Discovering Latent Semantic Structure in Human Mobility Traces

We use human mobility models, for which we are experts, and attach a virus infection dynamics to it, for which we are not experts but have taken it from the literature, including recent publications. This results in a virus spreading dynamics model. The results should be verified, but because of the current time pressure, we publish them in their current state. Recommendations for improvement are welcome. We come to the following conclusions: 1. Complete lockdown works. About 10 days after lockdown, the infection dynamics dies down. This assumes that lockdown is complete, which can be guaranteed in the simulation, but not in reality. Still, it gives strong support to the argument that it is never too late for complete lockdown. 2. As a rule of thumb, we would suggest complete lockdown no later than once 10% of hospital capacities available for COVID-19 are in use, and possibly much earlier. This is based on the following insights: a. Even after lockdown, the infection dynamics continues at home, leading to another tripling of the cases before the dynamics is slowed. b. There will be many critical cases coming from people who were infected before lockdown. Because of the exponential growth dynamics, their number will be large. c. Researchers with more detailed disease progression models should improve upon these statements. 3. Our simulations say that complete removal of infections at child care, primary schools, workplaces and during leisure activities will not be enough to sufficiently slow down the infection dynamics. It would have been better, but still not sufficient, if initiated earlier. 4. Infections in public transport play an important role. In the simulations shown later, removing infections in the public transport system reduces the infection speed and the height of the peak by approximately 20%. Evidently, this depends on the infection parameters, which are not well known. – This does not point to reducing public transport capacities as a reaction to the reduced demand, but rather use it for lower densities of passengers and thus reduced infection rates. 5. In our simulations, removal of infections at child care, primary schools, workplaces, leisure activities, and in public transport may barely have been sufficient to control the infection dynamics if implemented early on. Now according to our simulations it is too late for this, and (even) harsher measures will have to be initiated until possibly a return to such a restrictive, but still somewhat functional regime will again be possible. Evidently, all of these results have to be taken with care. They are based on preliminary infection parameters taken from the literature, used inside a model that has more transport/movement details than all others that we are aware of but still not enough to describe all aspects of reality, and suffer from having to write computer code under time pressure. Optimally, they should be confirmed independently. Short of that, given current knowledge we believe that they provide justification for “complete lockdown” at the latest when about 10% of available hospital capacities for COVID-19 are in use (and possibly earlier; we are no experts of hospital capabilities). What was not investigated in detail in our simulations was contact tracing, i.e. tracking down the infection chains and moving all people along infection chains into quarantine. The case of Singapore has so far shown that this may be successful. Preliminary simulation of that tactic shows that it is difficult to implement for COVID-19, since the incubation time is rather long, people are contagious before they feel sick, or maybe never feel sufficiently sick at all. We will investigate in future work if and how contact tracing can be used together with a restrictive, but not totally locked down regime. When opening up after lockdown, it would be important to know the true fraction of people who are already immune, since that would slow down the infection dynamics by itself. For Wuhan, the currently available numbers report that only about 0.1% of the population was infected, which would be very far away from “herd immunity”. However, there have been and still may be many unknown infections (Frankfurter Allgemeine Zeitung GmbH 2020).

Inspired by the literature on side-channel attacks against cryptographic implementations, we describe a framework for the analysis of location privacy. It allows us to revisit (continuous) re-identification attacks with a combination of information theoretic and security metrics. Our results highlight conceptual differences between re-identification attacks exploiting leakages that are internal or external to a pseudonymised database. They put forward the amount of data to collect in order to estimate a predictive model as an important – yet less discussed – dimension of privacy assessments. They finally leverage recent results on the security evaluations/certification of cryptographic implementations to connect information theoretic and security metrics, and to formally bound the risk of re-identification with external leakages.

Research in location determination for GSM phones has gained interest recently as it enables a wide set of location based services. RSSI-based techniques have been the preferred method for GSM localization on the handset as RSSI information is available in all cell phones. Although the GSM standard allows for a cell phone to receive signal strength information from up to seven cell towers, many of today's cell phones are low-end phones, with limited API support, that gives only information about the associated cell tower. In addition, in many places in the world, the density of cell towers is very small and therefore, the available cell tower information for localization is very limited. This raises the challenge of accurately determining the cell phone location with very limited information, mainly the RSSI of the associated cell tower. In this paper we propose a Hidden Markov Model based solution that leverages the signal strength history from only the associated cell tower to achieve accurate GSM localization. We discuss the challenges of implementing our system and present the details of our system and how it addresses the challenges. To evaluate our proposed system, we implemented it on Android-based phones. Results for two different testbeds, representing urban and rural environments, show that our system provides at least 156% enhancement in median error in rural areas and at least 68% enhancement in median error in urban areas compared to current RSSI-based GSM localization systems.

Many location-based services require a mobile user to continuously prove his location. In absence of a secure mechanism, malicious users may lie about their locations to get these services. Mobility trace, a sequence of past mobility points, provides evidence for the user's locations. In this paper, we propose a Continuous Location Integrity and Provenance (CLIP) Scheme to provide authentication for mobility trace, and protect users' privacy. CLIP uses low-power inertial accelerometer sensor with a light-weight entropy-based commitment mechanism and is able to authenticate the user's mobility trace without any cost of trusted hardware. CLIP maintains the user's privacy, allowing the user to submit a portion of his mobility trace with which the commitment can be also verified. Wireless Access Points (APs) or colocated mobile devices are used to generate the location proofs. We also propose a light-weight spatial-temporal trust model to detect fake location proofs from collusion attacks. The prototype implementation on Android demonstrates that CLIP requires low computational and storage resources. Our extensive simulations show that the spatial-temporal trust model can achieve high (> 0.9) detection accuracy against collusion attacks.

Understanding how the brain extracts the behavioral meaning carried by specific vocalization types that can be emitted by various vocalizers and in different conditions is a central question in auditory research. This semantic categorization is a fundamental process required for acoustic communication, and presupposes discriminative and invariance properties of the auditory system for conspecific vocalizations. Songbirds have been used extensively to study vocal learning, but the communicative function of all their vocalizations and their neural representation has yet to be examined. In this study, we first generated a library containing almost the entire zebra finch vocal repertoire, and organised communication calls along nine different categories according to their behavioral meaning. We then investigated the neural representations of these semantic categories in the primary and secondary auditory areas of six anesthetised zebra finches. To analyse how single units encode these call categories, we described neural responses in terms of their discrimination, selectivity and invariance properties. Quantitative measures for these neural properties were obtained with an optimal decoder using both spike counts and spike patterns. Information theoretic metrics show that almost half of the single units encode semantic information. Neurons achieve higher discrimination of these semantic categories by being more selective and more invariant. These results demonstrate that computations necessary for semantic categorization of meaningful vocalizations are already present in the auditory cortex, and emphasise the value of a neuro-ethological approach to understand vocal communication.

Navigation in mobile robotics involves two tasks, keeping track of the robot's position and moving according to a control strategy. In addition, when no prior knowledge of the environment is available, the problem is even more difficult, as the robot has to build a map of its surroundings as it moves. These three problems ought to be solved in conjunction since they depend on each other. <br/>This thesis is about simultaneously controlling an autonomous vehicle, estimating its location and building the map of the environment. The main objective is to analyse the problem from a control theoretical perspective based on the EKF-SLAM implementation. <br/>The contribution of this thesis is the analysis of system's properties such as observability, controllability and stability, which allow us to propose an appropriate navigation scheme that produces well-behaved estimators, controllers, and consequently, the system as a whole. <br/>We present a steady state analysis of the SLAM problem, identifying the conditions that lead to partial observability. It is shown that the effects of partial observability appear even in the ideal linear Gaussian case. This indicates that linearisation alone is not the only cause of SLAM inconsistency, and that observability must be achieved as a prerequisite to tackling the effects of linearisation. Additionally, full observability is also shown to be necessary during diagonalisation of the covariance matrix, an approach often used to reduce the computational complexity of the SLAM algorithm, and which leads to full controllability as we show in this work.<br/>Focusing specifically on the case of a system with a single monocular camera, we present an observability analysis using the nullspace basis of the stripped observability matrix. The aim is to get a better understanding of the well known intuitive behaviour of this type of systems, such as the need for triangulation to features from different positions in order to get accurate relative pose estimates between vehicle and camera. Through characterisation the unobservable directions in monocular SLAM, we are able to identify the vehicle motions required to maximise the number of observable states in the system. <br/>When closing the control loop of the SLAM system, both the feedback controller and the estimator are shown to be asymptotically stable. Furthermore, we show that the tracking error does not influence the estimation performance of a fully observable system and viceversa, that control is not affected by the estimation. Because of this, a higher level motion strategy is required in order to enhance estimation, specially needed while performing SLAM with a single camera. Considering a real-time application, we propose a control strategy to optimise both the localisation of the vehicle and the feature map by computing the most appropriate control actions or movements. The actions are chosen in order to maximise an information theoretic metric. Simulations and real-time experiments are performed to demonstrate the feasibility of the proposed control strategy.

A grammatical gender system divides a lexicon into a small number of relatively fixed grammatical categories. How similar are these gender systems across languages? To quantify the similarity, we define gender systems extensionally, thereby reducing the problem of comparisons between languages’ gender systems to cluster evaluation. We borrow a rich inventory of statistical tools for cluster evaluation from the field of community detection (Driver and Kroeber, 1932; Cattell, 1945), that enable us to craft novel information theoretic metrics for measuring similarity between gender systems. We first validate our metrics, then use them to measure gender system similarity in 20 languages. We then ask whether our gender system similarities alone are sufficient to reconstruct historical relationships between languages. Towards this end, we make phylogenetic predictions on the popular, but thorny, problem from historical linguistics of inducing a phylogenetic tree over extant Indo-European languages. Of particular interest, languages on the same branch of our phylogenetic tree are notably similar, whereas languages from separate branches are no more similar than chance.

Joint entropy analysis of anterior-posterior and medial-lateral body sway.

This technical report describes preliminary research investigating the relationship between information theory and Bayes' theory for estimation of the probability of clone overlap for the use in DNA restriction mapping. A number of languages (along with information theoretic metrics) capable of describing a hypothesized overlap are presented. For each language, the MML criterion is applied to the encoded overlaps of a pair of clones to search for that overlap which is most probable. The objective is to order the pair's encoded overlaps, based on the MML criterion, from the most to the least probable. This ordering is compared to the ordering suggestion by the Bayesian probabilistic approach.

Most wireless health monitoring devices are designed to continuously acquire, process, store and transmit photoplethysmogram (PPG) signal(s) that may undergo different kinds of waveform distortions at the processing stages of denoising, compression and transmission to be performed at the wireless biosensing node. Therefore, in this paper, we present and evaluate the performance of the information-theoretic metrics, including the mutual information (MI) and Kullback-Leibler divergence (KLD) by using various kinds of compression and reconstructed signals obtained based on the compressed sensing, discrete cosine transform, predictive coding, and discrete wavelet transform. Evaluation results on the 3-point and 2-point scale ratings of subjective quality evaluation test showed that the MI metric provides promising results in terms of higher Pearson correlation coefficient with subjective quality scores and prediction accuracy in correctly predicting the quality group as compared to that of the KLD metric.

In broadband fixed wireless access (BFWA) networks mainly operating above 20 GHz, a clear line-of-sight (LOS) path is necessary between the base stations (BS) and the subscribers (S). In this letter, a Markov-based method to estimate the packet loss rate (PLR) statistical characteristics over a dynamically, due to propagation, varying LOS BFWA channel is presented. The method is verified by means of information theoretic metrics and tested against simulation results.

Side-Channel Analysis (SCA) plays a crucial role in hardware security evaluation. However, side-channel acquisitions (a.k.a. traces) usually contain noises that often impose negative effects on key-recovery efficiency. In this paper, we propose convolutional denoising autoencoder (CDAE) for noise reduction in SCA. CDAE is composed of multiple layers of convolution operators, learning an end-to-end mapping from noisy traces to clean traces by minimizing the \(\ell _2\) loss of noisy-clean trace pairs. The convolutional layers capture the abstraction of the traces while eliminating noises. We argue that CDAE is very suitable for profiled SCA especially when the attacker has a large amount of traces in the offline profiling phase. Once the network training is done, our denoising network can be applied to individual new noisy traces for the attacker to launch online attacks. To validate the effectiveness of our method, we train CDAE to denoise traces and then perform Template Attacks (TA) in three high noise jamming scenarios, including unprotected (GPU and FPGA based) and protected (MCU based) AES implementations. Our method can significantly outperform the state-of-the-art Singular Spectrum Analysis (SSA) denoising method on both information theoretic metrics and security metrics. Results show that CDAE achieves at least \(\sim 4\times \) Signal-to-Noise Ratio (SNR) gain, thus TA with denoising preprocessing requires at most 50% of the traces in the attack phase.

In this paper, we consider a recently introduced framework that investigates physically observable implementations from a theoretical point of view. The model allows quantifying the effect of practically relevant leakage functions with a combination of security and information theoretic metrics. More specifically, we apply our evaluation methodology to an exemplary block cipher. We first consider a Hamming weight leakage function and evaluate the efficiency of two commonly investigated countermeasures, namely noise addition and masking. Then, we show that the proposed methodology allows capturing certain non-trivial intuitions, e.g. about the respective effectiveness of these countermeasures. Finally, we justify the need of combined metrics for the evaluation, comparison and understanding of side-channel attacks.

Methods on modelling the human brain as a Complex System have increased remarkably in the literature as researchers seek to understand the underlying foundations behind cognition, behaviour, and perception. Computational methods, especially Graph Theory-based methods, have recently contributed significantly in understanding the wiring connectivity of the brain, modelling it as a set of nodes connected by edges. Therefore, the brain’s spatiotemporal dynamics can be holistically studied by considering a network, which consists of many neurons, represented by nodes. Various models have been proposed for modelling such neurons. A recently proposed method in training such networks, called full-Force, produces networks that perform tasks with fewer neurons and greater noise robustness than previous least-squares approaches (i.e. FORCE method). In this paper, the first direct applicability of a variant of the full-Force method to biologically-motivated Spiking RNNs (SRNNs) is demonstrated. The SRNN is a graph consisting of modules. Each module is modelled as a Small-World Network (SWN), which is a specific type of a biologically-plausible graph. So, the first direct applicability of a variant of the full-Force method to modular SWNs is demonstrated, evaluated through regression and information theoretic metrics. For the first time, the aforementioned method is applied to spiking neuron models and trained on various real-life Electroencephalography (EEG) signals. To the best of the authors’ knowledge, all the contributions of this paper are novel. Results show that trained SRNNs match EEG signals almost perfectly, while network dynamics can mimic the target dynamics. This demonstrates that the holistic setup of the network model and the neuron model which are both more biologically plausible than previous work, can be tuned into real biological signal dynamics.

WiFi Access Points (APs) can be used to offload data or computation tasks while users are commuting. However, due to APs&#x2019; limited coverage, offloading performance is heavily impacted by the users&#x2019; mobility. This work proposes to leverage human mobility to inform offloading tasks, taking a data based approach leveraging granular mobility datasets from two cities: Porto and Beijing. We define Offloading Regions (ORs) as areas where a commuter&#x2019;s mobility would enable offloading, and propose an unsupervised learning methodology to extract ORs from mobility traces. Then, we characterise and analyse ORs according to offloading opportunity metrics such as type, availability, total time to offload, and offloading delay. Results show that in 50&#x0025; of the trips, users spend more than 48&#x0025; of the travel time inside ORs extracted according to the proposed methodology. The ability to predict the next ORs would benefit offloading orchestration. Offloading mobility predictability, although crucial, proves to be challenging, expressed by the poor predictive performance of well-known models (<inline-formula> <tex-math notation="LaTeX">$\approx $ </tex-math></inline-formula> 37&#x0025; acc. for the best predictor). We show that mobility regularity properties improve predictive performance up to <inline-formula> <tex-math notation="LaTeX">$\approx $ </tex-math></inline-formula> 35&#x0025;. Finally, we look into the impact of further OR extraction and prediction parameters. We show that the exploration phase length does not impact the discovery of low relevance ORs, and that both filtering low relevance OR and predicting multiple ORs increase predictability. By characterising the trade-off between mobility predictability and offloading opportunities in transit, we highlighting the need for offloading systems to adopt hybrid strategies, i.e., mixing opportunistic and predictive strategies. The conclusions and findings on offloading mobility properties are likely to generalise for varied urban scenarios given the high degree of similarity between the results obtained for the two different and independently collected mobility datasets.