Miracle-dz/UniFAR-FTUs huggingface dataset:
Secret Key and Private Key Constructions for Simple Multiterminal Source Models

We propose an approach for constructing secret and private keys based on the long-known Slepian-Wolf code, due to Wyner, for correlated sources connected by a virtual additive noise channel. Our work is motivated by results of Csiszár and Narayan which highlight innate connections between secrecy generation by multiple terminals that observe correlated source signals and Slepian-Wolf near-lossless data compression. Explicit procedures for such constructions and their substantiation are provided. The performance of low-density parity check channel codes in devising a new class of secret keys is examined.
Learning First-Order De nitions of Functions

First-order learning involves nding a clause-form de nition of a relation from examples of the relation and relevant background information. In this paper, a particular rst-order learning system is modi ed to customize it for nding de nitions of functional relations. This restriction leads to faster learning times and, in some cases, to de nitions that have higher predictive accuracy. Other rst-order learning systems might bene t from similar specialization.
Effect of Tuned Parameters on a LSA Multiple Choice

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Effect of Tuned Parameters on a LSA Multiple Choice Questions Answering Model Alain Lifchitz, Sandra Jhean-Larose, Guy Denhière
Aesthetic-based Clothing Recommendation

Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers’ decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural networks (CNN features) and the scale-invariant feature transform algorithm (SIFT features), color histograms, and so on. Nevertheless, one important type of features, the aesthetic features , is seldom considered. It plays a vital role in clothing recommendation since a users’ decision depends largely on whether the clothing is in line with her aesthetics, however the conventional image features cannot por-tray this directly. To bridge this gap, we propose to introduce the aesthetic information, which is highly relevant with user preference, into clothing recommender systems. To achieve this, we first present the aesthetic features extracted by a pre-trained neural network, which is a brain-inspired deep structure trained for the aesthetic assessment task. Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner. We conduct extensive experiments on real-world datasets, which demonstrate that our approach can capture the aesthetic preference of users and significantly outperform several state-of-the-art recommendation methods.
Efficient particle-based online smoothing in general hidden Markov models : the PaRIS algorithm

This thesis consists of two papers studying online inference in general hidden Markov models using sequential Monte Carlo methods.The first paper present an novel algorithm, the particle-based, rapid incremental smoother (PaRIS), aimed at efficiently perform online approximation of smoothed expectations of additive state functionals in general hidden Markov models. The algorithm has, under weak assumptions, linear computational complexity and very limited memory requirements. The algorithm is also furnished with a number of convergence results, including a central limit theorem.The second paper focuses on the problem of online estimation of parameters in a general hidden Markov model. The algorithm is based on a forward implementation of the classical expectation-maximization algorithm. The algorithm uses the PaRIS algorithm to achieve an efficient algorithm.
Covert Message Passing over Public Internet Platforms Using Model-Based Format-Transforming Encryption

We introduce a new type of format-transforming encryption where the format of ciphertexts is implicitly encoded within a machine-learned generative model. Around this primitive, we build a system for covert messaging over large, public internet platforms (e.g., Twitter). Loosely, our system composes an authenticated encryption scheme, with a method for encoding random ciphertext bits into samples from the generative model’s family of seed-indexed token-distributions. By ﬁxing a deployment scenario, we are forced to consider system-level and algorithmic solutions to real challenges — such as receiver-side parsing ambiguities, and the low information-carrying capacity of actual token-distributions — that were elided in prior work. We use GPT-2 as our generative model so that our system cryptographically transforms plaintext bit-strings into natural-language covertexts suitable for posting to public platforms. We consider adversaries with full view of the internet platform’s content, whose goal is to surface posts that are using our system for covert messaging. We carry out a suite of experiments to provide heuristic evidence of security and to explore tradeoffs between operational efﬁciency and detectability.
Learning Agent State Online with Recurrent Generate-and-Test by

The concept of state is fundamental to a reinforcement learning agent. The state is the input to the agent’s action-selection policy, value functions, and environmental model. A reinforcement learning agent interacts with the environment by performing actions and receiving observations, resulting in the agent’s data stream of experience. In many cases, the observations only provide partial information about the state, and the agent needs to learn the state directly based on the data stream of experience. We refer to the state directly learned from the data stream of experience as the agent state. Existing methods based on gradient descent, including Real-Time Recurrent Learning (RTRL) and Backpropagation Through Time (BPTT), can learn the agent state. However, these methods are computationally expensive, making them unsuitable for online learning of the agent state. In this thesis, we propose computationally efficient methods based on the generate-and-test approach to learn the agent state. We study the effectiveness of our generate-and-test methods for learning the agent state on two partially observable multi-step prediction problems—the trace conditioning problem and the trace patterning problem. The trace conditioning problem focuses on the agent’s ability to remember a cue presented in the past to predict a signal in the future. The trace patterning problem is an extension of the trace conditioning problem in which a non-linear combination of observation signals triggers the arrival of a temporally distant signal. In the trace patterning problem, the agent must learn non-linear configurations of observation signals to predict ii the signal of interest accurately. Our experiments show that the proposed generate-and-test methods can learn the agent state online and make accurate predictions in both problems mentioned above.
Hazelnut : A Bidirectionally Typed Structure Editor Calculus ( Research Paper Draft )

Programs are rich inductive structures, but human programmers typically construct and manipulate them only indirectly, through flat textual representations. This indirection comes at a cost – programmers must comprehend the various subtleties of parsing, and it can require many text editor actions to make a single syntactically and semantically well-defined change. During these sequences of text editor actions, or when the programmer makes a mistake, programmers and programming tools must contend with malformed or semantically ill-defined program text, complicating the programming process. Structure editors promise to alleviate these burdens by exposing only edit actions that produce sensible changes to the program structure. Existing designs for structure editors, however, are complex and somewhat ad hoc. They also focus primarily on syntactic well-formedness, so programs can still be left semantically ill-defined as they are being constructed. In this paper, we report on our ongoing efforts to develop Hazelnut, a minimal structure editor defined in a principled type-theoretic style where all edit actions leave the program in both a syntactically and semantically well-defined state. Uniquely, Hazelnut does not force the programmer to construct the program in a strictly “outside-in” fashion. Formally, Hazelnut is a bidirectionally typed lambda calculus extended with 1) holes (which mark subterms that are being constructed from the inside out); 2) a focus model ; and 3) a bidirectional action model equipped with a useful action sensibility theorem.
Design and Implementation of a Self-Securing Storage Device

Self-securing storage prevents intruders from undetectably tampering with or permanently deleting stored data. To accomplish this, self-securing storage devices internally audit all requests and keep all versions of all data for a window of time, regardless of the commands received from potentially compromised host operating systems. Within the window, system administrators are guaranteed to have this valuable information for intrusion diagnosis and recovery. The S4 implementation combines log-structuring with novel metadata journaling and data replication techniques to minimize the performance costs of comprehensive versioning. Experiments show that self-securing storage devices can deliver performance that is comparable with conventional storage. Further, analyses indicate that several weeks worth of all versions can reasonably be kept on state-of-the-art disks, especially when di erencing and compression technologies are employed. The authors would like to thank the member companies of the Parallel Data Consortium (CLARiiON Array Development, EMC Corporation, Hewlett-Packard Labs, Hitachi, In neon Technologies, Intel Corporation, LSI Logic, MTI Technology Corporation, Novell, Inc., PANASAS, L.L.C., Procom Technology, Quantum Corporation, Seagate Technology, Sun Microsystems, Veritas Software Corporation, and 3Com Corporation). The authors also thank IBM Corporation and CMU's Data Storage Systems Center for their support of this project.
PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Studies

Understanding a plant’s root system characteristics is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping root systems non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying root system traits features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are more than 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths in the soil. All of the images have been annotated with weak imagelevel labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of in vivo root system characteristics with deep learning and other image analysis algorithms.
A Simple Machine Learning Method for Commonsense Reasoning? A Short Commentary on Trinh & Le (2018)

This is a short Commentary on Trinh & Le (2018) ("A Simple Method for Commonsense Reasoning") that outlines three serious flaws in the cited paper and discusses why data-driven approaches cannot be considered as serious models for the commonsense reasoning needed in natural language understanding in general, and in reference resolution, in particular.
Using Small Proxy Datasets to Accelerate Hyperparameter Search

One of the biggest bottlenecks in a machine learning workflow is waiting for models to train. Depending on the available computing resources, it can take days to weeks to train a neural network on a large dataset with many classes such as ImageNet. For researchers experimenting with new algorithmic approaches, this is impractically time consuming and costly. We aim to generate smaller "proxy datasets" where experiments are cheaper to run but results are highly correlated with experimental results on the full dataset. We generate these proxy datasets using by randomly sampling from examples or classes, training on only the easiest or hardest examples and training on synthetic examples generated by "data distillation". We compare these techniques to the more widely used baseline of training on the full dataset for fewer epochs. For each proxying strategy, we estimate three measures of "proxy quality": how much of the variance in experimental results on the full dataset can be explained by experimental results on the proxy dataset. Experiments on Imagenette and Imagewoof (Howard, 2019) show that running hyperparameter search on the easiest 10% of examples explains 81% of the variance in experiment results on the target task, and using the easiest 50% of examples can explain 95% of the variance, significantly more than training on all the data for fewer epochs, a more widely used baseline. These "easy" proxies are higher quality than training on the full dataset for a reduced number of epochs (but equivalent computational cost), and, unexpectedly, higher quality than proxies constructed from the hardest examples. Without access to a trained model, researchers can improve proxy quality by restricting the subset to fewer classes; proxies built on half the classes are higher quality than those with an equivalent number of examples spread across all classes.
Asymptotically exact error analysis for the generalized equation-LASSO

Given an unknown signal x 0 ∈ ℝn and linear noisy measurements y = Ax 0 + σv ∈ ℝm, the generalized equation-LASSO solves equation. Here, ƒ is a convex regularization function (e.g. l 1 -norm, nuclear-norm) aiming to promote the structure of x 0 (e.g. sparse, low-rank), and, λ ≥ 0 is the regularizer parameter. A related optimization problem, though not as popular or well-known, is often referred to as the generalized l 2 -LASSO and takes the form equation, and has been analyzed by Oymak, Thrampoulidis and Hassibi. Oymak et al. further made conjectures about the performance of the generalized equation-LASSO. This paper establishes these conjectures rigorously. We measure performance with the normalized squared error equation. Assuming the entries of A are i.i. d. Gaussian N(0, 1/m) and those of v are i.i. d. N(0, 1), we precisely characterize the “asymptotic NSE” aNSE :=lim σ→0 NSE(σ) when the problem dimensions tend to infinity in a proportional manner. The role of λ, ƒ and x 0 is explicitly captured in the derived expression via means of a single geometric quantity, the Gaussian distance to the subdifferential. We conjecture that aNSE = sup σ>0 NSE(σ). We include detailed discussions on the interpretation of our result, make connections to relevant literature and perform computational experiments that validate our theoretical findings.
A pr 2 00 2 Statistical Mechanics of Broadcast Channels Using Low Density Parity Check Codes

Abstract. We investigate the use of Gallager’s low-density parity-check (LDPC) codes in a broadcast channel, one of the fundamental models in network information theory. Combining linear codes is a standard technique in practical network communication schemes and is known to provide better performance than simple timesharing methods when algebraic codes are used. The statistical physics based analysis shows that the practical performance of the suggested method, achieved by employing the belief propagation algorithm, is superior to that of LDPC based timesharing codes while the best performance, when received transmissions are optimally decoded, is bounded by the timesharing limit.
Security of Quantum Key Distribution Against All Collective Attacks

Quantum theory introduced us with new methods of keys exchan ge. The security of those methods, has been investigated in the last few y ears, reaching several different results. In this paper we shall review one of the se curity proofs against a specific (though quite general) attack named the collective attack. This seminar is based on the work of Eli Biham, Michel Boyer, Gilles Brassar, Jeroen van de Graff and Tal Mor [1].
Period of K system generator of pseudorandom numbers

We analyze the structure of the periodic trajectories of the matrix generator of pseudorandom numbers which has been earlier proposed in [1, 2]. The structure of the periodic trajectories becomes more transparent when the rational sublattice coincides with the Galois field GF [p] [3, 4]. We are able to compute the period of the trajectories as a function of p and the dimension of the matrix d.
The Haar Wavelet Transform of a Dendrogram – I

While there is a very long tradition of approximating a data array by projecting row or column vectors into a lower dimensional subspace the direct approximation of a data matrix through smoothing is less common. Applications of data array smoothing include visualization; filtering of less relevant, and thus harder to interpret, values; and as a means towards compression. Wavelet smoothing or regression is a term applied to data filtering in wavelet space, followed by data reconstruction. Due to boundaries, and invariance of rows and columns, applying a wavelet transform to a data array is very problematic, unlike applying a wavelet transform to a two-dimensional pixelated image. We develop a new wavelet transform for application to data arrays. This is based on prior hierarchical clustering, which takes internal data structure and interrelationships into account. We motivate and describe the integrated clustering and wavelet transform in this work, and discuss its use for data array smoothing, and for approximating a dendrogram by “collapsing” clusters. In a companion paper (Paper II) we explore background theory and address the question as to how this new post-processing analysis of hierarchical clustering is indeed a wavelet transform.
Auto-Scaling Web Applications in Clouds

Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. It allows cloud users to acquire or release computing resources on demand, which enables web application providers to automatically scale the resources provisioned to their applications without human intervention under a dynamic workload to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this article, we comprehensively analyze the challenges that remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scalers according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weaknesses in this field. Moreover, based on the analysis, we propose new future directions that can be explored in this area.
Flow Fields : Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation 1

This supplementary material document is only intended for readers that have read the article “Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation”, as we assume the notations terms and experiments introduced/presented in the article to be known. First, we present the parameters determined for the public results of our approach in Section 2. In Section 3 we describe why we did not incorporate the matching error for outlier filtering. In Section 4 we provide guidelines for parameter selection. This section was created for our conference approach [1], but should also be mostly valid for our improved approach. In Section 5 we evaluate the variance of our approach due to random search. In Section 6 we describe inaccuracies of the ∼Flow Fields curve. In Section 7 we test our conference approach [1] with different data terms. Figure 7 shows an example for an evolving flow field. Figure 7 in the paper shows snippets from here. In Figure 1 we show what happens if the experiment presented in Figure 8 a) in the paper is only performed with one sample as initialization (see figure caption).
Course Corpus 1 ( e . g , MIT course web pages ) Course Corpus 2 MOOC Corpus ( video sequences ) Concept Space Neural Net overfittingPartial derivative Lin

The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.
Inference in Binary Pairwise Markov Random Fields through Self-Avoiding Walks

In a recent result, Weitz [31] established equivalence betw e n the marginal distribution of a node, say v, in any binary pair-wise Markov Random Field (MRF), say G, with the marginal distribution of the root node in the selfavoid walk tree of theG starting atv. In this paper, we exploit this remarkable connection to obt ain insights in the performance of the widely popular Belief Propagation heuri stic for computing marginal distribution (sum-product) and max-marginal distribution (max-product). We obtain a tight characterization of the size of self-avoid ing walk tree for any connected graph as a function of number of edges. This may be of interest in its own right.