Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check

Chinese spelling check (CSC) is a challenging yet meaningful task, which not only serves as a preprocessing in many natural language processing(NLP) applications, but also facilitates reading and understanding of running texts in peoples’ daily lives. However, to utilize data-driven approaches for CSC, there is one major limitation that annotated corpora are not enough in applying algorithms and building models. In this paper, we propose a novel approach of constructing CSC corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to the OCR- and ASR-based methods, respectively. Upon the constructed corpus, different models are trained and evaluated for CSC with respect to three standard test sets. Experimental results demonstrate the effectiveness of the corpus, therefore confirm the validity of our approach.
Coils layout optimization of dynamic wireless power transfer system to realize output voltage stable

In order to solve the problem of the received voltage drop caused by the decrease of the coiling coefficient between the primary and secondary coils at the junction of the primary coils of dynamic wireless power transfer (DWPT), a partial overlap arrangement method of primary coil for DWPT system is proposed. The two adjacent primary coils are overlapped partially, and this two coils are used simultaneously to supply power when the secondary coil is located at the junction to ensure less voltage drop without any additional controls. The LCC compensation topology is used to ensure that the output voltage of the system is not affected by mutual inductance between the primary coils. The coil transfer efficiency can be maintained at a high level at the junction. The validity of the proposed arrangement method is verified by experiments.
Automatic Plant-Controller Input/Output Matching using Evolutionary Algorithms

Automation of software development is an actively researched problem. Search-based software engineering aims to apply various search-based techniques to software engineering problems. Recently we proposed the method for automatic generation of function block application using evolutionary algorithms and model checking and applied it to the problem of automatic generation of data connections in distributed control system. The aim of this paper is to further study this method on the problem of matching of input and output connections in a closed-loop plant-controller system. The computed fitness function distribution shows that the evaluated method successfully determines the correct input and output connections between the controller and the plant. Additionally, we evaluate how the composition of specification requirements in the fitness function affects the performance of the (1+1) evolutionary algorithm. We show that additional liveness formulas can improve the performance of the algorithm, while the introduction of safety formulas significantly decreases it.
FPGA-based Dynamic Duty Cycle and Frequency Controller for a Class-E2 DC-DC Converter

This paper presents a controller that achieves zero-voltage and zero-dv/dt switching (ZVS and ZVDS) for a class-E2 dc-dc converter under a wide variation in the input and output voltages. One of the major problems in using the class-E topology for dc-dc conversion is that the increased switching loss significantly degrades the efficiency when the voltage condition deviates from the point which the circuit is designed for. The proposed controller modulates the duty cycle and the frequency of the gate driving signal so that ZVS and ZVDS are always achieved under the voltage variation, while regulating the output voltage by on-off control. We demonstrate an FPGA-based prototype controller in conjunction with a 2 MHz isolated class-E2 dc-dc converter that minimizes the efficiency drop due to the switching loss for the input voltage from 80 V to 200 V and the output voltage from 5 V to 20 V.
Online Class Imbalance Learning for Quality Estimation in Manufacturing

Online machine learning has become increasingly important recently as more and more machines are being connected and data is being sent to the decision making node in real time. Traditional batch based machine learning is no longer suitable for such streaming data scenario. Here, an online classification algorithm to classify good and defective product, under imbalance streaming environment, is proposed. The proposed method exploits the assumption that different classes should be far away from each other. Even when the raw data might appear to be close, the algorithm learns and projects them into some specific manifold where different classes are far from each other. The algorithm classifies good and defective product in an imbalanced environment where good product outweighs defective product. The algorithm uses only single pass of the data, where the data is used once and then discarded. The approach is then being validated using industry data and the result indicates better performance in term of G-Mean and F1-score.
Empirical Evaluation of Hybrid Gaze-Controller Selection Techniques in a Gaming Context

Controller-based interaction is popular due to the increasing prevalence of console and couch-based games, but is known to be slower and less accurate than aiming with a mouse. In this study we evaluated the performance of five interaction techniques for games to answer the question, if gaze interaction can improve the performance of controller interaction. We compared mouse only, controller only, gaze only with two commonly used gaze and controller hybrid interactions: gaze teleportation and gaze panning. We implemented a targeting game that resembled a Fitts' Law test to evaluate performance, effort, and preference. Our findings show that mouse was the fastest technique and gaze was both the slowest and most error-prone. For the controller-based techniques, players preferred gaze teleportation over the other techniques; however, it only improved performance over the controller for targets that were small and far away.
A Comparative Investigation of Iron Loss Models for Electrical Machine Design Using FEA and Experimental Validation

This paper proposes a contribution by presenting a comprehensive comparison of different iron loss models currently applied on electrical machine design process. Three core loss methods have been selected following premises of relatively easy implementation and coefficients obtained from standardized tests. A Finite Element Analysis (FEA) is used for accurate estimation of the electromagnetic fields in a motor case study. The results are post-processed with approaches at frequency and time domains by the referred iron loss models and confronted with measurements in a low power Brushless DC (BLDC) motor prototype, inverter-driven, whose output uses pulsewidth-modulation (PWM) voltage waveform. The results show that, for an accurate prediction of motor efficiency, variable iron losses coefficients and PWM corrections must be applied.
Improving Influenza Forecasting with Web-Based Social Data

Improving seasonal influenza forecasting combining official data sources with web search and social media is a recent research topic which can enhance situational awareness of healthcare organizations when monitoring the outbreak of seasonal flu. In this paper, a prediction model based on autoregression that combines data coming from official influenza surveillance system, with data from web search and social media regarding influenza is proposed. The model is evaluated on the two influenza seasons 2016–2017 and 2017–2018, restricted to Italy. The results show that by using Web-based social data, like Google search queries and tweets, we can obtain accurate weekly influenza predictions up to four weeks in advance. The proposed approach improves real-time influenza forecast compared to traditional surveillance systems based on data from sentinel doctors: the prediction error is reduced up to 47%, while the Pearson's correlation is improved of about 24%.
Socialbots' First Words: Can Automatic Chatting Improve Influence in Twitter?

Online social networks (OSNs) such as Twitter and Facebook constitute an open space for developers to create sophisticated machines that imitate human users by automating their social network activities. The existence of socialbots is so powerful that bots can be transformed to influential users. Previous studies use fundamental functions such as posting a tweet or creating links to a specific target group to investigate the ability to infiltrate of these accounts. Our study analyzes the role of automated chatting in bots' infiltration. Our analysis is compared with the state of the art of this kind and reveals that the chat functionally was able to improve Klout and follow ratio on about 24% and 123% respectively. Also, the advanced communications skills contribute to more message interactions between socialbots and other accounts. Based on our empirical experimental study the conversational socialbots infiltrate more successfully in the Twitter-sphere.
Finding Related Forum Posts through Content Similarity over Intention-Based Segmentation (Extended Abstract)

We study the problem of finding related forum posts to a post at hand. We developed a multi-segment matching technique that considers posts as a set of segments each one written with a different goal in its author mind and computes the relatedness between two posts based on the similarity of their respective segments that are intended for the same goal. The questions are how our method identifies such segments, how it figures out for what each segment is intended and how it exploits this information to rank the posts. We experimentally illustrate the effectiveness and efficiency of our segmentation method and overall approach of finding related forum posts.
Drug Resistance in Toxoplasma gondii

Toxoplasma gondii (T. gondii) is a global protozoan parasite infecting up to one-third of the world population. Pyrimethamine (PYR) and sulfadiazine (SDZ) are the most widely used drugs for treatment of toxoplasmosis; however, several failure cases have been recorded as well; suggesting the existence of drug resistant strains. This review aims to give a systematic and comprehensive understanding of drug resistance in T. gondii including mechanisms of resistance and sites of drug action in parasite. Analogous amino acid substitutions in the Toxoplasma enzyme were identified to confer PYR resistance. Moreover, resistance to clindamycin, spiramycin, and azithromycin is encoded in the rRNA genes of T. gondii. However, T. gondii SDZ resistance mechanism has not been proved yet. Recently there has been a slight increase in SDZ resistance. That is why the majority of studies were carried out using SDZ. Six strains resistant to SDZ were found in clinical cases between 2013 and 2017 which among Brazilian T. gondii isolates, TgCTBr11, Ck3, and Pg1 were identified in human toxoplasmosis, as well as in livestock intended for human consumption. In conclusion, recent experimental studies in clinical cases have clearly shown that drug resistance in Toxoplasma is ongoing. Thus, establishing a more effective therapeutic scheme in the treatment of toxoplasmosis is critically needed. The emergence of T. gondii strains resistant to current drugs, reviewed here, represents a concern not only for treatment failure but also for increased clinical severity in immunocompromised patients. To improve the therapeutic outcome in patients, a greater understanding of the exact mechanisms of drug resistance in T. gondii should be developed. Thus, monitoring the presence of resistant parasites, in food products, would seem a prudent public health program.
High-Mobility Normally OFF Al2O3/AlGaN/GaN MISFET With Damage-Free Recessed-Gate Structure

In this letter, partial recessed-gate AlAl<sub>2</sub>O<sub>3</sub>/AlGaN/GaN MISFET is experimentally demonstrated based on selective-area growth. The device features different contents of Al in the AlGaN barriers that were grown in the recessed and accessed regions that contributed to a higher <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\textsf {th}}$ </tex-math></inline-formula> and lower ON-resistance. As a result, it achieves a positive shift in <inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {th}}$ </tex-math></inline-formula> as compared to the reference (from 1.8 to 2.5 V). Besides, this method accomplished the highest reported peak <inline-formula> <tex-math notation="LaTeX">$\mu _{\textsf {FE}}$ </tex-math></inline-formula> value of 2033 cm<sup>2</sup>/<inline-formula> <tex-math notation="LaTeX">$\textsf {V}\cdot \textsf {s}$ </tex-math></inline-formula> and a lower gate channel sheet resistance of <inline-formula> <tex-math notation="LaTeX">$519~\Omega /\Box $ </tex-math></inline-formula> (access region, <inline-formula> <tex-math notation="LaTeX">${R}_{\textsf {sh}} $ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$418~\Omega /\Box $ </tex-math></inline-formula>) owing to reserving damage-free AlGaN/GaN hetero-structure in the recessed-gate. Notably, the formed device also exhibits a low hysteresis, low gate leakage, and a slight current collapse.
Using active learning to expand training data for implicit discourse relation recognition

We tackle discourse-level relation recognition, a problem of determining semantic relations between text spans. Implicit relation recognition is challenging due to the lack of explicit relational clues. The increasingly popular neural network techniques have been proven effective for semantic encoding, whereby widely employed to boost semantic relation discrimination. However, learning to predict semantic relations at a deep level heavily relies on a great deal of training data, but the scale of the publicly available data in this field is limited. In this paper, we follow Rutherford and Xue (2015) to expand the training data set using the corpus of explicitly-related arguments, by arbitrarily dropping the overtly presented discourse connectives. On the basis, we carry out an experiment of sampling, in which a simple active learning approach is used, so as to take the informative instances for data expansion. The goal is to verify whether the selective use of external data not only reduces the time consumption of retraining but also ensures a better system performance. Using the expanded training data, we retrain a convolutional neural network (CNN) based classifer which is a simplified version of Qin et al. (2016)’s stacking gated relation recognizer. Experimental results show that expanding the training set with small-scale carefully-selected external data yields substantial performance gain, with the improvements of about 4% for accuracy and 3.6% for F-score. This allows a weak classifier to achieve a comparable performance against the state-of-the-art systems.
A 0.04MM316NW Wireless and Batteryless Sensor System with Integrated Cortex-M0+ Processor and Optical Communication for Cellular Temperature Measurement

This paper demonstrates a complete wireless sensor node for accurate cellular temperature measurement that includes a fully programmable Cortex-M0+ processor, custom SRAM, optical energy harvesting, 2-way communication, and a subthreshold temperature sensor. The temperature resolution is 0.034°C RMS, and the transmit distance extends to 15.6cm. The 0.04mm3 (~500× smaller than a grain of rice) fully assembled cellular temperature sensing system (CTS) is 24× smaller than prior programmable sensing systems [3], enabling implantation in a cluster of cells or large egg cells for biological studies.
K-Truss Decomposition of Large Networks on a Single Consumer-Grade Machine

k-truss decomposition of a graph is a method to discover cohesive subgraphs and to study the hierarchical structure among them. The existing algorithms for computing k-truss of today's massive networks mainly focus on reducing the runtime using parallel computation on a powerful multi-core server. Our focus, by contrast, is to investigate the feasibility of computing the k-truss on a single consumer-grade machine within a reasonable amount of time. We engineer two efficient k-truss decomposition algorithms: the edge-peeling algorithm proposed by J. Wang and J. Cheng and the asynchronous h-index-updating algorithm proposed by A. E. Sariyuce, C. Seshadhri, and A. Pinar. We reduce their memory usage significantly by optimizing the underlying data structures and by using WebGraph, an efficient framework for graph compression. With our optimized implementation, we show that we can efficiently compute k-truss decomposition of large networks (e.g., a graph with 1.2 billion edges) on a single consumer-grade machine.
Automated VNF Testing with Gym: A Benchmarking Use Case

In the growing landscape of Virtualized Network Function (VNF) development processes and methodologies fueled by enabling technologies for virtualization, the myriad of customization options unveil unprecedented SW/HW configuration knobs and hazards. Underlying execution environments multiplex resources imposing hard-to-predict relationships between VNF performance metrics (e.g., latency), allocated infrastructure assets (e.g., vCPU), and stimuli workloads. Gym is a framework designed to enable automated testing and to extraction of such relationships by the means of VNF performance profiles, walking through a cause-effect path towards agile DevOps methodologies for NFV. The demo showcases the implementation of Gym through exemplified live extraction of VNF metrics for analytics use cases, such as comparison factors between physical network functions and pre-deployment infrastructure dimensioning.
Application of the Internet of Things (IoT) Technology in Consumer Electronics - Case Study

In this paper, an alternative and new approach to embed sophisticated Internet of Things (IoT) functionality into consumer electronic devices has been proposed. The approach has been evaluated by implementing it into an electric kettle. The proposed concept includes a new communication architecture, data models as well as the possibility to create user profiles. Moreover, it is compatible with the building automation and control systems (BACS) and other available IoT solutions. The presented evaluation shows that by careful consideration of the power consumption, there is a high potential to a safe significant amount of energy, especially if it is scaled up to the number of potential users. Furthermore, it is shown that the proposed architecture is very flexible and can be extended with several other functionalities or profiles, depended on the requirements and addressed device type.
MTF -Storm: a High Performance Fuzzer for Modbus/TCP

MTF -Storm is a highly effective fuzzer for industrial systems employing Modbus/TCP connectivity. It achieves high fault coverage, while offering high performance and quick testing of the System-Under- Test (SUT). Analogously to its predecessor MTF, MTF -Storm operates in 3 phases: reconnaissance, fuzz testing and failure detection. Reconnaissance identifies the memory organization of the SUT and the supported functionality, enabling selection and synthesis of fuzz testing sequences that are effective for the specific SUT. MTF -Storm develops its test sequences systematically, starting with single field tests and proceeding with combined field tests, adopting techniques for automated combinatorial software testing and reducing the test space through partitioning field value ranges. MTF -Storm has been used to evaluate 9 different Modbus/TCP implementations and has identified issues with all of them, ranging from out-of-spec responses to successful denial-of-service attacks and crashes.
Efficient Information Flow Maximization in Probabilistic Graphs (Extended Abstract)

In this paper, we address the problem of optimizing information propagation in uncertain networks given a constrained budget of edges. We show that this problem requires to solve two NP-hard subproblems: the computation of expected information flow, and the optimal choice of edges. To compute the expected information flow to a source vertex, we propose the F-tree as a specialized data structure, that identifies independent components of the graph for which the information flow can either be computed analytically and efficiently, or for which traditional Monte-Carlo sampling can be applied independently of the remaining network.
Coercivity enhancement of Nd-Ce-Fe-B sintered magnets by the grain boundary diffusion process using Nd-Al-Cu alloys.

Nd-Fe-B based sintered magnets have a wide range of applications in energy efficient technologies such as traction motors of hybrid electric vehicles and wind generators [1]. In general, Nd-Fe-B based sintered magnets have a large amount of rare earth elements, such as Nd, Pr and Dy. From an economic point of view, new type of magnets incorporating light rare earth elements such as La and Ce into Nd-Fe-B based sintered magnets has stimulated considerable research efforts due to the analogous structure recently [2]. However, magnets incorporated with Ce exhibit much deteriorated magnetic properties due to the inferior intrinsic magnetic properties of Ce 2 Fe 14B, which saturation magnetization, ambient anisotropic field and Curie temperature were reported to be 1.17 T, 2.6 T and $152 ^{\circ}\mathrm {C}$, respectively [3]. Many efforts had been paid to improve the performance. One wellknown method to increase the coercivity of sintered magnets is to refine the grain boundary (GB) structure and chemistry through grain boundary diffusion (GBD) [4], [5]. Previous investigations have demonstrated that the coercivity of Ce-based magnets is enhanced by diffusion process with minor amounts of Nd 70 Cu 30 and Nd 80 Ag20 [6]. In this work, we investigated the microstructure of Nd-Ce-Fe-B sintered magnets and the eutectic grain boundary diffusion process using Nd-Al-Cu in order to comprehend the origin of the coercivity enhancement by the Nd-Al-Cu diffusion process.
Unsupervised Identification of Study Descriptors in Toxicology Research: An Experimental Study

Identifying and extracting data elements such as study descriptors in publication full texts is a critical yet manual and labor-intensive step required in a number of tasks. In this paper we address the question of identifying data elements in an unsupervised manner. Specifically, provided a set of criteria describing specific study parameters, such as species, route of administration, and dosing regimen, we develop an unsupervised approach to identify text segments (sentences) relevant to the criteria. A binary classifier trained to identify publications that met the criteria performs better when trained on the candidate sentences than when trained on sentences randomly picked from the text, supporting the intuition that our method is able to accurately identify study descriptors.
Counterexample-guided inference of controller logic from execution traces and temporal formulas

We developed an algorithm for inferring controller logic for cyber-physical systems (CPS) in the form of a state machine from given execution traces and linear temporal logic formulas. The algorithm implements an iterative counterexample-guided strategy: constraint programming is employed for constructing a minimal state machine from positive and negative traces (counterexamples) while formal verification is used for discovering new counterexamples. The proposed approach extends previous work by (1) considering a more intrinsic model of a state machine making the algorithm applicable to synthesizing CPS controller logic, and (2) using closed-loop verification which allows considering more expressive temporal properties.