Miracle-dz/UniFAR-FTUs huggingface dataset:
CLUSTERING ORGANIZATION STRUCTURE IN PRODUCT DEVELOPMENT PROJECTS USING SIMILARITY

A product development (PD) project is a complex social network, in which teams have their own attributes and are related by information flow. Similar team attributes and the complex patterns of technical dependency among teams both affect organization modularity. This paper provides an innovative spectral clustering approach that merge team attributes and relationship of teams. To measure the similarity of PD teams, we analyze the similar attributes of team and build structural models to capture the technical communication dependency among teams via the product-organization multi-domain matrix (MDM). We use two metrics to evaluate the clustering solutions and confirm that the proposed approach provides effective reduction of PD coordination complexity.
Designing Optimal Educational Experiences

“Flow” is an optimal experience, where a participant is so positively engaged with the activity that he looses all sense of time. Product designers have introduced the concept of “flow” to improve the experience that users have with their products. Can the same concepts used by product designers be applied to the development of instructional materials to improve their effectiveness? This paper discusses these concepts as variables and provides a useful framework to utilize these variables in curriculum design. Sometimes a picture is worth a thousand words. The unusual comic book style used to present this paper is an illustration of the use of media to promote flow in reading complex information. Even serious work can be presented in formats that grab attention and provoke memory formation in its readers. Pi ps qu ea k Pr od uc ti on s LL C © 2 00 8
DEVELOPMENT OF A NEURAL NETWORK FOR THE IDENTIFICATION OF MULTIPHASE FLOW PATTERNS

This work is concerned with the development of an artificial neural network (ANN), capable of classifying two-phase flow patterns, such as discrete bubbles, stratified, slug-flow, intermittent and annular regimes. Experimental operating data from the literature and the physical properties of the fluids were used to define and calculate dimensionless numbers. These numbers constitute the inputs of the neural model. They successfully describe the flow because they account for the competing forces occurring within the multiphase fluid. The training procedure was performed using a Levenberg-Marquardt algorithm. The methodology used to find the best network architecture is described in detail. All the flow regimes were accurately classified presenting only a small deviation. The final goal is to develop an automatic classification tool for multiphase flow patterns aimed at laboratories and field applications.
Testing the limits of non-adjacent dependency learning: Statistical segmentation and generalization across domains

Achieving linguistic proficiency requires identifying words from speech, and discovering the constraints that govern the way those words are used. In a recent study of non-adjacent dependency learning, Frost and Monaghan (2016) demonstrated that learners may perform these tasks together, using similar statistical processes — contrary to prior suggestions. However, in their study, non-adjacent dependencies were marked by phonological cues (plosive- continuant-plosive structure), which may have influenced learning. Here, we test the necessity of these cues by comparing learning across three conditions; fixed phonology, which contains these cues, varied phonology, which omits them, and shapes, which uses visual shape sequences to assess the generality of statistical processing for these tasks. Participants segmented the sequences and generalized the structure in both auditory conditions, but learning was best when phonological cues were present. Learning was around chance on both tasks for the visual shapes group, indicating statistical processing may critically differ across domains.
Training video surveillance system for the purpose of object identification with the help of neural networks

Research on the development of algorithms for the use of neural networks associated with video surveillance systems on the identification of the desired objects today go to a whole new technological level. Categorization of classes-objects of research, determination of actual zones of fixation, integration of functions-cloud computing, analysis of incoming data and their evaluation determine the relevance in the creation of a trainable model that allows to solve a wide range of problems when working with the existing and the flow of incoming data in real time both at the state and at the level of transnational companies.
Refactoring as formal refinements

Program restructuring in the context of object-oriented programming is known as refactoring. This consists of changes made to the internal structure of software in order to improve its legibility and make it easier to modify without changing its external behaviour. In practice, refactoring usually relies on compilation and tests in order to guarantee behaviour preservation. Works like those by Opdyke and Roberts have already been done in the direction of refactoring formalisation by means of the identification of conditions that must be satisfied to guarantee that a change to a program is behaviour preserving. The conditions, which are usually written in the predicate calculus, are introduced as preand postconditions of the refactorings. Other approaches for the proof of refactoring behaviour preservation use formalisms such as concept analysis and graph rewriting. However, there is no algebraic technique that presents refactorings as behaviour preserving transformations, with proofs carried out. This avoids changes of notation and facilitates mechanisation. Our contribution is to present refactorings as transformations of programs written in the language rool (Refinement object-oriented Language), which is a Java-like imperative language with classes, visibility control for attributes, dynamic binding, and recursion. It allows reasoning about object-oriented programs and specifications, as both kinds of constructs are mixed as in Morgan’s refinement calculus. The semantics of rool, as usual for refinement calculi, is based on weakest preconditions. A set of programming laws is available for the imperative constructs of rool as well as for its object-oriented features. The correctness of these laws, which is also a contribution of the present work, is proved against the semantics of rool. We present refactorings as algebraic refinement rules involving program terms. The proof that these rules are behaviour preserving is accomplished by the application of the programming laws of one of the sides of the rule to obtain the other side. The proofs of some refactoring rules also involve data refinement of classes. We generalise the standard data refinement technique from single modules (classes) to class hierarchies. Design patterns arise as a natural objective for refactoring a system. The literature on design patterns already presents works that propose the formalisation of design patterns. They usually concentrate on the formal description of patterns, not on the transformation of a system with the intention of obtaining a final system structure according to a design pattern. In this work, we also present how to obtain a system that is in accordance with design patterns by the application of refactoring rules proved to be behaviour preserving. We also present the transformation of a monolithic application to a well-structured one according to an architectural pattern.
Large Dataset and Language Model Fun-Tuning for Humor Recognition

The task of humor recognition has attracted a lot of attention recently due to the urge to process large amounts of user-generated texts and rise of conversational agents. We collected a dataset of jokes and funny dialogues in Russian from various online resources and complemented them carefully with unfunny texts with similar lexical properties. The dataset comprises of more than 300,000 short texts, which is significantly larger than any previous humor-related corpus. Manual annotation of 2,000 items proved the reliability of the corpus construction approach. Further, we applied language model fine-tuning for text classification and obtained an F1 score of 0.91 on a test set, which constitutes a considerable gain over baseline methods. The dataset is freely available for research community.
Her Story or their own stories? Digital game-based learning, student creativity, and creative writing

Abstract This qualitative study investigates a media transfer project in which a digital game was used to promote student creativity in an English as a foreign language (EFL) class. The paper first addresses the potential of opportunities for stimulating student creativity and motivation. Creativity has been highlighted as a core competency and has garnered considerable interest in many fields in recent years; however, creativity and creative writing are rarely cultivated in EFL writing classrooms. This study uses a digital game and designs a creative writing project to provide an authentic learning opportunity through which students were able to develop their creativity, use the target language in a meaningful way, and enjoy learning. A murder mystery game, Her Story, was selected because it provides a springboard for creative writing due to its fragmented and ambiguous narrative. Based on the game plot, 25 Korean university students reimagined the narrative in creative writing projects of their own. The study analyzes the students’ writing according to three constructs in Torrance’s model of creativity: originality, flexibility, and elaboration. The results demonstrate how the students’ writing exhibits considerable creativity in all three constructs. Student reflection papers and surveys indicate that participating in the project enhanced the students’ motivation for and engagement in learning.
Optimization of Frequency-Scaling in Time-Triggered Multi-Core Architectures using Scenario-Based Meta-Scheduling

Nowadays, energy-efficiency, energy-management, and energy-saving are essential issues in embedded systems. Many real-time embedded systems need to adapt at run-time to critical events such as faults which may arise within the system or changing environmental conditions. Time-triggered systems are frequently used in safety-critical applications, because precomputed schedules improve predictability and fault containment. Nevertheless, flexibility and resource-efficiency can be conceded by shifting between static scenario-based schedules that are generated by meta-scheduling tools and techniques. Application models, platform models and context models are used by meta-scheduling tools to plan reactions to events and generate meta-schedules. An example of a significant event is dynamic-slack, which can be used to increase the energy-efficiency in systems by changing communication and execution time plans. While methods and algorithms for energy-efficiency, energy-management and energy-saving are used in many applications (e.g., mobile phones, smart TVs), their applicability is still restricted in safety-critical systems. In a multi-core chip, the NoC technology contributes significantly to the overall energy consumption of an MPSoC. Hence, this paper proposes a meta-scheduler for Dynamic Voltage and Frequency Scaling (DVFS) in time-triggered multi-core architectures. The evaluation of correctness and energy-efficiency for cores and routers is carried out experimentally and analytically for dynamic slack-events. The results are monitored, analyzed and validated with a meta-scheduling visualization tool (MeSViz).
Innovations in Monitoring Conveyor Belts with Implemented RFID Technology

Belt conveyors are used as efficient conveyance systems for the continuous transportation of materials. However, they are also used for a wide range of applications in the transport of ores and non-metallic materials. The Radio Frequency Identification (RFID) system represents a key technology in many industries due to its ability of automatic data processing. The purpose of the present article is to discuss the implementation of RFID tags into fabric conveyor belts as carriers of the information on conveyor belts (ozone-induced ageing or accelerated thermal ageing, damage to cover layers and the carcass, and ignition). During the monitoring, it is important to record conveyor belt failures and damage causes, as well as other problems arising during the use of belt conveyors. The article presents the results of the experimental research on RFID tags and the analysis of their behaviour in accelerated thermal ageing. This facilitates simulating the conditions of hot vulcanisation of conveyor belts, especially when splicing (as well as producing) them, and of ozone-induced ageing of conveyor belts. The output of the article is the verification of the possibility to implement the RFID technology in the transport of mineral materials by belt conveyance systems in real operations.
Performance Analysis of Strapdown Inertial Navigation Algorithms

In this work the theory of strapdown inertial navigation is introduced. The strapdown inertial navigation algorithms using Euler angles, direction cosines, and quaternions methods are derived. Reference flow charts of strapdown INS algorithms for attitude calculation based on Euler angles, direction cosines, and quaternions methods are derived.Simulation examples applied on the navigation of Aerosonde UAV. Simulation results are analyzed to explore the differences between the different algorithms to conclude the most reliable algorithm of strapdown inertial navigation algorithms.
Link Prediction with Signed Latent Factors in Signed Social Networks

Link prediction in signed social networks is an important and challenging problem in social network analysis. To produce the most accurate prediction results, two questions must be answered: (1) Which unconnected node pairs are likely to be connected by a link in future? (2) What will the signs of the new links be? These questions are challenging, and current research seldom well solves both issues simultaneously. Additionally, neutral social relationships, which are common in many social networks can affect the accuracy of link prediction. Yet neutral links are not considered in most existing methods. Hence, in this paper, we propose a s igned l atent f actor (SLF) model that answers both these questions and, additionally, considers four types of relationships: positive, negative, neutral and no relationship at all. The model links social relationships of different types to the comprehensive, but opposite, effects of positive and negative SLFs. The SLF vectors for each node are learned by minimizing a negative log-likelihood objective function. Experiments on four real-world signed social networks support the efficacy of the proposed model.
Understanding Consumer Journey using Attention based Recurrent Neural Networks

Paths of online users towards a purchase event (conversion) can be very complex, and guiding them through their journey is an integral part of online advertising. Studies in marketing indicate that a conversion event is typically preceded by one or more purchase funnel stages, viz., unaware, aware, interest, consideration, and intent. Intuitively, some online activities, including web searches, site visits and ad interactions, can serve as markers for the user's funnel stage. Identifying such markers can potentially refine conversion prediction, guide the design of ad creatives (text and images), and lead to higher ad effectiveness. We explore this hypothesis through a set of experiments designed for two tasks: (i) conversion prediction given a user's activity trail, and (ii) funnel stage specific targeting and creatives. To address challenges in the two tasks, we propose an attention based recurrent neural network (RNN) which ingests a user activity trail, and predicts the user's conversion probability along with attention weights for each activity (analogous to its position in the funnel). Specifically, we propose novel attention mechanisms, which maintain a global weight for each activity across all user trails, and also indicate the activity's funnel stage. Use of the proposed attention mechanisms for the first task of conversion prediction shows significant AUC lifts of 0.9% on a public dataset (RecSys 2015 challenge), and up to 3.6% on three proprietary datasets from a major advertising platform (Yahoo Gemini). To address the second task, the activity weights from the proposed mechanisms are used to automatically assign users to funnel stages via a scalable scoring method. Offline evaluation shows that such activity weights are more aligned with editorially tagged activity-funnel stages compared to weights from existing attention mechanisms and simpler conversion models like logistic regression. In addition, results of online ad campaigns in Yahoo Gemini with funnel specific user targeting and ad creatives show strong performance lifts further validating the connection across online activities, purchase funnel stages, stage-specific custom creatives, and conversions.
Towards Comprehensive Description Generation from Factual Attribute-value Tables

The comprehensive descriptions for factual attribute-value tables, which should be accurate, informative and loyal, can be very helpful for end users to understand the structured data in this form. However previous neural generators might suffer from key attributes missing, less informative and groundless information problems, which impede the generation of high-quality comprehensive descriptions for tables. To relieve these problems, we first propose force attention (FA) method to encourage the generator to pay more attention to the uncovered attributes to avoid potential key attributes missing. Furthermore, we propose reinforcement learning for information richness to generate more informative as well as more loyal descriptions for tables. In our experiments, we utilize the widely used WIKIBIO dataset as a benchmark. Besides, we create WB-filter based on WIKIBIO to test our model in the simulated user-oriented scenarios, in which the generated descriptions should accord with particular user interests. Experimental results show that our model outperforms the state-of-the-art baselines on both automatic and human evaluation.
A Risk-Based Decision Framework for the Distribution Company in Mutual Interaction With the Wholesale Day-Ahead Market and Microgrids

One of the emergent prospects for active distribution networks (<italic>DN</italic>) is to establish new roles to the distribution company (<italic>DISCO</italic>). The <italic>DISCO</italic> can act as an aggregator of the resources existing in the <italic>DN</italic>, also when parts of the network are structured and managed as microgrids (<italic>MG</italic>s). The new roles of the <italic>DISCO</italic> may open the participation of the <italic>DISCO</italic> as a player trading energy in the wholesale markets, as well as in local energy markets. In this paper, the decision making aspects involving the <italic>DISCO</italic> are addressed by proposing a bilevel optimization approach in which the <italic>DISCO</italic> problem is modeled as the upper-level problem and the <italic>MG</italic>s problems and day-ahead wholesale market clearing process are modeled as the lower-level problems. To include the uncertainty of renewable energy sources, a risk-based two-stage stochastic problem is formulated, in which the <italic>DISCO</italic>'s risk aversion is modeled by using the conditional value at risk. The resulting nonlinear bilevel model is transformed into a linear single-level one by applying the Karush–Kuhn–Tucker conditions and the duality theory. The effectiveness of the model is shown in the application to the IEEE 33-bus <italic>DN</italic> connected to the IEEE RTS 24-bus power system.
EXPLAINABLE, TRUSTABLE AND EMPHATIC ARTIFICIAL INTELLIGENCE FROM FORMAL ARGUMENTATION THEORY TO ARGUMENTATION FOR HUMANS

argumentation, (ii) reasoning about trust in multiagent systems, and (iii) reasoning about coalitions in multiagent systems. After my PhD, my research topics gradually evolved from formal computational models of argument to natural models of arguments, where arguments in natural language are considered and mined from heterogeneous sources. I am one of the very first initiators of the research topic, very popular nowadays, called Argument Mining. I have also studied the impact of emotions on the argumentation online debaters express on the Web. These two research lines are highly multidisciplinary, and they have been addressed thanks to a fruitful collaboration with linguists and cognitive scientists. The rationale behind my research work (and more generally behind the whole research activity of the WIMMICS team) is that decision (being them from artificial or human agents) need to be supported and justified through arguments and other factors, like emotions, mental states and trust, and the Web represents a invaluable interaction architecture and information source to be leveraged in order to support decision making and the subsequent justification about such deliberation.
AFFORDANCES OF WEB 2.0 INTERFACES FOR THE TEACHING/LEARNING OF L2 IN THE FLIPPED CLASSROOM

The aim of this study is to analyze affordances and limitations of some web 2.0 interfaces for the teaching and learning of English as foreign, second or additional language (hereafter L2) in the flipped classroom format. With that aim, the study analyzed four digital interfaces with web 2.0 tools for L2 teaching/learning in regards to their potential to the flipped classroom approach to develop both linguistic and digital literacy. The four online spaces analyzed were the Edmodo (https://www.edmodo.com), the Socrative (http://www.socrative.com), the Edcanvas (http://www.web2teachingtools.com/edcanvas.html) and the Screencast (https://www.screencast.com). Preliminary results of the study suggest that the interfaces analyzed are relevant possibilities for the development of both L2 and digital literacy skills that can be used in the traditional class format as well as in hybrid or blended approaches such as in the flipped/inverted classroom format.
Methoden zur Erstellung von Bedeutungsparaphrasenangaben

Abstract Once a new word or a new meaning is added to a monolingual dictionary, the lexicographer is to provide a definition of this item. This paper focuses on the methodological challenges in writing such definitions. After a short discussion of the central terminology (method and definition), the article describes factors which inform this process: linguistic theories, linguistic and lexicographical methods, and types of definitions. Using the example of elexiko, a dictionary project of the Institute for the German language (IDS) in Mannheim, Germany, the paper finally showcases the compilation of definitions in a monolingual online dictionary of contemporary German.
MCˆ2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension

Conversational machine reading comprehension (CMRC) extends traditional single-turn machine reading comprehension (MRC) by multi-turn interactions, which requires machines to consider the history of conversation. Most of models simply combine previous questions for conversation understanding and only employ recurrent neural networks (RNN) for reasoning. To comprehend context profoundly and efficiently from different perspectives, we propose a novel neural network model, Multi-perspective Convolutional Cube (MCˆ2). We regard each conversation as a cube. 1D and 2D convolutions are integrated with RNN in our model. To avoid models previewing the next turn of conversation, we also extend causal convolution partially to 2D. Experiments on the Conversational Question Answering (CoQA) dataset show that our model achieves state-of-the-art results.