Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Lingual Argumentative Relation Identification: from English to Portuguese

Argument mining aims to detect and identify argument structures from textual resources. In this paper, we aim to address the task of argumentative relation identification, a subtask of argument mining, for which several approaches have been recently proposed in a monolingual setting. To overcome the lack of annotated resources in less-resourced languages, we present the first attempt to address this subtask in a cross-lingual setting. We compare two standard strategies for cross-language learning, namely: projection and direct-transfer. Experimental results show that by using unsupervised language adaptation the proposed approaches perform at a competitive level when compared with fully-supervised in-language learning settings.
Aurum: A Data Discovery System

Organizations face a data discovery problem when their analysts spend more time looking for relevant data than analyzing it. This problem has become commonplace in modern organizations as: i) data is stored across multiple storage systems, from databases to data lakes, to the cloud; ii) data scientists do not operate within the limits of well-defined schemas or a small number of data sources—instead, to answer complex questions they must access data spread across thousands of data sources. To address this problem, we capture relationships between datasets in an enterprise knowledge graph (EKG), which helps users to navigate among disparate sources. The contribution of this paper is AURUM, a system to build, maintain and query the EKG. To build the EKG, we introduce a Two-step process which scales to large datasets and requires only one-pass over the data, avoiding overloading the source systems. To maintain the EKG without re-reading all data every time, we introduce a resource-efficient sampling signature (RESS) method which works by only using a small sample of the data. Finally, to query the EKG, we introduce a collection of composable primitives, thus allowing users to define many different types of discovery queries. We describe our experience using AURUM in three corporate scenarios and do a performance evaluation of each component.
On the Impact of Meteorological Conditions on the Initiation of Upward Lightning Flashes from Tall Structures

Upward lightning flashes that originate from tall grounded structures have been classified as either 'self-triggered' or 'other-triggered' based on preceding lightning events in their vicinity, i.e., strokes or pulses from cloud-to-ground (CG) or intracloud (IC) discharges occurring around the tower. A total of 165 upward flashes recorded between March 2015 to July 2016 at the Säntis Tower in the eastern Swiss Alps are analyzed considering the related meteorological conditions. The results are compared to similar previous studies to reveal some general ideas on how meteorological conditions influence the initiation of self- and other- triggered upward flashes.
MSMO: Multimodal Summarization with Multimodal Output

Multimodal summarization has drawn much attention due to the rapid growth of multimedia data. The output of the current multimodal summarization systems is usually represented in texts. However, we have found through experiments that multimodal output can significantly improve user satisfaction for informativeness of summaries. In this paper, we propose a novel task, multimodal summarization with multimodal output (MSMO). To handle this task, we first collect a large-scale dataset for MSMO research. We then propose a multimodal attention model to jointly generate text and select the most relevant image from the multimodal input. Finally, to evaluate multimodal outputs, we construct a novel multimodal automatic evaluation (MMAE) method which considers both intra-modality salience and inter-modality relevance. The experimental results show the effectiveness of MMAE.
Five Shades of Untruth: Finer-Grained Classification of Fake News

Prior work on algorithmic truth assessment on unreliable content, has mostly pursued binary classifiers - factual vs. fake - and disregarded the finer shades of untruth. On the other hand, manual analysis of questionable content has proposed a more fine-grained classification: distinguishing between hoaxes, irony and propaganda, or the six-way rating by the PolitiFact community. In this paper, we present a principled approach to capture these finer shades in automatically assessing and classifying news articles and claims. We systematically explore a variety of signals from both news and social media, and give an analysis of the underlying features.
Sounds of Silence Breakers: Exploring Sexual Violence on Twitter

Gender-based-violence is a serious concern in recent times. Due to the social stigma attached to these assaults, victims rarely come forward. Implementing policy measures to prevent sexual violence get constrained due to lack of crime statistics. However, the recent outcry on the Twitter platform allows us to address this concern. Sexual assaults occur at workplaces, public places, educational institutes and also at home. Policy level approaches and awareness campaign for these assaults would not be similar. So, we want to identify the risk factor associated with these sexual assaults. We extracted 0.7 million tweets during the #MeToo social media movement. Next, we employ deep learning techniques to classify these sexual violences. We observe that sexual assaults by a family member at own home is a more serious concern than harassment by a stranger at public places. This study reveals assaults by a known person are more prevalent than assaults by unknown strangers.
Hackers Hedging Bets: A Cross-Community Analysis of Three Online Hacking Forums

Online hacking forums have been used as communities where users, possibly cybercriminals, can learn and exchange knowledge, and purchase the necessary tools and information to commit various offences such as hacking, credit card/identity fraud, money laundering, and even cyberattacks on infrastructure. Monitoring these forums and identifying key players are important when investigating emergent threats and developing efficient disruption strategies. Literature shows the lack of studies regarding users' cross-forum activity. This paper presents an analysis of forum users' cross-posting in three hacking forums including user overlap among different hacking communities/forums and identify user roles based on the type of posts and their frequencies. This allows us to assess the impact of users and forums in terms of cybercrime victimization.
5G MIMO Data for Machine Learning: Application to Beam-Selection Using Deep Learning

The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies. Deep learning has proven successful in ML tasks such as speech processing and computational vision, with a performance that scales with the amount of available data. The lack of large datasets inhibits the flourish of deep learning applications in wireless communications. This paper presents a methodology that combines a vehicle traffic simulator with a ray-tracing simulator, to generate channel realizations representing 5G scenarios with mobility of both transceivers and objects. The paper then describes a specific dataset for investigating beam-selection techniques on vehicle-to-infrastructure using millimeter waves. Experiments using deep learning in classification, regression and reinforcement learning problems illustrate the use of datasets generated with the proposed methodology.
Disparities in the Diagnosis of Hypertrophic Obstructive Cardiomyopathy: A Narrative Review of Current Literature

Hypertrophic obstructive cardiomyopathy (HOCM) is a disorder of abnormal thickening of the myocardium that affects 0.2% of the population. HOCM is a frequently implicated cause of sudden cardiac death (SCD) in young athletes. In this manner, this condition has the capacity for tremendous emotional, social, financial, and medical burdens for families and communities across the country. Multiple factors including genetics and hormonal elements are believed to play a role in the development of this cardiomyopathy. HOCM is an autosomal dominant trait with variable expressivity. It is associated with several genetic changes in the myosin heavy chain genes. Current treatment includes optimization of cardiac risk factors and medical management with beta-adrenergic blockade with definitive treatment of surgical intervention to reduce cardiac workload. The demographic most likely to be diagnosed with HOCM based upon clinical registry data is Caucasian boys and men. However, a growing body of data supports increased prevalence in African American populations and percentages equal to, if not higher than, Caucasian males in Hispanic populations. Similarly, males of African American ethnicity that participate in basketball are the most likely demographic to be affected by HOCM based on the data available from the National Collegiate Athletic Association (NCAA). Further, though rates of diagnosis may be up to 1.5 times higher in males than in females, an increasing number of studies demonstrate an increased prevalence of HOCM in females, often presenting with worse symptoms and an increased incidence of disease progression. Similarly, data suggest that age of diagnosis is associated with various prognostic factors including annual mortality. In addition, consideration of the social determinants of health undoubtedly impacts the rate of diagnosis, access to care, and HOCM-related complications in underserved populations. Effective screening including auscultation and electrocardiography (EKG) with confirmatory echocardiography in these communities supports equitable surveillance and management of HOCM.
Querying Cohesive Subgraphs by Keywords

Keyword search problem has been widely studied to retrieve related substructures from graphs for a keyword set. However, existing well-studied approaches aim at finding compact trees/subgraphs containing the keywords, and ignore a critical measure, density, to reflect how strongly and stablely the keyword nodes are connected in the substructure. In this paper, we study the problem of finding a cohesive subgraph containing the query keywords based on the k-truss model, and formulate it as minimal dense truss search problem, i.e., finding minimal subgraph with maximum trussness covering the keywords. We first propose an efficient algorithm to find the dense truss with the maximum trussness containing keywords based on a novel hybrid KT-Index (Keyword-Truss Index). Then, we develop a novel refinement approach to extract the minimal dense truss based on the anti-monotonicity property of k-truss. Experimental studies on real datasets show the outperformance of our method.
Exacting Eccentricity for Small-World Networks

This paper studies the efficiency issue on computing the exact eccentricity-distribution of a small-world network. Eccentricity-distribution reflects the importance of each node in a graph, which is beneficial for graph analysis. Moreover, it is key to computing two fundamental graph characters: diameter and radius. Existing eccentricity computation algorithms, however, are either inefficient in handling large-scale networks emerging nowadays in practice or approximate algorithms that are inappropriate to small-world networks. We propose an efficient approach for exact eccentricity computation. Our approach is based on a plethora of insights on the bottleneck of the existing algorithms — one-node eccentricity computation and the upper/lower bounds update. Extensive experiments demonstrate that our approach outperforms the state-of-the-art up to three orders of magnitude on real large small-world networks.
Numerical Implementation of Local Degradation Profiles in Soft Magnetic Materials

This paper focuses on the numerical implementation of a continuous model for local degradation in soft magnetic materials. Cutting and punching processes during the production of electrical machines influence the magnetic properties of magnetic materials. A simplified but effective model for local degradation of magnetic properties is described in detail as well as the procedure to obtain the parameters from the measurements. The proposed model is then implemented in the source code of a numerical simulation tool where the material characteristics depending on the distance from the cut edge are assigned directly at the mesh level. The proposed method allows avoiding the drawbacks of multilayer approaches like model complexity and mesh size. A comparison between simulation results and measurements of lamination rings is presented and discussed.
Multi-Objective Design of Compact Microwave Components with Data-Driven Surrogates and Pareto Front Decomposition

The paper discusses low-cost multi-objective optimization of compact microwave components using variablefidelity EM simulation models and data-driven surrogates. Our approach builds upon a recently reported method where the initial approximation of the Pareto set is obtained by optimizing the kriging surrogate constructed from sampled data of the coarsediscretization EM model of the structure at hand, with selected designs further refined to obtain the high-fidelity Pareto set. The drawback of the method is a large number of training data samples required to set up the surrogate. Here, considerable savings concerning the training data set size are achieved by Pareto front decomposition based on auxiliary points identified along the front and setting up the kriging models in the corresponding subdomains. The key factor is that the total volume of the sub-domains is considerably smaller than the volume of the original domain. Our considerations are illustrated using a compact rat-race coupler with design optimization cost savings of 29- and 30-percent for two and three sub-domains, respectively.
High-Power, High-SFDR, Heterogeneously Integrated III-V on Si MZI Modulators

Heterogeneously integrated III-V/Si MZI modulators measured at high optical power levels demonstrate applicability for high SFDR analog fiber-optic links without an optical amplifier. Optical power up to 100 mW injected into the modulator shows no degradation in linearity, demonstrating 110 dB.Hz2/3 typical SFDR.
On random primitive sets, directable NDFAs and the generation of slowly synchronizing DFAs

We tackle the problem of the randomized generation of slowly synchronizing deterministic automata (DFAs) by generating random primitive sets of matrices. We show that when the randomized procedure is too simple the exponent of the generated sets is O(n log n) with high probability, thus the procedure fails to return DFAs with large reset threshold. We extend this result to random nondeterministic automata (NDFAs) by showing, in particular, that a uniformly sampled NDFA has both a 2-directing word and a 3-directing word of length O(n log n) with high probability. We then present a more involved randomized algorithm that manages to generate DFAs with large reset threshold and we finally leverage this finding for exhibiting new families of DFAs with reset threshold of order $ \Omega(n^2/4) $.
Optimized Flux-Weakening Control of Induction Motor for Torque Enhancement in Voltage Extension Region

It is known that higher torque capability and less torque ripple cannot be obtained at the same time for flux-weakening control of induction motor in voltage extension region (outside the inscribed circle but within the hexagon). Therefore, a tradeoff strategy for maximum toque and suppressed torque ripple is necessary. Meanwhile, in many previous research studies, VCFS (voltage closed-loop flux-weakening scheme) based voltage extension methods rely on the reconstructed voltage, which means the control performance is affected by the accuracy of reconstruction algorithm and the stability of DC-Link voltage. In this paper, an optimized flux-weakening controller is proposed with the following features: a proper balance between torque capability and torque ripple, an enhanced DC-Link overvoltage rejection and a better torque performance for load change conditions. Simulation and experiment results verify the validity of the proposed scheme.
On The Ignition, Propagation and Termination Of The Neuronal Bursting Activity During Ictogenesis In Epileptic Patients

Epilepsy creates a persistent increase in the probability of spontaneous seizures. An ictal episode evolves due to acute disturbance of the fine-tuned balance between excitatory vs. inhibitory inputs within a neural network in favor of excitation. The current literature that proposes the activity-dependent disinhibition as a valid mechanism of chronic epilepsy, does not provide clues on why this mechanism emerges only in epileptic patients and how the vicious circle resulting of an activity-dependent disinhibition in over-active ictogenic network would end. A new model, which presents chronic epilepsy as a disease of faulty architecture of the neural circuit, is discussed. Wherein; variable genetic or acquired predisposing factors drive abnormalities in the construction of multiple neural circuits resulting in an activity-dependent positive feedback excitatory loops which transform normal neural circuits into ictal foci. Such new mechanism, for igniting an activity-dependent unstable excitation with subsequent relatively stable disinhibition, leads to an ictal escape rhythm. The propagation of such bursting activity occurs either electrochemically via synaptic communication to remote susceptible circuits, or chemically via a trigger wave which recruits the non-connected proximal neurons. Termination occurs abruptly when the inhibitory interneurons functionally recover and reimpose their inhibitory effect on the ictogenic circuit to transform the escape rhythm into a normal, under-control output. The proposed model elucidates various enigmatic features of the disease; and illustrates both the end-result ictogenic mechanism arising from the wide variety of etiologies of human spontaneous and acquired epilepsy, and the timing of episodic transitions from normal activity to seizures.
What Banking and Phone Data Tell us about the Socioeconomic Groups and Their Consumption Patterns?

This paper makes use of a large dataset of anonymized banking transactions and phone calls to classify individuals into socioeconomic groups (SEGs) and social networks, determine their consumption patterns, and compare the latter with equivalent information available from household surveys. The results obtained demonstrate that classification into SEGs by aggregated bank income provides a robust breakdown of the population that is validated by a social network analysis of the phone data. In addition, the spending profile obtained for each SEG shows that individuals behave similarly according to their income and their spending can be accurately categorized. Furthermore, the consumption patterns obtained from this novel approach can be contrasted directly with those obtained from national household surveys, potentially overcoming some of the limitations of traditional approaches in terms of coverage and inclusiveness. The work presented here shows the feasibility and capabilities of Big Data sources and tools to understand the consumption behavior of the population.
Enhancing Activity Recognition of Self-Localized Robot Through Depth Camera and Wearable Sensors

Robots will become part of our everyday life as helpers and companions, sharing the environment with us. Thus, robots should become social and able to naturally interact with the users. Recognizing human activities and behaviors will enhance the capabilities of the robot to plan an appropriate action and tailor the approach according to what the user is doing. Therefore, this paper addresses the problem of providing mobile robots with the ability to recognize common daily activities. The fusion of heterogeneous data gathered by multiple sensing strategies, namely wearable inertial sensors, depth camera, and location features, is proposed to improve the recognition of human activity. In particular, the proposed work aims to recognize 10 activities using data from a depth camera mounted on a mobile robot able to self-localize in the environment and from customized sensors worn on the hand. Twenty users were asked to perform the selected activities in two different relative positions between them and the robot while the robot was moving. The analysis was carried out considering different combinations of sensors to evaluate how the fusion of the different technologies improves the recognition abilities. The results show an improvement of 13% in the F-measure when different sensors are considered with respect to the use of the sensors of the robot. In particular, the system is able to recognize not only the performed activity, but also the relative position, enhancing the robot capabilities to interact with the users.
3.3kV All-SiC Module for Electric Distribution Equipment

We developed 3.3kV All-SiC power module on which SiC-MOSFET and SiC-SBD were applied for electric distribution equipment. This power module has characteristic structure of pin-connection and resin-molded package. These features lead to long-term reliability. In addition, low static loss and low switching loss is achieved by using SiC devices. Total switching loss of All-SiC module in the inverter driving is 64% lower than a module which uses Si-IGBT and Si-FWD.
Static Flow Analysis of the Action Language for Foundational UML

One of the major advantages of Model-Driven Engineering is the possibility to early assess crucial system properties, in order to identify issues that are easier and cheaper to solve at design level than at code level. An example of such a property is the timing behaviour of a real-time application, where an early indication that the timing constraints might not be met can help avoiding costly re-designs late in the development process. In this paper we provide a model-driven round-trip transformation chain for (i) applying a flow analysis to executable models described in terms of the Action Language for Foundational UML (AU), and (ii) back-propagating analysis results to Alf models for further investigation. Alf models are transformed into the input format for an analysis tool that identifies flow facts, i.e., information about loop bounds and infeasible paths in the model. Flow facts can be used, for instance, when estimating the worst-case execution time for the analysed model. We evaluated the approach through a set of benchmark models of various size and complexity.
A Lumped Thermal Model Including Thermal Coupling Effects and Boundary Conditions for Capacitor Banks

Capacitors are widely used in power electronic converters to buffer the pulsation power, filter the harmonics and support voltage for stable operation. For these applications where single capacitor can not fulfill the voltage rating or capacitance requirements, capacitor bank is always used as the energy buffer by connecting several capacitors in parallel for larger capacitance, or in series for higher voltage capability. The existing design considerations for the capacitor bank are in terms of voltage ripple, electromagnetic-interference, power loss, weight and volume. With more stringent constrains on volume for high power density applications, reliability as well as temperature of capacitor banks should be considered into the design phase. In order to estimate the temperature of capacitor bank with sufficient accuracy and computational efficiency, this paper proposes a lumped thermal model for a capacitor bank by taking into account the boundary conditions (e.g., ambient temperature, power loss for each cell) and the thermal coupling effect among the capacitor cells. Considering the variable boundary with different loading conditions in FEM simulation, fast thermal resistance extraction method is investigated to obtain the self-heating and coupling thermal impedance. Critical uneven temperature distribution among capacitors in a bank may be observed. A case study of an electrolytic capacitor bank is presented to verify the accuracy of the proposed method.