Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing Classifiers' Performance under Differential Privacy

The application of differential privacy in privacy-preserving data analysis has gained momentum in recent years. In particular, it provides an effective solution for the construction of privacy-preserving classifiers, in which one party owns the data and another party is interested in obtaining a classifier model from this data. While several approaches have been proposed in the literature to employ differential privacy for the construction of classifiers, an understanding of the difference in performance of these classifiers is currently missing. This knowledge enables the data owner and the analyst to select the most appropriate classification algorithm and training parameters in order to guarantee high privacy requirements while minimizing the loss of accuracy. In this study, we investigate the impact of the use of differential privacy on three well-known classifiers, i.e., Naïve Bayes, SVM, and Decision Tree classifiers. To this end, we show how these classifiers can be trained in a differential privacy setting and perform extensive experiments to evaluate the effect of this privacy enforcement on their performance.
Inducing Semantic Roles Without Syntax

Semantic roles are a key component of linguistic predicate-argument structure, but developing ontologies of these roles requires signiﬁ-cant expertise and manual effort. Methods exist for automatically inducing semantic roles using syntactic representations, but syntax can also be difﬁcult to deﬁne, annotate, and predict. We show it is possible to automatically induce semantic roles from QA-SRL, a scalable and ontology-free semantic annotation scheme that uses question-answer pairs to represent predicate-argument structure. By associating arguments with distributions over QA-SRL questions and clustering them in a mixture model, our method outperforms all previous models as well as a new state-of-the-art baseline over gold syntax. We show that our method works because QA-SRL acts as surrogate syntax , capturing non-overt arguments and syntactic alternations, which are central motivators for the use of semantic role labeling systems. 1
Upper Limb Anthropometric Parameter Estimation through Convolutional Neural Network Systems and Image Processing

: Anthropometry is a versatile tool for evaluating the human body proportions. This tool allows the orientation of public health policies and clinical decisions. But in order to optimize the obtaining of anthropometric measurements, different methods have been developed to determine anthropometry automatically using artiﬁcial intelligence. In this work, we apply a convolutional neural network to estimate the upper limb’s anthropometric parameters. With this aim, we use the OpenPose estimator system and image processing for segmentation with U-NET from a complete uncalibrated body image. The parameter estimation system is performed with total body images from 4 different volunteers. The system accuracy is evaluated through a global average percentage of 71% from the comparison between measured values and estimated values. A ﬁne-tuning of algorithm hyper-parameters will be used in future works to improve the estimation.
Using Meta-heuristics to Optimize the Parameterization of Algorithms in Simulation Models

The research topic of the laboratory Science Pour l’Environnement (SPE) and the laboratory STELLA MARE of Université de Corse, focus on solving the environmental problems of our time. Various research teams focus their work on modeling and simulation of complex systems and behavioral modeling of species. Generally, in this modeling process (abstractions from the real world), we observe that the parameterization of the models is usually very tedious, carried out in an empirical or intuitive way based on assumptions specific to each modeler. There are also several modeling techniques which are generally parameterized intuitively and empirically. We have therefore proposed an approach to optimize the parameterization of models based on the algorithms of these models. This approach uses meta-heuristics, a class of optimization algorithms inspired by nature for which we obtain remarkable results. The use of meta-heuristics in this approach is justified by the nature of the problem to be solved. Indeed, the parameterization of models can be considered as a complex problem with a very large solution space that needs to be explored in an intelligent way. The risk of a combinatorial explosion is also very high because of the number of variables to be optimized. The advantage of this approach that we propose is that it allows an evolutive optimization of the model parameterization as the data arrives. For the validation of this approach, we used simulated data from a theoretical model. The validation of this theoretical model opens possibilities of applications on real world models.
Enhanced Metaphor Detection via Incorporation of External Knowledge Based on Linguistic Theories

Use of external knowledge is an important and effective method applied widely in metaphor detection. Although existing knowledge-based methods perform well, when leveraging ex-ternal knowledge, they take little consideration on linguistic theories of metaphor detection. Based on Metaphor Identiﬁcation Pro-cedure (MIP) and Select Preference Violation (SPV), directly using examples and deﬁnitions of words from the Oxford Dictionary 1 , we pro-pose two BERT-based models for metaphor detection: ExampleBERT and DeﬁnitionBERT. Experimental results show that our methods achieve state-of-the-art performance on two established metaphor datasets. Furthermore, we show that our DeﬁnitionBERT is highly inter-pretable.
Information and algorithmic support of a multi-level integrated system for the investment strategies formation

The article summarizes the accumulated practical experience of the authors in the development of algorithms for the formation of investment strategies. For this purpose, the optimization of the studied parameters, information support of investment activities, verification, monitoring and adjustment in the testing mode and the subsequent practical application of the described tools are considered. The system is based on the main provisions of the Markowitz portfolio theory. The analytical block of the Information System Portfolio Investor includes Profitability-Risk model; empirical models of optimal complexity; hybrid predictive model systems; the principle of combining (integrating) both models and forecasts, as well as decision rules; optimization of the training sample length (modified Markowitz model); optimization of the frequency of monitoring and adjusting the composition of the investment portfolio. The principles of design and development of the information block of the system, its replenishment and functioning are described in detail. All the above listed components of the algorithmic content of the investment decision making system are described sequentially. The system modules have been successfully tested on a wide class of financial instruments: ordinary shares, preferred shares, government and corporate bonds, exchange commodities, stock, commodity, industry and bond indices, exchange-traded investment funds and real estate funds. The implemented Markowitz model with a dynamic database of historical data can significantly increase the efficiency of investment decisions, which is facilitated by taking into account the characteristics of both the markets under study and the corresponding financial instruments.
P-Stance: A Large Dataset for Stance Detection in Political Domain

Stance detection determines whether the author of a text is in favor of, against or neutral to a speciﬁc target and provides valuable insights into important events such as presidential election. However, progress on stance detection has been hampered by the absence of large annotated datasets. In this paper, we present P-S TANCE , a large stance detection dataset in the political domain, which contains 21,574 labeled tweets. We provide a detailed description of the newly created dataset and develop deep learning models on it. Our best model achieves a macro-average F1-score of 80.53%, which we improve further by using semi-supervised learning. Moreover, our P-S TANCE dataset can facilitate research in the ﬁelds of cross-domain stance detection such as cross-target stance detection where a classiﬁer is adapted from a different but related target. We publicly release our dataset and code. 1
Estimation of the Features Influence on Cluster Partition

: The use of machine learning and clustering tools for production management, operational and strategic planning is an urgent task. Industrial automation and Industry 4.0 in general stimulate the use of new technologies. So, for the analytics of many business processes and tasks, it is possible to use clustering. This paper evaluates the clustering performance for supplier evaluation considering the influence of preference features. Clustering is mostly unsupervised procedure, and most clustering algorithm depend on some certain assumptions. Subgroups present in the dataset are formed on the base of these assumptions. Consequently, in most cases, the resulting cluster groups require validation and reliability assessment.
IndoCollex: A Testbed for Morphological Transformation of Indonesian Word Colloquialism

Indonesian language is heavily riddled with colloquialism whether in written or spoken forms. In this paper, we identify a class of Indonesian colloquial words that have undergone morphological transformations from their standard forms, categorize their word formations, and propose a benchmark dataset of Indonesian Colloquial Lexicons (IndoCollex) consisting of informal words on Twitter ex-pertly annotated with their standard forms and their word formation types/tags. We evaluate several models for character-level transduction to perform morphological word normalization on this testbed to understand their failure cases and provide baselines for future work. As IndoCollex catalogues word formation phenomena that are also present in the non-standard text of other languages, it can also provide an attractive testbed for methods tailored for cross-lingual word normalization and non-standard word formation.
SecSDN: A Novel Architecture for a Secure SDN

: Security of SDN has been an important focus of research. Attempts to uncover security vulnerabilities in SDN points to two major causes: (i) Inherent assumption of switches being severely limited in intelligence, (ii) Lack of authentication in the communication between controllers and switches. The assumption that switches have limited intelligence, and can only do the task of packet forwarding, further leads to the inference of switches never being actively corrupt or operated by malicious entities. While such an assumption is reasonable for SDN data centers operated within the bounds of a single organization, it is incorrect for larger scaled inter-networking. In this paper, we propose SecSDN, an architecture and a protocol using repetitive hashing to authenticate the communicating parties, securely verify consistency of ﬂow tables residing inside the switches and detect their malicious behaviour within a predeﬁned constant time frame. Using such a technique, we arrive at an infrastructure that can securely perform functions as envisaged in SDN. We establish the correctness of SecSDN and the simulations show that the overhead incurred is virtually non-existent.
Annotation and Evaluation of Coreference Resolution in Screenplays

Screenplays refer to characters using different names, pronouns, and nominal expressions. We need to resolve these mentions to the correct referent character for better story understanding and holistic research in computational narratology. Coreference resolution of character mentions in screenplays becomes challenging because of the large document lengths, unique structural features like scene headers, interleaving of action and speech passages, and reliance on the accompanying video. In this work, we ﬁrst adapt widely-used annotation guidelines to address domain-speciﬁc issues in screenplays. We develop an automatic screenplay parser to extract the structural information and design coreference rules based upon the structure. Our model exploits these structural features and outperforms a benchmark coreference model on the screenplay coreference resolution task.
Analysis of Tree-Structured Architectures for Code Generation

Code generation is the task of generating code snippets from input user speciﬁcations in nat-ural language. Leveraging the linguistically-motivated hierarchical structure of the input can beneﬁt code generation, especially since the speciﬁcations are complex sentences containing multiple variables and operations over various data structures. Moreover, recent advances in Transformer architectures have led to improved performance with tree-to-tree style generation for other seq2seq tasks e.g., machine translation. Hence, we present an empirical analysis of the signiﬁcance of input parse trees for code generation. We run text-to-tree, linearized tree-to-tree, and structured tree-to-tree models, using constituency-based parse trees as input, where the target is Ab-stract Syntax Tree (AST) of the code. We evaluate our models on the Python-based code generation dataset CoNaLa and a semantic parsing dataset ATIS. We ﬁnd that constituency trees encoded using a structure-aware model improve performance for both datasets. We also provide an analysis of those aspects of the input parse trees which are most impact-ful. For instance, we ﬁnd that structure-aware encodings are better at modelling inputs with multiple variables and capturing long-range dependencies for code generation. 1
Using Social and Linguistic Information to Adapt Pretrained Representations for Political Perspective Identification

Understanding the political perspective shaping the way events are discussed in the media is increasingly important due to the dramatic change in news distribution. With the advance in text classiﬁcation models, the performance of political perspective detection is also improving rapidly. However, current deep learning based text models often require a large amount of supervised data for training, which can be very expensive to obtain for this task. Meanwhile, models pre-trained on the general source and task (e.g. BERT) lack the ability to focus on bias-related text span. In this paper, we propose a novel framework that pre-trains the text model using signals from the rich social and linguistic context that is readily available, including entity mentions, news sharing, and frame indicators. The pre-trained models beneﬁt from tasks related to bias detection and therefore are easier to train with the bias labels. We demonstrate the effectiveness of our proposed framework by experiments on two news bias datasets. The models with pre-training achieve signiﬁcant improvement in performance and are capable of identifying the text span for bias better.
Quantitative Study of Capillary Supply to the Skeletal Muscles of Crucian Carp Carassius carassius L.: Effects of Hypoxic Acclimation

In aerated water at 15 C the routine oxygen consumption (V̇o2) of crucian carp (Carassius carassius L.) was 75.7 ml O2·kg⁻¹h-1. On exposure to progressive hypoxia in closed respirometers, V̇o2 fell parallel with the decrease in water Po₂(PWo₂). Fish were acclimated to PWo₂ 1.5 kPa for 6 wk. Following hypoxia acclimation, carp maintained near constant V̇o2 until PWo2, fell to below 3.5-5.8 kPa. V̇o2 at PWo2 1.5 kPa was significantly higher for fish acclimated to hypoxic water (34.0 ml O2 · kg⁻¹h⁻¹) than for fish held in aerated water (16.2 ml O2 · kg⁻¹h⁻¹). The volume densities of mitochondria (Vvmt,f) and surface (Svc,f) and volume (Vvc,f) densities of capillaries were determined for fast and slow myotomal muscles using low-power electron micrographs. These parameters provide information on the structural limits relating to oxygen demand and supply to the swimming muscles. Mitochondrial volume densities for normoxic carp were 0. 15 for slow and 0.02 for fast muscles. The surface density of capillaries was 6.4 times higher for slow than for fast muscle, reflecting the greater potential for aerobic metabolism. Muscle capillary density was unchanged following acclimation to hypoxia, whereas slow fiber mitochondrial volume density increased by 67%. It is suggested that the increased slow-muscle Vvmt,f is an adaptation to increase the utilization of circulating oxygen stores at low PWo2. This would be an advantage since increased O2 extraction by the muscles would reduce venous Po2 and hence increase the rate of O2 transfer across the gills.
Better Chinese Sentence Segmentation with Reinforcement Learning

A long-standing challenge in Chinese–English machine translation is that sentence boundaries are ambiguous in Chinese orthography, but inferring good splits is necessary for obtaining high quality translations. To solve this, we use reinforcement learning to train a segmentation policy that splits Chinese texts into segments that can be independently translated so as to maximise the overall translation quality. We compare to a variety of segmentation strategies and ﬁnd that our approach improves the baseline BLEU score on the WMT2020 Chinese–English news translation task by +0.3 BLEU overall and improves the score on input segments that contain more than 60 words by +3 BLEU.
Research on Phishing Email Detection Based on URL Parameters Using Machine Learning Algorithms

Abstract – phishing is the most frequent data breach problem in cybersecurity. Cyber scammers use the phishing approach to outwit or obtain sensitive information without a consumer agreement. The victims might receive an email that promotes clicking on the following malicious links that lead to sensitive data leaks. This problem is especially relevant to large companies. Attackers tend to prepare emails that contain work-related information and include familiar keywords in phishing URL 1 s. This paper addresses the URL Boolean classification problem using various machine learning methods such as Support Vector Machine, Random Forest, Decision Tree, Linear Discriminant Analysis, and Logistic Regression. This paper provides a comparative study on these algorithms applied for two different URL classification datasets.
High-Quality Dialogue Diversification by Intermittent Short Extension Ensembles

Many task-oriented dialogue systems use deep reinforcement learning (DRL) to learn policies that respond to the user appropriately and complete the tasks successfully. Training DRL agents with diverse dialogue trajectories prepare them well for rare user requests and unseen situations. One effective diversification method is to let the agent interact with a diverse set of learned user models. However, trajectories created by these artificial user models may contain generation errors, which can quickly propagate into the agent's policy. It is thus important to control the quality of the diversification and resist the noise. In this paper, we propose a novel dialogue diversification method for task-oriented dialogue systems trained in simulators. Our method, Intermittent Short Extension Ensemble (I-SEE), constrains the intensity to interact with an ensemble of diverse user models and effectively controls the quality of the diversification. Evaluations on the Multiwoz dataset show that I-SEE successfully boosts the performance of several state-of-the-art DRL dialogue agents.
GoG: Relation-aware Graph-over-Graph Network for Visual Dialog

Visual dialog, which aims to hold a meaningful conversation with humans about a given image, is a challenging task that requires models to reason the complex dependencies among visual content, dialog history, and current questions. Graph neural networks are recently applied to model the implicit relations between objects in an image or dialog. However, they neglect the importance of 1) coreference relations among dialog history and dependency relations between words for the question representation; and 2) the representation of the image based on the fully represented question. Therefore, we propose a novel relation-aware graph-over-graph network (GoG) for visual dialog. Specifically, GoG consists of three sequential graphs: 1) H-Graph, which aims to capture coreference relations among dialog history; 2) History-aware Q-Graph, which aims to fully understand the question through capturing dependency relations between words based on coreference resolution on the dialog history; and 3) Question-aware I-Graph, which aims to capture the relations between objects in an image based on fully question representation. As an additional feature representation module, we add GoG to the existing visual dialogue model. Experimental results show that our model outperforms the strong baseline in both generative and discriminative settings by a significant margin.
Multilingual Simultaneous Neural Machine Translation

Simultaneous machine translation (S I MT) involves translating source utterances to the target language in real-time before the speaker utterance completes. This paper proposes the multilingual approach to S I MT, where a single model simultaneously translates between multiple language-pairs. This not only results in more efﬁciency in terms of the number of models and parameters (hence simpler deployment), but may also lead to higher performing models by capturing commonalities among the languages. We further explore simple and effective multilingual architectures based on two strong recently proposed S I MT models. Our results on translating from two Germanic languages (German, Dutch) and three Romance languages (French, Italian, Romanian) into English show (i) the single multilingual model is on-par or better than individual models, and (ii) multilingual S I MT models trained based on language families are on-par or better than the universal model trained for all languages. 1
Automatic Feedback Generation for Supporting User Interface Design

Although the interest for User Interfaces (UI) has increased their study, their design is a difficult process to learn. Novel UI designers, therefore, need guidance through the learning process of UI design to obtain better results. Feedback is a key factor to improve knowledge and skills acquisition. However, providing individual feedback is a complex and time-consuming task and requires a fair amount of expertise. This paper describes a solution to this problem: Feedback ENriched user Interface Simulation (FENIkS). FENIkS is a model-driven engineering UI design simulation tool able to automatically provide instant feedback to the students about how they apply UI design principles. While designing the UI, the novice designer receives feedback on how design principles are applied through the options he/she selects. Then, when generating a working prototype from the models, feedback explaining the application of principles is incorporated in the prototype. An experimental evaluation was conducted, demonstrating FENIkS improves students' understanding of UI design principles. The perceived usability was also positively evaluated. This paper explains FENIkS' design: the meta-model, how design options, design principles and types of feedback are used to generate automated feedback on the observation of design principles in the tool and the generated prototype.
Over Two Years of Challenging Environmental Conditions for Localization: The IPLT Dataset

This paper presents a new challenging dataset for autonomous driving applications: Institut Pascal Long-Term — IPLT — Dataset which was collected over two years and it contains, at the moment, 127 sequences and it still growing. This dataset has been captured in a parking lot where our experimental vehicle has followed the same path with slight lateral and angular deviations while we made sure to incorporate various environmental conditions caused by luminance, weather, seasonal changes.