bounds on parallel routing. In practice, we only need to perform partial permutations, so we generalize routing via matchings to that setting. We give new routing procedures for common architecture graphs and for the generalized hierarchical product of graphs, which produces subgraphs of the Cartesian product. Secondly, for serial routing, we consider the token swapping framework and extend a 4-approximation algorithm for general graphs to support partial permutations. We apply these routing procedures to give several circuit transformations, using various heuristic qubit placement subroutines. We implement these transformations in software and compare their performance for large quantum circuits on grid and modular architectures, identifying strategies that work well in practice.
Using stigmergy as a computational memory in the design of recurrent neural networks

In this paper, a novel architecture of Recurrent Neural Network (RNN) is designed and experimented. The proposed RNN adopts a computational memory based on the concept of stigmergy. The basic principle of a Stigmergic Memory (SM) is that the activity of deposit/removal of a quantity in the SM stimulates the next activities of deposit/removal. Accordingly, subsequent SM activities tend to reinforce/weaken each other, generating a coherent coordination between the SM activities and the input temporal stimulus. We show that, in a problem of supervised classification, the SM encodes the temporal input in an emergent representational model, by coordinating the deposit, removal and classification activities. This study lays down a basic framework for the derivation of a SM-RNN. A formal ontology of SM is discussed, and the SM-RNN architecture is detailed. To appreciate the computational power of an SM-RNN, comparative NNs have been selected and trained to solve the MNIST handwritten digits recognition benchmark in its two variants: spatial (sequences of bitmap rows) and temporal (sequences of pen strokes).
Classification via local manifold approximation

Classifiers label data as belonging to one of a set of groups based on input features. It is challenging to achieve accurate classification when the feature distributions in the different classes are complex, with nonlinear, overlapping and intersecting supports. This is particularly true when training data are limited. To address this problem, we propose a new type of classifier based on obtaining a local approximation to the support of the data within each class in a neighbourhood of the feature to be classified, and assigning the feature to the class having the closest support. This general algorithm is referred to as local manifold approximation classification. As a simple and theoretically supported special case, which is shown to have excellent performance across a broad variety of examples, we use spheres for local approximation, obtaining a spherical approximation classifier.
Towards Higher Spectral Efficiency: Spatial Path Index Modulation Improves Millimeter-Wave Hybrid Beamforming

The combination of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems and index modulation (IM) technique has recently constituted a novel form of mmWave hybrid beamforming. Numerous studies have been conducted to show that such a system has the potential to outperform conventional mmWave-MIMOs with respect to spectral efficiency (SE). Most of the current works focused only on designing hybrid beamforming structures to empirically achieve higher SE performance. However, a fundamental question whether IM technique can truly improve the SE of current mmWave hybrid beamforming is still left unanswered. Against such a background, in this paper, we first extend the IM-assisted mmWave system to a more generalized version, i.e., a spatial path index modulation aided mmWave (SPIM-mmWave) system, which subsumes both IM-assisted and conventional mmWave-MIMO systems as its special cases. Based on the framework of SPIM-mmWave, a fundamental study is conducted towards a theoretic condition under which SPIM-mmWave guarantees to outperform conventional mmWave-MIMO schemes. It is demonstrated that, under a specific channel condition and noise level (which will be explicitly given by our study), the SPIM-mmWave guarantees to outperform conventional mmWave-MIMO systems.
FRAMER: A Software-based Capability Model.

Fine-grained memory protection for C and C++ programs must track individual objects (or pointers), and store bounds information per object (pointer). Its cost is dominated by metadata updates and lookups, making efficient metadata management the key for minimizing performance impact. Existing approaches reduce metadata management overheads by sacrificing precision, breaking binary compatibility by changing object memory layout, or wasting space by excessive alignment or large shadow memory spaces. We propose FRAMER, a software capability model for object-granularity memory protection. Its efficient per-object metadata management mechanism enables direct access to metadata by calculating their location from a tagged pointer to the object and, for large objects, a compact supplementary table. The number of bits in this tag and the size of the supplementary table are balanced to minimize both using a novel technique. FRAMER is a general proposal for object metadata management with potential applications in memory safety, type safety, thread safety and garbage collection that improves over previous solutions by (1) increasing locality of reference by having objects carry their metadata, (2) streamlining expensive metadata lookups, (3) saving space by avoiding superfluous alignment and padding, (4) avoiding internal object memory layout changes.
Scaling up budgeted reinforcement learning

Can we learn a control policy able to adapt its behaviour in real time so as to take any desired amount of risk? The general Reinforcement Learning framework solely aims at optimising a total reward in expectation, which may not be desirable in critical applications. In stark contrast, the Budgeted Markov Decision Process (BMDP) framework is a formalism in which the notion of risk is implemented as a hard constraint on a failure signal. Existing algorithms solving BMDPs rely on strong assumptions and have so far only been applied to toy-examples. In this work, we relax some of these assumptions and demonstrate the scalability of our approach on two practical problems: a spoken dialogue system and an autonomous driving task. On both examples, we reach similar performances as Lagrangian Relaxation methods with a significant improvement in sample and memory efficiency.
Logarithmic Regret for parameter-free Online Logistic Regression

We consider online optimization procedures in the context of logistic regression, focusing on the Extended Kalman Filter (EKF). We introduce a second-order algorithm close to the EKF, named Semi-Online Step (SOS), for which we prove a O(log(n)) regret in the adversarial setting, paving the way to similar results for the EKF. This regret bound on SOS is the first for such parameter-free algorithm in the adversarial logistic regression. We prove for the EKF in constant dynamics a O(log(n)) regret in expectation and in the well-specified logistic regression model.