Miracle-dz/UniFAR-FTUs huggingface dataset:
Fragment-Based Case Management Models: Metamodel, Consistency, & Correctness

Knowledge-intensive processes are inherently complex. Thus, modeling them is hard as the models have to capture various perspectives often using sub-models with hidden dependencies, e.g., the behavior of a process is constrained by cardinality constraints in a domain model. Yet, models are desirable as they are the primary tool in business process management to analyze, design, implement, and enact processes. We present a metamodel, consistency and correctness criteria for fragment-based case management. The criteria can i) be verified automatically and ii) used to assist modelers at design-time. Thereby, mistakes can be detected and even prevented during design, so model quality improves.
Choosing a Creativity Technique for Requirements Elicitation

Creativity and creativity techniques are relevant for requirements elicitation. There are many different creativity techniques, but these techniques are not used as widely as expected. To investigate the reasons for this situation, we assumed the viewpoint of a requirements engineer looking for decision-making guidelines to facilitate the choice among the considerable number of available creativity techniques. The results of the search highlighted the gap between existing information and what is needed. To this end, we propose a logical framework based on two matrices for choosing creativity techniques and methods in view of their application in requirements elicitation.
Custom-MADE - Leveraging Agile Rationale Management by Employing Domain-Specific Languages

Managing rationale in software development projects can be a cumbersome task with a potentially low return on investment. Especially in the agile context, documentation is therefore very unpopular. Research has not yet properly addressed an agile documentation workﬂow. In this paper, the author presents an integrated approach to agile rationale management based on a highly-ﬂexible modelling approach using domain-speciﬁc languages. It facilitates the complete documentation workﬂow from capture to reuse, partially automates it and oﬀers various customisation opportunities
Semantic Frames for Classifying Temporal Requirements: An Exploratory Study

Temporal requirements express the time-related system behaviors and properties. In engineering critical systems, experience has shown that temporal requirements are the most problematic type of requirements to verify. Researchers have thus used natural language processing (NLP) techniques—most notably, part-of-speech (PoS) tagging—to develop practical classifiers to distinguish temporal requirements from non-temporal ones. In this paper, we explore frame semantics—a linguistic approach to labeling a word’s role in a sentence with respect to the events of interest—to augment the temporal requirements classification task. Our experiments of 111 requirements sentences from the regulatory documents show that the best classification accuracy of 90.9% is achieved when PoS features are replaced with, rather than combined with, frame semantics features. The results suggest the promising role of semantics-augmented NLP support in an understudied requirements engineering task.
Parallel Discrepancy Detection and Incremental Detection

This paper studies how to catch duplicates, mismatches and conflicts in the same process. We adopt a class of entity enhancing rules that embed machine learning predicates, unify entity resolution and conflict resolution, and are collectively defined across multiple relations. We detect discrepancies as violations of such rules. We establish the complexity of discrepancy detection and incremental detection problems with the rules; they are both NP-complete and W[1]-hard. To cope with the intractability and scale with large datasets, we develop parallel algorithms and parallel incremental algorithms for discrepancy detection. We show that both algorithms are parallelly scalable, i.e. , they guarantee to reduce runtime when more processors are used. Moreover, the parallel incremental algorithm is relatively bounded. The complexity bounds and algorithms carry over to denial constraints, a special case of the entity enhancing rules. Using real-life and synthetic datasets, we experimentally verify the effectiveness, scalability and efficiency of the algorithms.
Automatic Detection of Fake News

Following the American presidential election in 2016, the terms”fake news” was popularized and has since been a common term in the public vocabulary While quite recently popularized, fake news is a phenomenon that is as old as news itself and is most commonly defined as purposeful disinformation used to untrue information or skewed reporting intended to push a certain narrative In recent years, fake news has seen frequently in attempts to influence elections or by organized crime organizations in various efforts to make money, not least drawing from the ongoing CoVid-19 pandemic We argue that the phenomenon must be researched from technical as well as from social aspects, since it involved using technical tools to spread information targeted humans In this paper, we identify key methods for automatic fake news detection in order to lay the foundation for end-user support system designed to help users identify and avoid fake news © 2020 Copyright for this paper by its authors
Delayed Rewards in the Context of Reinforcement Learning based Recommender Systems

We present a Reinforcement Learning (RL) based approach to implement Recommender systems. The results are based on a real-life Wellness app that is able to provide personalized health related content to users in an interactive fashion. Unfortunately, current recommender systems are unable to adapt to continuously evolving features, e.g. user sentiment, and scenarios where the RL reward needs to be computed based on multiple and unreliable feed-back channels (e.g., sensors, wearables). To overcome this, we pro-pose three constructs: (i) weighted feedback channels, (ii) delayed rewards, and (iii) rewards boosting, which we believe are essential for RL to be used in Recommender Systems. Finally, we also provide some implementation details on how the Wellness App based on Azure Personalizer was extended to accommodate the above RL constructs.
The Nectar of Missing Position Prediction for Story Completion

The story creation by providing automatic support to writers is a challenging and crucial task. Recently, in the ﬁeld of story generation and understanding, story completion has been proposed as an approach of generating missing parts of an incomplete story. Despite the usefulness of this approach in providing creative support, its applicability is limited. This limitation is owing to the prior knowledge requirement to the user regarding the missing part of the story. To overcome this limitation, we proposed a novel approach called “Missing Position Prediction” [MYMH20]. It is necessary to predict the position of the missing part in an incomplete story. Through our study, we found that the estimation accuracy when the ﬁrst or last sentence of a short story was missing was higher than when the middle sentences were missing. In this study, we further analyze this result using sentence similarity and part-of-speech tagging.
Quantitative Analysis of Passives with Agent Phrase Based on Multilingual Parallel Data

In this paper I discuss the advantages of using parallel data in linguistic research and demonstrate preliminary results of the study devoted to passives with agent phrase. For the study I used a parallel corpus of texts in nine European languages, the data set contained 983 fully aligned translation units. In my experiment I aimed to check whether the distribution of passives with agent phrase and related constructions used in translations depends on the semantic role of the participant that corresponds to the oblique argument of the passive.
Deep sequential model for anchor recommendation on live streaming platforms

Live streaming has grown rapidly in recent years, attracting increasingly more participation. As the number of online anchors is large, it is difficult for viewers to find the anchors they are interested in. Therefore, a personalized recommendation system is important for live streaming platforms. On live streaming platforms, the viewer’s and anchor’s preferences are dynamically changing over time. How to capture the user’s preference change is extensively studied in the literature, but how to model the viewer’s and anchor’s preference changes and how to learn their representations based on their preference matching are less studied. Taking these issues into consideration, in this paper, we propose a deep sequential model for live streaming recommendation. We develop a component named the multi-head related-unit in the model to capture the preference matching between anchor and viewer and extract related features for their representations. To evaluate the performance of our proposed model, we conduct experiments on real datasets, and the results show that our proposed model outperforms state-of-the-art recommendation models.
From Legal Contracts to Formal Specifications: A Progress Report

Smart contracts are software systems that partially automate, monitor and control the execution of legal contracts. The requirements of such systems consist of a formal specification of the legal contract whose execution is to be monitored and controlled. Legal contracts are always available as text expressed in natural language. We have been working on the translation of such text documents into formal specifications. Our translation process consists of four steps that (a) Semantic annotation of text identifying obligations, powers, contracting parties and assets, (b) Identification of relationships among the concepts identified in (a), (c) Generation of a domain model for terms used in the contract, as well as identification of parameters and local variables for the contract, (d) Generation of formal expressions that formalize the constituents of obligations and powers. This paper reports on the status of the project and the results that have been achieved.
Latent Chords: Generative Piano Chord Synthesis with Variational Autoencoders

Advances in the latest years on neural generative models such as GANs and VAEs have unveiled a great potential for creative applications supported by artificial intelligence methods. The most known applications have occurred in areas such as image synthesis for face generation as well as in natural language generation. In terms of tools for music composition, several systems have been released in the latest years, but there is still space for improving the possibilities of music co-creation with neural generative tools. In this context, we introduce Latent Chords , a system based on a Variational Autoencoder architecture which learns a latent space by reconstructing piano chords. We provide details of the neural architecture, the training process and we also show how Latent Chords can be used for a controllable exploration of chord sounds as well as to generate new chords by manipulating the latent representation. We make our training dataset, code and sound examples open and available at https://github.com/CreativAI-UC/TimbreNet
Cococo: AI-Steering Tools for Music Novices Co-Creating with Generative Models

In this work 1 , we investigate how novices co-create music with a deep generative model, and what types of interactive controls are important for an effective co-creation experience. Through a needfinding study, we found that generative AI can overwhelm novices when the AI generates too much content, and can make it hard to express creative goals when outputs appear to be random. To better match co-creation needs, we built Cococo, a music editor web interface that adds interactive capabilities via a set of AI-steering tools. These tools restrict content generation to particular voices and time measures, and help to constrain non-deterministic output to specific high-level directions. We found that the tools helped users increase their control, self-efficacy, and creative ownership, and we describe how the tools affected novices’ strategies for composing and managing their interaction with AI.
A Vision of Understanding the Users' View on Software

Requirements Engineering is focused on eliciting, specifying and validating what customers and potential users say about the software they need. However, we have only little insight how users talk about software; this insight comes from short utterances in user forums and app stores. How do they describe the features of existing software? What are the terms and concepts they use? What do they have in mind and associate with the software? Is it the user interface, underlying functionality, or domain issues? We stipulate that an improved understanding of the user language will also improve the communication between users and developers about new features. Therefore, we propose to study comprehensive user utterances, and to define the so-called user view language. It comprises the concepts and relationships with which users describe their view of software. In this paper we describe our vision of the user view language. Then, we describe planned studies on user utterances and the challenges we see in investigating these utterances with natural language processing techniques. Our data provides an interesting testbed for such techniques and their combination.
Case study: How Well Can IBM's "Requirements Quality Assistant" Review Automotive Requirements?

In the last years natural language processing evolved immensely. Tools based on that technology, like e.g., the Requirements Quality Assistant (RQA) by IBM or QVscribe by QRA claim to improve the requirements process for natural language requirements. In this paper we evaluate whether RQA supports a requirements engineer at Bosch in evaluating the quality of automotive requirements. In our case study, we come to the conclusion, that pure syntactical checks, based on the INCOSE rules of writing requirements, are not sufficient to address the needs of Bosch. We think that tool vendors should integrate semantic checks to their syntactical ones to further increase the practical use of their tools.
Efficient Size-Bounded Community Search over Large Networks

The problem of community search, which aims to find a cohesive subgraph containing user-given query vertices, has been extensively studied recently. Most of the existing studies mainly focus on the cohesiveness of the returned community, while ignoring the size of the community, and may yield communities of very large sizes. However, many applications naturally require that the number of vertices/members in a community should fall within a certain range. In this paper, we design exact algorithms for the general size-bounded community search problem that aims to find a subgraph with the largest min-degree among all connected subgraphs that contain the query vertex q and have at least l and at most h vertices, where q, l, h are specified by the query. As the problem is NP-hard, we propose a branch-reduce-and-bound algorithm SC-BRB by developing nontrivial reducing techniques, upper bounding techniques, and branching techniques. Experiments on large real graphs show that SC-BRB on average increases the minimum degree of the community returned by the state-of-the-art heuristic algorithm GreedyF by a factor of 2.41 and increases the edge density by a factor of 2.2. In addition, SC-BRB is several orders of magnitude faster than a baseline approach, and all of our proposed techniques contribute to the efficiency of SC-BRB.
Narrative Trends of COVID-19 Misinformation

The COVID-19 crisis has seen the rise of many harmful online narratives These narratives have seeped into the real world and pose tangible health risks For this reason, we have leveraged existing techniques and developed tools to further our understanding of online misinformation and its dynamics This provides policy makers with more tools to sift through otherwise impossibly large data sets To ensure this, we worked closely with the Arkansas Attorney General Copyright © by the paper's authors
Predicting Respiratory Failure in Patients with COVID-19 pneumonia: a case study from Northern Italy

The Covid-19 crisis caught health care services around the world by surprise, putting unprecedented pressure on Intensive Care Units (ICU) To help clinical staff to manage the limited ICU capacity, we have developed a Machine Learning model to estimate the probability that a patient admitted to hospital with COVID-19 symptoms would develop severe respiratory failure and require Intensive Care within 48 hours of admission The model was trained on an initial cohort of 198 patients admitted to the Infectious Disease ward of Modena University Hospital, in Italy, at the peak of the epidemic, and subsequently refined as more patients were admitted Using the Light- GBM Decision Tree ensemble approach, we were able to achieve good accuracy (AUC = 0 84) despite a high rate of missing values Furthermore, we have been able to provide clinicians with explanations in the form of personalised ranked lists of features for each prediction, using only 20 out of more than 90 variables, using Shapley values to describe the importance of each feature Copyright © 2020 for this paper by its authors
Adolescent Threats and Violence: An Intriguing Difference

Relatively little is known about young people who engage in targeted acts of violence. In this study, we explored a sample of young people referred to a youth forensic mental health service for a risk assessment of general violence risk, targeted violence risk, or both general and targeted violence risk. Exploratory comparisons were made across mental health issues and criminogenic risk factors, and results indicated that young people referred for a threat-only assessment were around 20 times more likely to have a depressive mood disorder than young people referred for a violence-only assessment. Furthermore, young people referred for a threat assessment had lower rates of physical abuse, family mental health issues, and prior offending. These results indicate that young people referred for an assessment of targeted violence risk may be distinguished from young people referred for general violence risk, which has important implications for their assessment and treatment.
A Novel Rotor Topology for High-Performance Fractional Slot Concentrated Winding Interior Permanent Magnet Machine

This article presents a finite-element-based, multiobjective design optimization study of the fractional-slot, concentrated wound, permanent magnet synchronous machine (FSCW PMSM). Design objectives included maximization of efficiency, minimization of cost and low ripple without sacrificing torque density and wide constant power speed range. A large-scale optimization study revealed that while a V-type rotor provides high torque density, a spoke-type rotor has the benefit of low torque ripple. Quest for a design that can combine the goodness of both V- and spoke type rotors for an FSCW stator has led to a novel interior permanent magnet rotor topology referred here as Y-type. The goals of achieving maximum efficiency, minimum cost and wide CPSR were also accomplished in the proposed Y-type FSCW IPMSM. For experimental verification purpose, three fully optimized rotors - V-, spoke- and Y-type were constructed for a 12-slot/10-pole FSCW stator. Extensive experimental tests were conducted on three machines for a detailed comparison study. It will be shown that the proposed Y-type FSCW IPMSM outperforms both V and spoke-type configurations. A scaled-up version of the Y-type FSCW IPMSM shown to have satisfied many of the Freeedomcar 2020 targets, which is promising for application in electric vehicles.
An Analytical Method for Parameter Extraction in Oxide Semiconductor Field-Effect Transistors

This article presents an analytical method for parameter extraction in oxide semiconductor field-effect transistors (OS FETs), including threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula>), effective channel length (<inline-formula> <tex-math notation="LaTeX">${L}_{\text {eff}}$ </tex-math></inline-formula>), source–drain series resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{\text {SD}}$ </tex-math></inline-formula>), and intrinsic effective mobility (<inline-formula> <tex-math notation="LaTeX">$\mu _{\text {int}}$ </tex-math></inline-formula>). An analytical expression of effective mobility (<inline-formula> <tex-math notation="LaTeX">$\mu _{\text {eff}}$ </tex-math></inline-formula>) is implemented with the consideration of percolation conduction and <inline-formula> <tex-math notation="LaTeX">${R}_{\text {SD}}$ </tex-math></inline-formula> effects. This method relies on the concept of <inline-formula> <tex-math notation="LaTeX">${Y}$ </tex-math></inline-formula>-function method and the channel length dependent measurement. The extraction results are compared to those from the combination of transfer length method (TLM). It has been shown that a better accuracy of the extracted parameters is achieved with the proposed method. Furthermore, transfer characteristics are resimulated based on the extracted parameters, in which a good match between measurement and simulation is obtained.
Multi-Agent Reinforcement Learning Based Channel Access Scheme for Underwater Optical Wireless Communication Networks

In this paper, we investigate the channel access problem in underwater optical wireless communication (UOWC) networks, wherein multiple transmitters randomly select receivers to send data. The objective is to find a joint multi-transmitter strategy that maximizes the network throughput while guaranteeing the reliability of the data transmission. Considering that the underwater channel conditions are dynamically changing, we propose a multi-agent Deep-Q network (DQN) algorithm based on the reinforcement learning. In this design, the transmitters, each acting as an agent, simultaneously interact with the communication environment. These agents receive observations and evaluate the reward, then learn to choose a receiver using the gained experiences. Simulation results reveal the effectiveness of the proposed scheme, and demonstrate that the multiple agents can learn to generate appropriate access strategies to improve the total transmission capacity while retaining the reliability performances.
Influences of PM Number and Shape of Spoke Array PM Flux Reversal Machines

Spoke array permanent magnet (PM) flux reversal machines (SAFRPMMs), which are suitable for low speed and high torque applications, are able to enhance torque density due to the flux focusing effect. In this paper, what influence both the number of PM per slot and the PM shape have on their electromagnetic performance are investigated. The results show that both the PM number and the PM shape have significant effects on the performance of SAFRPMMs, and proper selections are critical in designing high performance machines. When the PM number per slot is 2, the SAFRPMM exhibits the best electromagnetic performances, i.e., the highest torque, power, efficiency, and power factor. Meanwhile, by properly designing the PM shape, the torque can be further improved by around 11%. A prototype is made and tested to validate the analyses.