Miracle-dz/UniFAR-FTUs huggingface dataset:
Conversational Networks for Automatic Online Moderation

Moderation of user-generated content in an online community is a challenge that has great socio-economic ramifications. However, the costs incurred by delegating this paper to human agents are high. For this reason, an automatic system able to detect abuse in user-generated content is of great interest. There are a number of ways to tackle this problem, but the most commonly seen in practice are word filtering or regular expression matching. The main limitations are their vulnerability to intentional obfuscation on the part of the users, and their context-insensitive nature. Moreover, they are language dependent and may require appropriate corpora for training. In this paper, we propose a system for automatic abuse detection that completely disregards message content. We first extract a conversational network from raw chat logs and characterize it through topological measures. We then use these as features to train a classifier on our abuse detection task. We thoroughly assess our system on a dataset of user comments originating from a French massively multiplayer online game. We identify the most appropriate network extraction parameters and discuss the discriminative power of our features, relatively to their topological and temporal nature. Our method reaches an $F$ -measure of 83.89 when using the full feature set, improving on existing approaches. With a selection of the most discriminative features, we dramatically cut computing time while retaining the most of the performance (82.65).
Visual Hindsight Experience Replay

Reinforcement Learning algorithms typically require millions of environment interactions to learn successful policies in sparse reward settings. Hindsight Experience Replay (HER) was introduced as a technique to increase sample efficiency through re-imagining unsuccessful trajectories as successful ones by replacing the originally intended goals. However, this method is not applicable to visual domains where the goal configuration is unknown and must be inferred from observation. In this work, we show how unsuccessful visual trajectories can be hallucinated to be successful using a generative model trained on relatively few snapshots of the goal. As far as we are aware, this is the first work that does so with the agent policy conditioned solely on its state. We then apply this model to training reinforcement learning agents in discrete and continuous settings. We show results on a navigation and pick-and-place task in a 3D environment and on a simulated robotics application. Our method shows marked improvement over standard RL algorithms and baselines derived from prior work.
Semisupervised Ordinal Regression Based on Empirical Risk Minimization

Abstract Ordinal regression is aimed at predicting an ordinal class label. In this letter, we consider its semisupervised formulation, in which we have unlabeled data along with ordinal-labeled data to train an ordinal regressor. There are several metrics to evaluate the performance of ordinal regression, such as the mean absolute error, mean zero-one error, and mean squared error. However, the existing studies do not take the evaluation metric into account, restrict model choice, and have no theoretical guarantee. To overcome these problems, we propose a novel generic framework for semisupervised ordinal regression based on the empirical risk minimization principle that is applicable to optimizing all of the metrics mentioned above. In addition, our framework has flexible choices of models, surrogate losses, and optimization algorithms without the common geometric assumption on unlabeled data such as the cluster assumption or manifold assumption. We provide an estimation error bound to show that our risk estimator is consistent. Finally, we conduct experiments to show the usefulness of our framework.
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets

PURPOSE Accurate tumor segmentation is a requirement for magnetic resonance (MR)-based radiotherapy. Lack of large expert annotated MR datasets makes training deep learning models difficult. Therefore, a cross-modality (MR-CT) deep learning segmentation approach that augments training data using pseudo MR images produced by transforming expert-segmented CT images was developed. METHODS Eighty one T2-weighted MRI scans from 28 patients with non-small cell lung cancers (9 with pre-treatment and weekly MRI and the remainder with pre-treatment MRI scans) were analyzed. Cross-modality prior encoding the transformation of CT to pseudo MR images resembling T2w MRI was learned as a generative adversarial deep learning model. This model augmented training data arising from 6 expert-segmented T2w MR patient scans with 377 pseudo MRI from non-small cell lung cancer CT patient scans with obtained from the Cancer Imaging Archive. This method was benchmarked against shallow learning using random forest, standard data augmentation and three state-of-the art adversarial learning-based cross-modality data (pseudo MR) augmentation methods. Segmentation accuracy was computed using Dice similarity coefficient (DSC), Hausdorff distance metrics, and volume ratio. RESULTS The proposed approach produced the lowest statistical variability in the intensity distribution between pseudo and T2w MR images measured as Kullback-Leibler divergence of 0.069. This method produced the highest segmentation accuracy with a DSC of (0.75 ± 0.12) and the lowest Hausdorff distance of (9.36 mm ± 6.00mm) on the test dataset using a U-Net structure. This approach produced highly similar estimations of tumor growth as an expert (P = 0.37). CONCLUSIONS A novel deep learning MR segmentation was developed that overcomes the limitation of learning robust models from small datasets by leveraging learned cross-modality information using a model that explicitly incorporates knowledge of tumors in modality translation to augment segmentation training. The results show the feasibility of the approach and the corresponding improvement over the state-of-the-art methods. This article is protected by copyright. All rights reserved.
Utilité d'un couplage entre Word2Vec et une analyse sémantique latente : expérimentation en catégorisation de données textuelles

Nous reexaminons dans cet article les methodes de vectorisation de textes dans le cadre dune etude de classification de documents. Nous etudions les methodes basees sur des plongements de mots (word2vec) ou de documents (analyse semantique latente, ou sac de mots associees a diverses ponderations) ainsi que certaines combinaisons de ces methodes. A cette fin, nous evaluons ces methodes de vectorisation en utilisant trois modeles de classification (un percep- tron multicouches, une machine lineaire a vecteurs supports optimisee par des- cente de gradient stochastique et un classifieur multinomial naif de Bayes). Nos resultats montrent que le modele propose pour associer les methodes word2vec et LSA, qui conjugue les deux caracterisations complementaires du contexte doccurrence des mots (local pour word2vec et global pour LSA), permet de produire une vectorisation robuste, en general plus discriminante que les autres approches testees.
Interactive data visualisation for accounting information: a three-fit perspective

ABSTRACT The volume of freely available accounting information is rapidly becoming overwhelming. To be useful, information needs to be delivered to users in a suitable, relevant, and understandable form. Interactive data visualisation (IDV) can help address this need for useful information by organising accounting information, especially financial reports, into forms with these qualities. Given both their prevalence and their likelihood of being future users of IDV, the purpose of this research is to examine the appropriateness of IDV for non-professional investors’ use when they access accounting information. This research uses a 2 × 2 experimental approach involving 404 participants representing non-professional investors from diverse demographic backgrounds. This research suggests that IDV mitigates non-professional investors’ restricted investment capabilities by presenting information that is more salient, thus reducing non-professional investors’ cognitive effort. This combination allows such investors to better perform both simple and multipart investment tasks. By integrating three information systems’ fit perspectives (i.e. task technology, information quality, and cognitive), this research explains IDV’s suitability and fit within the accounting domain. We also discuss how the findings can inform practice and span interdisciplinary research into data and information visualisation.
A Robust Authentication Protocol for Multi-server Architecture Using Elliptic Curve Cryptography

The multi-server architecture authentication scheme enables users access to the multiple distributed servers with only one single registration procedure. It provides a scalable solution for repeated registration issue in multi-server environment. In this paper, we present a secure remote authentication scheme for multiple servers architecture with elliptic curves cryptosystem (ECC). The proposed scheme could resolve many grave ﬂaws and provide message authenticity, while preserving user anonymity. In the security analysis, we prove the completeness of the proposal BAN-logic, which one of the important formal methods for evaluating information exchange protocols. Noticeable, our scheme also shows impressive eﬃciency and practicability comparing with other related schemes.
Provably Secure Searchable Attribute-Based Authenticated Encryption Scheme

In cloud storage system, attribute-based encryption can support ﬁne-gained access control over encrypted data. Furthermore, searchable attribute-based encryption can allow data users to retrieve encrypted data from a cloud storage system. However, these encryption algorithms cannot provide authenticity. In this paper, we propose a new concept—–searchable attribute-based authenticated encryption and establish its security model framework. Then, we embed ingeniously search mechanism into key-policy attribute-based signcryption, and present a concrete searchable attribute-based authenticated encryption scheme. Finally, according to the proposed framework, our scheme is proven to achieve (1) Ciphertext indistin-guishability under the Decisional Bilinear Diﬃe-Hellman Exponent hardness assumption; (2) Existential unforge-ability based on the hardness assumption of Computational Diﬃe-Hellman Exponent problem; (3) Selective security against chosen-keyword attack under the Decisional Linear hardness assumption; (4) Keyword secrecy based on the one-way hardness of hash function.
On the order of appearance of product of consecutive Fibonacci numbers

Let $F_{n}$ be the $n$th Fibonacci number. For each positive integer $m$, the order of appearance of $m$, denoted by $z(m)$, is the smallest positive integer $k$ such that $m$ divides $F_k$. Recently, D. Marques has obtained a formula for $z(F_{n}F_{n+1})$, $z(F_{n}F_{n+1}F_{n+2})$, and $z(F_{n}F_{n+1}F_{n+2}F_{n+3})$. In this paper, we extend Marques' result to the case $z(F_{n}F_{n+1}\cdots F_{n+k})$, for every $4\leq k \leq 6$. For instance, we prove that, for $n\geq1$, \[z(F_{n}F_{n+1}F_{n+2}F_{n+3}F_{n+4}) =\begin{cases}a, & \text{if $n\equiv1,2,3,4,5,6,7,10 \pmod {12}$, or $n\equiv8,60 \pmod {72}$};\\2a, & \text{if $n\equiv9,11\pmod {12}$, or $n\equiv24,44 \pmod {72}$};\\3a, & \text{if $n\equiv12,32,36,56 \pmod {72}$}; \\6a, & \text{if $n\equiv0,20,48,68 \pmod {72}$}\end{cases}\] where $a=[n,n+1,n+2,n+3,n+4]$.
Smart vs. Solid Solutions in Computational Linguistics—Machine Translation or Information Retrieval

Smart solutions and solid solutions are two different approaches for computational linguistics. To receive a satisfactory result at a minimum cost, smart solutions use special properties of the on-hand application. Solid solutions, in contrast, more generally analyze the application in order that these solutions can be used for a whole class of applications. By extending a well-maintained solid solution, it may be re-used once and again, thus saving time, money, and other resources in the long run. Therefore, the research focuses on solid solutions. It To implement smart solutions, machine translation is used while information retrieval is for solid solutions.
Extraction of the Underlying Structure of Systematic Risk from Non-Gaussian Multivariate Financial Time Series Using Independent Component Analysis: Evidence from the Mexican Stock Exchange

Regarding the problems related to multivariate non-gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT.
Les groupes polychrones pour capturer l'aspect spatio-temporel de la mémorisation

Nous nous basons sur le principe de la polychronisation, introduit par Izhikevich (2006), pour chercher une representation synthetique de la trace generee en memoire par des stimuli qu'un reseau de spiking neurones a appris a distinguer. Les spikes raster plots vehiculent une information trop dense. Nous extrayons de l'ensemble des neurones actifs ceux qui declenchent des groupes polychrones. Nous retenons, comme representation de la trace memoire d'un stimulus, les groupes polychrones majoritairement actives pour la classe a laquelle appartient le stimulus. Nous montrons que cette representation est suffisante pour distinguer les classes. Cette demarche rejoint celle du rank order coding proposee par Thorpe.
Approximation du score CFOF de détection d'anomalie dans un arbre d'indexation iSAX : Application au contexte SI de la SNCF

La finalite de notre travail est la detection des anomalies dans les traces de fonctionnement de l'infrastructure de communication du Systeme d'Information (SI) de la SNCF. Deux techniques recentes et independantes semblentparticuli erement appropriees dans notre cas. Il s'agit d'une part du stockage et de l'indexation de series temporelles dans un arbre appele arbre iSAX, et d'autre part d'un score de detection d'anomalie nomme CFOF dont la robustesse au phenomene de concentration en haute dimension a ´ eteeteetablie de facon formelle. Dans cet article nous montrons qu'il est possible d'utiliser la structuration des in-formations dans l'arbre iSAX pour determiner rapidement une approximation du score CFOF. La valeur obtenue est proche du score exact sur des donnees synthetiques et reelles. Les premiers retours d'expertises in-diquent que la methode semble pertinente pour le declenchement d'alarmes sur les donnees issues de trace d'activite du SI de la SNCF.
How persuasive is phishing email? The role of authentic design, influence and current events in email judgements

ABSTRACT Fraudulent emails, otherwise known as phishing emails, use a range of influence techniques to persuade individuals to respond, such as promising a monetary reward or invoking a sense of urgency. The current study explored a number of factors that may impact the persuasiveness and trustworthiness of emails by examining participant judgements of 20 pre-designed emails that varied according to (a) whether they used loss or reward-based influence techniques, (b) whether they contained particular authentic design cues, (c) whether they referenced a salient current event (the Rio Olympics), and (d) whether participants had been previously exposed to information regarding online scams in general. Results suggest that the presence of authentic design cues and the type of influence technique used significantly impacted participant judgements. Findings are discussed in relation to theoretical models of phishing susceptibility.
Experimental Results of Testing a Direct Monocular Visual Odometry Algorithm Outdoors on Flat Terrain under Severe Global Illumination Changes for Planetary Exploration Rovers

We present the experimental results obtained by testing a monocular visual odometry algorithm on a real robotic platform outdoors, on flat terrain, and under severe changes of global illumination. The algorithm was proposed as an alternative to the long-established feature based stereo visual odometry algorithms. The rover's 3D position is computed by integrating the frame to frame rover's 3D motion over time. The frames are taken by a single video camera rigidly attached to the rover looking to one side tilted downwards to the planet's surface. The frame to frame rover's 3D motion is directly estimated by maximizing the likelihood function of the intensity differences at key observation points, without establishing correspondences between features or solving the optical flow as an intermediate step, just directly evaluating the frame to frame intensity differences measured at key observation points. The key observation points are image points with high linear intensity gradients. Comparing the results with the corresponding ground truth data, which was obtained by using a robotic theodolite with a laser range sensor, we concluded that the algorithm is able to deliver the rover's position in average of 0.06 seconds after an image has been captured and with an average absolute position error of 0.9% of distance traveled. These results are quite similar to those reported in scientific literature for traditional feature based stereo visual odometry algorithms, which were successfully used in real rovers here on Earth and on Mars. We believe that they represent an important step towards the validation of the algorithm and make us think that it may be an excellent tool for any autonomous robotic platform, since it could be very helpful in situations in which the traditional feature based visual odometry algorithms have failed. It may also be an excellent candidate to be merged with other positioning algorithms and/or sensors.
Optimal state estimation for discrete-time Markovian Jump Linear Systems, in the presence of delayed output observations

In this paper, we investigate the design of optimal state estimators for Markovian jump linear systems. We consider that the state has two components: the first component is finite valued and is denoted as mode, while the second (continuous) component is in a finite dimensional Euclidean space. The continuous state is driven by a zero mean, white and Gaussian process noise. The observation output has two components: the first is the mode and the second is a linear combination of the continuous state observed and zero mean, white Gaussian noise. Both output components are affected by delays, not necessarily equal. Our paradigm is to design optimal estimators for the current state, given the current output observation. We provide a solution to this paradigm by giving a recursive estimator for the continuous state, in the minimum mean square sense, and a finitely parameterized recursive scheme for computing the probability mass function of the current mode conditioned on the observed output. We show that when the mode is observed with a greater delay then the continuous output component, the optimal estimator nonlinear in the observed outputs.
Active learners' characterization in MOOC forums and their generated knowledge

This study explores and characterizes learners' participation patterns in MOOC forums, as well as the factors that correlate with learners' participation. Educational data mining and learning analytics methods were used to retrieve and analyze the learners' interpersonal interaction data, which had accumulated in the Coursera log files. The content in the forums was categorized based on Henri's criteria and converted into quantitative values that could be compared and visualized. It was found that only 20% of the learners were collaborating in the forums throughout the entire course and were responsible for 50% of the total posts. A portion of them earned the name "Super Active. " The analyses not only demonstrated the volume of activity and its pattern but also revealed the content of the discussions, which helped to highlight the needs and reasons for students' usage of the forums. The content analysis showed intensity in the "Cognitive" and "Discipline" categories. Thus, forum participants benefit from discussions not only socially but disciplinarily and cognitively as well. Furthermore, even though a strong significant correlation was found between the learners' completion status and their activity in the forums, a group of learners, who did not complete the course, was highly active. [ABSTRACT FROM AUTHOR]
Entre factorisation de matrices et apprentissage profond pour la recommandation dans le domaine du pneumatique

Résumé. Les moteurs de recommandations ont aujourd’hui une place de plus en plus importante dans l’aiguillage de nos choix de consommation sur internet. Cependant, les données disponibles pour effectuer une recommandation varient selon les utilisateurs, les moyens de l’industrie et le type des produits. Dans cet article, nous nous intéressons d’une part, aux performances des modèles de factorisation de matrice ainsi qu’à l’apprentissage profond, et d’autre part, à la recommandation sur des données massives. Une étude comparative entre plusieurs modèles de l’état de l’art est proposée dans un cadre applicatif lié à un industriel spécialisé dans la captation et la gestion des données sur le marché des pneumatiques.
An LSTM Approach to Short Text Sentiment Classification with Word Embeddings

Sentiment classification techniques have been widely used for analyzing user opinions. In conventional supervised learning methods, hand-crafted features are needed, which requires a thorough understanding of the domain. Since social media posts are usually very short, there’s a lack of features for effective classification. Thus, word embedding models can be used to learn different word usages in various contexts. To detect the sentiment polarity from short texts, we need to explore deeper semantics of words using deep learning methods. In this paper, we investigate the effects of word embedding and long short-term memory (LSTM) for sentiment classification in social media. First, words in posts are converted into vectors using word embedding models. Then, the word sequence in sentences are input to LSTM to learn the long distance contextual dependency among words. The experimental results showed that deep learning methods can effectively learn the word usage in context of social media given enough training data. The quantity and quality of training data greatly affects the performance. Further investigation is needed to verify the performance in different social media sources.
Comprehgensive Security for Body Area Networks: A Survey

Body Area Networks (BANs) are composed of multiple devices that measure, collect, forward and analyze physiological and medical data that may be used for diﬀerent purposes like activity tracking, health monitoring or medical treatments. Given the type of data BANs manage, several security requirements must be addressed: conﬁ-dentiality, integrity, privacy, authentication and authorization. This survey studies various proposals that aim to satisfy BAN security requirements, their advances and remaining challenges. We found that the mentioned requirements have not been comprehensively considered; the majority of the studied proposals do not address the entire BAN architecture, they focus on speciﬁc components. Although supporting security of individual BAN components is relevant, a comprehensive security view of an entire BAN environment is needed.