Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Functions Of Probability Distributions From A Finite Set Of Samples

This paper addresses the problem of estimating a function of a probability distribution from a finite set of samples of that distribution. A Bayesian analysis of this problem is presented, the optimal properties of the Bayes estimators are discussed, and as an example of the formalism, closed form expressions for the Bayes estimators for the moments of the Shannon entropy function are derived. Then numerical results are presented that compare the Bayes estimator to the frequency-counts estimator for the Shannon entropy. We also present the closed form estimators, all derived elsewhere, for the mutual information, chi-squared, covariance, and some other statistics. PACS numbers: 02.50. +s, 05.20. -y
TANSIVTx: Time-Accurate Network Simulation Interconnecting VMs with Hardware Virtualization Towards Stealth Analysis

Executive summary: This project aims at conﬁning malware samples for analysis in a secure environment while ensuring that these samples do not evade the analysis by detecting the environment. The speciﬁc challenge to address in this project is to support the analysis of malware samples that check the verisimilitude of network performances to detect analysis environments. To this end the project combines experiences from the Inria/Myriads team on simulation-based studies of distributed systems and from the DGA in cybersecurity and virtualization. The proposed internship focuses on studying a hardware virtualization implementation of the analysis environment.
The complexity of acyclic conjunctive queries

We show that the problem of evaluating acylic Boolean database-queries is LOGCFL-complete and thus highly parallelizable. We present a parallel database algorithm solving this problem with a logarithmic number of parallel join operations. It follows from our main result that the acylic versions of the following important database and Al problems are LOGCFL-complete: The query output tuple problem for conjunctive queries, conjunctive query containment, clause subsumption, and constraint satisfaction.
Recommended impact assessment methods for ILCD and Environmental Footprint: challenges, opportunities and updates

Within the International Reference Life Cycle Data System (ILCD) Handbook, 14 methods were recommended for impact assessment in LCA. The Product Environmental Footprint (PEF) builds on these recommendations. The ILCD analysis, evaluation and selection of recommended methods refers to methods available by 2008. Several impact categories have experienced a significant development and should be subject to updates in ILCD. Thus, there is the clear need for an update of the recommendations both from a harmonisation perspectives as well as for scientific credibility. In the context of the PEF, the EC-JRC is in the process of updating ILCD recommendations, focusing mainly on 4 impact categories, namely: water depletion (WD), resource depletion (RD), land use (LU) and respiratory inorganics (RI). In order to support an update of the ILCD and PEF recommendations a three-step evaluation procedure has been put in place: i) qualitative screening for newly developed methods, ii) benchmark among the available methods; iii) applicability analysis wit reference to lifecycle inventory data and elementary flows. This approach will ensure identifying not only the (theoretically/scientifically) best LCIA method but also whether there any significant gaps in LCI data availability to feed the LCIA models or issues regarding unexpected results and anomalies that need further attention. The limits of the LCIA methods related to day-to-day applicability will be carefully evaluated and some results of the screening are provided. As regards the LU, it seems reasonable to expand the LCIA method, at least at mid-point level, accounting more than one impact category, considering the multi-functionality of land. The selection of method(s) was also based on the availability of scientific literature and case-studies, and in order to facilitate the applicability with the currently available datasets containing inventory data on LU. Concerning WU, special attention is given to aspects such as consumptive use only vs. overall withdrawals, spatial differentiation, temporal scale environmental water requirements, as well as the applicability of the methods to currently available datasets. In case of RD, the three-step evaluation procedure is preceded by a careful consideration of what should be assessed in terms of impact on the Area of Protection Natural Resources (AoP), given the poor substantiation of this AoP in the LCA community so far. URI: Authors: SALA Serenella DEWULF Jo BENINI Lorenzo FAZIO Simone PANT Rana Publication Year: 2015 Publisher: Setac ISSN: 2309-8031 Citation: 25th setac Europe meeting p. 231 Source URL: https://ec.europa. eu/jrc/en/publication/recommended-impact-assessment-methods-ilcd-and-environmental-footprint-ch allenges-opportunities-and
Invariant Representation Learning for Infant Pose Estimation with Small Data.

With the increasing maturity of the human pose estimation domain, its applications have become more and more broaden. Yet, the state-of-the-art pose estimation models performance degrades significantly in the applications that include novel subjects or poses, such as infants with their unique movements. Infant motion analysis is a topic with critical importance in early developmental studies. However, models trained on large-scale adult pose datasets are barely successful in estimating infant poses due to the significant differences in their body ratio and the versatility of poses they can take compared to adults. Moreover, the privacy and security considerations hinder the availability of adequate infant images required for training of a robust pose inference model from scratch. Here, we present an invariant representation learning strategy that allows us to augment the limited available real infant pose data by incorporating the knowledge from the adjacent domains of adult poses as well as synthetic infant models. We introduce a multi-stage training strategy to gradually transfer these knowledge into our fine-tuned domain-adapted infant pose (FiDIP) estimation model. In developing FiDIP, we also built and publicly released a synthetic and real infant pose (SyRIP) dataset with small yet diverse real infant images as well as generated synthetic infant data. We demonstrated that our FiDIP model outperforms state-of-the-art human pose estimation model for the infant pose estimation, with the mean average precision (AP) as high as 90.1.
Speaker Embedding-aware Neural Diarization: a Novel Framework for Overlapped Speech Diarization in the Meeting Scenario

In this paper, we reformulate overlapped speech diarization as a single-label prediction problem, which is always treated as a multi-label classiﬁcation task in previous studies. Speciﬁcally, the multiple labels of each frame are encoded into a single label with the power set, which represents the possible combinations of different speakers. Through this formulation, we pro-pose the speaker embedding-aware neural diarization (SEND) system. In SEND, the speech encoder, speaker encoder, similarity scores, and post-processing network are optimized to predict the power set encoded labels according to the similarities between speech features and speaker embeddings. Experimental results show that our method signiﬁcantly outperforms the variational Bayesian hidden Markov model-based clustering al-gorithm (VBx). Besides, the proposed method has two ben-eﬁts compared with the target-speaker voice activity detection (TSVAD). First, SEND can achieve lower diarization error rates in the real meeting scenario. Second, when the training data has a high overlap ratio, the learning process of SEND is more stable than TSVAD.
Products of Hidden Markov Models

We present products of hidden Markov models (PoHMM's), a way of combining HMM's to form a distributed state time series model. Inference in a PoHMM is tractable and eÆcient. Learning of the parameters, although intractable, can be e ectively done using the Product of Experts learning rule. The distributed state helps the model to explain data which has multiple causes, and the fact that each model need only explain part of the data means a PoHMM can capture longer range structure than an HMM is capable of. We show some results on modelling character strings, a simple language task and the symbolic family trees problem, which highlight these advantages.
A note on the empirical comparison of RBG and Ludii

We present an experimental comparison of the efficiency of three General Game Playing systems in their current versions: Regular Boardgames (RBG 1.0), Ludii~0.3. 0, and a Game Description Language (GDL) propnet. We show that in general, RBG is currently the fastest GGP system. For example, for chess, we demonstrate that RBG is about 37 times faster than Ludii, and Ludii is about 3 times slower than a GDL propnet. Referring to the recent comparison [An Empirical Evaluation of Two General Game Systems: Ludii and RBG, CoG 2019], we show evidences that the benchmark presented there contains a number of significant flaws that lead to wrong conclusions.
Text2Time: Transformer-based article time period predictor

—We explore the problem of predicting the publication period of text document, such as a news article, using the text from that document. In order to do so, we created our own extensive labeled dataset of over 350,000 news articles published by The New York Times over six decades. We then provide an implementation of a simple Naive Bayes baseline model, which surprisingly achieves decent performance in terms of accuracy. Finally, for our approach, we use a pretrained BERT model ﬁne-tuned for the task of text classiﬁcation. This model exceeds our expectations and provides some very impressive results in terms of accurately classifying news articles into their respective publication decades. The results beat the performance of the few previously tried models for this relatively unexplored task of time prediction from text.
2 Model and Preliminaries 2 . 1 A Regression Setting

We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to report their data to the analyst truthfully constrains our design to mechanisms that provide a privacy guarantee to the participants; we use differentially privacy to model individuals’ privacy losses. This immediately poses a problem, as differentially private computation of a linear model necessarily produces a biased estimation, and existing approaches to design mechanisms to elicit data from privacy-sensitive individuals do not generalize well to biased estimators. We manage to overcome this this challenge, through appropriate design of the computation and payment scheme.
2 A Variable-Splitting Augmented Lagrangian Framework

We consider a class of sparse learning problems in high dimensional feature space regularized by a structured sparsity-inducing norm that incorporates prior knowledge of the group structure of the features. Such problems often pose a considerable challenge to optimization algorithms due to the non-smoothness and non-separability of the regularization term. In this paper, we focus on two commonly adopted sparsity-inducing regularization terms, the overlapping Group Lasso penalty l1/l2-norm and the l1/l∞-norm. We propose a unified framework based on the augmented Lagrangian method, under which problems with both types of regularization and their variants can be efficiently solved. As one of the core building-blocks of this framework, we develop new algorithms using a partial-linearization/splitting technique and prove that the accelerated versions of these algorithms require O( 1 √ ) iterations to obtain an -optimal solution. We compare the performance of these algorithms against that of the alternating direction augmented Lagrangian and FISTA methods on a collection of data sets and apply them to two real-world problems to compare the relative merits of the two norms.
Toward robust early-warning models : A horse race , ensembles and model uncertainty I

This paper presents first steps toward robust models for crisis prediction. We conduct a horse race of conventional statistical methods and more recent machine learning methods as early-warning models. As individual models are in the literature most often built in isolation of other methods, the exercise is of high relevance for assessing the relative performance of a wide variety of methods. Further, we test various ensemble approaches to aggregating the information products of the built models, providing a more robust basis for measuring country-level vulnerabilities. Finally, we provide approaches to estimating model uncertainty in early-warning exercises, particularly model performance uncertainty and model output uncertainty. The approaches put forward in this paper are shown with Europe as a playground. Generally, our results show that the conventional statistical approaches are outperformed by more advanced machine learning methods, such as k-nearest neighbors and neural networks, and particularly by model aggregation approaches through ensemble learning.
AquaLog : An ontology-driven Question Answering system as an interface to the Semantic Web

The semantic web vision is one in which rich, ontology-based semantic markup will become widely available. The availability of semantic markup on the web opens the way to novel, sophisticated forms of question answering. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input, and returns answers drawn from one or more knowledge bases (KBs). We say that AquaLog is portable because the configuration time required to customize the system for a particular ontology is negligible. AquaLog presents an elegant solution in which different strategies are combined together in a novel way. It makes use of the GATE NLP platform, string metric algorithms, WordNet and a novel ontology-based relation similarity service to make sense of user queries with respect to the target KB. Moreover it also includes a learning component, which ensures that the performance of the system improves over the time, in response to the particular community jargon used by end users.
Optimal random access and random spectrum sensing for an energy harvesting cognitive radio

We consider a secondary user with energy harvesting capability. We design access schemes for the secondary user which incorporate random spectrum sensing and random access, and which make use of the primary automatic repeat request (ARQ) feedback. The sensing and access probabilities are obtained such that the secondary throughput is maximized under the constraints that both the primary and secondary queues are stable and that the primary queueing delay is kept lower than a specified value needed to guarantee a certain quality of service (QoS) for the primary user. We consider spectrum sensing errors and assume multipacket reception (MPR) capabilities. Numerical results are presented to show the enhanced performance of our proposed system over a random access system, and to demonstrate the benefit of leveraging the primary feedback.
THE METHOD OF FORMATION OF THE STATUS OF PERSONALITY UNDERSTANDING BASED ON THE CONTENT ANALYSIS

4 ration and sympathy in relation to others); integrity (discipline, diligence and focus on result); neuroticism (degree of emotional stability, level of control of impulses and anxiety); openness to experience (degree of intellectual curiosity, the desire for new and diverse experiences, impressions). Each personality is ranked by five dispositions [4]. Thus psychologists make up a personality model. Such a model is used to predict the actions of a human, formation of conclusions about his/her professional suitability, prospects for professional growth, opportunities to work in a team, etc. [5]. When recruiting, a number of organizations perform mandatory psychological testing of candidates for compiling their personality model [6]. This is a fairly long procedure that requires certain resources and time costs. Most often, in addition to the interview, they use the users’ profiles in social networks for analysis [7]. Automated processing of user-generated content considerably simplifies the process of recruiting [7]. Similarly, for a marketing company to manually analyze potential audience of consumers of advertised products is quite a costly process [8]. Automated analysis of audience through thematic activity in social networks significantly facilitates the process of promotion of products to the market [9]. It also accelerates expanding target audience of consum-
Offline handwritten signature identification and verification using contourlet transform and Support Vector Machine

In this paper, a new method for signature identification and verification based on contourlet transform (CT) is proposed. This method uses contourlet coefficient as the feature extractor and Support Vector Machine (SVM) as the classifier. In proposed method, first signature image is normalized based on size. After preprocessing, contourlet coefficients are computed on specified scale and direction. Next, all extracted coefficients are fed to a layer of SVM classifiers as feature vector. The number of SVM classifiers is equal to the number of classes. Each SVM classifier determines if the input image belongs to the corresponding class or not. The main characteristic of proposed method is independency to nation of signers. Two experiments on two signature sets are performed. The first is on a Persian signature set and the other is on Stellenbosch (Turkish) signature set. Based on these experiments, we achieve a 100% recognition (identification) rate and more than 96.5% on Persian and Turkish signature sets respectively and 4.5% error in verification.
Optimized cell programming for flash memories with quantizers

Multi-level flash memory cells represent data by the amount of charge stored in them. Certain voltages are applied to the flash memory cells to inject charges when programming and the cell level can be only increased during the programming process as a result of the high cost of block erasures. To achieve a high speed during writing, parallel programming is used, whereby a common voltage is applied to a group of cells to inject charges simultaneously. The voltage sharing simplifies the circuitry and increases the programming speed, but it also affects the precision of charge injection and limits the storage capacity of flash memory cells. Another factor that limits the precision of cell programming is the thermal electronics noise induced in charge injection. In this paper, we focus on noiseless parallel programming of multiple cells and noisy programming of a single cell. We propose a new criterion to evaluate the performance of the cell programming which is more suitable for flash memories in practice and then we optimize the parallel programming strategy accordingly. We then proceed to noisy programming and consider the two scenarios where feedback on cell levels is either available during programming or not. We study the optimization problem under both circumstances and present algorithms to achieve the optimal performance.
Well-Structured Futures and Cache Locality

In fork-join parallelism, a sequential program is split into a directed acyclic graph of tasks linked by directed dependency edges, and the tasks are executed, possibly in parallel, in an order consistent with their dependencies. A popular and effective way to extend fork-join parallelism is to allow threads to create futures. A thread creates a future to hold the results of a computation, which may or may not be executed in parallel. That result is returned when some thread touches that future, blocking if necessary until the result is ready. Recent research has shown that although futures can, of course, enhance parallelism in a structured way, they can have a deleterious effect on cache locality. In the worst case, futures can incur Ω(PT∞ + tT∞) deviations, which implies Ω (CPT∞ + CtT∞) additional cache misses, where C is the number of cache lines, P is the number of processors, t is the number of touches, and T∞ is the computation span. Since cache locality has a large impact on software performance on modern multicores, this result is troubling. In this article, we show that if futures are used in a simple, disciplined way, then the situation is much better: if each future is touched only once, either by the thread that created it or by a later descendant of the thread that created it, then parallel executions with work stealing can incur at most O(CPT2∞) additional cache misses—a substantial improvement. This structured use of futures is characteristic of many (but not all) parallel applications.
Handwritten Urdu Character Recognition using 1-Dimensional BLSTM Classifier

The recognition of cursive script is regarded as a subtle task in optical character recognition due to its varied representation. Every cursive script has different nature and associated challenges. As Urdu is one of cursive language that is derived from Arabic script, thats why it nearly shares the same challenges and difficulties even more harder. We can categorized Urdu and Arabic language on basis of its script they use. Urdu is mostly written in Nastaliq style whereas, Arabic follows Naskh style of writing. This paper presents new and comprehensive Urdu handwritten offline database name Urdu-Nastaliq Handwritten Dataset (UNHD). Currently, there is no standard and comprehensive Urdu handwritten dataset available publicly for researchers. The acquired dataset covers commonly used ligatures that were written by 500 writers with their natural handwriting on A4 size paper. We performed experiments using recurrent neural networks and reported a significant accuracy for handwritten Urdu character recognition.
Polar codes for the m-user multiple access channel and matroids

In this paper, a polar code for the m-user multiple access channel (MAC) with binary inputs is constructed. In particular, Arikan’s polarization technique applied individually to each user will polarize any m-user binary input MAC into a finite collection of extremal MACs. The extremal MACs have a number of desirable properties: (i) the ‘uniform sum rate’1 of the original channel is not lost, (ii) the extremal MACs have rate regions that are not only polymatroids but matroids and thus (iii) their uniform sum rate can be reached by each user transmitting either uncoded or fixed bits; in this sense they are easy to communicate over. Provided that the convergence to the extremal MACs is fast enough, the preceding leads to a low complexity communication scheme that is capable of achieving the uniform sum rate of an arbitrary binary input MAC. We show that this is indeed the case for arbitrary values of m.
Subspace Polynomials and Cyclic Subspace Codes

Subspace codes have received an increasing interest recently due to their application in error correction for random network coding. In particular, cyclic subspace codes are possible candidates for large codes with efficient encoding and decoding algorithms. In this paper, we consider such cyclic codes and provide constructions of optimal codes for which their codewords do not have full orbits. We further introduce a new way to represent subspace codes by a class of polynomials called subspace polynomials. We present some constructions of such codes, which are cyclic and analyze their parameters.