Miracle-dz/UniFAR-FTUs huggingface dataset:
Trust Modelling and Management System for a Hyper-connected World of Internet of Everything

The Internet of Everything (IoE) applications require device to device communication with minimal or no input from users to complete transactions or whole processes. This requires the development of software agents that will act on behalf of the users. These software agents will need to make decisions on what communications are trustworthy enough to accept data from and transact business with. This is with the objective of making trust decision making transparent to the end users. There is therefore the need to model trust for IoE applications for the IoE devices to compute the appropriate trust values and make decisions based on the type and critical nature of the applications involved. This paper models the concept of trust in the context of IoE applications and designs a trust management system for it.
Detection of Induction Motor Coupling Unbalanced and Misalignment via Advanced Transient Current Signature Analysis

In industry, vibration analysis is often employed for the detection of mechanical failures in rotating electrical machinery. However, there are some applications in which the installation of vibration sensors is not feasible or the access to the motor is limited. In these cases, the use of alternative techniques, such as the analysis of motor currents may bring significant advantages. In this context, over these recent years, advanced methods based on the analysis of transient motor currents have proven to be very effective for the detection of certain faults, avoiding the drawbacks of classical current analysis methods. This paper proposes the analysis of the starting current to detect different types of mechanical faults in induction motors. Motor misalignment caused by loosened bolts and soft foot as well as coupling unbalance are considered in the work. The results show that the advanced analysis of starting currents brings very useful information to detect the presence of these anomalies, becoming an excellent informational source for the diagnosis of these types of failures.
High-precision Rotor Position Estimation for High-speed SPMSM Drive Based on State Observer and Harmonic Elimination

A sensorless field-oriented control scheme (FOC) based on state observer is proposed. The estimated position and speed fluctuations caused by the high-order harmonics are analyzed. A harmonics filter is used to eliminate the harmonics. A high-speed surface-mounted permanent magnet synchronous motor (SMPSM) drive testbench is built to demonstrate the proposed control scheme. It is shown in the experimental results that the proposed method can significantly reduce the fluctuations of estimated position and speed.
Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging

Part-of-Speech (POS) tagging for Twitter has received considerable attention in recent years. Because most POS tagging methods are based on supervised models, they usually require a large amount of labeled data for training. However, the existing labeled datasets for Twitter are much smaller than those for newswire text. Hence, to help POS tagging for Twitter, most domain adaptation methods try to leverage newswire datasets by learning the shared features between the two domains. However, from a linguistic perspective, Twitter users not only tend to mimic the formal expressions of traditional media, like news, but they also appear to be developing linguistically informal styles. Therefore, POS tagging for the formal Twitter context can be learned together with the newswire dataset, while POS tagging for the informal Twitter context should be learned separately. To achieve this task, in this work, we propose a hypernetwork-based method to generate different parameters to separately model contexts with different expression styles. Experimental results on three different datasets show that our approach achieves better performance than state-of-the-art methods in most cases.
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism

Named entity recognition (NER) is an important task in natural language processing area, which needs to determine entities boundaries and classify them into pre-defined categories. For Chinese NER task, there is only a very small amount of annotated data available. Chinese NER task and Chinese word segmentation (CWS) task have many similar word boundaries. There are also specificities in each task. However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS. In this paper, we propose a novel adversarial transfer learning framework to make full use of task-shared boundaries information and prevent the task-specific features of CWS. Besides, since arbitrary character can provide important cues when predicting entity type, we exploit self-attention to explicitly capture long range dependencies between two tokens. Experimental results on two different widely used datasets show that our proposed model significantly and consistently outperforms other state-of-the-art methods.
A State-transition Framework to Answer Complex Questions over Knowledge Base

Although natural language question answering over knowledge graphs have been studied in the literature, existing methods have some limitations in answering complex questions. To address that, in this paper, we propose a State Transition-based approach to translate a complex natural language question N to a semantic query graph (SQG), which is used to match the underlying knowledge graph to find the answers to question N. In order to generate SQG, we propose four primitive operations (expand, fold, connect and merge) and a learning-based state transition approach. Extensive experiments on several benchmarks (such as QALD, WebQuestions and ComplexQuestions) with two knowledge bases (DBpedia and Freebase) confirm the superiority of our approach compared with state-of-the-arts.
A 3GPP NR Compliant Beam Management Framework to Simulate End-to-End mmWave Networks

The advent of the next iteration of mobile and wireless communication standards, the so called 5G, is already a reality. 3GPP released in December 2017 the first set of specifications of the 5G New Radio (NR), which introduced important innovations with respect to legacy networks. One of the main novelties is the use of very-high frequencies in the radio access, which requires highly-directional transmissions or beams to overcome the severe propagation losses. Therefore, it is paramount to manage these beams in an efficient manner in order to always choose the optimum set of beams. In this work, we describe the first NR-compliant beam management framework for the ns-3 network simulator. We aim at providing an open-source and fully-customizable solution to let the scientific community implement their solutions and assess their impact on the end-to-end network performance. Additionally, we describe the necessary modifications in ns-3 to align the radio frame structure to what the 3GPP standards mandate. Finally, we validate our results by running a simple mobility scenario.
Syntactical Analysis of the Weaknesses of Sentiment Analyzers

We carry out a syntactic analysis of two state-of-the-art sentiment analyzers, Google Cloud Natural Language and Stanford CoreNLP, to assess their classification accuracy on sentences with negative polarity items. We were motivated by the absence of studies investigating sentiment analyzer performance on sentences with polarity items, a common construct in human language. Our analysis focuses on two sentential structures: downward entailment and non-monotone quantifiers; and demonstrates weaknesses of Google Natural Language and CoreNLP in capturing polarity item information. We describe the particular syntactic phenomenon that these analyzers fail to understand that any ideal sentiment analyzer must. We also provide a set of 150 test sentences that any ideal sentiment analyzer must be able to understand.
A Game-Theoretic Lexical Link Analysis for Discovering High-Value Information from Big Data

We demonstrate a machine learning and artificial intelligence method, i.e., lexical link analysis (LLA) to discover high-value information from big data. In this paper, high-value information refers to the information that has the potential to grow its value over time. LLA is a unsupervised learning method that does not require manually labeled training data. New value metrics are defined based on a game-theoretic framework for LLA. In this paper, we show the value metrics generated from LLA in a use case of analyzing business news. We show the results from LLA are validated and correlated with the ground truth. We show that by using game theory, the high-value information selected by LLA reaches a Nash equilibrium by superpositioning popular and anomalous information, and at the same time generates high social welfare, therefore, contains higher intrinsic value.
Feature-Oriented Evolution of Automation Software Systems in Industrial Software Ecosystems

In the domain of industrial automation many companies nowadays need to serve a mass market while at the same time customers demand individual customer-specific solutions. Such customizations often apply to individual products only but may also be needed at the level of product lines for whole market segments. To handle this problem, development is frequently organized in software ecosystems (SECOs), i.e., interrelated software product lines involving internal and external developers. This paper introduces an approach supporting feature-oriented, distributed development and evolution in industrial SECOs. It is common industrial practice to first derive initial products from a product line, then adding and adapting features to satisfy individual customer requirements, possibly followed by merging back these changes into the original product line. Our approach goes beyond this practice and also allows to share new or updated features by transferring them to other product lines in the ecosystem. This is for instance useful when a feature developed in an individual customer project becomes relevant for another market segment or when updates of features need to be transferred to related products in the ecosystem. We describe and motivate research challenges based on the industrial ecosystem of an industry partner. We outline the key elements and operations of our approach, including an implementation in our FORCE2development environment. We demonstrate application scenarios from the well-known Pick-and-Place Unit (PPU) system as a proof of concept.
All-Metal Domestic Induction Heating Using Single-Frequency Double-Layer Coils.

In the past few decades, induction heating based on near-field magnetic coupling has been actively developed, aiming to improve the efficiency, controllability and safety. For traditional induction heating, the heating coil is energized by an AC power source to generate an alternating magnetic field, which in turn induces the eddy current in the workpiece to produce the heating effect [1]. Nevertheless, the workpiece made of non-ferromagnetic materials like stainless steel, aluminum and copper cannot be properly heated, which dramatically limits its application as compared with traditional open-fire heating. There was an attempt to heat the non-ferromagnetic workpiece using a strong magnetic field provided by superconducting windings [2], but it was not practical for domestic induction heating. By using low-frequency AC (around 20 kHz) to heat the ferromagnetic pan and high-frequency AC (over 70 kHz) to heat the non-ferromagnetic pan, the selective operating frequency technique was developed for all-metal domestic induction heating [3]. However, the corresponding switching and winding losses significantly increased, which inevitably resulted in low heating efficiency. In addition, the frequency selection caused serious electromagnetic interference and increased complexity and cost. Recently, the homogeneity and flexibility of induction heating has been improved by adopting the magnetic resonant coupling (MRC) mechanism [4], [5]. In this paper, a new all-metal domestic heating system is proposed and implemented. The key is to employ double-layer coils and utilizes the MRC mechanism to induce a high current in the resonant coil when heating the non-ferromagnetic pans, thus successfully heating both the ferromagnetic and non-ferromagnetic pans at 30 kHz resonant frequency. Consequently, the advantages of simplicity, high heating efficiency and single operating frequency can be attained for all-metal induction heating.
Activity Recognition in Sensor Data Streams for Active and Assisted Living Environments

In active and assisted living environments, a major service that can be provided is the automated assessment of elderly people’s well-being. Therefore, activity recognition is required to detect what types of help disabled persons need to support them in their daily life activities. Unfortunately, it is still a difficult task to estimate the size of the required window for online sensor data streams to recognize a specific activity, especially when new sensor events are recorded. This paper proposes a windowing algorithm, which presents promising results to recognize complex human activities for multi-resident homes. The approach is based on the analysis of the sensor data to identify the best fitting sensors that should be considered in a specified window. Moreover, the second part of this paper proposes a set of different statistical spatio-temporal features to recognize human activities. In order to check the overall performance, this approach is tested using the CASAS data set and artificially generated laboratory data using our HBMS simulator. The results show high performance based on different evaluation metrics compared to other approaches. We believe that the proposed windowing approach provides a good approximation of the required window size in order to robustly detect human activities in comparison to other windowing approaches.
Dissipation Loop for Shoot-Through Faults in HVDC Converter Cells

Converter cells for HVDC applications store large amounts of energy. This energy might be dissipated in a very short time in case of a shoot-through fault. Measures to avoid shoot-through or handle the extreme currents during a fault and prevent damage from neighboring components are essential to ensure a continued operation of the converter. With future high-voltage silicon carbide semiconductors, cell voltages can be increased leading to higher stored energy per cell. In cells with thyristor-based semiconductors, e.g. IGCTs, a di/dt reactor may have to be employed. This paper presents a method to handle the dissipated energy during shoot-through which makes use of the inherently needed di/dt reactor. The majority of the stored energy in the cell can be dissipated in a dedicated discharge loop formed by the reactor and an antiparallel bypass thyristor. After diverting the fault current into the dissipation loop, there is no current through any other component of the cell.
Automated Camera-Based Estimation of Rehabilitation Criteria Following ACL Reconstruction

Anterior cruciate ligament (ACL) reconstruction necessitates months of rehabilitation, during which a clinician evaluates whether a patient is ready to return to sports or occupation. Due to their time- and cost-intensive nature, these screenings to assess progress are unavailable to many. This paper introduces an automated, markerless, camera-based method for estimating rehabilitation criteria following ACL reconstruction. To evaluate the performance of this novel technique, data were collected weekly from 12 subjects as they used a leg press over the course of a 12-week rehabilitation period. The proposed camera-based method for estimating displacement and force was compared to encoder and force plate measurements. The leg press displacement and force values were estimated with 89.7% and 85.3% accuracy, respectively. These values were then used to calculate lower-limb symmetry and to track patient progress over time.
Balance Control of Split Capacitor Potential for Magnetically Levitated Motor System Using Zero-Phase Current

We have previously proposed a driving method for both a three-phase permanent magnet motor and one- degree-of-freedom controlled magnetic suspension using only a three-phase inverter. In this driving system, the suspension winding for magnetic suspension is connected between a neutral- point of the Y-connected three-phase motor winding and a middle point of two DC voltage sources. The current flowing in this part is denoted by the zero- phase current here. The suspension force is actively controlled by the zero- phase current in this system. This paper proposes a further simplified configuration, where the DC-link voltage is split by two capacitors using a DC voltage source. The split capacitor potential, however, is unbalanced by the DC component of the zero- phase current, owing to the offset detection error of the current sensor and the bias current of the magnetic suspension. This paper addresses the balance control of the split capacitor potential. We propose two balance control methods, including the voltage feedback control and the voltage sensorless control. The experimental results demonstrate the effectiveness of the proposed balance control.
Characterizing Infrastructure Damage After Earthquake: A Split-Query Based IR Approach

Retrieving relevant information from social media based on specific requirements has become a focus area for researchers. In this paper, we propose a framework for online retrieval of tweets providing information about possible infrastructure damages, caused due to earthquakes and use the same to determine a damage score for the possibly affected locations. Identifying such tweets would not only provide a holistic view of the affected areas but would also help in taking necessary relief actions. Existing works on this topic fail to effectively capture the semantic variation in the tweets, possibly due to poor content quality, thereby providing scopes for further improvement in the mechanisms involved. Our proposed technique relies on a novel split-query based mechanism along with a pseudo-relevance feedback approach to identify the relevant tweets. The pseudo-relevance feedback approach expands on an initial set of seed tweets obtained using a semi-automatic query generation mechanism that couples topic based clustering with human annotation. Empirical validation of our proposed method on a manually annotated ground truth data reveals a considerable improvement in precision, recall and mean average precision over several baseline methods.
Effect of Volume in Resistance Training on Inhibitory Control in Young Adults: A Randomized and Crossover Investigation

Purpose: The aim of the study is to compare the effect of resistance training volume on inhibitory control in young adults with previous experience in resistance training. Method: All the 27 participants underwent 40-week experiment, divided in three training phases of 8-week duration. A washout period of 8 weeks between each of the training phases was carried out. The participants performed 1, 3, or 5 sets of the same exercises with equalized intensity (loading zones) and rest. Inhibitory control was assessed by the Stroop Test. Results: Interaction effect was found for inhibitory control accuracy [F(5,22) = 56.88, p < 0.01] and mean response time [F(5,22) = 83.02, p < 0.01] for 3 sets (p = 0.01; ES = 0.6) and 5 sets (p = 0.01; ES = 0.8) when compared to 1 set. Conclusion: In conclusion, 1 set of resistance training may provide insufficient volume stimulus for positive adaptation in inhibitory control when compared to 3 or 5 sets.
The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation

Neural sequence-to-sequence models have proven very effective for machine translation, but at the expense of model interpretability. To shed more light into the role played by linguistic structure in the process of neural machine translation, we perform a fine-grained analysis of how various source-side morphological features are captured at different levels of the NMT encoder while varying the target language. Differently from previous work, we find no correlation between the accuracy of source morphology encoding and translation quality. We do find that morphological features are only captured in context and only to the extent that they are directly transferable to the target words.
Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention

Distantly supervised relation extraction employs existing knowledge graphs to automatically collect training data. While distant supervision is effective to scale relation extraction up to large-scale corpora, it inevitably suffers from the wrong labeling problem. Many efforts have been devoted to identifying valid instances from noisy data. However, most existing methods handle each relation in isolation, regardless of rich semantic correlations located in relation hierarchies. In this paper, we aim to incorporate the hierarchical information of relations for distantly supervised relation extraction and propose a novel hierarchical attention scheme. The multiple layers of our hierarchical attention scheme provide coarse-to-fine granularity to better identify valid instances, which is especially effective for extracting those long-tail relations. The experimental results on a large-scale benchmark dataset demonstrate that our models are capable of modeling the hierarchical information of relations and significantly outperform other baselines. The source code of this paper can be obtained from https://github.com/thunlp/HNRE.
Should I Stay or Should I Go?: Exploiting Visitor Movements to Derive Individualized Recommendations in Museums

Nowadays, mobile recommender systems running on user's smart devices have become popular. However, most implemented mechanisms require continuous user interaction to provide personalized recommendations, and thus weaken the usability. This paper provides an innovative approach for taking advantage of user's movement data as implicit user feedback for deriving recommendations. By means of a real-world museum scenario a beacon infrastructure for tracking sojourn times is presented. Then we show how sojourn times can be integrated in both collaborative filtering and content-based mechanism approaches. An exhaustive experimental evaluation shows the suitability of our approach.
Lightning Protection of a Compact MV Power Line Sharing the same Poles of a HV Line

This paper deals with the assessment of the lightning performance of a double-circuit HV-MV overhead line. We focus on the protection of the 15-kV line with compact configuration that shares the same concrete poles of a 69-kV or 138-kV transmission line. Due to the presence of the HV conductors, the number of direct events to the multi-circuit line is higher than the one of a typical MV line alone in open terrain. The presence of the HV conductors and of the overhead ground wire reduces the induced overvoltages in the MV conductors due to indirect lightning events. For the considered configurations, the number of flashovers per year expected in the double-circuit line is lower than in a compact MV line in open terrain. It is shown that such a number depends mainly on the line withstand voltage, on the grounding resistance and on the presence of surge arresters, along with their location along the line. Such a dependence is quantitatively discussed in the paper.