Miracle-dz/UniFAR-FTUs huggingface dataset:
A Lens into Employee Peer Reviews Via Sentiment-Aspect Modeling

Given a corpus of employee peer reviews from a large corporation where each review is structured into pros and cons, what are the prevalent traits that employees talk about? How can we describe the performance of an employee with just a few sentences, that help us interpret what their work is praised and criticized for? What is the best way to summarize an employee's reviews, while preserving the content and sentiment as well as possible? In this work, we study a large collection of corporation-wide employee peer reviews from a technology enterprise. Motivated by the challenges we outline in our analysis of employee review data, our work makes two main contributions in the domain of people analytics: (a) Sentiment-Aspect Model (SAM): we introduce a stylized log-linear model that identifies the hidden aspects and sentiment within an employee peer review corpus, (b) Interpretable Sentiment-Aspect Representations (EMPLOYEE2VEC): using SAM, we produce a vector space embedding for each employee, containing an overall sentiment score per aspect, and (c) Summarization of Employee Peer Reviews (PEERSUM): we summarize an employee's peer reviews with just a few sentences which reflect the most prevalent traits and associated sentiment for the employee as much as possible. We show that our model SAM can use the structure present in the dataset as supervision to discover meaningful latent traits and sentiment embodied in the reviews. Our employee vector representations Employee2vecprovide a compact, interpretable overview of their evaluation. The review summaries extracted by PeersUmprovide text that explains the professional performance of an employee in a succinct and objectively quantifiable way. We also show how to use our techniques for people analytics tasks such as the analysis of thematic differences between departments, regions, and genders.
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection

State-of-the-art networks that model relations between two pieces of text often use complex architectures and attention. In this paper, instead of focusing on architecture engineering, we take advantage of small amounts of labelled data that model semantic phenomena in text to encode matching features directly in the word representations. This greatly boosts the accuracy of our reference network, while keeping the model simple and fast to train. Our approach also beats a tree kernel model that uses similar input encodings, and neural models which use advanced attention and compare-aggregate mechanisms.
Analytical Study of Influence of Carrier Frequency of Inverter on Rotating Performance of Permanent Magnet Synchronous Motor

This paper presents analytical study results for the relationship between carrier frequency of inverter and performance of permanent magnet synchronous motor (PMSM), of which mechanical output is 60 kW. Compared with traditional methods, the proposed method can improve the total efficiency, comprehensively considering the inverter's losses and motor's loss. The analysis is carried out based on voltage equations and an equation of torque. And the inverter's losses which include the conducting loss and switching loss are considered for estimating the total power dissipation. The relationship among carrier frequency, total harmonic distortion of current and torque is also analyzed. The optimal carrier frequency can be clarified according to the total loss of inverter and motor, the average torque, and the total harmonic distortion. Thus, it can increase the total efficiency of the system which includes inverter and motor. Different optimal carrier frequencies of inverter at different rotating speed of motor can be chosen, providing reference for minimizing losses of system in motor's whole speed region.
HTML Document Error Detector and Visualiser for Novice Programmers

Learning HTML poses similar challenges as learning conventional programming language by novice programmer. Apart from the HTML validator, there are limited tools to help novice programmer address errors in their HTML code. In this study, we employ visualisation techniques to display the structural and contextual information of the HTML code. We look at condensing and visually representing the important aspects of the HTML code. This is to enable novice programmers gain insights on the HTML code structure and locate any underlying syntax and semantic errors.
Prognosis of the electromagnetic field parameters of numerically controlled machine tools in industrial conditions

The problem of high-frequency electromagnetic field at the workplace is critical due to its effect on the operators of technological machines. For this reason, the exposure to electromagnetic fields at the workplace should be subject to regular monitoring. The paper presents the use of simple models for forecasting of magnetic and electric fields components. This study investigates the high-frequency electromagnetic field emitted in CNC machine tools area (DMU 65 Mono Block). Experimental tests were conducted for CNC machine tool. The measurements of high-frequency electric field strength were performed in compliance with the method specified in the PN-T- 06580–3: 2002 standard.
Comparison of Current Control Strategies in Modular Multilevel Converter

There is a need to transmit the power generated from windfarm in remote locations to the residential and industrial load. One of the suitable and economic solution for such long distance transmission of power is the High Voltage DC (HVDC) transmission. The Modular Multilevel Converter (MMC) based HVDC transmission is a proven technology with very high power quality. A multi-objective control is used to control the MMC, one of the objectives is to deliver the power from windfarm to grid, the other objective of the control is to achieve stable operation of the MMC. In order to achieve a stable operation, the capacitors in the MMC should be balanced and the currents circulating within the phases of the MMC need to be controlled. Many control strategies have been proposed to suppress such Circulating Current (CC). However, there is no clear comparison between these control strategies.In this paper control strategies for the MMC are reviewed specifically the circulating current suppressor. The cascaded control based on PI-controller and the Model Predictive Control (MPC) for outer current control are compared. Results show that traditional method has better steady state performance, while MPC based method has much faster dynamic response and has the advantage of involving less control loops in controlling systems with multiple control aspects. The control methods for capacitor voltage balancing and the circulating current suppression are simulated and compared.
ProfileGen: Generation of Automatic and Realistic Artificial Profiles

One of the most effective approaches for detecting malicious activity in online social networks (OSNs) involves the use of social network honeypots - artificial profiles. Therefore, there is a growing need for the ability to reliably generate realistic artificial honeypot profiles in OSNs. In this research we present ‘ProfileGen’ - a method for the automated generation of profiles for professional social networks, giving particular attention to producing realistic education and employment records. ‘ProfileGen’ creates honeypot profiles that are similar to actual data by extrapolating the characteristics and properties of real data items. Evaluation by 70 domain experts confirms the method's ability to generate realistic artificial profiles that are indistinguishable from real profiles, demonstrating that our method can be applied to generate realistic artificial profiles for a wide range of applications.
Electromagnetic Design of an Interior Permanent Magnet Motor for Vehicle Traction

This paper reveals the design process of a 15:1 constant power speed range 75 kW motor for an electric vehicle application. The paper starts with a summary of the conceptual design process, which analyzes the pros and cons of various motor topologies (induction, spoke-type IPM and multi-barrier IPM) and type of magnet (high energy NdFeB and low energy ferrites). Then, the tradeoffs affecting the results of the preliminary design are shown. Finally, a detailed description of the final design is disclosed, focusing on motor performance, efficiency, torque oscillation mitigation and risk of demagnetization of the permanent magnets.
Fluid Overload in Critically Ill Children

Background: A common practice in the management of critically ill patients is fluid resuscitation. An excessive administration of fluids can lead to an imbalance in fluid homeostasis and cause fluid overload (FO). In pediatric critical care patients, FO can lead to a multitude of adverse effects and increased risk of morbidity. Objectives: To review the literature highlighting impact of FO on a multitude of outcomes in critically-ill children, causative vs. associative relationship of FO with critical illness and current pediatric fluid management guidelines. Data Sources: A literature search was conducted using PubMed/Medline and Embase databases from the earliest available date until June 2017. Data Extraction: Two authors independently reviewed the titles and abstracts of all articles which were assessed for inclusion. The manuscripts of studies deemed relevant to the objectives of this review were then retrieved and associated reference lists hand-searched. Data Synthesis: Articles were segregated into various categories namely pathophysiology and sequelae of fluid overload, assessment techniques, epidemiology and fluid management. Each author reviewed the selected articles in categories assigned to them. All authors participated in the final review process. Conclusions: Recent evidence has purported a relationship between mortality and FO, which can be validated by prospective RCTs (randomized controlled trials). The current literature demonstrates that “clinically significant” degree of FO could be below 10%. The lack of a standardized method to assess FB (fluid balance) and a universal definition of FO are issues that need to be addressed. To date, the impact of early goal directed therapy and utility of hemodynamic parameters in predicting fluid responsiveness remains underexplored in pediatric resuscitation.
Analysis of Fault- Tolerant PM Motors with Independent Phases by Finite Element Method

This paper presents the finite element analysis of a multiphase permanent-magnet motor designed to have independent phases structure according to the principles of fault tolerance. The goal of the study is to verify and quantify the independence of the phases from the electromagnetic point of view, one of the key requirements of fault-tolerant machines. The case study refers to a five-phase permanent-magnet motor designed for an aircraft flap application. The typical operating conditions of motors designed to operate in fault tolerant environments are considered, such as the opening of one or more phases after the occurrence of a failure. The actual performance of the multiphase motor is compared to that achieved by the superposition of the effects of each phase fed independently, which is the straightforward approach in usual modeling. The results demonstrate that modeling by independent phases leads to negligible errors in almost all the operating conditions.
The Efficiency Analysis of Various Structural Solutions of the Wheel Motor Cooling Systems

The analysis of the performance of various structural solutions of the cooling system for the in wheel car motor is presented in the work. The analysis was conducted on the prepared spatial calculation models using the CFD software. The maximum motor temperature for various solutions of the stator support structure and for different shapes of the water jacket channel was determined as a result of simulation. The analysis for a steady state with constant losses corresponding to the S1 motor operation point and the constant flow of the cooling medium was carried out. The calculations were made by building separate discrete models for each analyzed case. The information how the structural changes affect the efficiency of the cooling system and how to improve the prototype of the in wheel car motor was given as a result of conducted thermal simulations.
An Information Geometry-Based Distance Between High-Dimensional Covariances for Scalable Classification

Modeling images/videos with covariance matrices has attracted increasing attentions in various vision tasks, especially in visual classification. For covariances-based visual classification, measuring the distances between covariances is one of the key issues and has been studied for decades. Since the space of covariances is a Riemannian manifold, the geometrical structure of covariances should be favorably considered when designing distance metrics. Although this problem has been widely studied, designing an effective and efficient metric between high-dimensional covariances (HDCOV) for scalable classification is still an open problem. In this paper, we present an information geometry-based distance (IGBD) to tackle this challenge from the perspective of information geometry. Our idea is based on the fact that each covariance can be viewed as a zero-mean Gaussian distribution, and thus the distances between covariances are measured by those between the corresponding Gaussian distributions. The core of our method is to project each distribution, in the form of a set of random samples, to a vector on the tangent space of a common, known distribution on the statistical manifold, based on Fisher information metric and maximum likelihood method. On the tangent space, the Euclidean norm can be used to measure the distances between those sets of projection vectors (or equivalently distributions). The proposed IGBD for HDCOV is computationally efficient and easily combined with a linear support vector machine, suitable for scalable visual classification. The experiments are conducted on various kinds and sizes of benchmarks, and results show the proposed method is efficient and the combination of HDCOV can achieve very competitive performance.
Time-Aware Sub-Trajectory Clustering in Hermes@PostgreSQL

In this paper, we present an efficient in-DBMS framework for progressive time-aware sub-trajectory cluster analysis. In particular, we address two variants of the problem: (a) spatiotemporal sub-trajectory clustering and (b) index-based time-aware clustering at querying environment. Our approach for (a) relies on a two-phase process: a voting-and-segmentation phase followed by a sampling-and-clustering phase. Regarding (b), we organize data into partitions that correspond to groups of sub-trajectories, which are incrementally maintained in a hierarchical structure. Both approaches have been implemented in Hermes@PostgreSQL, a real Moving Object Database engine built on top of PostgreSQL, enabling users to perform progressive cluster analysis via simple SQL. The framework is also extended with a Visual Analytics (VA) tool to facilitate real world analysis.
Trajectory Generation for Millimeter Scale Ferromagnetic Swimmers: Theory and Experiments

Microrobots have the potential to impact many areas such as microsurgery, micromanipulation and minimally invasive sensing. Due to their small size, microrobots swim in a regime that is governed by low Reynolds number hydrodynamics. In this paper, we consider small scale artificial swimmers that are fabricated using ferromagnetic filaments and locomote in response to time varying external magnetic fields. We motivate the design of previously proposed control laws using tools from geometric mechanics and also demonstrate how new control laws can be synthesized to generate net translation in such swimmers. We further describe how to modify these control inputs to make the swimmers track rich trajectories in the workspace by investigating stability properties of their limit cycles in the orientation angles phase space. Following a systematic design optimization, we develop a principled approach to encode internal magnetization distributions in millimeter scale ferromagnetic filaments. We verify and demonstrate this procedure experimentally and finally show translation, trajectory tracking and turning in place locomotion in these optimal swimmers using a Helmholtz coils setup.
Dynamic GPU Parallel Sparse LU Factorization for Fast Circuit Simulation

Lower–upper (LU) factorization is widely used in many scientific computations. It is one of the most critical modules in circuit simulators, such as the Simulation Program With Integrated Circuit Emphasis. To exploit the emerging graphics process unit (GPU) computing platforms, several GPU-based sparse LU solvers have been recently proposed. In this paper, efficient algorithms are presented to enhance the ability of GPU-based LU solvers to achieve higher parallelism as well as to exploit the dynamic parallelism feature in the state-of-the-art GPUs. Also, rigorous performance comparisons of the proposed algorithms with GLU as well as KLU, for both the single-precision and double-precision cases, are presented.
Distributed Multi-Player Bandits - a Game of Thrones Approach

We consider a multi-armed bandit game where N players compete for K arms for T turns. Each player has different expected rewards for the arms, and the instantaneous rewards are independent and identically distributed. Performance is measured using the expected sum of regrets, compared to the optimal assignment of arms to players. We assume that each player only knows her actions and the reward she received each turn. Players cannot observe the actions of other players, and no communication between players is possible. We present a distributed algorithm and prove that it achieves an expected sum of regrets of near-O\left(\log^{2}T\right). This is the first algorithm to achieve a poly-logarithmic regret in this fully distributed scenario. All other works have assumed that either all players have the same vector of expected rewards or that communication between players is possible.
Efficient Urban Railway Design integrating Train Scheduling, Onboard Energy Storage, and Traction Power Management

This paper presents a design of urban railway operation based on an integrated design approach. The integrated approach aims to integrate the design train schedule, optimizing scenario for installing onboard energy storage, and considering effective power management. The proposed integrated design is formulated as an optimization problem with two alternative form of objective function solved by Genetic Algorithm. First objective function is minimizing energy supplied from substation and energy capacity of energy storage. Alternative form of objective function is minimizing surplus regenerative energy and energy capacity of energy storage. Both objective functions deal with energy-saving and cost-saving concern. To verify the performance of proposed method, the numerical case studies of urban railway system in Thailand and China will be numerically performed. The results shown that the proposed integrated design with the first objective function in off-peak hour can provide up to 16% of energy-saving performance in the case of Chinese Jinan Metro system and 27% of energy-saving performance in the case of Bangkok Metro System.
Model Order Reduction of Bearingless Reluctance Motor Including Eccentricity

Eccentricity in a bearingless motor may occur during different operating states of the machine. This rises challenges in designing robust control for the machine with a lumped parameter model, due to the cross coupling of the windings with respect to the eccentric position of the rotor, the saturation of the ferromagnetic material, and spatial complexity. The non-linearity of the ferromagnetic material and the spatial harmonics can be considered in a finite element model of the machine, although applying it in a real time system is unreasonable. We propose a novel method based on orthogonal interpolation to reduce the order of the 2D finite element model of a bearingless synchronous reluctance motor, suitable for implementation in a real-time system. The winding currents and the eccentricity are given as inputs to the reduced model and the nodal values of the magnetic vector potential is obtained as the output, wherefrom the flux linkages, torque, and forces can be computed easily.
Holistic Influence Maximization for Targeted Advertisements in Spatial Social Networks

The problem of influence maximization has recently received significant attention. However, most studies focused on user influence via cyber interactions while ignoring their physical interactions which are important to gauge influence propagation. Additionally, targeted campaigns or advertisements have not received sufficient attention. To do this, we first devise a novel holistic influence diffusion model and then formulate a new holistic influence maximization query problem and develop three algorithms. Finally, we conduct extensive experiments to evaluate the effectiveness and efficiency of the proposed solutions.
Facts That Matter

This work introduces fact salience: The task of generating a machine-readable representation of the most prominent information in a text document as a set of facts. We also present SalIE, the first fact salience system. SalIE is unsupervised and knowledge agnostic, based on open information extraction to detect facts in natural language text, PageRank to determine their relevance, and clustering to promote diversity. We compare SalIE with several baselines (including positional, standard for saliency tasks), and in an extrinsic evaluation, with state-of-the-art automatic text summarizers. SalIE outperforms baselines and text summarizers showing that facts are an effective way to compress information.
Artificial Intelligence for Conversational Robo-Advisor

With the advent of the artificial intelligence (AI) era, the combination of AI with financial technology (FinTech) has become a development trend in the financial industry. However, deep learning (DL) on the application of automated financial management has been rarely investigated. Thus, this research focuses on the applications of FinTech and DL in asset allocation and aims to optimize investment portfolio. The best investment portfolio in index-based funds based on Taiwan's index-type security investment trust funds are the main investment targets. Time series models for DL, that is, long short-term memory, predict the increase of each investment target and find the best investment portfolio in combination with the relevant asset allocation theory. In this research, we use the Markowitz mean-variance and Black-Litterman models as our asset allocation models for robo-advisor. Results show that the Black-Litterman model has a better accumulated return performance than the Morkowitz model and outperforms other strategies. The Human-Computer Interaction (HCI) dialogue service adopts artificial intelligence markup language (AIML) and a generative model. The main contribution of this paper is that we have developed an integrated knowledge-based and generative-based models for AI conversational robo-advisor.