We take interest in the early assessment of risk for depression in social media users. We focus on the eRisk 2018 dataset, which represents users as a sequence of their written online contributions. We implement four RNN-based systems to classify the users. We explore several aggregations methods to combine predictions on individual posts. Our best model reads through all writings of a user in parallel but uses an attention mechanism to prioritize the most important ones at each timestep.
The @cite_21 is a simpler version of the that we used, that only takes into account the target hidden state. It is stated as such :
@cite_21
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We investigate alternative suspicion functions for Tardos traitor tracing schemes. In the simple decoder approach (computation of a score for every user independently) we derive suspicion functions that optimize a performance indicator related to the sufficient code length l in the limit of large coalition size c. Our results hold for the Restricted-Digit Model as well as the Combined-Digit Model. The scores depend on information that is usually not available to the tracer -- the attack strategy or the tallies of the symbols received by the colluders. We discuss how such results can be used in realistic contexts. We study several combinations of coalition attack strategy versus suspicion function optimized against some attack (another attack or the same). In many of these combinations the usual scaling l c2 is replaced by a lower power of c, e.g. c3 2. We find that the interleaving strategy is an especially powerful attack, and the suspicion function tailored against interleaving is effective against all considered attacks. In this paper we present a novel approach to disambiguate textual mentions of named entities against the Wikipedia knowledge base. The conditional dependencies between different named entities across Wikipedia are represented as a Markov network. In our approach, named entities are treated as hidden variables and textual mentions as observations. The number of states and observations is huge and naively using the Viterbi algorithm to find the hidden state sequence that emits the query observation sequence is computationally infeasible, given a state space of this size. Based on an observation that is specific to the disambiguation problem, we propose an approach that uses a tailored approximation to reduce the size of the state space, making the Viterbi algorithm feasible. Results show good improvement in disambiguation accuracy relative to the baseline approach and to some state-of-the-art approaches. Also, our approach shows how, with suitable approximations, HMMs can be used in such large-scale state space problems. We consider finite state space stationary hidden Markov models (HMMs) in the situation where the number of hidden states is unknown. We provide a frequentist asymptotic evaluation of Bayesian analysis methods. Our main result gives posterior concentration rates for the marginal densities, that is for the density of a fixed number of consecutive observations. Using conditions on the prior, we are then able to define a consistent Bayesian estimator of the number of hidden states. It is known that the likelihood ratio test statistic for overfitted HMMs has a nonstandard behaviour and is unbounded. Our conditions on the prior may be seen as a way to penalize parameters to avoid this phenomenon. Inference of parameters is a much more difficult task than inference of marginal densities, we still provide a precise description of the situation when the observations are i.i.d. and we allow for 2 possible hidden states. We investigate alternative suspicion functions for bias-based traitor tracing schemes, and present a practical construction of a simple decoder that attains capacity in the limit of large coalition size @math . We derive optimal suspicion functions in both the restricted-digit model and the combined-digit model. These functions depend on information that is usually not available to the tracer—the attack strategy or the tallies of the symbols received by the colluders. We discuss how such results can be used in realistic contexts. We study several combinations of coalition attack strategy versus suspicion function optimized against some attack (another attack or the same). In many of these combinations, the usual codelength scaling @math changes to a lower power of @math , e.g., @math . We find that the interleaving strategy is an especially powerful attack. The suspicion function tailored against interleaving is the key ingredient of the capacity-achieving construction. It is a fundamental problem to decide how many copies of an unknown mixed quantum state are necessary and sufficient to determine the state. Previously, it was known only that estimating states to error @math in trace distance required @math copies for a @math -dimensional density matrix of rank @math . Here, we give a theoretical measurement scheme (POVM) that requires @math copies to estimate @math to error @math in infidelity, and a matching lower bound up to logarithmic factors. This implies @math copies suffice to achieve error @math in trace distance. We also prove that for independent (product) measurements, @math copies are necessary in order to achieve error @math in infidelity. For fixed @math , our measurement can be implemented on a quantum computer in time polynomial in @math . We introduce a novel form of decoy-state technique to make the single-photon Bennett 1992 protocol robust against losses and noise of a communication channel. Two uninformative states are prepared by the transmitter in order to prevent the unambiguous state discrimination attack and improve the phase-error rate estimation. The presented method does not require strong reference pulses, additional electronics or extra detectors for its implementation. We consider the estimation of the number of hidden states (the order) of a discrete-time finite-alphabet hidden Markov model (HMM). The estimators we investigate are related to code-based order estimators: penalized maximum-likelihood (ML) estimators and penalized versions of the mixture estimator introduced by Liu and Narayan (1994). We prove strong consistency of those estimators without assuming any a priori upper bound on the order and smaller penalties than previous works. We prove a version of Stein's lemma for HMM order estimation and derive an upper bound on underestimation exponents. Then we prove that this upper bound can be achieved by the penalized ML estimator and by the penalized mixture estimator. The proof of the latter result gets around the elusive nature of the ML in HMM by resorting to large-deviation techniques for empirical processes. Finally, we prove that for any consistent HMM order estimator, for most HMM, the overestimation exponent is null. Hidden Markov models (HMMs) have proven to be one of the most widely used tools for learning probabilistic models of time series data. In an HMM, information about the past is conveyed through a single discrete variable—the hidden state. We discuss a generalization of HMMs in which this state is factored into multiple state variables and is therefore represented in a distributed manner. We describe an exact algorithm for inferring the posterior probabilities of the hidden state variables given the observations, and relate it to the forward–backward algorithm for HMMs and to algorithms for more general graphical models. Due to the combinatorial nature of the hidden state representation, this exact algorithm is intractable. As in other intractable systems, approximate inference can be carried out using Gibbs sampling or variational methods. Within the variational framework, we present a structured approximation in which the the state variables are decoupled, yielding a tractable algorithm for learning the parameters of the model. Empirical comparisons suggest that these approximations are efficient and provide accurate alternatives to the exact methods. Finally, we use the structured approximation to model Bach‘s chorales and show that factorial HMMs can capture statistical structure in this data set which an unconstrained HMM cannot. Abstract In presence of incomplete information about the world we need to distinguish between the state of the world and the state of the agent's knowledge about the world. In such a case the agent may need to have at its disposal sensing actions that change its state of knowledge about the world and may need to construct more general plans consisting of sensing actions and conditional statements to achieve its goal. In this paper we first develop a high-level action description language that allows specification of sensing actions and their effects in its domain description and allows queries with conditional plans. We give provably correct translations of domain description in our language to axioms in first-order logic, and relate our formulation to several earlier formulations in the literature. We then analyze the state space of our formulation and develop several sound approximations that have much smaller state spaces. Finally we define regression of knowledge formulas over conditional plans. Information-hiding in natural language text has mainly consisted of carrying out approximately meaning-preserving modifications on the given cover text until it encodes the intended mark. A major technique for doing so has been synonym-substitution. In these previous schemes, synonym substitutions were done until the text "confessed", i.e., carried the intended mark message. We propose here a better way to use synonym substitution, one that is no longer entirely guided by the mark-insertion process: It is also guided by a resilience requirement, subject to a maximum allowed distortion constraint. Previous schemes for information hiding in natural language text did not use numeric quantification of the distortions introduced by transformations, they mainly used heuristic measures of quality based on conformity to a language model (and not in reference to the original cover text). When there are many alternatives to carry out a substitution on a word, we prioritize these alternatives according to a quantitative resilience criterion and use them in that order. In a nutshell, we favor the more ambiguous alternatives. In fact not only do we attempt to achieve the maximum ambiguity, but we want to simultaneously be as close as possible to the above-mentioned distortion limit, as that prevents the adversary from doing further transformations without exceeding the damage threshold; that is, we continue to modify the document even after the text has "confessed" to the mark, for the dual purpose of maximizing ambiguity while deliberately getting as close as possible to the distortion limit. The quantification we use makes possible an application of the existing information-theoretic framework, to the natural language domain, which has unique challenges not present in the image or audio domains. The resilience stems from both (i) the fact that the adversary does not know where the changes were made, and (ii) the fact that automated disambiguation is a major difficulty faced by any natural language processing system (what is bad news for the natural language processing area, is good news for our scheme's resilience). In addition to the above mentioned design and analysis, another contribution of this paper is the description of the implementation of the scheme and of the experimental data obtained. Considerons une marche aleatoire branchante surcritique a temps discret. Nous nous interessons a la probabilite qu'il existe un rayon infini du support de la marche aleatoire branchante, le long duquel elle croit plus vite qu'une fonction lineaire de pente y - e, ou γ designe la vitesse asymptotique de la position de la particule la plus a droite dans la marche aleatoire branchante. Sous des hypotheses generales peu restrictives, nous prouvons que, lorsque e → 0, cette probabilite decroit comme exp —β+o(1) e 1 2 , ou β est une constante strictement positive dont la valeur depend de la loi de la marche aleatoire branchante. Dans le cas special ou des variables aleatoires i.i.d. de Bernoulli(p) (avec 0 < p < 1 2 ) sont placees sur les aretes d'un arbre binaire enracine, ceci repond a une question ouverte de Robin Pemantle (Ann. Appl. Probab. 19 (2009) 1273-1291). For the Tardos traitor tracing scheme, we show that by combining the symbol-symmetric accusation function of ? with the improved analysis of Blayer and Tassa we get further improvements. Our construction gives codes that are up to four times shorter than Blayer and Tassa's, and up to two times shorter than the codes from ? Asymptotically, we achieve the theoretical optimal codelength for Tardos' distribution function and the symmetric score function. For large coalitions, our codelengths are asymptotically about 4.93 of Tardos' original codelengths, which also improves upon results from We present a single-database computationally private information retrieval scheme with polylogarithmic communication complexity. Our construction is based on a new, but reasonable intractability assumption, which we call the φ-Hiding Assumption (φHA): essentially the difficulty of deciding whether a small prime divides φ(m), where m is a composite integer of unknown factorization. In this paper we present a tracker, which is radically different from state-of-the-art trackers: we apply no model updating, no occlusion detection, no combination of trackers, no geometric matching, and still deliver state-of-theart tracking performance, as demonstrated on the popular online tracking benchmark (OTB) and six very challenging YouTube videos. The presented tracker simply matches the initial patch of the target in the first frame with candidates in a new frame and returns the most similar patch by a learned matching function. The strength of the matching function comes from being extensively trained generically, i.e., without any data of the target, using a Siamese deep neural network, which we design for tracking. Once learned, the matching function is used as is, without any adapting, to track previously unseen targets. It turns out that the learned matching function is so powerful that a simple tracker built upon it, coined Siamese INstance search Tracker, SINT, which only uses the original observation of the target from the first frame, suffices to reach state-of-theart performance. Further, we show the proposed tracker even allows for target re-identification after the target was absent for a complete video shot. In this paper, we present two differential fault analyses on PRESENT-80 which is a lightweight block cipher. The first attack is a basic attack which induces a fault on only one bit of intermediate states, and we can obtain the last subkey of the block cipher, given 48 faulty cipher texts on average. The second attack can retrieve the master key of the block cipher, given 18 faulty cipher texts on average. In the latter attack, we assume that we can induce faults on a single nibble of intermediate states. Given those faulty cipher texts, the computational complexity of attacks is negligible. In this paper we present a tracker, which is radically different from state-of-the-art trackers: we apply no model updating, no occlusion detection, no combination of trackers, no geometric matching, and still deliver state-of-the-art tracking performance, as demonstrated on the popular online tracking benchmark (OTB) and six very challenging YouTube videos. The presented tracker simply matches the initial patch of the target in the first frame with candidates in a new frame and returns the most similar patch by a learned matching function. The strength of the matching function comes from being extensively trained generically, i.e., without any data of the target, using a Siamese deep neural network, which we design for tracking. Once learned, the matching function is used as is, without any adapting, to track previously unseen targets. It turns out that the learned matching function is so powerful that a simple tracker built upon it, coined Siamese INstance search Tracker, SINT, which only uses the original observation of the target from the first frame, suffices to reach state-of-the-art performance. Further, we show the proposed tracker even allows for target re-identification after the target was absent for a complete video shot. In multi-target tracking, the maintaining of the correct identity of targets is challenging. In the presented tracking method, accurate target identification is achieved by incorporating the appearance information of the spatial and temporal context of each target. The spatial context of a target involves local background and nearby targets. The first contribution of the paper is to provide a new discriminative model for multi-target tracking with the embedded classification of each target against its context. As a result, the tracker not only searches for the image region similar to the target but also avoids latching on nearby targets or on a background region. The temporal context of a target includes its appearances seen during tracking in the past. The past appearances are used to train a probabilistic PCA that is used as the measurement model of the target at the present. As the second contribution, we develop a new incremental scheme for probabilistic PCA. It can update accurately the full set of parameters including a noise parameter still ignored in related literature. The experiments show robust tracking performance under the condition of severe clutter, occlusions and pose changes. This paper describes a simple method for extracting metadata fields from citations using hidden Markov models. The method is easy to implement and can achieve levels of precision and recall for heterogeneous citations comparable to or greater than other HMM-based methods. The method consists largely of string manipulation and otherwise depends only on an implementation of the Viterbi algorithm, which is widely available, and so can be implemented by diverse digital library systems. This paper describes a method for checking the acceptability of paraphrases in context. We use the Google n-gram data and a CCG parser to certify the paraphrasing grammaticality and fluency. We collect a corpus of human judgements to evaluate our system. The ultimate goal of our work is to integrate text paraphrasing into a Linguistic Steganography system, by using paraphrases to hide information in a cover text. We propose automatically generated paraphrases as a new and useful source of transformations for Linguistic Steganography, and show that our method for checking paraphrases is effective at maintaining a high level of imperceptibility, which is crucial for effective steganography. We study the problem of finding a hidden code k in the domain 1, ..., m n in the presence of an oracle which, for any x in the domain, answers a pair of numbers a(x,k) and b(x,k) such that a(x,k) is the number of components coinciding in x and k and, b(x, k) is the sum of a(x, k) and the number of components occurring in both x and k but, not at the same position. We show that ( m n )+ 2nlogn + 2n + 2 queries are sufficient to find any hidden code if m ≥ n.