Miracle-dz/UniFAR-FTUs huggingface dataset:
PWM Strategy for Parallel Operation of Three Phase Converters Tied to Grid

When three phase converters operate, tied to the grid, interleaved PWM with coupled inductor can be used to enhance current quality. As the results, the harmonics of the output current are reduced significantly because of the increased equivalent switching frequency due to interleaving operation. However, the circulating current increases between converters. The coupled inductor is employed to suppress this circulating current. In this system, optimization of PWM pulse pattern by interleaved carrier would be the best strategy to enhance harmonics characteristics of output voltage and current. In this paper, based on harmonics analysis of PWM, a new PWM strategy for interleaved PWM is proposed and an offset voltage of each converter is optimized in terms of the harmonics of output voltage and current. As the results, the grid side inductor can be significantly reduced by decreasing voltage and current harmonics of the system to the grid. The validity, feasibility and effectiveness of the proposed method have been confirmed by computer simulation and experimental results.
Somm: Into the Model

To what extent could the sommelier profession, or wine stewardship, be displaced by machine leaning algorithms? There are at least three essential skills that make a qualified sommelier: wine theory, blind tasting, and beverage service, as exemplified in the rigorous certification processes of certified sommeliers and above (advanced and master) with the most authoritative body in the industry, the Court of Master Sommelier (hereafter CMS). We propose and train corresponding machine learning models that match these skills, and compare algorithmic results with real data collected from a large group of wine professionals. We find that our machine learning models outperform human sommeliers on most tasks — most notably in the section of blind tasting, where hierarchically supervised Latent Dirichlet Allocation outperforms sommeliers’ judgment calls by over 6% in terms of F1-score; and in the section of beverage service, especially wine and food pairing, a modified Siamese neural network based on BiLSTM achieves better results than sommeliers by 2%. This demonstrates, contrary to popular opinion in the industry, that the sommelier profession is at least to some extent automatable, barring economic (Kleinberg et al., 2017) and psychological (Dietvorst et al., 2015) complications.
EClone: detect semantic clones in Ethereum via symbolic transaction sketch

The Ethereum ecosystem has created a prosperity of smart contract applications in public blockchains, with transparent, traceable and programmable transactions. However, the flexibility that everybody can write and deploy smart contracts on Ethereum causes a large collection of similar contracts, i.e., clones. In practice, smart contract clones may amplify severe threats like security attacks, resource waste etc. In this paper, we have developed EClone, a semantic clone detector for Ethereum. The key insight of our clone detection is Symbolic Transaction Sketch, i.e., a set of critical semantic properties generated from symbolic transaction. Sketches of two smart contracts will be normalized into numeric vectors with a same length. Then, the clone detection problem is modeled as a similarity computation process where sketches and other syntactic information are combined. We have applied EClone in identifying semantic clones of deployed Ethereum smart contracts and achieved an accuracy of 93.27%. A demo video of EClone is at https://youtu.be/IRasOVv6vyc.
WarningsGuru: integrating statistical bug models with static analysis to provide timely and specific bug warnings

The detection of bugs in software systems has been divided into two research areas: static code analysis and statistical modeling of historical data. Static analysis indicates precise problems on line numbers but has the disadvantage of suggesting many warning which are often false positives. In contrast, statistical models use the history of the system to suggest which files or commits are likely to contain bugs. These course-grained predictions do not indicate to the developer the precise reasons for the bug prediction. We combine static analysis with statistical bug models to limit the number of warnings and provide specific warnings information at the line level. Previous research was able to process only a limited number of releases, our tool, WarningsGuru, can analyze all commits in a source code repository and we currently have processed thousands of commits and warnings. Since we process every commit, we present developers with more precise information about when a warning is introduced allowing us to show recent warnings that are introduced in statistically risky commits. Results from two OSS projects show that CommitGuru's statistical model flags 25% and 29% of all commits as risky. When we combine this with static analysis in WarningsGuru the number of risky commits with warnings is 20% for both projects and the number commits with new warnings is only 3% and 6%. We can drastically reduce the number of commits and warnings developers have to examine. The tool, source code, and demo is available at https://github.com/louisq/warningsguru.
Text Mining for Plagiarism Detection: Multivariate Pattern Detection for Recognition of Text Similarities

The problem of plagiarism the recent years has been intensified by the availability of information in digital form and the accessibility of the electronic libraries through the Internet. As a result, plagiarism detection has been transformed into a big data analytics problem since the number of digital sources is extravagant and a new document needs to be compared with millions of other existing documents. In this paper, a text mining methodology is proposed that can detect all common patterns between a document and the documents in a reference database. The technique is based on a pattern detection algorithm and the corresponding data structure that enables the algorithm to detect all common patterns. The methodology has been applied in a well-defined dataset providing very promising results identifying difficult cases of plagiarism such as technical disguise.
Winning the app production rally

When a user looks for an Android app in Google Play Store, a number of apps appear in a specific rank. Mobile apps with higher ranks are more likely to be noticed and downloaded by users. The goal of this work is to understand the evolution of ranks and identify the variables that share a strong relationship with ranks. We explore 900 apps with a total of 4,878,011 user-reviews in 30 app development areas. We discover 13 clusters of rank trends. We observe that the majority of the subject apps (i.e., 61%) dropped in the rankings over the two years of our study. By applying a regression model, we find the variables that statistically significantly explain the rank trends, such as the number of releases. Moreover, we build a mixed effects model to study the changes in ranks across apps and various versions of each app. We find that not all the variables that common-wisdom would deem important have a significant relationship with ranks. Furthermore, app developers should not be afraid of a late entry into the market as new apps can achieve higher ranks than existing apps. Finally, we present the findings to 51 developers. According to the feedback, the findings can help app developers to achieve better ranks in Google Play Store.
Research on Key Technology and Equipment for Zhangbei 500kV DC Grid

With the capacity of new energy resources such as wind power and photovoltaic in Zhangbei region increasing rapidly, the reliable transmission and consumption capabilities of large-scale and high-proportion new energy resources are urgently improved. Considering the advantages of the VSC-HVDC transmission technology, Zhangbei project is designed as symmetrical bipolar system which adopts half-bridge modular multi-level converter (MMC) topology and overhead lines for power transmission. In this paper, a system scheme is presented, and some results are provided. Meanwhile, a novel modular cascaded hybrid DC breaker concept is proposed, and 500kV VSC valve and DC breaker prototype are developed to meet the requirement of the Zhangbei four-terminal dc grid.
Optimized Selection and Utilization of DC-Link Capacitor in a Single-Phase PV Grid Inverter System

Single-phase PV grid inverters usually require large reservoir capacitors on the DC-Link to absorb 2nd order harmonics. This paper shows in a design how to improve the utilization of metalized polypropylene film capacitor (MPFC) specifications in multiple dimensions simultaneously: breakdown voltage rating, capacitance, ripple current capability and warranted reliability performance. As a result, the system size and cost can be reduced, while the overall energy capturing efficiency is guaranteed. Under low-capacitance conditions the DC-Link voltage varies within a fundamental cycle; a boost converter is placed between the PV panel and the DC-Link to provide MPPT (maximum power point tracking). The modulation index of the output inverter can be dynamically varied for waveform compensation. Comparison is made with electrolytic capacitors and it is found that when using an optimized capacitor selection strategy, it is possible to mitigate the increased size and cost implications of film capacitors. Simulation demonstrates the design of a 2.5kW system, while experiment is conducted to prove the design concept.
Two-Stage Protection for Multi-Channel Power Electronic Converters Fed Large Asynchronous Hydro-Generating Unit

Multi-channeled voltage source inverter (VSI) provided in rotor circuit of doubly fed induction machine (DFIM) ensures control of real and reactive power of the machine alongwith speed variation. VSI is significantly impacted during grid disturbances since DFIM is very sensitive to the grid disturbances as stator/rotor circuit is directly connected to the grid. This paper presents two stage protection circuit for the multi-channeled VSI since it is useful for the continuous operation of the unit in case of marginal grid disturbances. Control system for the dc link chopper circuit protection and crowbar protection is developed based on the dc link voltage of the VSI. In addition, it provides the activation status of the protection circuits during grid disturbances which is economically helpful for the plant authorities. Two-stage protection circuit for a 250 MW DFIM with five channel back-to-back three level VSI is simulated in Matlab/Simulink. A 2.2 kW DFIM is tested in the laboratory to validate the simulation results.
Boost Integrated Three-Phase Solar Inverter using Current Unfolding and Active Damping Methods

This paper proposes a three-phase grid connected solar inverter with integrated boost function. The circuit operating principle is based on current unfolding and injection method, which is similar to that of a SWISS rectifier. This approach requires only two high frequency switches operating at only half voltage stress, thus leading to a significant reduction in switching losses. Other switches only operate at line frequency, and therefore can be optimized to reduce conduction losses. The proposed inverter therefore can deliver high efficiency. This paper discusses the basic operating principle as well as control method for the inverter. It is revealed that the output currents of the proposed inverter contains intrinsic oscillation due to current unfolding operation. In order to solve this problem, an active damping method is proposed to stabilize the operation. As a result, stable operation of the proposed method is confirmed by simulation. The feasibility of the proposed inverter is also confirmed using a mini laboratory prototype.
A Promising Gas Sensor Based on Monolayer  $\alpha $ -SbN to Detect SO2 Among SF6 Decompositions

The adsorption of five decomposed components (SO<sub>2</sub>, SOF<sub>2</sub>, SO<sub>2</sub>F<sub>2</sub> , H<sub>2</sub>S, and HF) of SF<sub>6</sub> on monolayer -SbN was investigated using the density functional theory. Based on the adsorption configurations, we built a simulated gas sensing device using monolayer -SbN, and the response to five gases was discussed and compared. The results showed that SO<sub>2</sub> and HF adsorption exhibited relatively larger adsorption energy and electron transfer with states overlaps in the density of states. Only SO<sub>2</sub> adsorption leads to the obvious decrease of band gap from about 1.47 to 1.15eV. The currentvoltage property demonstrates that the simulated gas sensor only had high response to SO<sub>2</sub> among these five gases. Our work suggests that there is a promising prospect for detecting SO<sub>2</sub> among SF<sub> 6</sub> decompositions using a monolayer -SbN based gas sensor.
Feasible Annotation Scheme for Capturing Policy Argument Reasoning using Argument Templates

Most of the existing works on argument mining cast the problem of argumentative structure identification as classification tasks (e.g. attack-support relations, stance, explicit premise/claim). This paper goes a step further by addressing the task of automatically identifying reasoning patterns of arguments using predefined templates, which is called argument template (AT) instantiation. The contributions of this work are three-fold. First, we develop a simple, yet expressive set of easily annotatable ATs that can represent a majority of writer’s reasoning for texts with diverse policy topics while maintaining the computational feasibility of the task. Second, we create a small, but highly reliable annotated corpus of instantiated ATs on top of reliably annotated support and attack relations and conduct an annotation study. Third, we formulate the task of AT instantiation as structured prediction constrained by a feasible set of templates. Our evaluation demonstrates that we can annotate ATs with a reasonably high inter-annotator agreement, and the use of template-constrained inference is useful for instantiating ATs with only partial reasoning comprehension clues.
Language Use Similarities in Small Dutch Twitter Communities

In this paper, we show that there are measurable differences between the language use of different small (Dutch) Twitter communities. We measured distances between n-gram probability distributions in 1,000-word samples of tweets by about 13,000 Twitter user communities ranging from 4 to 51 participants, discussing one of five selected broad topic areas, in which strong content-bearing words were masked. We found that the language use is more similar in users from the same community than in users from different communities. This was to be expected for discussions of the same topic, where the community might focus on specific subtopics, but the effect was also found across-topic.
Estimating Marginal Probabilities of n-grams for Recurrent Neural Language Models

Recurrent neural network language models (RNNLMs) are the current standard-bearer for statistical language modeling. However, RNNLMs only estimate probabilities for complete sequences of text, whereas some applications require context-independent phrase probabilities instead. In this paper, we study how to compute an RNNLM’s em marginal probability: the probability that the model assigns to a short sequence of text when the preceding context is not known. We introduce a simple method of altering the RNNLM training to make the model more accurate at marginal estimation. Our experiments demonstrate that the technique is effective compared to baselines including the traditional RNNLM probability and an importance sampling approach. Finally, we show how we can use the marginal estimation to improve an RNNLM by training the marginals to match n-gram probabilities from a larger corpus.
Geo-Location Identification of Facebook Pages

Online Social Network (OSN) communities serve as different platforms for multiple users' interaction - people behaving diversely among distinctive communities - such as entertainment, global and local discussion communities. However, attribute identification among online discussion communities remain largely unexplored. In this paper, we describe and analyze the geo-location property of large-scale Facebook public pages (15M pages). We propose a framework utilizing the connectivity of the page-like graph to predict the missing geo-location information based on Breadth-First Search (BFS). Our method achieves a satisfyingly high accuracy (89 %) on identifying the state location attribute of unknown United States (US) pages. Our empirical results offer a better understanding of regional social analysis and target audience broadcasting.
Pole-Slot Selection Considerations for Double Layer Three-phase Tooth-Coil Wound Electrical Machines

This work presents a combination of top-down and bottom-up design procedures to select a suitable pole-slot combination for a tooth-coil wound machine (TCWM), also known as non-overlapped fractional slot concentrated winding synchronous machine. Top-down features such as size and speed are determined by the intended application and affect the selection of slot and pole number. Bottom-up properties are quantified through key performance indicators (KPI) such as fundamental winding factor, periodicity, cogging multiplier and MMF harmonic leakage factor (HLF). Furthermore a compact and intuitive graphical way of presenting the properties of the double layer TCWMs is shown in this paper for slot number up to 39, highlighting the similarities among machines through the key winding factor concept. Analytical formulas for KPls are presented and the results are compared and visualized with FEM simulations.
Multi-Level Topology Based Linear Amplifier Family for Realization of Noise-Less Inverters

Nowadays, all of power converters utilize switching operation to reduce losses of semiconductor power devices. However, the switching operation causes high harmonics, high electromagnetic interference (EMI), and enlarging passive components in a circuit. These are the typical issues in the power electronics, because dv/dt and di/dt in a power converter tend to high due to practical use of SiC and GaN devices with high-speed switching capability. In this paper, multi-level topology based linear amplifiers (MLLA) without the switching operation are proposed for realization of noise-less inverters. Although the conventional linear amplifier such as the class-B amplifier generate large loss due to a linear operation, MLLA can reduce the loss by multiple series connection of MOSFETs. Three basic topologies, diode-clamped linear amplifier (DCLA), flying-capacitor linear amplifier (FCLA), and modular-cascaded linear amplifier (MCLA) are introduced and summarized as the MLLA family. The operation principles of the three basic topologies are quite similar, and loss generated during a linear operation in the MOSFETs become smaller, as the number of series connected MOSFET increases. These features enable to realize a noise-less inverter with high efficiency even compared with the PWM inverters. Finally, it is verified that 4-series circuits of the DCLA, FCLA, and MCLA achieve higher efficiency of 82% despite of the linear amplifier without the harmonics and EMI.
Followee Management: Helping Users Follow the Right Users on Online Social Media

User timelines in Online Social Media (OSM) remains filled with a significant amount of information received from followees. Given that content posted by followee is not under user's control, this information may not always be relevant. If there is large presence of not so relevant content, then a user may end up overlooking relevant content, which is undesirable. To address this issue, in the first part of our work, we propose suitable metrics to characterize the user-followee relationship. We find that most of the users choose their followees primarily due to the content that they post (content-conscious behavior, measured by content similarity scores). For a small number of followees, a high degree of social engagement (likes and shares) irrespective of the content posted by them is observed (user-conscious behavior, measured by user affinity scores). We evaluate our proposed approach on 26,516 followees across 100 random users on Twitter who have cumulatively posted 234,403 tweets. We find that on average for 60 % of their followees, users exhibit very low degree of content similarity and social engagement. These findings motivate the second part of our work, where we develop a Followee Management Nudge (FMN) through a browser extension (plugin) that helps users remain more informed about their relationship with each of their followees. In particular, the FMN nudges a user with a list of followees with whom they have least (or never) engaged in the past and also exhibit very low similarity in terms of content, thereby helping a user to make an informed decision (say by unfollowing some of these followees). Results from a preliminary controlled lab study show that 62.5 % of participants find the nudge to be quite useful.
Selection & Detection of Skin and Skin Color Background Under Complex Background

Skin detection is the process of finding skin colored pixels and regions in an image or a video. Skin detection plays an important role in a wide range of image processing applications ranging from face detection, face tracking, gesture analysis, and content-based image retrieval systems and to various human computer interaction domains. Skin color has proven to be a useful and robust cue for face detection, localization and tracking. During skin detection some non-skin pixels are usually detected as skin pixels. It is very difficult in images to select skin areas with complex background. The purposed work is an improved skin detection method that uses color spaces and combines color spaces to detect skin areas. The experimental result can separate skin and non-skin color under complex background.
Comparative Study of Permanent Magnet Machines with Single-Sided and Dual-Sided Magnets

This paper presents a comparative study of permanent magnet machines (PMMs) with single-sided (SS) and dual-sided (DS) magnets, respectively. The SS-PMM employs homopolar PMs between either adjacent stator teeth or iron rotor poles, while the DS-PMM is geometrically characterized by PM arrangement on both rotor and stator sides. Different from the SS-PMM, the DS-PMM can operate based on a bidirectional field modulated effect, which enables the torque density improvement. The machine topologies and field modulated effect are firstly introduced. Then, the basic electromagnetic characteristics of three 12 slot/10 pole pairs PMMs with SS and DS magnet arrangements are comprehensively analyzed and compared by finite element analysis (FEA). Moreover, the torque quality of the PMMs with different pole pairs are investigated and compared to offer a general guideline of the feasible stator/rotor combination.
Automatic Poetry Generation with Mutual Reinforcement Learning

Poetry is one of the most beautiful forms of human language art. As a crucial step towards computer creativity, automatic poetry generation has drawn researchers’ attention for decades. In recent years, some neural models have made remarkable progress in this task. However, they are all based on maximum likelihood estimation, which only learns common patterns of the corpus and results in loss-evaluation mismatch. Human experts evaluate poetry in terms of some specific criteria, instead of word-level likelihood. To handle this problem, we directly model the criteria and use them as explicit rewards to guide gradient update by reinforcement learning, so as to motivate the model to pursue higher scores. Besides, inspired by writing theories, we propose a novel mutual reinforcement learning schema. We simultaneously train two learners (generators) which learn not only from the teacher (rewarder) but also from each other to further improve performance. We experiment on Chinese poetry. Based on a strong basic model, our method achieves better results and outperforms the current state-of-the-art method.