Miracle-dz/UniFAR-FTUs huggingface dataset:
Frustratingly Easy Model Ensemble for Abstractive Summarization

Ensemble methods, which combine multiple models at decoding time, are now widely known to be effective for text-generation tasks. However, they generally increase computational costs, and thus, there have been many studies on compressing or distilling ensemble models. In this paper, we propose an alternative, simple but effective unsupervised ensemble method, post-ensemble, that combines multiple models by selecting a majority-like output in post-processing. We theoretically prove that our method is closely related to kernel density estimation based on the von Mises-Fisher kernel. Experimental results on a news-headline-generation task show that the proposed method performs better than the current ensemble methods.
Investigation of Various Position Estimation Accuracy Issues in Pulse-Injection-based Sensorless Drives

For low speed sensorless drives of Surface mounted Permanent Magnet Synchronous Machine (SPMSM), pulse vectors are often injected into the drive to estimate the rotor position. There are many factors that could affect the accuracy of the estimated position such as inverter voltage error, cross-saturation effects and the presence of the speed dependent back electromotive force (EMF). This paper analyzes and evaluates the position estimation error caused by aforementioned factors with experimental results. Detailed quantitative results are given. Meanwhile the interaction between these factors is briefly studied.
Dmap: Automating Domain Name Ecosystem Measurements and Applications

Behind each Internet domain name, there is a set of entities/companies responsible for delivering the various services associated with it, such as Web hosting and e-mail. Together, they form what we refer to as DNS ecosystem. Currently, there is no single measurement tool designed to measure this ecosystem altogether. As a result, researchers that aim at analyzing (parts of) this ecosystem often have to spend significant amounts of time preparing and executing the multiple application measurements and post-processing their heterogeneous raw datasets. Given that time is a scare resource, this complexity diverts researcher's time from actual analysis, ultimately limiting how far many studies go. To help researchers facing this situation, we present Dmap, an active measurement application that reduces the complexity of executing both measurements and analysis. It does so by (i) automating the crawling of several application protocols (DNS, HTTP, TLS/SSL, SMTP, both over IPv4 and IPv6) and (ii) storing the results into a relational data base, enabling researchers to quickly perform hypothesis tests within interactive response times using SQL. Dmap current version has 40 classifiers that generate 166 derived features (e.g., CMS detection, page language), which can be used by researchers and operators to build applications and services. We present an evaluation of Dmap and show three applications that it can be used for, such as profiling the Alexa 1 million domains. We use Dmap at SIDN (.nl registry) for research on the. nl zone and make it open-source for researchers.
Specific Absorption Rate Evaluation in Human Body Model Comprising of Metallic Implant

In this paper, the human body absorption of electromagnetic energy emitted by a dipole antenna operating at the current mobile phone technology frequencies is investigated. The specific absorption rate within a realistic anatomical model comprising of the metallic implant was evaluated. The research was carried out using electromagnetic numerical modeling based on the Finite Integration method. The primary objective was to compare levels of absorbed electromagnetic energy as a function of the antenna distance and mobile phone carrier frequency. The effect of implants presence was also studied. The results show that SAR varies with respect to the antenna distance as well as to the considered mobile phone technology standard. However, the antenna shift in the tens of millimeters provides significant SAR decrease below the safety limit. The presence of metallic implant in superficial tissue layers causes significant SAR increase, particularly for PM implant while the SAR enhancement for needle implant was negligible.
Faster R-CNN for Object Location in a Virtual Environment for Sorting Task

This paper presents the implementation of a mobile robotic arm simulation whose task is to order different objects randomly distributed in a workspace. To develop this task, it is used a Faster R-CNN which is going to identify and locate the disordered elements, reaching 99% accuracy in validation tests and 100% in real-time tests, i.e. the robot was able to collect and locate all the objects to be ordered, taking into account that the virtual environment is controlled and the size of the input image obtained from the workspace to be entered to the network should be 700x525 px.
Design and Optimization of a Two-Pole Line-Start Ferrite Assisted Synchronous Reluctance Motor

To meet the new international efficiency (IE) standards, a 2-pole multi-barrier line-start ferrite assisted synchronous reluctance motor is proposed for a bore-well submersible water pumping application. Low-cost ferrite permanent magnets (PM) are used instead of the costly rare-earth magnets to make the motor economically viable while being efficient. Further, a multiple rotor flux-barrier is employed with a simple cage and end-ring geometry which minimizes the number of rotor bridges. This also eases the process of embedding the magnets in the flux barriers. The rotor flux barriers are designed to maximize the reluctance torque while protecting the ferrite magnets from demagnetization due to the in-rush currents. To ascertain these claims, a 2-D finite element (FE) analysis is carried-out and the results are presented. Later, the initial rotor geometry is optimized using FE analysis to further improve the motor performance.
Topological Fragility Versus Antifragility: Understanding the Impact of Real-Time Repairs in Networks Under Targeted Attacks

Network science fosters our understanding of many natural and man-made complex systems by providing models for evolution, emergence and reliability. Existing studies on network robustness mainly focus on targeted node attacks, where the topology shrinks until the network loses its basic functionality. Here, we bring a novel aspect into perspective. First, we investigate the fragility of networks when dealing with edge attacks instead of node attacks - a highly relevant scenario in dynamical social systems where, because of the frequent interactions, edges are dynamically created or destroyed. Second, we propose an intuitive edge repair model to respond after edge attacks. Accordingly, we study different strategies for targeted edge attack and edge repair, to find that real-world networks are generally more robust than synthetic ones, that high betweenness and high degree nodes present structural weak-spots for targeted attacks, and that repairing edges incident to high-degree nodes first offers the most effective response to an attack. Finally, we find evidence of a behaviour we tag as topological antifragility for networks that actually grow stronger when being under attack, due to the triggered edge repair strategy.
An improved Robust Stability Criteria of Neutral Lur'e Systems with Time-Varying Delay via Nonlinearity Decomposition Approach

This paper considers the robust absolute stability for uncertain mixed neutral and Lur'e systems with time-varying delay and sector-bounded nonlinear. A novel nonlinearity decomposition approach is proposed. By constructing a suitable augmented L-K functional, and combining nonlinearity decomposition approach, Writing-integral inequality and reciprocally convex approach, the new delay-dependent robust stability criteria for uncertainly neutral Lur'e systems are derived in the form of linear matrix inequalities (LMIs). Two numerical examples are presented to show that effectiveness and less conservatism of proposed methods and can improve the existing methods.
An Intrusion Detection System for Constrained WSN and IoT Nodes Based on Binary Logistic Regression

In this paper we evaluate the feasibility of running a lightweight Intrusion Detection System within a constrained sensor or IoT node. We propose mIDS, which monitors and detects attacks using a statistical analysis tool based on Binary Logistic Regression (BLR). mIDS takes as input only local node parameters for both benign and malicious behavior and derives a normal behavior model that detects abnormalities within the constrained node.We offer a proof of correct operation by testing mIDS in a setting where network-layer attacks are present. In such a system, critical data from the routing layer is obtained and used as a basis for profiling sensor behavior. Our results show that, despite the lightweight implementation, the proposed solution achieves attack detection accuracy levels within the range of 96% - 100%.
Characterizing the Effectiveness of Query Optimizer in Spark

In the big data community, Spark has been widely used for processing interactive queries. Spark employs a query optimizer, called Catalyst, to provides a set of optimization rules and supports Cost-Based Optimization (CBO). In this paper, we investigated the effectiveness of the optimization rules and costbasedoptimization in Catalyst. We conducted comprehensive validation experiments by varying the data volume and cluster scale, and found that the execution time of most TPC-H queries were reduced slightly even when query optimizations are applied. We derived some interesting observations on Catalyst, which can help the community better understand and improve the queryoptimizer of Spark in future.
Testing and validating end user programmed calculated fields

This paper reports on an approach for systematically generating test data from production databases for end user calculated field program via a novel combination of symbolic execution and database queries. We also discuss the opportunities and challenges that this specific domain poses for symbolic execution and shows how database queries can help complement some of symbolic execution's weaknesses, namely in the treatment of loops and also of path conditions that exceed SMT solver capabilities.
A Multimodal Analytics Platform for Journalists Analyzing Large-Scale, Heterogeneous Multilingual, and Multimedia Content

Analysts and journalists face the problem of having to deal with very large, heterogeneous, and multilingual data volumes that need to be analyzed, understood, and aggregated. Automated and simplified editorial and authoring process could significantly reduce time, labor, and costs. Therefore, there is a need for unified access to multilingual and multicultural news story material, beyond the level of a nation, ensuring context-aware, spatiotemporal, and semantic interpretation, correlating also and summarizing the interpreted material into a coherent gist. In this paper, we present a platform integrating multimodal analytics techniques, which are able to support journalists in handling large streams of real-time and diverse information. Specifically, the platform automatically crawls and indexes multilingual and multimedia information from heterogeneous resources. Textual information is automatically summarized and can be translated (on demand) into the language of the journalist. High-level information is extracted from both textual and multimedia content for fast inspection using concept clouds. The textual and multimedia content is semantically integrated and indexed using a common representation, to be accessible through a web-based search engine. The evaluation of the proposed platform was performed by several groups of journalists revealing satisfaction from the user side.
ULISSE: ULtra Compact Index for Variable-Length Similarity Search in Data Series

Data series similarity search is an important operation and at the core of several analysis tasks and applications related to data series collections. Despite the fact that data series indexes enable fast similarity search, all existing indexes can only answer queries of a single length (fixed at index construction time), which is a severe limitation. In this work, we propose ULISSE, the first data series index structure designed for answering similarity search queries of variable length. Our contribution is two-fold. First, we introduce a novel representation technique, which effectively and succinctly summarizes multiple sequences of different length. Based on the proposed index, we describe efficient algorithms for approximate and exact similarity search, combining disk based index visits and in-memory sequential scans. We experimentally evaluate our approach using several synthetic and real datasets. The results show that ULISSE is several times (and up to orders of magnitude) more efficient in terms of both space and time cost, when compared to competing approaches.
Efficient Low-Power Digital Baseband Transceiver for IEEE 802.15.6 Narrowband Physical Layer

Wireless body area network (WBAN) is a propitious new technology, which enables the efficient delivery of healthcare rights to patients and aging people in outback areas. WBANs consist of resource-limited nodes that make the power consumption one of the most important challenges. This paper proposes a low-power yet efficient architecture design of a narrowband physical layer baseband transceiver for WBANs at the 2.4-GHz band. The proposed design targets high data reliability with scalable data rates for transmission. A low-cost adjustable data rate modulation/demodulation architecture is proposed with a maximum data rate of 971.4 kb/s at 6-MHz clock rate. A digital timing synchronization scheme is also proposed, which can achieve coarse and fine-timing synchronization. Moreover, a low-complexity phase error correction algorithm is proposed with a low-cost pulse deshaping matched filter, to enhance the reliability of the proposed receiver. The proposed baseband transceiver is implemented in 130-nm CMOS technology with a 1.2-V power supply. The proposed baseband design only consumes $72.12~\mu \text{W}$ in the transmitter and $330.42~\mu \text{W}$ in the receiver for 971.4-kb/s data rate with 6-MHz clock frequency. To demonstrate the reliability performance of the proposed design, the dedicated packet error rate (PER) postlayout simulations are reported. The overall PER for the proposed transceiver is about 1% at a signal-to-noise ratio of 9.6 dB.
A New Control Method for Triple-Active Bridge Converter with Feed Forward Control

Triple Active Bridge (TAB) DC/DC converter contributes to the realization of micro grid by controlling the power flow among distributed power systems and load. The control system of the TAB converter has characteristics of a 2-input 2-output MIMO structure and strong nonlinearity. There is a trade-off problem between the accuracy of the decoupler and the simplicity of the controller structure. Hence, this paper proposes the new control method that enables the decoupler to operate in a wide range while keeping the simplicity of the controller structure. Also, this paper describes experimental results of 1 kW prototype TAB converter in order to verify the validity of the theoretical considerations. As a result, it was confirmed that the interference is suppressed by the proposed controller.
Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications

Cooperative Intelligent Transport Systems (C-ITS) make road traffic safer and more efficient, but require the mobile networks to handle time-critical applications. While some applications may need new dedicated communications technologies such as IEEE 802.11p or 5G, other applications can use current cellular networks. This study evaluates the performance that connected vehicles can expect from existing networks, and estimates the potential gain of multi-access by simultaneously transmitting over several operators. We upload time-critical warning messages from buses in Sweden, and characterise transaction times and network availability. We conduct the experiments with different protocols: UDP, TCP, and HTTPS. Our results show that when using UDP, the median transaction time for sending a typical warning message is 135 ms. We also show that multi-access can bring this value down to 73 ms. For time-critical applications requiring transaction times under 200 ms, multi-access can increase availability of the network from to 57.4% to 92.0%.
Methods for Selecting Multicomponent Layers Which Match the Acoustic Impedance of Ultrasound Transducers to Various Media

The problem of optimal selection of materials for matching layers e.g. of piezoceramic transducers for ultrasonic emission and reception is closely related to the type of medium into which ultrasonic energy is radiated. Due to the unfavoruable ratio of acoustic impedance between piezoelectric ceramics and gaseous, liquid and biological media, the conversion of electrical to acoustic energy introduces significant losses. The paper presents, among other methods of impedance-matching layer selection, a method based on genetic algorithms. The method enables the search of state-space of possible solutions - in this case, of combinations matching layers' materials. The optimal choice of computation method depends closely on ultrasonic impulse parameters such as: impulse shape, impulse energy, transmission band shape and width. When using a single matching layer, its impedance can be calculated based on upon Chebyshev's, DeSilets' or Souquet's criterion. The choice of criterion depends on, among other factors, the expected shape of the transfer function; the calculated values of matching layer's impedance exhibit large variability, so the choice of suitable material is a complex task. The solution might be to use multiple matching layers. The use of a genetic algorithm allows for finding the optimal matching layers combination, fulfilling the chosen matching criterion.
BRRA: A Bid-Revisable Reverse Auction based Framework for Incentive Mechanisms in Mobile Crowdsensing Systems

Mobile Crowdsensing (MCS) applications take advantage of the ubiquity and sensing power of smartphones in data gathering. Designing an incentive mechanism for motivating the individuals to participate in such systems is vital. Reverse Auction (RA) is a popular framework in which the participants bid their expected returns for their contributions, and a task creator selects a subset of them with a view to maximise the cumulative contribution within a prescribed budget. In RA, the participants are not aware of their winning probability before the auction is closed. If the participants are given some statistical information about the returns associated with their bid, they may reduce their bid in order to increase their returns. In this paper, we propose Bid-Revisable Reverse Auction (BRRA), as well as an enhancement called BRRA with Virtual Contribution (BRRA-VC), wherein the participants are allowed to revise their bids during the auction, based on the feedback they receive about the winning probability of their submitted bids. Through conducting extensive experiments, we show that in comparison to RA, the BRRA schemes not only benefit the task creator by increasing the return on investment (i.e., the total contribution for the same budget) and also by decreasing the participant dropout ratio, but also profit the participants who are open to revise their bids by increasing their received rewards as well as their winning chances.
Network Alarm Flood Pattern Mining Algorithm Based on Multi-dimensional Association

In the process of network operation, a large number of alerts are generated every day, which reflect the occurrence of some abnormal conditions. Traditional methods depend too much on the knowledge of equipment manufacturers and industry experts, so we need some novel ways to overcome this problem in the network management. The application of data mining technology to alarm pattern analysis has become the focus of current research. Researchers developed many kinds of algorithms fitting different application characteristics. This paper proposes the concept of association matrix pattern mining, which means that before mining the data, we use the multi-dimensional information of the data to construct the association matrices between the items. And we develop a conditional pattern mining algorithm based on the association matrix which aims to find out less but more meaning results. Our experiments validate that with the multi-dimensional information stored in association matrix, the algorithm performs better than traditional pattern mining methods in finding out the detailed alarm pattern from network alarm flood.
Phys: probabilistic physical unit assignment and inconsistency detection

Program variables used in robotic and cyber-physical systems often have implicit physical units that cannot be determined from their variable types. Inferring an abstract physical unit type for variables and checking their physical unit type consistency is of particular importance for validating the correctness of such systems. For instance, a variable with the unit of ‘meter’ should not be assigned to another variable with the unit of ‘degree-per-second’. Existing solutions have various limitations such as requiring developers to annotate variables with physical units and only handling variables that are directly or transitively used in popular robotic libraries with known physical unit information. We observe that there are a lot of physical unit hints in these softwares such as variable names and specific forms of expressions. These hints have uncertainty as developers may not respect conventions. We propose to model them with probability distributions and conduct probabilistic inference. At the end, our technique produces a unit distribution for each variable. Unit inconsistencies can then be detected using the highly probable unit assignments. Experimental results on 30 programs show that our technique can infer units for 159.3% more variables compared to the state-of-the-art with more than 88.7% true positives, and inconsistencies detection on 90 programs shows that our technique reports 103.3% more inconsistencies with 85.3% true positives.
Evaluation of a Sequence Tagging Tool for Biomedical Texts

Many applications in biomedical natural language processing rely on sequence tagging as an initial step to perform more complex analysis. To support text analysis in the biomedical domain, we introduce Yet Another SEquence Tagger (YASET), an open-source multi purpose sequence tagger that implements state-of-the-art deep learning algorithms for sequence tagging. Herein, we evaluate YASET on part-of-speech tagging and named entity recognition in a variety of text genres including articles from the biomedical literature in English and clinical narratives in French. To further characterize performance, we report distributions over 30 runs and different sizes of training datasets. YASET provides state-of-the-art performance on the CoNLL 2003 NER dataset (F1=0.87), MEDPOST corpus (F1=0.97), MERLoT corpus (F1=0.99) and NCBI disease corpus (F1=0.81). We believe that YASET is a versatile and efficient tool that can be used for sequence tagging in biomedical and clinical texts.
Outer-Ferrite-PM-Rotor BLAC Motor Characterization: FEM Assisted Optimal Design

The present paper is dedicated designing a Ferrite PM outer rotor synchronous machine for home appliances. The target requirement is to maximize efficiency at 4500rpm and 1kW. An 9 stator teeth/8 rotor poles configuration topology with rotor surface PM is chosen for the investigation. To meet this requirement, an optimal design procedure employing the Hooke Jeeves algorithm and an analytical model of the BLAC machine is used. FEM based online checks and corrections of the analytical model are embedded in the optimization process to improve accuracy of the analytical model and limit the computation effort. Further, an extended FEM analysis of the optimization result is presented and compared with experimental results on a build prototype. Conclusions are drawn at the end.