Reviewerly/exHarmony huggingface dataset:
Parameter Estimation Using Unscented Kalman Filter on the Gray-Box Model for Dynamic EEG System Modeling

Model parameters' estimation is one of the most important tasks in the analysis and design process of a nonlinear dynamic system in real time, especially in the presence of noise. This article presents a novel approach in estimating important parameters of gray-box model for such a system on real nonlinear EEG to simulate efficiently the dynamic characteristics of neurons. Specifically, the proposed methodology exploits unscented Kalman filter (UKF) that is combined with chaos neural population model to formulate the interaction between the cortical areas. The proposed methodology is compared with the state-of-the-art parameters' estimation techniques to verify the efficiency of the UKF on the gray-box model. Experimental results show that the proposed method demonstrates the lowest error value of root mean square error (RMSE) among existing parameter estimation methods. The robustness of the proposed approach is further validated in its convergence and automation, with minimum error relatively than others and without any user-specified input, respectively.
Research on Collaborative Filtering Recommendation Algorithm Based on Mahout and User Model

Abstract Based on the research status of recommendation system, the traditional collaborative filtering algorithm is explored. By establishing the concept of training sample and Mahout framework, a collaborative filtering algorithm based on Mahout and extracted user model can be proposed. Using this algorithm in the system of recommendation aims to solve the problem of transforming the traditional recommendation problem into the classification problem in machine learning. In this way, it can provide personalized recommendations for users. The analysis of the experimental results of the collected some data sets suggests that the algorithm based on user model and machine learning is effective and optimal, at the same time, the efficiency of the algorithm under Mahout framework is also verified.
Using keyword spotting systems as tools for the transcription of historical handwritten documents: Models and procedures for performance evaluation

The paper proposes a performance model for estimating the improvement of the time needed to transcribe small collections of handwritten documents by using a keyword spotting system (KWS) with respect to the time for manually achieving the transcription. The proposed model assumes that no other information than those obtained from the samples and the KWS system performance on the training set are available, and, depending on them, establishes analytically the condition the performance measures must satisfy to make it profitable to use the system, and, in the affirmative case, estimates the gain and the accuracy of such estimation. The model is complemented by a step-by-step procedure for building the training set, running the KWS on it, estimating the performance parameters on the data set, and eventually estimating the overall improvement and the accuracy of this estimate.
Basic Ensembles of Vanilla-Style Deep Learning Models Improve Liver Segmentation From CT Images

Segmentation of the liver from 3D computer tomography (CT) images is one of the most frequently performed operations in medical image analysis. In the past decade, Deep Learning Models (DMs) have offered significant improvements over previous methods for liver segmentation. The success of DMs is usually owed to the user’s expertise in deep learning as well as to intricate training procedures. The need for bespoke expertise limits the reproducibility of empirical studies involving DMs. Today’s consensus is that an ensemble of DMs works better than the individual component DMs. In this study we set off to explore the potential of ensembles of publicly available, ‘vanilla style DM segmenters. Our ensembles were created from four off-the-shelf DMs: U-Net, Deepmedic, V-Net, and Dense V-Networks. To prevent further overfitting and to keep the overall model simple, we use basic non-trainable ensemble combiners: majority vote, average, product and min/max. Our results with two publicly available data sets (CHAOS and 3Dircadb1) demonstrate that ensembles are significantly better than the individual segmenters on four widely used metrics.
Motion Sickness Predictors in College Students and Their First Experience Sailing at Sea

INTRODUCTION: Individual motion sickness susceptibility can be rapidly estimated by the motion sickness susceptibility questionnaire (MSSQ), but its stability is affected by various factors. The purpose of this study was to investigate the involved predictive factors of motion sickness screened with uniform samples of Chinese college students and to verify the individual susceptibility difference in marine navigation. METHODS: A total of 1051 college students (719 men, 332 women; mean age: 18.32 ± 0.65 yr) completed the MSSQ. Another 42 men (mean age: 21.12 ± 1.10 yr) took part in 2 separate voyages. MSSQ data were collected before sailing and Graybiel motion sickness questionnaire (GMSQ) data were collected within 24 h after sailing and 24 h before landing. RESULTS: The internal consistency of the MSSQ was 0.685. The mean subscore of the MSSQ-A (18.47 ± 19.49) was significantly higher than that of the MSSQ-B (12.69 ± 14.97). Women had significantly higher MSSQ scores (38.29 ± 33.49) than men (27.87 ± 30.27). The mean MSSQ score of the inland subjects (33.97 ± 33.35) was significantly higher than that of the coastal subjects (27.81 ± 29.24). Nearly 93% of new seafarers experienced seasickness during their first navigation. The MSSQ score was positively correlated with seasickness symptoms (r = 0.706). CONCLUSION: Gender, age, and birthplace appear to be important predictors of motion sickness for Chinese college students. Specifically, women, younger people, and people who were born in inland China seem more prone to the syndrome. A high MSSQ score is a risk factor for seasickness. However, long-term voyages can lead to habituation, which reduces the occurrence of seasickness. Zhang X, Sun Y. Motion sickness predictors in college students and their first experience sailing at sea . Aerosp Med Hum Perform. 2020; 91(2):71–78.
Training in Task Space to Speed Up and Guide Reinforcement Learning

Recent breakthroughs in the reinforcement learning (RL) community have made significant advances towards learning and deploying policies on real world robotic systems. However, even with current state-of-the-art algorithms and computational resources, these methods are still plagued with high sample complexity, and thus long training times, especially for high degree of freedom (DOF) systems. There are also concerns arising from lack of perceived stability or robustness from emerging policies. This paper aims at mitigating these drawbacks by: (1) modeling a complex, high DOF system with a representative simple one, (2) making explicit use of forward and inverse kinematics without forcing the RL algorithm to “learn” them on its own, and (3) learning locomotion policies in Cartesian space instead of joint space. In this paper, these methods are applied to JPL's Robosimian, but they can be readily used on any system with a base and end effector(s). These locomotion policies can be produced in just a few minutes, trained on a single laptop. We compare the robustness of the resulting learned policies to those of other control methods.
Ensembles of cost-diverse Bayesian neural learners for imbalanced binary classification

Combining traditional diversity and re-balancing techniques serves to design effective ensembles for solving imbalanced classification problems. Therefore, to explore the performance of new diversification procedures and new re-balancing methods is an attractive research subject which can provide even better performances. In this contribution, we propose to create ensembles of the recently introduced binary Bayesian classifiers, that show intrinsic re-balancing capacities, by means of a diversification mechanism which is based on applying different cost policies to each ensemble learner as well as appropriate aggregation schemes. Experiments with an extensive number of representative imbalanced datasets and their comparison with those of several selected high-performance classifiers show that the proposed approach provides the best overal results.
Recommendation System of Information Technology Jobs using Collaborative Filtering Method Based on LinkedIn Skills Endorsement

Students who are graduated from Informatics Engineering have wide employment opportunities in the information technology work field, such as database administrator, data scientist, UI designer, IT project manager, network engineer, system analyst, software engineer and UX designer. Each job in Information Technology field has different skill requirement for the interest of work field. Therefore, IT skill classification is needed to find out the suitable career recommendation for Informatics Engineering students. Data from IT professionals which are obtained from LinkedIn account of IT professionals will be processed as reference for students. Data are processed using K-Means Clustering algorithm to find out how is feasible IT professionals data are used as a reference. Then, Collaborative Filtering method by the K-NN algorithm is used to determine classification based on the proximity between student skills and information technology job field. The output is recommendation of information technology job field which are generated from calculate of IT student skills. Result has been tested by testing one of user that has been labeled software engineer produce a recommendation output as a software engineer.
Triple Cache Approach for Preserving Privacy and Enhancing Performance of LBS

Location Based Services (LBS) have become widespread recently, especially with great increase in the number of devices and applications that use the Global Positioning System and its related services. But, these applications and services have created new threats and challenges related to the privacy of users’ data in addition to the performance. Where server provider (SP) can tracing the locations of user frequently and analyzing them to reveal many of sensitive information about his life as his customs, religion, hobbies, job, ethics, social status, friends, and many of other private issues. This research proposes a new approach titled “Triple Cache Approach”. This approach dealing with peers cooperation methods and integrating with cache techniques with a novel idea of using three caches, one in each user device, and two in the access point of each cell/area. The proposed approach solves the drawbacks, that existing in previous approaches, which are related to performance, cache hit-ratio, level of privacy, and trust issue.
From Cooperation to Collaboration: Toward a Framework for Deepening Library Partnerships

Librarians are encouraged to collaborate in their work, but examples in LIS literature suggests the term “collaboration” is applied to a range of activities that vary widely in terms of interaction, resource-sharing, cooperative planning, and cooperative service delivery. Collaboration has been studied in other fields but remains largely unexplored beyond case studies in LIS. Through a series of focus groups with librarians and a range of potential community partners, this research identified barriers to collaboration and factors that enable successful partnerships, resulting in a general framework that could act as a guide to planning and developing deep collaborations.
QR code based authentication method for IoT applications using three security layers

A quick response code-based authentication method (QRAM) is proposed. QRAM is applicable for lots of internet of things (IoT) applications. QRAM aims to verify requests of such an access to IoT applications. Requests are made using a quick response code (QRC). To authenticate contents of QRC, users will scan QRC to access IoT applications. To authenticate contents of QRC, three procedures are applied. QRAM contributes to IoT automatic access systems or smart applications in terms of authentication and safety of access. QRAM is evaluated in term of security factors (e.g., authentication). Computation time of authentication procedures for several IoT applications has become a considerable issue. QRAM aims to reduce computation time consumed to authenticate each QRC. Some authentication techniques still face difficulties when an IoT application requires fast response to users; therefore, QRAM aims to enhance so to meet real-time applications. Thus, QRAM is compared to several competitive methods used to verify QRC in term of computation time. Results confirmed that QRAM is faster than other competitive techniques. Besides, results have shown a high level of complexity in term of decryption time needed to deduce private contents of QRC. QRAM also is robust against unauthorized requests of access.
On the clique number of Paley graphs of prime power order

Finding a reasonably good upper bound for the clique number of Paley graphs is an open problem in additive combinatorics. A recent breakthrough by Hanson and Petridis using Stepanov's method gives an improved upper bound on Paley graphs defined on a prime field $\mathbb{F}_p$, where $p \equiv 1 \pmod 4$. We extend their idea to the finite field $\mathbb{F}_q$, where $q=p^{2s+1}$ for a prime $p\equiv 1 \pmod 4$ and a non-negative integer $s$. We show the clique number of the Paley graph over $\mathbb{F}_{p^{2s+1}}$ is at most $\min \bigg(p^s \bigg\lceil \sqrt{\frac{p}{2}} \bigg\rceil, \sqrt{\frac{q}{2}}+\frac{p^s+1}{4}+\frac{\sqrt{2p}}{32}p^{s-1}\bigg)$.
An Evaluation Method for Visual Search Stability in Urban Tunnel Entrance and Exit Sections Based on Markov Chain

Tunnel section is the throat of transportation and attracts lots of attentions.This paper proposed a method to evaluate the driver's visual search stability based on the Markov Chain properties of eye movements.Firstly, visual and physiological data about 16 participants driving through 13 urban tunnels were collected.Then, the view area was divided into six AOIs (Area of Interest) by fast clustering of the drivers' fixation points.The one-step fixation transition probability and the stable distribution of different lane changing behavior were obtained based on the division of the view area.The probability of transition from the forward windscreen to the left rearview mirror and other 6 visual parameters were selected as indexes by correlation tests.And the first four principal components which covered 96.1% of all information were extracted.Then an evaluation method for visual search stability was implemented by principal component analysis.In order to validate the method, average lane change times, average speed and SDNN (Standard Deviation of NN Intervals) of the drivers' heart rate were clustered into two categories.According to the consistency between the evaluation results and the clustering results, the evaluation method proposed in this paper has been proven to be reliable.Finally, the score threshold for judging the driver's stability was obtained as E = 0.313.The method could be applied to adjustment of tunnel facilities, assistance in driving training and development of auto driving system by assessing whether a driver can take over the control of the vehicle or not.
Exploration of End-to-End Framework for Code-Switching Speech Recognition Task: Challenges and Enhancements

The end-to-end (E2E) framework has emerged as a viable alternative to conventional hybrid systems in automatic speech recognition (ASR) domain. Unlike the monolingual case, the challenges faced by an E2E system in code-switching ASR task include (i) the expansion of target set to account for multiple languages involved, (ii) the requirement of a robust target-to-word (T2W) transduction, and (iii) the need for more effective context modeling. In this paper, we aim to address those challenges for reliable training of the E2E ASR system on a limited amount of code-switching data. The main contribution of this work lies in the E2E target set reduction by exploiting the acoustic similarity and the proposal of a novel context-dependent T2W transduction scheme. Additionally, a novel textual feature has been proposed to enhance the context modeling in the case of code-switching data. The experiments are performed on a recently created Hindi-English code-switching corpus. For contrast purposes, the existing combined target set based system is also evaluated. The proposed system outperforms the existing one and yields a target error rate of 18.1% along with a word error rate of 29.79%.
Head Gesture Recognition using a 6DOF Inertial IMU

The recognition of the head movements is a challenging task in the Human-Computer Interface domain. The medical, automotive, or computer games domains are only several fields where this task can find practical applicabilities. Currently, the head movement recognition is performed using complex systems based on video information or using an IMU sensor with nine freedom degrees. In this paper, we describe a new approach for recognizing head movements using a new type of IMU sensor with six freedom degrees placed on top of a headphone pair. The system aims to provide an easy control method for people suffering from tetraplegia for a specific set of activities. The system collects data from the inertial sensor placed on top of the headphone to analyze and then extract the features for head movement recognition. We did construct and evaluated eight predictive models of classifying head movements activity to determine which one is the best fit for the proposed head movement recognition system. The comparison and performance evaluation provided by each predictive model lead to demonstrate the performances delivered by our new system for head movements recognition.
COVID-19: A revelation – A reply to Ian Mitroff

This is a rejoinder of Dr. Ian Mitroff's perspective titled "Corona Virus: A Prime Example of A Wicked Mess". While I agree with Dr. Mitroff on the complexity of the COVID-19 outbreak and the urgency to address it, I disagree with him on points related to the role of wet market in this specific outbreak and whether China had acknowledged the existence of the outbreak. I also consider key highlights of the outbreak are the importance of collaboration across national and state borders and a balance between privacy and public health under such situations.
Can Compact Optimisation Algorithms Be Structurally Biased?

In the field of stochastic optimisation, the so-called structural bias constitutes an undesired behaviour of an algorithm that is unable to explore the search space to a uniform extent. In this paper, we investigate whether algorithms from a subclass of estimation of distribution algorithms, the compact algorithms, exhibit structural bias. Our approach, justified in our earlier publications, is based on conducting experiments on a test function whose values are uniformly distributed in its domain. For the experiment, 81 combinations of compact algorithms and strategies of dealing with infeasible solutions have been selected as test cases. We have applied two approaches for determining the presence and severity of structural bias, namely an (existing) visual and an (updated) statistical (Anderson-Darling) test. Our results suggest that compact algorithms are more immune to structural bias than their counterparts maintaining explicit populations. Both tests indicate that strong structural bias is found only in the cBFO algorithm, regardless of the choice of strategy of dealing with infeasible solutions, and cPSO with mirror strategy. For other test cases, statistical and visual tests disagree on some cases classified as having mild or strong structural bias: the former one tends to make harsher decisions, thus needing further investigation.
Adversarial autoencoder for detecting anomalies in soldered joints on printed circuit boards

The inspection of solder joints on printed circuit boards is a difficult task because defects inside the joints cannot be observed directly. In addition, because anomalous samples are rarely obtained in a general anomaly detection situation, many methods use only normal samples in the learning phase. However, sometimes a small number of anomalous samples are available for learning. We propose a method to improve performance using a small number of anomalous samples for training in such situations. Specifically, our proposal is an anomaly detection method using an adversarial autoencoder (AAE) and Hotelling’s T-squared distribution. First, the AAE learns features of the solder joint following the standard Gaussian distribution from a large number of normal samples and a small number of anomalous samples. Then, the anomaly score of a solder joint is calculated by Hotelling’s T-squared method from the features learned by the AAE. Finally, anomaly detection is performed by thresholding using this anomaly score. In experiments, we show that our method performs anomaly detection with few false positives in such situations. Moreover, we confirmed that our method outperforms the conventional method using handcrafted features and a one-class support vector machine.
Skeleton extraction and inpainting from poor, broken ESPI fringe with an M-net convolutional neural network

Extracting skeletons from fringe patterns is the key to the fringe skeleton method, which is used to extract phase terms in electronic speckle pattern interferometry (ESPI). Because of massive inherent speckle noise, extracting skeletons from poor, broken ESPI fringe patterns is challenging. In this paper, we propose a method based on a modified M-net convolutional neural network for skeleton extraction from poor, broken ESPI fringe patterns. In our method, we pose the problem as a segmentation task. The M-net performs excellent segmentation, and we modify its loss function to suit our task. The broken ESPI fringe patterns and corresponding complete skeleton images are used to train the modified M-net. The trained network can extract and inpaint the skeletons simultaneously. We evaluate the performance of the network on two groups of computer-simulated ESPI fringe patterns and two groups of experimentally obtained ESPI fringe patterns. Two related recent methods, the gradient vector fields based on variational image decomposition and the U-net based method, are compared with our method. The results demonstrate that our method can obtain accurate, complete, and smooth skeletons in all cases, even where fringes are broken. It outperforms the two compared methods quantitatively and qualitatively.
Comment: Operational aspects of solar forecasting

• This paper discusses the operational forecasting issues. • CAISO operational forecast submission requirements are elaborated. • Forecast verification based on the operational requirements is discussed.