Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Author Name Disambiguation Based on Fusion Features and Semantic Fingerprints

Author name disambiguation has been a challenging problem in many applications. In order to promote researches to solve name disambiguation, Aminer launched the Open Academic Data Challenge 2018 jointly with Chinese Association for Artificial Intelligence and China Knowledge Centre for Engineering and Technology. Aminer is a scholar-cantered academic search and mining platform covering more than 200 million papers and more than 100 million scholars in various academic fields. Our team proposed a name disambiguation method based on fusion features and semantic fingerprint technique to participate in the competition. The method identified authors with same names through organization feature and co-author feature at first, and then it solves ambiguity names by way of semantic fingerprints which are 128-bit binary vector generated from textual features of papers by Simhash algorithm. Our method scored 0.609 on the verification set and 0.879 on the test set ranking first in the final submission.
Assembly Method for Satellite Propulsion System based on HoloLens

As the satellite design changes from the traditional 2D mode to the 3D digital mode, the satellite assembly also enters the 3D assembly mode. An effective and accurate propulsion system assembly information transmission method from designers to operators is not formed, in the process of complex propulsion assembly, operators need to frequently confirm status back and forth between satellite and computer stations, and accidents with incorrect polarity in component installation often occur. In order to improve efficiency and quality of satellite propulsion system installation, a system based on HoloLens is designed. The successful application of this system provides a new solution for satellite propulsion system installation and marks a new step in the digital assembly capacity.
Indoor Localization System Based on Hybrid Wi-Fi/BLE and Hierarchical Topological Fingerprinting Approach

Wi-Fi-based localization using received signal strength (RSS) with pedestrian dead reckoning (PDR) algorithm is widely used to track pedestrians in indoor environments. However, the unsatisfactory deployment of Wi-Fi access points (APs) in buildings and the unstable performance of PDR are still key problems that lead to low localization accuracy. In this paper, we make contributions on proposing a hybrid Wi-Fi and Bluetooth Low Energy (BLE) indoor localization system (ILS) based on an efficient BLE deployment strategy and hierarchical topological fingerprinting (HTF). For the BLE deployment strategy, we deploy BLE beacons in places that do not have clear Wi-Fi signals for localization. This efficiently increases the localization accuracy. For HTF, we hierarchically localize targets based on a topological fingerprint (TF) map. First of all, we quickly localize the room in which the target is located by Dendogram-based support vector machine (DSVM). Then, the specific position of the target is estimated by fusing Wi-Fi and BLE signals with the TF map. The new BLE-based fingerprinting algorithm is used to localize targets in environments sparsely populated by BLE beacons. We conduct physical experiments in a real building. The experimental results demonstrate that the beacons deployed based on our proposed deployment strategy results in greater localization accuracy. Furthermore, the HTF approach performs better than the other commonly used localization methods.
Multi-Leader–Follower Game for MEC-Assisted Fusion-Based Vehicle On-Road Analysis

Multi-source data fusion to support vehicle on-road analysis is a promising service offered by mobile edge computing (MEC) for vehicles. With the fusion results delivered in near real-time, vehicle users (VUs) can peek around the corner, extend sensing range, reinforce and validate local observations. Consequently, there has emerged a new market between smart mobility service providers (SMSPs) and VUs in offering and purchasing multi-source data fusion results to support vehicle on-road analysis. Each SMSP and each VU compete with their peers to maximize their own profits. Also, VUs can receive multiple fusion results from several SMSPs and combine them to achieve a better inference. In this paper, we develop a multi-leader-follower game to model this complicated coupled problem. For the single-SMSP scenario, we analyze the properties of the leader-follower (L/F) Nash equilibrium and then reformulate the game as a mathematical program with equilibrium constraints (MPEC) to obtain the equilibrium. For the multi-SMSP scenario, the game is reinterpreted as an equilibrium problem with equilibrium constraints (EPEC), for which we analyze the local Nash equilibrium (LNE) with the assistance of variational inequality (VI) theory. Then, the block coordinate descent (BCD) method, which is low-complexity, is applied to solve the EPEC. Finally, numerical results are provided to validate the theoretical analysis and show that our proposed strategies maximize the utilities for both SMSPs and VUs.
Training Data Augmentation for Detecting Adverse Drug Reactions in User-Generated Content

Social media provides a timely yet challenging data source for adverse drug reaction (ADR) detection. Existing dictionary-based, semi-supervised learning approaches are intrinsically limited by the coverage and maintainability of laymen health vocabularies. In this paper, we introduce a data augmentation approach that leverages variational autoencoders to learn high-quality data distributions from a large unlabeled dataset, and subsequently, to automatically generate a large labeled training set from a small set of labeled samples. This allows for efficient social-media ADR detection with low training and re-training costs to adapt to the changes and emergence of informal medical laymen terms. An extensive evaluation performed on Twitter and Reddit data shows that our approach matches the performance of fully-supervised approaches while requiring only 25% of training data.
The Option Keyboard: Combining Skills in Reinforcement Learning

The ability to combine known skills to create new ones may be crucial in the solution of complex reinforcement learning problems that unfold over extended periods. We argue that a robust way of combining skills is to define and manipulate them in the space of pseudo-rewards (or "cumulants"). Based on this premise, we propose a framework for combining skills using the formalism of options. We show that every deterministic option can be unambiguously represented as a cumulant defined in an extended domain. Building on this insight and on previous results on transfer learning, we show how to approximate options whose cumulants are linear combinations of the cumulants of known options. This means that, once we have learned options associated with a set of cumulants, we can instantaneously synthesise options induced by any linear combination of them, without any learning involved. We describe how this framework provides a hierarchical interface to the environment whose abstract actions correspond to combinations of basic skills. We demonstrate the practical benefits of our approach in a resource management problem and a navigation task involving a quadrupedal simulated robot.
Advances in Ordinary Kriging Using the Stampede and Bridges Supercomputers

A new approach for the implementation of variogram models and ordinary kriging using the R statistical language, in conjunction with Fortran, the MPI (Message Passing Interface), and the “pbdDMAT” package within R on the Bridges and Stampede Supercomputers will be described. This new technique has led to great improvements in timing as compared to those in R alone, or R with C and MPI. These improvements include processing and forecasting vectors of size 25,000 in an average time of 6 minutes on the Stampede Supercomputer and 2.5 minutes on the Bridges Supercomputer as compared to previous processing times of 3.5 hours.
Living With Muscular Dystrophy

Nätterlund, B. 2001. Living with muscular dystrophy. Illness experience, activities of daily living, coping, quality of life and rehabilitation. Acta Universitatis Upsaliensis. Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine 1025. 91 pp. Uppsala. ISBN 91-554-4997-2 The overall aim was to study and gain knowledge about what it means living with muscular dystrophy and to study rehabilitation out from the patients perspective, among adults with muscular dystrophy in three Swedish counties: Örebro, Östergötland and Norrbotten. The thesis comprises two qualitative and three quantitative studies. Thirty interviews about illness experience were subjected to content analysis and 37 interviews about perceived support in rehabilitation were analysed according to phenomenological guidelines. Data were also collected by the Assessment of Problem-focused Coping (APC), the ADL Staircase, the elf-report ADL, the Mental Adjustment to Cancer Scale, the Sickness Impact Profile, the Psychosocial well-being questionnaire and the Quality of Life Profile. The APC was developed for assessment of problem-focused coping and covers also questions concerning the extent to which activities are experienced as problems and satisfaction with activities. The result shows that the experience of illness is largely similar in the three diagnostic groups (proximal MD, Myotonic muscular dystrophy, Myopathia distalis tarda hereditaria). The persons reported many restrictions of everyday activities, most often in mobility and transportation. Over half were dependent on other people in activities of daily living, and the illness was experienced mainly as having negative consequences for everyday life. A lower quality of life may be partly explained by a reduced capacity for activities. Problem-focused coping was used only to a limited extent, and ‘Fighting spirit’ was the dominant coping strategy. Rehabilitation was experienced as very valuable, particularly the education about the muscle disease, technical aids, grants and physical training. Over a five-year period, disability and quality of life of the study participants deteriorated significantly, and the dependence on other people increased.
Feature-binding in working memory through neuronal synchronization

Swap-errors occur in working memory (WM) tasks when a wrong response is in fact accurate relative to a non-target stimulus. These errors reflect the failure to bind in memory the conjunction of features that define one object, and the mechanisms implicated remain unknown. Here, we tested the mechanism of synchrony across featurespecific neural assemblies. We built a biophysical neural network model for WM composed of two 1D attractor networks for WM, one representing colors and the other one locations. Within each network, gamma-oscillations were induced during bump-attractor activity through the interplay of fast recurrent excitation and slower feedback inhibition. These two networks are then connected via weak excitation, accomplishing color-location binding through the selective synchronization of pairs of bumps across the networks. Association-encoding was accomplished by stimulating simultaneously the corresponding bumps in each network, and feature-decoding by stimulating the cued location with a . 5s pulse, which impacted strongly the corresponding phase-locked bump. In some simulations, “color bumps” abruptly changed their phase relationship with “location bumps” from which we derived a neural prediction: swap-errors are associated with a lower phase consistency of oscillatory activity in the delay period. Finally, we tested this prediction in MEG recorded from n=30 humans.
Advancements in co‐simulation techniques in combined transmission and distribution systems analysis

Common practice in transmission and distribution (T&D) systems analysis has been to treat them independently by decoupling those at the point of common coupling (PCC). With the large amount of distributed energy resources (DER) being added in distribution networks, the interactions between T&D systems at PCC produce unacceptable errors. This study discusses an alternate approach for the combined T&D systems analysis using co-simulation technique. The proposed framework couples the analysis of the two systems by iteratively exchanging the power flow variables at the PCC. The approach is implemented for an IEEE 9-bus transmission test system coupled with the IEEE 13-bus distribution test system and the analysis is validated against an equivalent stand-alone combined T&D system model.
Limiting THD of Grid Connected Photovoltaic System using PWM Switching Frequency Selection Based on Solar Irradiance Changing

Photovoltaic (PV) system becomes the best alternative energy to produce electricity that can replace fuel resource. Grid Connected Photovoltaic (GC-PV) system will supply the AC power generated to the utility grid then distributed to the consumer. Solar irradiance in Malaysia varies daily and throughout the year. The consequence of low irradiance level is the output of the PV array is low and thus the input of the inverter relatively becoming low. The inverter will exhibit large non-linearity when the inverter is operating at low power input during low level of solar irradiance and Total Harmonic Distortion (THD) will increased. Pulse Width Modulation (PWM) techniques that are Continuous Pulse Width Modulation (CPWM) and Discontinuous PWM (DPWM) are implemented in the inverter control of the GC-PV system using MATLAB/Simulink environment to investigate the effect of switching frequency on THD at different level of solar irradiance. THD is limit within standard requirement by selecting appropriate value of switching frequency. Results shows that CPWM has better performance in THD and switching losses at selected switching frequency.
Predictive wheel–rail management in London Underground: Validation and verification

London Underground is facing the challenge of increasing timetables against spending cuts across renewals and maintenance in all assets. In order to meet this challenge, it is reviewing all maintenance practices to make sure that they are appropriate for the current asset conditions. Management of the wheel–rail interface is critical to maximising the life of wheels and rails through preventative maintenance regimes that ensure all activities offer value for money and safe operation. Detailed monitoring of the asset condition using novel non-destructive techniques has allowed the identification of the problems which currently occur at the wheel–rail interface on the London Underground network. These problems are discussed in this paper along with some of the solutions proposed to manage them. Site observations from a range of rail rolling contact fatigue monitoring sites have also been compared to the outputs from vehicle dynamic simulations. These outputs were post-processed using a circle plotting technique, which illustrates the location, direction and severity of the forces, and the Whole Life Rail Model to predict the susceptibility to rail damage for two rail steel grades. The outputs from these comparisons have helped to illustrate the wheel–rail contact conditions and forces which are driving the observed damage and potential future enhancements to improve the accuracy of the models for predicting the observed rolling contact fatigue damage.
The Shoulder. 4th Ed.

Charles A. Rockwood Jr., Frederick A. Matsen III, Michael A. Wirth, and Steven B. Lippitt, editors. Philadelphia: Saunders-Elsevier; 2009. 1584 pages. $359.00. ISBN: 978-1-4160-3427-8. When reviewing any textbook that is in its fourth edition, one must compare the most recent edition with the previous three editions. The changes that the editors have made to keep up with the times, as well as their inclusion of the explosion of new information that has emerged since the third edition, …
Modal Analysis of Loader Crane with Variable Configuration

Paper presents a method of simplifications used over numerical studies on dynamic properties of a hydraulic loader crane. Simplification concerns hydraulic actuators and allows for a significant reduction in the time of building model and calculations. Modal analysis of a loader crane using the finite element method, for both complex and simplified models, was carried out. The modal analysis computation times for both models were compared. Next, the results obtained on the basis of simplified model were compared with the results of experimental studies.
Macro-Cell Assisted Task Offloading in MEC-Based Heterogeneous Networks With Wireless Backhaul

Heterogeneous networks have allowed network operators to enhance the spectral efficiency and support large number of devices by deploying close small-cells. Recently, Multi-access Edge Computing (MEC) has become an enabler for modern latency-sensitive 5G services by pushing tasks computation to the network edge. In this paper, we study the problem of task offloading in a MEC-enabled heterogeneous network with low-cost wireless backhaul, where we minimize the total devices’ energy consumption while respecting their latency deadline. We explore the benefit of leveraging the macro-cell cloudlet for computing small-cell users’ tasks, where the allocation of backhaul radio resources is optimized. We also jointly optimize the partial offloading decision, transmit power, and the allocation of access radio and computational resources. We mathematically formulate our problem as a non-convex mixed-integer program, and due to its complexity, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) method that provides an approximate solution. Through numerical analysis, we perform simulations based on varying configurations, and demonstrate the performance and efficiency of our proposed solution.
Utilizando Métricas de Qualidade na Avaliação de uma Aplicação para Cidades Inteligentes

Diversas aplicações de software vêm modificando o estilo de vida das pessoas em aspectos tecnológicos e sociais. Um conjunto dessas aplicações possibilitam a criação das chamadas Cidades Inteligentes (CIs). Assim como qualquer outro tipo de aplicação, estas aplicações precisam ser avaliadas para garantir uma qualidade adequada e fornecer uma melhor experiência aos usuários. Contudo, aplicações no contexto de CIs podem conter caracterı́sticas tecnológicas especı́ficas que não são avaliadas em aplicações tradicionais, tais como aspectos de interoperabilidade e mudança de contexto da aplicação de acordo com a alteração do ambiente do usuário. Desta forma, este artigo apresenta um estudo sobre a avaliação de duas caracterı́sticas de qualidade relevantes para CIs (sensibilidade ao contexto e calmness). Adicionalmente, apresentamos uma discussão sobre essas caracterı́sticas e suas métricas em relação aos aspectos humanos da interação com a aplicação.
Semantic Similarity in Cheminformatics

Similarity in chemistry has been applied to a variety of problems: to predict biochemical properties of molecules, to disambiguate chemical compound references in natural language, to understand the evolution of metabolic pathways, to predict drug-drug interactions, to predict therapeutic substitution of antibiotics, to estimate whether a compound is harmful, etc. While measures of similarity have been created that make use of the structural properties of the molecules, some ontologies (the Chemical Entities of Biological Interest (ChEBI) being one of the most relevant) capture chemistry knowledge in machine-readable formats and can be used to improve our notions of molecular similarity. Ontologies in the biomedical domain have been extensively used to compare entities of biological interest, a technique known as ontology-based semantic similarity. This has been applied to various bio-logically relevant entities, such as genes, proteins, diseases, and anatomical structures, as well as in the chemical domain. This chapter introduces the fundamental concepts of ontology-based semantic similarity, its application in cheminformatics, its relevance in previous studies, and future potential. It also discusses the existing challenges in this area, tracing a parallel with other domains, particularly genomics, where this technique has been used more often and for longer.
Real-time Lipreading: Effects of Compression and Frame-rate

Speech recognition systems can be found all around us. From personal assistants in mobile phones and smart speakers to robots, we use speech recognition systems everyday. However, communicating with them can be troublesome in noisy environments because they only use audio signals for speech recognition. This problem can be solved by using visual speech recognition or lipreading systems. Research on lipreading systems has been going on since the 1980s but such systems are not being used in real-time systems yet. This can be attributed to the fact they need to process significantly higher amounts of data than audio speech processing which takes a lot of time and hence, they cannot be used in real-time. This thesis aims at finding out if frame rate, jpeg compression or presence of noise have any impact on the performance of lipreading system. The LipNet system is used for this thesis and the Lip Reading in the Wild (LRW) dataset is used for the purpose of experiments. The frame rate of videos of the dataset is varied from 11 to 25, with an increment of 2 for each experiment. Also, compression ratio is varied between no compression and 30 % quality, to find out how compression affects the performance of lipreading systems. Also, salt and pepper noise is artificially added to the dataset for the purpose of experiments. The results from the experiments showed that performance is not affected till frame rate 21, but it starts degrading gradually from frame rate 19 to 13 and after that there is sudden drop in the accuracy of LipNet. With compression of frames to 30 percent of their original quality, there is only a slight decrease in accuracy. However, there is a huge reduction in data size, which makes it easier to transmit data for cloud processing. We found substantial degradation in performance with the presence of noise with a probability of only 3 percent. This means that if we decrease frame rate to 21 and compress the frames to 30 % quality, memory usage can be decreased to 25 % without much impact on performance of the system. However, quality of video capturing cameras and data transmission to cloud needs to be monitored to avoid noise.
KEIDS: Kubernetes-Based Energy and Interference Driven Scheduler for Industrial IoT in Edge-Cloud Ecosystem

With the rapid explosion of Industrial Internet of Things (IIoT), the need for real-time data processing with enhanced flexibility and scalability has increased manifold. However, the newly evolved containerization technology offers lucrative advantages in comparison to the conventional virtual machines. However, management of these light-weight containers is a tedious task, but Google Kubernetes offers a consolidated container management and scheduling for successful execution of various lightweight containers. Nevertheless, the existing Kubernetes solutions fall short in efficiently handling the “interference” and “energy minimization” challenges in IIoT set-up. Hence, in this article, we present a competent controller, named Kubernetes-based energy and interference driven scheduler (KEIDS), for container management on edge-cloud nodes taking into account the emission of carbon footprints, interference, and energy consumption. The problem of task scheduling has been formulated using integer linear programming based on multiobjective optimization problem. In detail, KEIDS minimizes the energy utilization of edge-cloud nodes in IIoT for optimal green energy utilization. Henceforth, the applications are scheduled on the available nodes in less time with minimum interference from other applications, which in turn guarantees an optimal performance to the end-users. An extensive evaluation of the proposed KEIDS scheduler in comparison to the existing state-of-the-art schemes indicates its superior performance on real-time data acquired from Google compute cluster.