Miracle-dz/UniFAR-FTUs huggingface dataset:
Investing Data with Untrusted Parties using HE

Data such as an individual’s income, favorite sports team, typical commute route, vehicle maintenance history, medical records, etc. are typically not useful for making large-scale decisions such as where to build a new hospital, identifying which roads are in need of upkeep, and the like. However, aggregates of of these data across hundreds of individuals are useful to governments and to companies. Data cooperatives/unions offer a place for individuals to store their data and a service of data aggregation and interpretation to governments, non-profit organizations, and businesses while maintaining individuals’ anonymity. We propose the use of anonymization techniques coupled with graph algorithms over homomorphically encrypted (HE) graphs as a basis of analysis for this accumulated data. We believe this approach ensures individuals’ privacy and anonymity while preserving the usefulness of the plaintext data.
GRICE: A Grammar-based Dataset for Recovering Implicature and Conversational rEasoning

Understanding what we genuinely mean instead of what we literally say in conversations is challenging for both humans and machines; yet, this direction is mostly left untouched in modern open-ended dialogue systems. To ﬁll in this gap, we present a grammar-based dialogue dataset, G RICE , designed to bring impli-cature into pragmatic reasoning in the context of conversations. Our design of G RICE also incorporates other essential aspects of modern dialogue modeling ( e.g ., coreference). The entire dataset is systematically generated using a hierarchical grammar model, such that each dialogue context has intricate implicatures and is temporally consistent. We further present two tasks, the implicature recovery task followed by the pragmatic reasoning task in conversation, to evaluate the model’s reasoning capability. In experiments, we adopt baselines that claimed to have pragmatics reasoning capability; the results show a signiﬁcant performance gap between baseline methods and human performance. After integrating a simple module that explicitly reasons about implicature, the model shows an overall performance boost in conversational reasoning. These observations demonstrate the signiﬁcance of implicature recovery for open-ended dialogue reasoning and call for future research in conversational impli-cature and conversational reasoning.
Gamified Hands-on-Training in Business Information Systems: An Educational Design Experiment

The Covid-19-pandemic confronted lecturers worldwide with the sudden necessity to develop concepts suitable for distance education. Students' motivation became a crucial aspect of prolonged e-learning situations. This paper reports on an educational design experiment to change a hands-on-training on SAP ERP-Systems into a gamified self-paced e-learning environment. This training accompanies a lecture on Business Information Systems for first-semester students. Allowing mistakes to happen kept the attention high and made achievements within the learning environment more rewarding. An anonymous online survey confirmed the relevance of self-paced learning for learning efficiency. Even though a positive impact of “mistakes” on learning efficiency was not confirmed, comments and statements of the participants pointed towards an effect on learning, worth further research. We contribute to the body of knowledge by providing lessons learned on gamified self-paced e-learning within university courses. It could be verified that business process-related hands-on-training within an ERP-System could be implemented in a gamified self-paced e-learning environment without compromises regarding scope or scale of the content. Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
Word Graph Guided Summarization for Radiology Findings

Radiology reports play a critical role in communicating medical findings to physicians. In each report, the impression section summarizes essential radiology findings. In clinical practice, writing impression is highly demanded yet time-consuming and prone to errors for radiologists. Therefore, automatic impression generation has emerged as an attractive research direction to facilitate such clinical practice. Existing studies mainly focused on introducing salient word information to the general text summarization framework to guide the selection of the key content in radiology findings. However, for this task, a model needs not only capture the important words in findings but also accurately describe their relations so as to generate high-quality impressions. In this paper, we propose a novel method for automatic impression generation, where a word graph is constructed from the findings to record the critical words and their relations, then a Word Graph guided Summarization model (WGSum) is designed to generate impressions with the help of the word graph. Experimental results on two datasets, OpenI and MIMIC-CXR, confirm the validity and effectiveness of our proposed approach, where the state-of-the-art results are achieved on both datasets. Further experiments are also conducted to analyze the impact of different graph designs to the performance of our method.
Attribute Relation Modeling for Pulmonary Nodule Malignancy Reasoning

Predicting the malignancy of pulmonary nodules found in chest CT images have become much more accurate due to powerful deep convolutional neural networks. However, attributes, such as lobulation, spiculation, and texture, as well as the correlations and dependencies among such attributes have rarely been exploited in deep learning-based algorithms albeit they are frequently used by human experts during nodule assessment. In this paper, we propose a hybrid machine learning framework consisting of two relation modeling modules: Attribute Graph Network and Bayesian Network, which effectively take advantage of attributes and the correlations and dependencies among them to improve the classification performance of pulmonary nodules. According to experiments on the LIDC−IDRI benchmark dataset, our method achieves an accuracy of 93.59%, which gains a 4.57% improvement over the 3D Dense-FPN baseline.
BatchMixup: Improving Training by Interpolating Hidden States of the Entire Mini-batch

Usually, we train a neural system on a sequence of mini-batches of labeled instances. Each mini-batch is composed of k samples, and each sample will learn a representation vector. M IXUP implicitly generates synthetic samples through linearly interpolating inputs and their corresponding labels of random sample pairs in the same mini-batch. This means that M IXUP only generates new points on the edges connecting every two original points in the representation space. We observed that the new points by the standard M IXUP cover pretty limited regions in the entire space of the mini-batch. In this work, we propose B ATCH M IXUP —improving the model learning by interpolating hidden states of the entire mini-batch. B ATCH M IXUP can generate new points scattered throughout the space corresponding to the mini-batch. In experiments, B ATCH M IXUP shows superior performance than competitive baselines in improving the performance of NLP tasks while using different ratios of training data.
Model Inversion for Impersonation in Behavioral Authentication Systems

A Behavioral Authentication (BA) system uses behavioral characteristics of a user that is stored in their behavioral profile, to verify their future identity claims. BA profiles are widely used as a second factor to strengthen password based authentication systems. A BA verification algorithm takes the claimed identity of the user together with their presented verification data, and by comparing the data with the profile of the claimed identity it decides to accept or reject the claim. An efficient and highly accurate verification algorithms can be constructed by training a Deep Neural Network (DNN) on the users’ profiles. The trained DNN classifies the presented verification data and if the classification matches the claimed identity, accepts the claim, else reject it. This is a very attractive approach because it removes the need to maintain the profile database that is security and privacy sensitive. In this paper we show that query access to the DNN verification algorithm allows an attacker to break security of the authentication system by constructing the profile of a user in the original training database and succeed in impersonation attack. We show how to construct an inverse classifier when the attacker has black-box access to the DNN’s output prediction vectors, truncated to a single component (highest probability value). We use a substitute classifier to approximate the unknown components of the prediction vectors, and use the recovered vectors to train the inverse classifier and construct the profile of a user in the database. We implemented our approach on two existing BA systems and achieved the average success probability of 29.89% and 45.0%, respectively. Our approach is general and can be used in other DNN
A Specific Language for Developing Business Process by Refinement based on BPMN 2.0

This paper deals with the development by refinement of BPMN(Business Process Model and Notations) models. Indeed, Business Process (BP) development based on step-wise refinement (i) facilitates the understanding of complex BP (ii) specifies all the BP semantics that the BPMN model has to describe by focusing on the smallest detail of the BP since details are added gradually to the model under development. Hence, we propose an approach assisting a business process developer to gradually build his/her BPMN model ensuring an automatic syntax and semantic property checking. The approach allows in one hand a BPMN syntax driven refinement based on a BPMN context-free grammar and in the other hand a formal verification of semantic properties. To be validated, the proposed approach is illustrated throughout the development of an on line flight booking BP.
Optimization-based Trajectory Prediction Enhanced with Goal Evaluation for Omnidirectional Mobile Robots

In this paper, an optimization-based trajectory prediction enhanced with goal evaluation for omnidirectional mobile robots is proposed. The proposed approach tries to predict the mobile platform’s trajectory based on its previous positions. A two-stage strategy is introduced. At the first stage, the likely goal of the robot in the scenario is evaluated based on an improved Bayesian framework, which also predicts the possible waypoints in a discrete roadmap based on Monte-Carlo sampling in the future. Then, based on the predicted waypoints, an optimization problem is formulated based on the complementary progress constraints, the system dynamics, and the model constraints. After solving the proposed optimization problem, a more reasonable predicted trajectory can be generated. At the end, an experimental scenario is set up, and it is verified with the experimental data, whether the trajectories can be predicted well.
Synthetic Call Detail Records Generator

Researchers often have to deal with the problem of data availability and privacy restrictions. This is why synthetic data generation is usually a solution for the deficiency of research data and a way to provide the researchers with the data fitting specific situations. This paper proposes a system of synthetic Call Detail Records (CDR) data generation based on a real-life dataset. Our approach uses statistical analysis to extract the users’ behaviour from the real-life dataset. The generator is tested by simulating several unique situations, which appear in the real world. Since this kind of situations can be hard to capture into the dataset, we believe that the proposed system could be useful in many telco research fields.
Cloud Key Management using Trusted Execution Environment

Cloud storage represents a primordial component in most information technology infrastructures. Using cloud instead of on-premise storage raises several security issues, especially when secret keys are stored on the cloud. In such a setting, a robust cloud key management system is a must. Using traditional key management systems (KMS) in the cloud suffers from performance and scalability limitations. This paper, proposes an efficient and secure cloud KMS based on Trusted Execution Environment, precisely Intel SGX. The suggested system (KMSGX), while being deployed on the cloud, is fully controlled by the end-user. Therefore, KMSGX allows running on-premise software key management securely on the cloud provider side, protecting the exchanged and stored data. The security properties of the suggested design have been formalized using the Applied Pi Calculus and proved with ProVerif. The experimental results have demonstrated the system’s high performance in terms of the upload and download durations and the limited overhead compared to the plain
Impact of Duplicating Small Training Data on GANs

Emoticons such as (^_^) are face-shaped symbol sequences that are used to express emotions in text. However, the number of emoticons is miniscule. To increase the number of emoticons, we created emoticons using SeqGANs, which are generative adversarial networks for generating sequences. However, the small number of emoticons means that few emoticons can be used as training data for SeqGANs. This is concerning because as SeqGANs underfit small training data, generating emoticons using SeqGANs is difficult. To address this problem, we duplicate the training data. We observed that emoticons can be generated when the duplication magnification is of an appropriate value. However, as a trade-off, it was also observed that SeqGANs overfit the training data, i.e., they produce emoticons that are exactly the same as the training data.
Biomedical Dataset Recommendation

Dataset search is a special application of information retrieval, which aims to help scientists with finding the datasets they want. Current dataset search engines are query-driven, which implies that the results are limited by the ability of the user to formulate the appropriate query. In this paper we aim to solve this limitation by framing dataset search as a recommendation task: given a dataset by the user, the search engine recommends similar datasets. We solve this dataset recommendation task using a similarity approach. We provide a simple benchmark task to evaluate different approaches for this dataset recommendation task. We also evaluate the recommendation task with several similarity approaches in the biomedical domain. We benchmark 8 different similarity metrics between datasets, including both ontology-based techniques and techniques from machine learning. Our results show that the task of recommending scientific datasets based on meta-data as it occurs in realistic dataset collections is a hard task. None of the ontology-based methods manage to perform well on this task, and are outscored by the majority of the machine-learning methods. Of these ML methods only one of the approaches performs reasonably well, and even then only reaches 70% accuracy.
Zero-shot Label-Aware Event Trigger and Argument Classification

Identifying events and mapping them to pre-defined event types has long been an important natural language processing problem. Most previous work has been heavily relying on labor-intensive and domain-specific annotations while ignoring the semantic meaning contained in the labels of the event types. As a result, the learned models cannot effectively generalize to new domains, where new event types could be introduced. In this paper, we propose an unsupervised event extraction pipeline, which first identifies events with available tools (e.g., SRL) and then automatically maps them to pre-defined event types with our proposed unsupervised classification model. Rather than relying on annotated data, our model matches the semantics of identified events with those of event type labels. Specifically, we leverage pre-trained language models to contextually represent pre-defined types for both event triggers and arguments. After we map identified events to the target types via representation similarity, we use the event ontology (e.g., argument type"Victim"can only appear as the argument of event type"Attack") as global constraints to regularize the prediction. The proposed approach is shown to be very effective when tested on the ACE-2005 dataset, which has 33 trigger and 22 argument types. Without using any annotation, we successfully map 83% of the triggers and 54% of the arguments to the correct types, almost doubling the performance of previous zero-shot approaches.
Business Model Innovation to Enhance the Efficiency of Freight Logistics in the Maritime Supply Chain through Blockchain-based Industry Platforms

The emerging platform economy is transforming the maritime freight logistics industry. In particular, blockchain-based industry platforms offer enormous potential for enhancing efficiency in the supply chain network. This paper draws on the principles of platform business modelling to develop a framework for global information technology companies to enable a transformation of their current service and software-oriented operations into a platform business model. A qualitative study was conducted integrating theoretical insights from relevant extant literature and empirical evidence based on semi-structured interviews with 15 experts from a global information technology company and the maritime industry. A multi-layered approach to data analysis allowed the identification of a set of interdependent generative causal factors that underlie the platform business model transformation. The findings point to the cross-sector partnership and governance as the dominant causal mechanisms driving the platform business model transformation. This led to the development of a new platform business model framework named the 4/9 Platform Business Model Canvas (4/9 PBM-C) which can be seen as a significant development in the current conceptualisation of blockchainbased industry platforms as a means of enhancing the efficiency of maritime freight logistics. The paper concludes with a consideration of the practical implications of the 4/9 PBM-C and its application to other
A Graph-based Approach at Passage Level to Investigate the Cohesiveness of Documents

Approaches involving the representation of documents as a series of passages have been used in the past to improve the performance of ad-hoc retrieval systems. In this paper, we represent the top returned passages as a graph with each passage corresponding to a vertex. We connected the vertices (passages) that belongs to the same document to form a graph. The underlying intuition behind this approach is to identify some measure of the cohesiveness of the documents. We introduce a graph-based approach at the passage level to calculate the cohesion score of each document. The scores for both relevant and non-relevant documents are compared, and we illustrate that the cohesion score differs for relevant and non-relevant. Moreover, we also re-ranked the documents by applying the cohesion score with a document similarity score to inspect its impact on the system’s performance.
TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation

Conventional autoregressive models have achieved great success in text generation but suffer from the exposure bias problem in that token sequences in the training and in the generation stages are mismatched. While generative adversarial networks (GANs) can remedy this problem, existing implementations of GANs directly on discrete outputs tend to be unstable and lack diversity. In this work, we propose TILGAN , a T ransformer-based I mplicit L atent GAN , which combines a Transformer autoencoder and GAN in the latent space with a novel design and distribution matching based on the Kullback-Leibler (KL) divergence. Speciﬁcally, to improve local and global coherence, we explicitly introduce a multi-scale discriminator to capture the semantic information at varying scales among the sequence of hidden representations encoded by Transformer. Moreover, the decoder is enhanced by an additional KL loss to be consistent with the latent-generator. Experimental re-sults on three benchmark datasets demonstrate the validity and effectiveness of our model, by obtaining signiﬁcant improvements and a better quality-diversity trade-off in automatic and human evaluation for both unconditional and conditional generation tasks. 1
Time and Processes: Towards Engineering Temporal Requirements

Processes are ubiquitous for modeling dynamic phenomena in many areas like business, production, health care, robotics etc. Many of these applications require to adequately deal with temporal aspects. Nevertheless, temporal aspects are not yet prominently treated in requirements engineering. Models for representing requirements need to express temporal properties of the context resp. the environment, which have to be taken into account for designing systems. And they need to express temporal conditions, which have to be satisfied or which represent properties of goals that should be reached. Models, therefore, contain constructs for durations, temporal constraints like allowed time between events, and deadlines. Furthermore, these models need a notion of correctness and we discuss different notions like satisfiability and controllability, and techniques, which can be employed to check these properties of these models at design time.
When Time Makes Sense: A Historically-Aware Approach to Targeted Sense Disambiguation

As languages evolve historically, making computational approaches sensitive to time can improve performance on speciﬁc tasks. In this work, we assess whether applying historical language models and time-aware meth-ods help with determining the correct sense of polysemous words. We outline the task of time-sensitive Targeted Sense Disambiguation (TSD), which aims to detect instances of a sense or set of related senses in historical and time-stamped texts, and address two main goals: 1) we scrutinize the effect of applying historical language models on the performance of several TSD methods and 2) we assess different disambiguation methods that take into account the year in which a text was produced. We train historical BERT models on a corpus of nineteenth-century English books and draw on the Oxford English Dictionary (and its His-torical Thesaurus) to create historically evolving sense representations. Our results show that using historical language models consistently improves performance whereas time-sensitive disambiguation helps especially with older documents.
A Formidable Ability: Detecting Adjectival Extremeness with DSMs

While distributional semantic models (DSMs) can successfully capture the similarity structure within a semantic domain, less is known about their ability to represent abstract semantic properties that hold across domains. Such properties can form the basis for abstract semantic classes that are a crucial aspect of human semantic knowledge. For example, the abstract class of extreme adjectives (such as brilliant and freezing ) spans a wide range of domains (here, INTELLIGENCE and TEMPER - ATURE ). Using a model that compares query items to an aggregate DSM representation of a set of extreme adjectives, we show that novel adjectives can be classiﬁed accurately, supporting the insight that a cross-domain property like extremeness can be captured in a word’s DSM representation. We then use the extreme-ness classiﬁer to model the emergence of inten-siﬁer meaning in adverbs, demonstrating, in a separate task, the effectiveness of detecting this abstract semantic property.