Miracle-dz/UniFAR-FTUs huggingface dataset:
Bots and Gender Profiling using a Deep Learning Approach

This paper describes the system we developed for the Bots and gender profiling task, at PAN @ CLEF 2019. The task consists in, given a tweets set, automatically determine whether its author is a bot or a human. In case of human, identify her/his gender. We propose a deep learning based system, fed with the TFIDF representation from the texts instead of word embeddings representation as usual. Additionally, we use some linguistic features which improve the performance of the system according with the experimental results.
Mobility Management as a Radio Access Network Service

Fifth generation (5G) core network is serviceoriented. It uses service-based interfaces between the control plane functions which improve modularity and align better with cloud networking. In this paper we propose an approach to service oriented radio access network (RAN), where the functions of Next Generation Application Protocol (NGAP) are designed as services of small size that are easily to debug and test. The focus is on handover procedures, where the handover control is exposed as a service. Following the Representational State Transfer (REST) style, we define service resources and the supported methods. Service implementation is considered in terms of modeling the handover state as seen by the core network, source and target radio nodes. The models are formally described and it is proved that they expose equivalent behavior.
Inception-v3 Based Method of LifeCLEF 2019 Bird Recognition

In this paper, we present a method of bird recognition based on Inception-v3. The goal of the LifeCLEF2019 Bird Recognition is to detect and classify 659 bird species within the provided soundscape recordings. Log-Mel spectrograms are extracted as features and Inception-v3 is used for bird sound detection. Some data augmentation techniques are applied to improve the robustness and generalization of the model. Finally, we evaluated our system in BirdCLEF test data and achieved 0.055 of classification mean average precision (c-mAP).
Twitter Bots and Gender Detection using Tf-idf

As the amount of unstructured data increases, value (and the number) of models that can infer information from this data also increases. This paper presents another such model that can perform bots and gender detection on Twitter using just the tweets from the respective Twitter user. We show that a simple frequency based approach with a machine learning algorithm i.e., SVM can achieve high accuracy if the preprocessing is done right. In English language. our model detects bots with an accuracy of 91% and gender with an accuracy of 82%. Main strength of this model is its simplicity along-with the ease with which it can be used with other languages.
A multi-robot coordination system for manipulation of huge elliptical workpiece

This paper presents a kind of kinematics arithmetic of a multi-robot coordination system, facing to manipulation of huge elliptical ship module. This multi-robot system can adjust the position and pose of the elliptical workpiece in 6 DOF, by the coordination of multiple 3 DOF robots. Based on the motion strategy, the inverse kinematics arithmetic was given, and also a simplified arithmetic applied to real time control. A numerical example illuminates the feasibility and the precision.
Secure Data Sharing with Fast Access Revocation through Untrusted Clouds

With the introduction of recent regulations concerning data privacy, encrypting data before uploading it to a public cloud becomes a widespread approach. Such processing reinforces data confidentiality but raises a challenge for efficient access revocation. In this paper, we introduce a conceptual multi-cloud architecture supporting a fast access revocation of outsourced data. The proposed data processing method is based on a combination of encryption, all-or-nothing transformation, and data dispersal. Theoretical and experimental evaluations show that our technique is the fastest among the state-of-the- art solutions.
Author Profiling using Semantic and Syntactic Features

In this paper we present an approach for the PAN 2019 Author Profiling challenge. The task here is to detect Twitter bots and also to classify the gender of human Twitter users as male or female, based on a hundred select tweets from their profile. Focusing on feature engineering, we explore the semantic categories present in tweets. We combine these semantic features with part of speech tags and other stylistic features – e.g. character floodings and the use of capital letters – for our eventual feature set. We have experimented with different machine learning techniques, including ensemble techniques, and found AdaBoost to be the most successful (attaining an F1-score of 0.99 on the development set). Using this technique, we achieved an accuracy score of 89.17% for English language tweets in the bot detection subtask.
EyeHacker: gaze-based automatic reality manipulation

In this study, we introduce EyeHacker, which is an immersive virtual reality (VR) system that spatiotemporally mixes the live and recorded/edited scenes based on the measurement of the users' gaze. This system updates the transition risk in real time by utilizing the gaze information of the users (i.e., the locus of attention) and the optical flow of scenes. Scene transitions are allowed when the risk is less than the threshold, which is modulated by the head movement data of the users (i.e., the faster their head movement, the higher will be the threshold). Using this algorithm and experience scenario prepared in advance, visual reality can be manipulated without being noticed by users (i.e., eye hacking). For example, consider a situation in which the objects around the users perpetually disappear and appear. The users would often have a strange feeling that something was wrong and, sometimes, would even find what happened but only later; they cannot visually perceive the changes in real time. Further, with the other variant of risk algorithms, the system can implement a variety of experience scenarios, resulting in reality confusion.
Overview of the Style Change Detection Task at PAN 2019

The task of style change detection aims at segmenting documents into stylistically homogeneous passages. These segments can subsequently be utilized for distinguishing different authors of a document. In this year’s PAN style change detection task we asked the participants to answer the following questions for a given document: (1) Is the document written by one or more authors? and, (2) if the document is multi-authored, how many authors have collaborated? The task is performed and evaluated on a dataset compiled from an English Q&A platform, covering a set of diverse topics. The paper in hand introduces the style change detection task and the underlying dataset, surveys the participant’s submissions, and analyzes their performance.
TheEarthIsFlat's Submission to CLEF'19CheckThat! Challenge

This report details our investigations in applying state-ofthe-art pre-trained Deep Learning models to the problems of Automated Claim Detection and Fact Checking, as part of the CLEF’19 Lab: CheckThat! : Automatic Identification and Verification of Claims. The report provides an overview of the experiments performed on these tasks, which continue to be extremely challenging for current technology. The research focuses mainly on the use of pre-trained deep neural text embeddings that through transfer learning can allow for improved classification performance on small and unbalanced text datasets. We also investigate the effectiveness of external data sources for improving prediction accuracy on the claim detection and fact checking tasks. Our team submitted runs for every task/subtask of the challenge. The results appeared satisfactory for task 1 and promising but less satisfactory for task 2. A detailed explanation of the steps performed to obtain the submitted results is provided, including comparison tables between our submissions and other techniques investigated.
Taxonomy and Adversarial Strategies of Random Subdomain Attacks

Ever since the introduction of the domain name system (DNS), attacks on the DNS ecosystem have been a steady companion. Over time, targets and techniques have shifted, and in the recent past a new type of attack on the DNS has emerged. In this paper we report on the DNS random subdomain attack, querying floods of non-existent subdomains, intended to cause a denial-of-service on DNS servers. Based on five major attacks in 2018 obtained through backscatter measurements in a large network telescope, we show the techniques pursued by adversaries, and develop a taxonomy of strategies of this attack.
Multi-channel Open-set Cross-domain Authorship Attribution

This paper describes a multi-channel approach to open-set cross-domain authorship attribution (AA) for the PAN-CLEF 2019 AA shared task. The present work adapts the EACH-USP ensemble method presented at PAN-CLEF 2018 to an open-set scenario by defining a threshold value for unknown authors, and extends the previous architecture with an additional character ranking model built with the aid of the PageRank algorithm. Results are superior to a number of baseline systems, and remain generally comparable to those in the original closed-set ensemble approach.
Towards Practical Neural Prosthetic Interfaces

Author(s): Patel, Aashish N. | Advisor(s): Gilja, Vikash | Abstract: The connection between our brain and our muscles via the peripheral nerves enables us to communicate and act upon the world. Individuals suffering from disease and injury that affect these connections have limited treatment options and often rely upon prostheses. A fundamental challenge for such prostheses is the development of a control system that is capable of interpreting desired intentions accurately. Existing control schemes utilize activation by other functional systems using clever mechanical linkages or electromyography signal based control. Increasingly, neural interfaces are being considered for these applications due to their potential ability to restore behavioral function without co-opting existing motor functions. Implanted electrode based neural interfaces, such as electrocorticography (ECoG) hold promise for providing high signal to noise ratio measurements that are stable over long time periods while electroencephalography (EEG) provides convenient, non-invasive scalp surface measurements. Although these techniques provide powerful approaches to measuring neural signals, their application currently yields limited performance in neural interfacing applications.Addressing the complexity of interpreting neural signals, we explore different neural decoding algorithms and evaluate their performance and limitations. Taking advantage of the time-varying properties of neural oscillations, we propose algorithms for sequence learning and sequence transfer learning. We also explore the different user interaction scenarios and the associated performance limitations. Furthermore, in advancing the adoption and understanding of non-invasive neural interfaces, we outline a portable system capable of measuring complex montages of multi-modal bio-sensing data. The perspective gained from these analyses provide better insights to practical neural interface design.
Event Sentence Detection Task Using Attention Model

This paper describes and evaluates a model for event sentence detection in news articles using Attention Models with Bidirectional Gated Recurrent Network (GRU) and Word Embeddings. This model was developed for event sentence detection task in the competition that was organized by ProtestNews lab at CLEF 2019. We also evaluated the generalizability of NLP tools by training our model on data from one country and testing it on data from another country. The model was developed for this task was shown to have the highest score in the organized competition with average F1-score of 0.6547.
Low Power, High speed, Low leakage Floating Gate SRAM Cell using LECTOR Technique

Objective: Leakage power is the major concern of circuit designers in nano meter technology era. The objective of this work is to design a low leakage power floating gate MOS static random access memory. Methods/Statistical Analysis: The proposed design has been made using Floating Gate MOS (FGMOS) and Leakage Control Transistor (LECTOR) technique. FGMOS has been used in place of normal MOS on the conventional SRAM cell. In the LECTOR an NMOS transistor is incorporated between output and pull down the network and a PMOS transitor is incorporated between output and pull up a network. Findings: The SRAM cell has been designed and simulated in Cadence environment on 45 nm gpdk standard CMOS process technology. From the simulation results, it is found that LECTOR FGSRAM cell reduces 90.53% leakage power, 12% delay and 33.20% overall power consumption when compared to FGSRAM cell. A detailed comparison between FGSRAM cell and LECTOR FGSRAM cell performances has been reported during WRITE operation mode. Application/Improvements: It is found that hybrid technique should be added along with LECTOR to improve the parameters.
ELMo Word Representations For News Protection

Within framework of the research for this article a new state-of-the-art ELMO model of the words representation to improve the quality of classical models for solving the problem of binary classification in different interpretations is considered. The article contains a description of various methods applied to the processing of source texts for their transformation into the format necessary for many models, a description of their advantages and disadvantages, principles for constructing and operating the context-dependent representation of ELMo with a detailed description of the algorithm for using it within the target model. For a competent assessment of the results obtained, all experiments are carried out using a real dataset including news articles from various sources in China and India. Comparative analysis includes consideration of the results of adding an ELMo model to standard target models of solving a problem in comparison with using Word2Vec. A comparison is also made for different problem statements the classification of whole texts, individual sentences and the finding of specific passages.
Bots and Gender Profiling on Twitter using Sociolinguistic Features

Unfortunately, in social networks, software bots or just bots are becoming more and more common because malicious people have seen their usefulness to spread false messages, spread rumors and even manipulate public opinion. Even though the text generated by users in social networks is a rich source of information that can be used to identify different aspects of its authors, not being able to recognize which users are truly humans and which are not, is a big draw-back. In this work, we describe the properties of our multilingual classiﬁcation model submitted for PAN2019 that is able to recognize bots from humans, and females from males. This solution extracted 18 features from the user’s posts and applying a machine learning algorithm obtained good performance results.
Comparing Word Embeddings for Document Screening based on Active Learning

Document screening is a fundamental task within Evidencebased Medicine (EBM), a practice that provides scientific evidence to support medical decisions. Several approaches are attempting to reduce the workload of physicians who need to screen and label hundreds or thousands of documents in order to answer specific clinical questions. Previous works have attempted to semi-automate document screening, reporting promising results, but their evaluation is conducted using small datasets, which hinders generalization. Moreover, some recent works have used recently introduced neural language models, but no previous work have compared, for this task, the performance of different language models based on neural word embeddings, which have reported good results in the latest years for several NLP tasks. In this work, we evaluate the performance of two popular neural word embeddings (Word2vec and GloVe) in an active learning-based setting for document screening in EBM, with the goal of reducing the number of documents that physicians need to label in order to answer clinical questions. We evaluate these methods in a small public dataset (HealthCLEF 2017) as well as a larger one (Epistemonikos). Our experiments indicate that Word2vec have less variance and better general performance than GloVe when using active learning strategies based on uncertainty sampling.
Style Change Detection with Feed-forward Neural Networks

The majority of previous authorship attribution studies mainly focus on a dataset of documents (or parts of documents) with labeled authorship. This scenario, however, is not applicable to documents written by more than one author. Detecting the authorship switches within multi-author documents has been shown to be a challenging task in previous PAN tasks. A simplified version of the style change task is thus organized by PAN 2019, which aims at identifying the number of authors in a given document. To this end, we present a system consisting of two modules, one for distinguishing the single-author documents from the multiauthor documents and the other for determining the exact number of authors in the multi-author documents.
Competitive Market for Joint Access and Backhaul Resource Allocation in Satellite-Drone Networks

In this paper, the problem of user association and sub-channel allocation in integrated satellite-drone networks (ISDNs) is studied. In this model, drone base stations (DBSs) provide downlink connectivity, supplementally, to ground users whose demand cannot be satisfied by terrestrial small cell base station (SBSs). Meanwhile, a satellite system is used to satisfy the increased demand for backhaul resources stemming from DBSs and SBSs. In this case, one must jointly consider resource allocation over satellite-DBS/SBS backhaul links, terrestrial backhaul links, and DBS/SBS-user radio access links as well as user association with DBSs and SBSs. This joint backhaul and radio user association and sub-channel allocation problem is modeled using a competitive market setting in which all of the communication links are modeled as consumers while the user association outcomes and the wireless sub-channels are modeled as goods. In this competitive market, consumers seek to exchange goods to maximize their profits, in terms of users' data rates. The sum rate of communication links, defined as the social welfare in the competitive market, is maximized in a sequential manner. An optimal satellite frequency sub-channel allocation scheme is first proposed given the sub-channel allocation and user association scheme at DBSs and SBSs. Then, the problems of sub-channel allocation and user association at DBSs and SBSs are solved using a Hungarian-based solution, such that the sum rate of all communication links are maximized. Simulation results show that, the proposed algorithm can find a Walrasian equilibrium allocation scheme, at which the spectrum resources in the market is fully used, upon convergence. Meanwhile, the proposed algorithm can yield over two-fold gains in terms of the number of downlink service links with 35 Mbps rate and 3.4 times gains in the number of backhaul links with 1 Gbps rate.
Bots and Gender Classification on Twitter

In the modern era, we observed a massive increase in the activities of social-media due to suitable large group of users. As Twitter is highly popular social networking site, Twitter has also appealed the interests of the spammers like social bots to behave as social media actors. Actors like this can perform many wicked actions, including individual discussion inflators, swindler, and stock market exploiters, and so on. The hazard is even higher when the purpose is a political party. Furthermore, bots are usually associated with spreading fake contents. So, it is vital to deal with the classification of bots from an author profiling point of view from the perception of the marketing field, network security, and Data forensics. This article describes the contribution of the Data Science Lab of Ryerson University, Canada in task bots and gender profiling at PAN-19 evaluation lab. The goal of this paper is to detect (A) if the author of a Tweet is a bot or a human, (B) if human, identify the gender of that particular author. We participated in the English language only. In the proposed approach, we used bag of words model after applying different preprocessing techniques (stemming, stop words removal, lowercase, etc.). On the development dataset which was made available by PAN, we got best accuracies 79.51 on task A (binary class) by using MultinomialNB and 56.55 on task B (multi-class) by using Decision Tree classifier. In the evaluation phase on TIRA, our results are the same as in development dataset-2.
Recurrent Attention Networks for Medical Concept Prediction

This paper presents the working notes for the CRADLE group's participation in the ImageCLEF2019 medical competition. Our group focused on the concept detection task which challenged participants to approximate the mapping from radiology images to concept labels. Traditionally, such a task is often mod-elled as an image tagging or image retrieval problem. However, we empirically discovered that many concept labels had weak visual connotations; hence, image features alone are insufficient for this task. To this end, we utilize a recurrent neural network architecture which enables our model to capture the relational dependencies among concepts in a label set to supplement visual grounding when their association to image features is weak or unclear. We also exploit soft attention and visual gating mechanisms to enable our network to dynamically regulate “where” and “when” to extract visual data for concept generation.
Robust Model Predictive Control for Autonomous Underwater Vehicle – Manipulator System with Fuzzy Compensator

Abstract This paper proposes an improved Model Predictive Control (MPC) approach including a fuzzy compensator in order to track desired trajectories of autonomous Underwater Vehicle Manipulator Systems (UVMS). The tracking performance can be affected by robot dynamical model uncertainties and applied external disturbances. Nevertheless, the MPC as a known proficient nonlinear control approach should be improved by the uncertainty estimator and disturbance compensator particularly in high nonlinear circumstances such as underwater environment in which operation of the UVMS is extremely impressed by added nonlinear terms to its model. In this research, a new methodology is proposed to promote robustness virtue of MPC that is done by designing a fuzzy compensator based on the uncertainty and disturbance estimation in order to reduce or even omit undesired effects of these perturbations. The proposed control design is compared with conventional MPC control approach to confirm the superiority of the proposed approach in terms of robustness against uncertainties, guaranteed stability and precision.