Miracle-dz/UniFAR-FTUs huggingface dataset:
Immunodeficiency and autoimmunity during biological disease-modifying antirheumatic drug therapy

Biological disease-modifying antirheumatic drugs target specific components of the immune response related to pathogenesis of autoimmune and inflammatory diseases. Introduction of biologic therapies has enabled better disease control than conventional drugs and thus a reduction in comorbidity and mortality. However, there is concern about adverse effects of these drugs including infections, cancers and drug-induced autoimmune diseases. Patients undergoing biologic treatment are at small but significant risk of serious infections. The overall risk of malignancies in patients on biologics compared with the general population is not increased, but there is evidence of a higher risk of individual cancers. Surprisingly, biological treatment may induce autoantibody production and, rarely, development of autoimmune diseases. A growing body of literature has evaluated the risk of adverse effects during biologic therapies. This paper outlines adverse effects of biological disease-modifying antirheumatic drugs related to immune system disorders, both immunodeficiency and autoimmunity.
Automatic Assessment of Narrative Answers Using Information Retrieval Techniques

In this paper we propose a novel system for automatic assessment of narrative answers using information retrieval algorithms. It is designed to help professors to evaluate the answers that they receive from their students. It is a Java application that communicates through a REST API. This REST API has at its core the Lucene library and exposes all the great functionalities that Lucene has. The application has one UI for the students and one UI for the professor. The student will select the professor, select the question, upload the answer and send it. The professor will evaluate the student answer using the algorithms that will be discussed in this paper. Also in this paper a series of experiments will be presented, and their result will give us a better understanding of the algorithms and have a taste of how they work.
Greedy Incremental Support Vector Regression

Support Vector Regression (SVR) is a powerful supervised machine learning model especially well suited to the normalized or binarized data. However, its quadratic complexity in the number of training examples eliminates it from training on large datasets, especially high dimensional with frequent retraining requirement. We propose a simple two-stage greedy selection of training data for SVR to maximize its validation set accuracy at the minimum number of training examples and illustrate the performance of such strategy in the context of Clash Royale Challenge 2019, concerned with efficient decks’ win rate prediction. Hundreds of thousands of labelled data examples were reduced to hundreds, optimized SVR was trained on to maximize the validation R2 score. The proposed model scored the first place in the Cash Royale 2019 challenge, outperforming over hundred of competitive teams from around the world.
Beyond Textual Issues: Understanding the Usage and Impact of GitHub Reactions

Recently, GitHub introduced a new social feature, named reactions, which are "pictorial characters" similar to emoji symbols widely used nowadays in text-based communications. Particularly, GitHub users can use a pre-defined set of such symbols to react to issues and pull requests. However, little is known about the real usage and impact of GitHub reactions. In this paper, we analyze the reactions provided by developers to more than 2.5 million issues and 9.7 million issue comments, in order to answer an extensive list of nine research questions about the usage and adoption of reactions. We show that reactions are being increasingly used by open source developers. Moreover, we also found that issues with reactions usually take more time to be handled and have longer discussions.
FANE: A Firewall Appliance for the Smart Home

With the advent of the Internet of Things (IoT), many domestic devices have been equipped with information technology. By connecting IoT devices with each other and with the Internet, Smart Home installations exist that allow the automation of complex household tasks. A popular example is Google Nest that controls cooling, heating and home security. However, Smart Home users are tempted to neglect that such IoT devices pose IT-Security risks. Examples like the Mirai malware have already shown that insecure IoT devices can be used for large-scale network attacks. Thus, it is important to adapt security approaches to Smart Home installations. In this paper, we introduce FANE, our concept for a Firewall AppliaNcE for Smart Home installations. FANE makes a few realistic assumptions on the network segmentation and the communication profile of IoT devices. This allows FANE to learn firewall rules automatically. Our prototypical implementation indicates that FANE can secure a wide range of IoT devices without requiring network-security expertise from the Smart Home user.
Prediction of Mathematical Expression Declarations based on Spatial, Semantic, and Syntactic Analysis

Mathematical expressions (ME) and words are carefully bonded together in most science, technology, engineering, and mathematics (STEM) documents. They respectively give quantitative and qualitative descriptions of a system model under discussion. This paper proposes a general model for finding the co-reference relations between words and MEs, based on which we developed a novel algorithm for predicting the natural language declarations of MEs--the ME-Dec. The prediction algorithm is applied in a three-level framework, where the first level is a customized tagger to identify the syntactic roles of MEs and the part-of-speech (POS) tags of words in the ME-word mixed sentences. The second level screens the ME-Dec candidates based on the hypothesis that most ME-Dec are noun phrases (NP). A shallow chunker is trained from the fuzzy process mining algorithm, which uses the labeled POS tag series in the NTCIR-10 dataset as input to mine for the frequent syntactic patterns of NP. In the third level, using distance, word stem, and POS tag respectively as the spatial, semantic, and syntactic features, the bonding model between MEs and ME-Dec candidates is trained on the NTCIR-10 training set. The final prediction results are made upon the majority votes of an ensemble of Naïve Bayesian classifiers based on the three features. Evaluation of the model on the NTCIR-10 test set, the proposed algorithm achieved 75% and 71% average F1 score in soft matching and strict matching, respectively, which outperforms the state-of-the-art solutions by a margin of 5-18%. 1
EmbSE: A Word Embeddings Model Oriented Towards Software Engineering Domain

The representation of contexts is essential in tasks involving Natural Language Processing (NLP). In the field of software engineering, classifying similar texts within a specific context has been a complex task, considering the informality and the complexity inherent of the texts produced through many software development processes (e.g. agile methods). Word embeddings capture semantic and syntactic information about unique words, allowing them to be represented in a dense and low-dimensional format. This property makes the embeddings vectors an important input feature for machine learning algorithms that aim to classify texts. Although there has been much research around the application of word embeddings in several areas, up to this moment, there is no knowledge about studies that have explored its application in the creation of a specific model for the domain of the area of software engineering. Thus, this article presents the proposal to generate an embedding model, called embeddings model for software engineering (EmbSE), which can recognize specific and relevant terms in the software engineering context. This model can be used as the main entry in the classification of several textual artifacts generated during the software development project process. The results are promising, presenting a 48% improvement in the mAP values for the EmbSE concerning the model trained on the generic corpus. This reinforces the hypothesis that a model of this nature can bring significant improvements in the classification of texts of the area.
Deep Learning Hyper-parameter Tuning for Sentiment Analysis in Twitter based on Evolutionary Algorithms

The state of the art in Sentiment Analysis is defined by deep learning methods, and currently the research efforts are focused on improving the encoding of underlying contextual information in a sequence of text. However, those neural networks with a higher representation capacity are increasingly more complex, which means that they have more hyper-parameters that have to be defined by hand. We argue that the setting of hyper-parameters may be defined as an optimisation task, we thus claim that evolutionary algorithms may be used to the optimisation of the hyper-parameters of a deep learning method. We propose the use of the evolutionary algorithm SHADE for the optimisation of the configuration of a deep learning model for the task of sentiment analysis in Twitter. We evaluate our proposal in a corpus of Spanish tweets, and the results show that the hyper-parameters found by the evolutionary algorithm enhance the performance of the deep learning method.
Training Subset Selection for Support Vector Regression

As more and more data are available, training a machine learning model can be extremely intractable, especially for complex models like Support Vector Regression (SVR) training of which requires solving a large quadratic programming optimization problem. Selecting a small data subset that can effectively represent the characteristic features of training data and preserve their distribution is an efficient way to solve this problem. This paper proposes a systematic approach to select the best representative data for SVR training. The distributions of both predictor and response variables are preserved in the selected subset via a 2-layer data clustering strategy. A 2-layer step-wise greedy algorithm is introduced to select best data points for constructing a reduced training set. The proposed method has been applied for predicting deck’s win rates in the Clash Royale Challenge, in which 10 subsets containing hundreds of data examples were selected from 100k for training 10 SVR models to maximize their prediction performance evaluated using R-squared metric. Our final submission having a R2 score of 0.225682 won the 3rd place among over 1200 solutions submitted by 115 teams.
Searching and ranking questionnaires: an approach to calculate similarity between questionnaires

Questionnaires are useful tools for research purposes and are generally used for collecting information about a population of interest, by focusing on different intentions. During the questionnaire project, or for sharing data purposes, it may be useful to check if there is already a questionnaire with the same intention as that being carried out. Well-designed questions can induce respondents to provide better answers. However, examining research questionnaires is not a trivial task since a question can be structured in different ways. In this paper, we propose a similarity measure to match questionnaires that are characterized by the heterogeneity of their questions and to provide a ranking method based on variations of a given query. In determining the effectiveness of this approach, we evaluated it through an experimental study, using recall, precision, f-value, MAP and NDGC, and this enabled us to obtain more effective results than other proposals.
Social Tie Based Cooperative Jamming for D2D Communications in the Internet of Things

With the proliferation of short-range networks and the prevalence of devices connected to these networks, the Internet of Things (IoT) has held the promise of improving our lives. Devices-to-device communication is considered to be an important part of the IoT. To address the limitation of physical layer security approaches based on single-antenna systems, cooperative jamming is a promising approach to enhance efficient cooperation among devices. In this paper, we study the cooperative interference problem of a two-layer network in which the upper social network (S*) is composed of people with different social ties and the lower layer network (P*) is composed of various physical devices. S* overlays P* by a connecting degree ( Cd ). We introduce game theory to simulate the cooperation willingness of jammers in S*, and prove the existence of Nash Equilibrium (NE) and design an algorithm to calculate the secrecy outage probability (SOP). Moreover, we introduce a susceptible-infective-recovery (SIR) spreading model to evaluate the performance, which believes that each jammer’s initial state is likely to participate rather than certainly participate in the cooperation. Each jammer is considered to has three states: probable participation (P-state), obligatory participation (O-state) and non-participation (N-state). Experiments show that when social ties are strong among devices, jammers would be more willing to cooperate, contribute more to communication quality and have lower computational complexity on two-lay net-work. Our methods make jammers have more choices for updating status, and show that results would be better than without SIR characteristics under the same number of jammers.
Composition of Languages Embedded in Scala

Composition is amongst the major challenges faced in language engineering. Erdweg et al. offered a taxonomy for language composition. Mernik catalogued the use of the Language Definitional Framework LISA for composition sorts in that taxonomy. We produce a similar catalogue for embedded language engineering in Scala. We begin with techniques that are not specific to Scala. They are applicable in any host language with a module system and support for higher order functions. We, then, present two more techniques to examine Scala-specific language engineering. Interestingly enough, even though dealing with embedded languages, in terms of lines of code, our material is of comparable length to its LISA counterpart. Our work lends insight into Scala’s serviceability for composition, as a host for embedded language engineering.
Big Data Platform for Smart Grids Power Consumption Anomaly Detection

Big data processing in the Smart Grid context has many large-scale applications that require real-time data analysis (e.g., intrusion and data injection attacks detection, electric device health monitoring). In this paper, we present a big data platform for anomaly detection of power consumption data. The platform is based on an ingestion layer with data densification options, Apache Flink as part of the speed layer and HDFS/KairosDB as data storage layers. We showcase the application of the platform to a scenario of power consumption anomaly detection, benchmarking different alternative frameworks used at the speed layer level (Flink, Storm, Spark).
The Entrepreneurial Student's Experience Journey Through Engineering Education

The goal of this paper is to present a new perspective on how to design entrepre-neurial education in a wider sense. By applying the method of “customer journey mapping”, which is widely used in the field of marketing, the perspective of the student (“customer”) is put into the centre of attention. It intends to raise aware-ness for a more holistic and customer-centric view of the entrepreneurship-related “customer experience”. The paper shows the limitations of existing tools and procedures and gives an outlook on possible tools to describe customer experience journey in entrepre-neurship education.
Accurate Retrieval of Corporate Reputation from Online Media Using Machine Learning

Corporate reputation is an economic asset and its accurate measurement is of increasing interest in practice and science. This measurement task is difficult because reputation depends on numerous factors and stakeholders. Traditional measurement approaches have focused on human ratings and surveys, which are costly, can be conducted only infrequently and emphasize financial aspects of a corporation. Nowadays, online media with comments related to products, services, and corporations provides an abundant source for measuring reputation more comprehensively. Against this backdrop, we propose an information retrieval approach to automatically collect reputation-related text content from online media and analyze this content by machine learning-based sentiment analysis. We contribute an ontology for identifying corporations and a unique dataset of online media texts labelled by corporations’ reputation. Our approach achieves an overall accuracy of 84.4%. Our results help corporations to quickly identify their reputation from online media at low cost.
Masters of the Process: A Board Game Proposal for Teaching Software Management and Software Development Process

Software Engineering (SE) education has a current challenge to provide sufficient hands-on experience for their students. Game-based learning represents a promising alternative to teach computing in higher education, promoting an "active learning" and serving as entertaining means for drill and practice. This paper presents "Masters of the Process", a board game proposal for teaching software management and software development process competences. For this, the design, construction and evaluation steps of the proposed game are described, as well as the usability results together with a brief comparison among SE board games. As a result, a simple, interactive and multiplayer game was provided, able to teach important SE concepts in a practical, competitive and funny way.
TRIVIR: A Visualization System to Support Document Retrieval with High Recall

In this paper, we propose TRIVIR, a novel interactive visualization tool powered by an Information Retrieval (IR) engine that implements an active learning protocol to support IR with high recall. The system integrates multiple graphical views in order to assist the user identifying the relevant documents in a collection, including a content-based similarity map obtained with multidimensional projection techniques. Given representative documents as queries, users can interact with the views to label documents as relevant/not relevant, and this information is used to train a machine learning (ML) algorithm which suggests other potentially relevant documents on demand. TRIVIR offers two major advantages over existing visualization systems for IR. First, it merges the ML algorithm output into the visualization, while supporting several user interactions in order to enhance and speed up its convergence. Second, it tackles the problem of vocabulary mismatch, by providing term's synonyms and a view that conveys how the terms are used within the collection. Besides, TRIVIR has been developed as a flexible front-end interface that can be associated with distinct text representations and multidimensional projection techniques. We describe two use cases conducted with collaborators who are potential users of TRIVIR. Results show that the system simplified the search for relevant documents in large collections, based on the context in which the terms occur.
Design of a Distributed HIDS for IoT Backbone Components

Nowadays DDoS attacks using devices from IoT networks are frequent and extensive. Given that IoT network instances are distributed and deployed on the conventional Internet structure, DDoS countermeasures in IoT need to be fully distributed and coordinated all over the components that form each IoT instance. This paper presents a host-based intrusion detection system (HIDS) that was designed and prototyped to protect the components of IoT network backbones comprising conventional switches and routers, not IoT devices. In our design, a set of the proposed HIDS executes conventional security verification, like default username and password, known attacks signatures, usage of resources, processes, ports and open connections, while also interacting with a Controller of the HIDS set to allow the coordination of intrusion detection actions relative to DDoS attacks distributed all over the IoT instance. The designed distributed HIDS is evaluated in a controlled environment that, although being a local and isolated network, realistically represents IoT network instances. Keywords—Internet of Things (IoT), IoT Security, Distributed Denial of Service (DDoS), Host-based Intrusion Detection System (HIDS), Distributed HIDS, Mirai botnet.
Playing the Sprint Retrospective

In this paper, we report on a replication of the study by Przybylek & Kotecka [2017]. The aim of our study was to revise the work practices related to Sprint Retrospectives in Bluebay Polska Sp. z.o. o. by adopting collaborative games. The feedback received from two Scrum teams confirms the findings from the original study and indicates that collaborative games improve participants’ creativity, involvement, and communication as well as produce better results than the standard retrospective.
A mixed-methods measurement and evaluation methodology for mobile application usability studies

— Low usability of mobile application is thought to diminish the perceived level of the quality by a user whose experiences substantially determines its market success or failure. However, while a single method of measurement employed to study usability may produce an unreliable or incomplete evaluation outcome, in this paper, contrarily we propose to take advantage of both qualitative and quantitative methods adequate to collect data, that would describe all usability attributes. In particular, in the scope of mobile application usability studies, this paper ( i ) depicts the main assumptions of elaborated M4MAUME methodology, ( ii ) describes the self-developed software tool (RVDA) for retrospective video data analysis, ( iii ) specifies the experimental setup, and ( iv ) discusses the preliminary results obtained from 20 experiments performed on four different groups of mobile applications. Eventually our findings lead us to the conclusion that mixing different methods have produced reliable and valuable outcomes which may be used to improve and manage usability in current and future projects, as well as to enhance existing software quality assurance (SQA) programs.
Factors Affecting Software Development Productivity: An empirical study

The competitiveness has demanded from the software industry shorter delivery times for its products resulting in optimized life cycles, generating a need to increase its performance to maintain competitiveness in the markets where they operate. This context has made productivity study so fundamental that organizations not only evaluate their performance, but also provide means to improve it. The main goal of this paper is to investigate which factors affect productivity in software development projects and in open-source projects. In this work a Systematic Literature Review (SLR) was carried out in order to answer the research questions and a survey with practitioners community about their perception in relation to the factors of the productivity of the team. This empirical study led to the discovery of interesting factors that show how the different factors do (or do not) affect productivity. It was also found out that some factors appear to allow independence and responsibility of team, while others appear to cause a better distribution of tasks. The results show how factors such as people, product, organization, investment in technology, lack of contractual relations and engagement of open-source project contributors influence productivity.