Reviewerly/exHarmony huggingface dataset:
Decision Tree Learning for Uncertain Clinical Measurements

Clinical decision requires reasoning in the presence of imperfect data. DTs are a well-known decision support tool, owing to their interpretability, fundamental in safety-critical contexts such as medical diagnosis. However, learning DTs from uncertain data leads to poor generalization, and generating predictions for uncertain data hinders prediction accuracy. Several methods have suggested the potential of probabilistic decisions at the internal nodes in making DTs robust to uncertainty. Some approaches only employ probabilistic thresholds during evaluation. Others also consider the uncertainty in the learning phase, at the expense of increased computational complexity or reduced interpretability. The existing methods have not clarified the merit of a probabilistic approach in the distinct phases of DT learning, nor when the uncertainty is present in the training or the test data. We present a probabilistic DT approach that models measurement uncertainty as a noise distribution, independently realized: (1) when searching for the split thresholds, (2) when splitting the training instances, and (3) when generating predictions for unseen data. The soft training approaches (1, 2) achieved a regularizing effect, leading to significant reductions in DT size, while maintaining accuracy, for increased noise. Soft evaluation (3) showed no benefit in handling noise.
On Data Privacy in Modern Personal Vehicles

Personal vehicles are increasingly becoming smarter, safer and an extension of the wider Internet of Things (IoT) connected landscape. As manufactures continue to push the limits of automotive technology and engineering, they are increasingly missing foundational principles necessary to protect consumer privacy. The purpose of this paper is to explore vehicle personal privacy in light of the explosion of telematic information and connectivity of today's automotive devices. Automobile manufacturers are increasing the information they capture, store and transmit tied to an ever-expanding list of automobile systems, functions and services. This ever-increasing list generates many questions on the safety of automobile connectivity and the use and protection of the data collected by these amazing new automotive devices.
Identifying the Instrumental Gesture Parameters Affecting the Timbral Quality of a Classical Guitar

In this study, we explore the timbral properties produced in a classical guitar from different instrumental gesture parameters performed by different guitarists. We determine the parameters that significantly affect the tone through design of experiments and statistical analysis. We represent the tonal response as three numerical values, namely the spectral centroid, spectral flux, and spectral rolloff. We implement a full factorial design with three factors, namely the angle of attack, plucking position, and the fret-string combination. We also explore the effect of varying physiology of guitarists. We verified through this study that the errors introduced by varying physiology of guitarists significantly affect the tonal quality produced with a p-value of less than 0.01% for two responses. This is further verified by the computation of accuracy for different models since the average error on the test set reduced by around 22% when we construct a unique model for each individual rather than for all individuals with varying physiology. We also identified that the spectral centroid response greatly captures the tonal quality of the classical guitar, having an average error on the test set that is at least 51% lower compared to the other responses.
Discussions on Information Security Test Strategy for Digital Industrial Control System in Nuclear Power Plant

The control system decides the safety and economy as a neural center, the information security plays a critical role in control system. With the fusion of industrialize and information, and with the development of digital nuclear power plant, the increasing number of general software technologies including network communication, operational system, etc., have been used, the security problem from traditional IT system has been introduce into industrial control system. By the view of information security, this paper analyzes characteristics of industrial control system that used in nuclear power plant, and establishes a hierarchy model from company management to software entity, then provides the defensive method for probably and potential intrusion on each level. Finally, this paper summarizes defense in depth system from software design and development to social engineering, and provides references for the optimization of information security in industrial control system.
Detection of Copy-Move Forgery in Digital Image Based on SIFT Features and Automatic Matching Thresholds

Today the technology age is characterized by the spread of the digital images. It's the most common form of information transmission whether through the internet or newspaper. This huge use of images technology has been accompanied by an evolution in editing tools which makes modifying and editing an image very simple. This paper proposes an effective and fast method for copy-move forgery detection. The paper adopts a SIFT technique for features extraction and wavelet technique to estimate the matching threshold. The low-frequency components are used to compute a dynamic threshold rather than a fixed threshold. Also, a method to remove false positive areas is proposed in order to produce the best possible results. The method can detect accurately and quickly the forgery even after more complex transformations. The experimental results refer that the proposed method can also detect forgery against post-processing operation and multiple copies.
Incremental Estimation of Users’ Expertise Level

Estimating a user's expertise level based on observations of their actions will result in better human-robot collaboration, by enabling the robot to adjust its behaviour and the assistance it provides according to the skills of the particular user it's interacting with. This paper details an approach to incrementally and continually estimate the expertise of a user whose goal is to optimally complete a given task. The user's expertise level, here represented as a scalar parameter, is estimated by evaluating how far their actions are from optimal. The proposed approach was tested using data from an online study where participants were asked to complete various instances of a simulated kitting task. An optimal planner was used to estimate the “goodness” of all available actions at any given task state. We found that our expertise level estimates correlate strongly with observed after-task performance metrics and that it is possible to differentiate novices from experts after observing, on average, 33% of the errors made by the novices.
New System to Measure Motion Motion-to-Photon Latency of Virtual Reality Head Mounted Display

Head mounted display (HMD) is an important part of the virtual reality (VR) system and the main interface of virtual world. During the process of using HMD, there is a certain time gap between the user's movement and the triggered change of display, which is referred to herein as a " Motion-to- Photon " latency. The latency may cause cybersickness which has an adverse effect on user experience and even physical health, so it is necessary to evaluate the latency of an HMD. We propose an automated and universal HMD latency measurement system. By simulating a user's motion, we are able to measure "Motion-to-Photon" latency using the generated periodic signals in HMD, with accuracy and reliability confirmed by our experiments. The system is fully automatic and easy to use, and suitable for the HMD with inside-out or outside-in positioning methods. It helps to improve both the detection level of existing HMDs and the user experience. It is of guiding significance for the design and manufacture of new HMD.
Capsules Encoder and Capsgan for Image Inpainting

Convolutional neural networks (CNN) have solved a lot of tasks in the field of computer vision because of its powerful processing capacity in recent years. Many different kinds of CNN are used to implement in the image restoration tasks and achieved outstanding results. Hinton propose a new network architecture with dynamic routing called Capsule networks which has shown remarkable results for image classification on MNIST data. Its advantage over CNN is that the networks can preserve more information from original image feature map with dynamic routing and compress the image into a vector, which CNN process the image as a scalar. We expand the capsule networks to the task of image restoration for the first time. Our idea is based on the structure of encoder-decoder by replacing convolutional neural networks with capsules networks in encoder aspect. Especially, we add Generative Adversarial Capsule Network (CapsuleGAN) in decoder to constraint parameters for expanding generative space. We evaluate our method on the ImageNet datasets and achieved acceptable results.
Quality Control of Building Materials According to Uncertainty of Measurement and Stability of the Technological Process of Production

Information on the influence of the state of the production process of production and the uncertainty of the measurement on the decision on the product's suitability is given. It is shown on the example of brick production that the decision to accept a batch of bricks, taking into account the measurement uncertainty under unstable condition of the production process, may be erroneous. Three variants of the state of the production process for the production of 100-grade brick are considered. The first option - the average value of compressive strength does not coincide with the middle of the tolerance field, the production process is characterized as unstable, the second option - the average value of compressive strength does not coincide with the middle of the tolerance field, a greater value of the spread of the indicators compared with the first version and is characterized as an unstable process, the third option is the average value of the tensile strength, and compression coincides with the middle of the tolerance field, is characterized as a stable and reproducible process. It is established, that, if the production process is in a state of statistical controllability, i.e. stable, then taking into account the uncertainty of measurement, the brick grade remains 100. In an unstable process with an increase in the value of the standard uncertainty of type A, the controller's decision to identify the brand of brick 100 may be erroneous. Statistically stable and reproducible process makes it possible to increase the reliability of product quality control and to avoid mistakes in deciding on the rejection of products.
Rapid Light Field Depth Estimation with Semi-Global Matching

Running time of the light field depth estimation algorithms is typically high. This assessment is based on the computational complexity of existing methods and the large amounts of data involved. The aim of our work is to develop a simple and fast algorithm for accurate depth computation. In this context, we propose an approach, which involves Semi-Global Matching for the processing of light field images. It forms on comparison of pixels' correspondences with different metrics in the substantially bounded light field space. We show that our method is suitable for the fast production of a proper result in a variety of light field configurations.
Fault Tolerant Clustering with Outliers

In a clustering with outliers problem, we are required to cluster all but a specified number of points, called outliers. In a fault tolerant clustering problem, the objective function incorporates the distance of a point to its f-th closest center chosen in the solution. We combine these two orthogonal generalizations, and consider Fault Tolerant Clustering with Outliers problems for various clustering objectives, such as k-center, k-median, and sum of radii. We essentially reduce the Fault Tolerant Clustering with Outliers problem, to the corresponding (non Fault Tolerant) Clustering with Outliers problem, for which constant approximations are known. This can be seen as a generalization of the framework of Kumar and Raichel [20] to handle the presence of outliers. This reduction comes at a loss in the approximation guarantee; however, we show that it is bounded by O(1) for the k-center objective, whereas it is O(f) for k-median and sum of radii objectives, where f is the degree of Fault Tolerance required in the solution. This implies O(1) and O(f) approximations for these generalizations respectively.
Learning Word Embeddings from 10-K Filings Using PyTorch

In this paper, we generate word embeddings learned from corpus of 10-K filings by corporates in U.S. to S.E.C from 1993 to 2018 using word2vec model implemented in PyTorch. Word Embeddings learned from a general corpus of articles from Google News, Wikipedia etc. are readily available online for researchers to use in their models but embeddings learned from 10-K filings are not publicly available. We publish the word embeddings learned from 10-K filings on GitHub for other researchers to use in their NLP tasks such as document classification, document similarity, sentiment analysis, readability index etc. on 10-K filings or other financial documents. We show that using these learned word embeddings we can differentiate between different types of sentiment words in the widely used Loughran-McDonald word lists and generate average similarity scores between them. We also present an application of word embeddings where we can quantitatively track changes in 10-K documents using the learned embeddings.
Normalization of Unstructured Indonesian Tweet Text For Presidential Candidates Sentiment Analysis

Indonesian tweet text has many of unstructured text. This research aims to propose the pre-processing task for cleaning tweets from the abnormal text. The first step, we use the common pre-processing task (case folding, filtering, tokenizing). Second, we use normalization. Each word is found with an excess letter, the word abbreviation, the word coincide and the word slang in each document will be converted into a standard word and also if a word or letter that does not have meaning will be deleted. After the normal text form is formed, stopword removal and stemming are then carried out. The data are taken from 2018 and 2019 data. This study produced the highest accuracy (81%) in 2018 with 425 tweets of training data and 100 tweet testing data and positive sentiment of Prabowo's electability is 52%. This result means that Prabowo deserves to submit himself as a presidential candidate.
Stochastic gradient descent for linear systems with sequential matrix entry accumulation

Conventional stochastic iterative methods are often employed for solving linear systems of equations involving large matrix sizes using low memory footprint. However, their performances are often limited by the unavailability of all the matrix entries, which is often termed as the problem of missing data. Although Ma and Needell [1] have recently proposed a method, termed as mSGD, assuming a model for data missing that results in improved convergence, their result is also affected by constant large variance of the stochastic gradient. In this paper we propose a SGD type method termed as cumulative information SGD (CISGD) for solving a linear system with missing data with an additional provision to accumulate a very small number of matrix entries sequentially per iteration, termed as the sequential matrix entry accumulation (SEMEA) mechanism. CISGD uses the data collected by SEMEA mechanism along with the prior model for data missing mechanism of [1] to gradually reduce variance of the stochastic gradient. The convergence of the proposed CISGD is theoretically analyzed and some interesting implications of the result are investigated under a specific SEMEA mechanism. Finally, numerical experiments are performed along with simulations that corroborate the theoretical findings regarding the efficacy of the proposed CISGD method.
A Deep Neural Network Based Human Following Robot with Fuzzy Control

This research aims to build a human following robot system based on a deep neural network algorithm in which a fuzzy controller controls the robot velocity and keeps the target person in the centre position of robot's view. Firstly, the system utilizes the deep neural network algorithm to detect a target person in the video sequence captured from a real sense D435 depth camera mounted on the mobile robot. Then, the system calculates the centre position of the target human and acquires the depth value of target human. Finally, these data are used as the inputs of a fuzzy controller to control the velocity and steering of the robot during tracking. Especially, the velocity of the robot which is normally limited as a constant in most existing human following robot systems is controlled by a fuzzy controller in this paper. The proposed system is verified through the experiments for a four-wheel steered mobile robot.
Coherence-based measurement of non-Markovian dynamics in an open quantum system

We present a theoretical scheme and its experimental proof of principle for an open quantum system undergoing Markovian and non-Markovian evolutions. We exhibit these two regimes by diagnosing them with the relative entropy of coherence of two polarization qubits playing the roles of system and ancilla. These are initially prepared in a polarization maximally entangled state of a photon pair produced by spontaneous parametric down-conversion. We induce Markovian and non-Markovian regimes in the system's dynamics with the help of two auxiliary qubits, experimentally implemented by optical paths in a layout of Sagnac and Mach-Zehnder interferometers. We replicate system-environment interactions by means of an amplitude damping channel and a suitably designed inversion of it. In our scheme, one needs only two experimentally accessible parameters to achieve Markovian and non-Markovian regimes.
Correlation distance skip connection denoising autoencoder (CDSK-DAE) for speech feature enhancement

Performance of learning based Automatic Speech Recognition (ASR) is susceptible to noise, especially when it is introduced in the testing data while not presented in the training data. This work focuses on a feature enhancement for noise robust end-to-end ASR system by introducing a novel variant of denoising autoencoder (DAE). The proposed method uses skip connections in both encoder and decoder sides by passing speech information of the target frame from input to the model. It also uses a new objective function in training model that uses a correlation distance measure in penalty terms by measuring dependency of the latent target features and the model (latent features and enhanced features obtained from the DAE). Performance of the proposed method was compared against a conventional model and a state of the art model under both seen and unseen noisy environments of 7 different types of background noise with different SNR levels (0, 5, 10 and 20 dB). The proposed method also is tested using linear and non-linear penalty terms as well, where, they both show an improvement on the overall average WER under noisy conditions both seen and unseen in comparison to the state-of-the-art model.
EASA: Entity Alignment Algorithm Based on Semantic Aggregation and Attribute Attention

Numerous knowledge bases have been published on the web, and there are serious heterogeneous problems among them. Unifying these knowledge bases at the semantic level can better promote the development of the Linked Data Project. Various effective methods, the mainstream one of which is the iterative entity alignment algorithm based on TransE (Translation-based Embedding, an efficient knowledge graph embedding representation algorithm), have been put forward to solve the heterogeneous problems among knowledge bases. Although the TransE-based iterative entity alignment algorithm can shorten the distance between two entities with the same semantics, it has low accuracy because it ignores the importance of semantic aggregation generated by many attributes of entities in the entity alignment process. To solve this problem, a novel entity alignment algorithm based on semantic aggregation and attribute attention, named EASA, is proposed in this paper. On the one hand, semantic aggregation of entities can be generated by different attributes and attribute values. On the other hand, the addition of attribute attention can be used to distinguish the different roles of different attributes in the entity alignment process. The experimental results show that our method achieves significant improvements compared to baselines for entity alignment on Chinese and English datasets. The data and source code for this paper can be obtained from https://www.github.com/xinan711456/EASA.
Big data scalability based on Spark Machine Learning Libraries

The paper introduces the challenge of scalability in machine learning algorithms suitable for massive datasets. Today, big data has relevant applications in the industry due to improvements in the system performance and by turning information into knowledge. Big data challenges include the lack of strategies to process computational cost and the large amount of data when computing machine learning predictions. To overcome these scalability issues, it is convenient to work with distributed and parallelized architecture across multiple nodes. The approach is based on Apache Spark, an in-memory distributed application that offers extensive machine learning libraries. The main contribution of the study is to measure the scalability by calculating the execution time that a classifier achieves with larger workloads. We validate our classifier models with experiments on logistic regression and random forest by studying their adaptability to the Apache Spark framework. The present work expects to combine the areas of big data and machine learning on scalability, and the use of optimization methods, cache and persist. In addition, a comparison between the classifiers is provided. The evaluation experiments show that logistic regression performed the shortest execution time and best scalability.
Real-time Aging Friendly fall detection system

The fall is a crucial issue for elderly people. With the strong growth of information and communication technologies and the development of light and low-cost wearable technology, elderly fall detection has gained much attention. These technologies can help the elderly get timely assistance to reduce further injury. In this paper, we propose to develop a real-time aging friendly fall detection system based on acceleration and angular velocity values produced by the accelerometer and gyroscope sensors of the mobile device. We describe an effective and simple threshold-based solution to implement a real-time method to monitor the elderly and detect falls using a mobile phone. In case of a fall, our system will transmit an alert with location information to the contacts list via a notification. Thus, medical attention can be provided with minimal delay. The system was tested by volunteers and achieved high sensitivity, specificity and accuracy.