Reviewerly/exHarmony huggingface dataset:
Scalable Infrastructure and Workflow for Anomaly Detection in an Automotive Industry

Anomalies are unexpected instances which significantly deviate from the normal patterns formed by the majority of a dataset. The more an observation deviate from the normal pattern, the more likely it is an anomaly. The continuous increase in the number of car models and configuration possibilities has led to continuous increase in the complexity of logistics supply chain and production. Consequently, it has become difficult to manage the whole IT Landscape, a small anomaly/failure somewhere in the system could lead to a huge loss of money. Therefore, to identify and ultimately resolve quickly a problem in such a system is highly important. This paper addresses the challenge of identifying anomalies in a scalable way. The new data collected suffers from the problem of lack of labels for training. This challenge is addressed in the developed solution by using multiple unsupervised algorithms and reporting those observation as anomalies which are commonly reported as anomalies by all the algorithms. The developed solution also tackles the problem of data heterogeneity and big size by using Spark underneath for scalable data processing. Scalability test results demonstrate the reduction in training time of 100 transactions by 80% when using 10 cores instead of using 1 core. The results of the study have also pointed out that increasing the number of cores does not necessarily means reduction in the overall execution time, there are other factors like communications between the cores, non-spark related processing tasks, etc which can also influence the execution time.
Prototype Classifiers and the Big Fish: The Case of Prototype (Instance) Selection

This article tells the story of our collaboration on prototype selection and what has happened since. In prototype classification, the data live in some metric space ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> equipped with a distance. Depending on which discipline or school (or continent) you come from, you may have a different name for the elements of ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> . In pattern recognition, we call those objects, data points, or even patterns. In machine learning, you are more likely to call them instances or examples. In statistics, we talk about observations and (the dubious singular -plural) samples.
A Comparison between Different Classifiers for Diagnoses of Atrial Fibrillation

This study is a comparison study with the purpose to propose an approach for selecting the best classifier for diagnoses of atrial fibrillation (AF) in coronary heartbeats. The Physionet Computing in Cardiology Challenge 2017 turned into used as the data source for this study. Automatic ECG processing consists essentially of the detection and location of the signal characteristic points and is an important tool in the management of cardiac diseases. The most relevant task is the detection of the QRS complex after which a complete analysis and delineation of each beat can be obtained. In the preprocessing stage, the Discrete Wavelet Transform (DWT) is used for removing noise and tuning to the morphological characteristics of the waveform features. For feature extraction, a set of features that consists of both morphological and temporal features is extracted using DWT. A comparison study was conducted between five classifiers (Decision trees, Random forest, AdaBoost ensemble classifier, support vector machine (SVM) and K-nearest neighbor Algorithm (KNN)) to know which give the best diagnoses for each type of Arrhythmia. In this study we used four classes of coronary heart beats atrial fibrillation, normal, other rhythms or noise. Results show that the AdaBoost classifier gives 100 % Accuracy scores for all types of Arrhythmia in the training set. The AdaBoost algorithm obtained a mean improvement report for all classes in testing set (97.3% in Area under curve accuracy (AUC), 94.7% in classifier accuracy (CA), 96.7% in sensitivity (Recall), and of 98 % in positive predictive value (Precision)).
A Study on the Voice Security System Using Sensor Technology

A person goes through a speaking and listening process that repeats verbal, physiological, and acoustic steps for communication. Voice recognition technology utilizing a range of sensor technologies using biosignals that are measurable in these human voice activities has developed rapidly in recent years. In particular, with the development of voice recognition technology based on artificial intelligence, the related market has expanded and is being released into various services. But the problem with voice recognition still exists and there are more cases of abuse. In addition, despite advances in technology, there is a problem where the associated services are not available to patients with laryngectomy or impaired pronunciation. Therefore, this paper focuses on silent recognition, which does not require vocalization, and introduces the technologies that may be used in implementing voice security systems in the future, including sensor technology, and further discusses the need to consider issues and support policies related to privacy when using the technology.
An Empirical Study of College Students’ Fragmented English Learning

This paper takes college students in some domestic universities as survey objects, investigates college students’ English fragmented learning cognition, learning characteristics, and learning effects. It also analyzes the influencing factors of college students’ English fragmented learning, and puts forward the guidance and teaching strategies, in order to explore how college students can use fragmented learning in the era of information technology to effectively optimize the learning experience.
Sensors system methodology for artefacts identification in Virtual Reality games

The field of affective computing has significantly emerged over the recent years, either we talk about systems that recognize and interpret human emotions or applications with assistive or therapeutic purposes that aim to treat various psychological disorders. Our goal is to design and develop a system that would aid people suffering of a common anxiety disorder of our times - the phobia. Thus, we propose a system for treating acrophobia - fear of heights - that exposes the patients in a Virtual Reality scenario depicting a mountain landscape, while their physiological signals (electrodermal activity, heart rate and respiration rate) are continuously recorded. However, during exposure, certain artefacts can appear, caused by head or hand movements, controller or mouse clicks, body rotations - and thus affect the quality of the recording. As artefact identification is a fundamental step in obtaining clean data, in this paper we present a method of recognizing artefact patterns in the signals recorded during gameplay sessions, which consists of aligning the data segments corresponding to the moments when the users performed head/hand/body movements with the artefact signals recorded during a reference procedure. The results obtained in pilot experiment with 5 users showed that the bias is lower on the perfectly aligned segments than on the segments located a few steps before and after the moment when the user introduced the artefact in the game signal, but without statistical significance. In addition, we propose a new method of estimating respiration rate based on the distance between two HTC Vive trackers placed on an elastic band, on the chest and on the upper back torso of the user.
Prediction of traffic volume in BTS sites using deep learning

Payload and Throughput are the most essential parameters in determining the congestion as well as performance of the network. Predicting these parameters in advance is a proactive approach for LTE network management and planning. Mostly ARIMA which are linear models have been applied in predicting future trends. However in this paper we propose special kind of Recurrent neural Network architectures mainly, LSTM and GRU, as these networks are capable in remembering longer dependency, learning temporal patterns in a long sequence of random length. These networks are used for predicting the payload and throughput trend with the help of past Key performance indicator reports. In the proposed work KPI data of past 16 days from Nokia Networks Pvt Ltd is obtained for research purpose. In the proposed work, RNN architectures achieved state of the art results on live data set from Nokia Networks producing promising results in predicting payload and throughput.
Analyzing Deep Neural Networks with Noisy Labels

Training deep neural networks (DNNs) with noisy labels is a fundamental problem in achieving generalization in DNNs. Recent studies mainly adopt sample selection in which the samples with small losses are regarded as clean ones, based on the findings that DNNs tend to learn simple and easy patterns first, and then gradually memorize all data. In this paper, we further investigate the sample selection by observing the loss distribution of training samples with clean and noisy labels. In experimental results, we found that the loss distributions with clean and noisy samples are different at the early stage, and gradually converge into similar distributions. Besides, this phenomenon is notably different depending on the datasets and noise types, such as symmetric and pair. Based on these findings, we argue that the sample selection method should consider an early stopping condition in learning DNNs with noisy labels.
Breast cancer detection and validation using dual modality imaging

Early detection of malignancy is important in order to reduce morbidity and mortality rate. However, existing approaches are based on a single modality having limited performance. Hence, the key objective of this study is detection and validation of breast cancer images using multimodality approach by combining both ultrasonography and mammography. Further, we investigate a particular segmentation approach that can accurately detect breast lesions using a dual-modality system. Three popular techniques i.e. Fuzzy-c-Means (FCM), and K-means (KM) and DPSO (Darwinian Particle swarm optimization) are experimented and evaluated for detecting lesions using combined ultrasonography and mammography. Performance measures such as the area of breast mass lesions, Jaccard Index (JI) and Dice Similarity Coefficient (DSC) have been used for the evaluation of the proposed dual modality breast cancer detection model. The results show that out of the implemented techniques, FCM outperformed others in detecting the lesion on both ultrasonography and mammography images with JI: highest average value (0.748) and lowest SD (0.124), DSC: highest average value (0.851) and lowest SD (0.084) and segmentation accuracy: highest average value (0.92) and lowest SD (0.074).
Improvisation of Cleaning Process on Tweets for Opinion Mining

In the current scenario, high accessibility to computational facilities encourage generation of a large volume of electronic data. Expansion of the data has persuaded researchers towards critical analyzation so as to extract the maximum possible patterns for wiser decisiveness. Such analysis requires curtailing of text to a better structured format by pre-processing. This scrutiny focuses on implementing pre-processing in two major steps for textual data generated by dint of Twitter API. A NoSQL, document-based database named as MongoDB is used for accumulating raw data. Thereafter, cleaning followed by data transformation is executed on accumulated tweets related to Narender Modi, Honorable Prime Minister of India.
PURL: Periodic User Representation Learning from Temporal Event Records for Personalized Health Management

Activity logging applications for mobile health have gained wide attentions recently and tracking of user health states is becoming an emerging trend in human daily life. Modeling user behaviors from users' temporal event records is a promising research topic for enhancing the practicability of personal health management. However, user modeling has long been a very challenging problem due to the complexity in activities of each individual, which consists of two key sub-issues, periodicity and diversity. In this paper, we propose a novel user modeling approach, namely Periodic User Representation Learning (PURL), to learn dynamic representations of user behaviors from large-scale temporal event records. To deal with the periodicity issue, we utilize periodic frequent pattern mining to capture the periodic user behaviors from users' temporal event records. Next, to cope with the diversity of user behaviors, we further build a periodic temporal pattern embedding module, which yields interpretable user representations according to the presence of each distinct periodic temporal pattern. To the best of our knowledge, PURL is the first user behavior modeling method that tackles the periodicity and diversity issues of users' temporal event records. Based on a real-world large-scale user activity logging dataset, experimental results demonstrate that PURL delivers up to 47% improvement in terms of the accuracy on health state prediction task compared with other existing methods.
[A tooth cone beam computer tomography image segmentation method based on the local Gaussian distribution fitting].

Oral teeth image segmentation plays an important role in teeth orthodontic surgery and implant surgery. As the tooth roots are often surrounded by the alveolar, the molar's structure is complex and the inner pulp chamber usually exists in tooth, it is easy to over-segment or lead to inner edges in teeth segmentation process. In order to further improve the segmentation accuracy, a segmentation algorithm based on local Gaussian distribution fitting and edge detection is proposed to solve the above problems. This algorithm combines the local pixels' variance and mean values, which improves the algorithm's robustness by incorporating the gradient information. In the experiment, the root is segmented precisely in cone beam computed tomography (CBCT) teeth images. Segmentation results by the proposed algorithm are then compared with the classical algorithms' results. The comparison results show that the proposed method can distinguish the root and alveolar around the root. In addition, the split molars can be segmented accurately and there are no inner contours around the pulp chamber.口腔牙齿图像分割对于牙齿的正畸手术、义齿种植等有重要的意义。由于牙根被牙槽骨包围，磨牙拓扑结构复杂，以及在牙齿内部有牙髓腔的存在，导致分割时易出现过分割、内部轮廓等问题。为进一步提高分割精度，针对上述问题本文提出了一种基于局部高斯分布拟合与边缘检测相结合的分割算法。该算法融合了像素局部邻域的方差和均值，并将图像梯度信息引入边缘检测，提高了分割算法的稳定性。在实验中，基于锥形束计算机断层图像，完成了对牙根图像的精确分割，并与当前比较经典的算法进行了对比，结果表明，本文算法能够更好地区分牙根以及牙根周围的牙槽骨，能够更精确地分割出牙根及分裂的磨牙，没有出现牙髓腔内部轮廓的情况。.
Adjustable and Adaptive Control for an Unstable Mobile Robot Using Imitation Learning with Trajectory Optimization

In this contribution, we develop a feedback controller in the form of a parametric function for a mobile inverted pendulum. The control both stabilizes the system and drives it to target positions with target orientations. A design of the controller based only on a cost function is difficult for this task, which is why we choose to train the controller using imitation learning on optimized trajectories. In contrast to popular approaches like policy gradient methods, this approach allows us to shape the behavior of the system by including equality constraints. When transferring the parametric controller from simulation to the real mobile inverted pendulum, the control performance is degraded due to the reality gap. A robust control design can reduce the degradation. However, for the framework of imitation learning on optimized trajectories, methods that explicitly consider robustness do not yet exist to the knowledge of the authors. We tackle this research gap by presenting a method to design a robust controller in the form of a recurrent neural network, to improve the transferability of the trained controller to the real system. As a last step, we make the behavior of the parametric controller adjustable to allow for the fine tuning of the behavior of the real system. We design the controller for our system and show in the application that the recurrent neural network has increased performance compared to a static neural network without robustness considerations.
Towards A Guided Perturbation for Privacy Protection through Detecting Adversarial Examples with Provable Accuracy and Precision

In this paper, we aim to demonstrate a practical mechanism to determine the boundary which can guide the design and implementation of privacy protection through perturbation. We have leveraged the strategy of detecting adversarial examples through a set of detection methods to find the "blind corners" of detection, and use them as the guidance of design and implementation of perturbation for privacy protection. To ensure the confidence of this approach, we have created a detection mechanism which has a very high accuracy and precision. First we use a classical statistical method to detect most of adversarial examples. Then, in considering that small examples sets will impact the confidence of statistical tests, we applied second detection method to discover the adversarial examples escaped from the first round detection by comparing similarity of the loss curves of training on the original data and tested data. Experiment results have shown that our enhanced detection mechanism not only extends the capability of detecting more adversarial examples in a much broad scope of perturbation, but also provides us the set of boundaries, represented by the values of a set of thresholds of exploring the "blind corners" of the detection methods, which can be practically used to effectively direct the design and implementation of perturbation for privacy protection. This is also the main contribution of this paper.
Dominant Feature Prediction By Improved Structural Similarity Computation

Recently features based on Machine learning approaches, such as face detection model trained with Convolutional Architecture for Fast Feature Embedding (caffe) framework, have been shown to be more effective than conventional hand crafted features and have become the state-of-the-art in object detection, including face detection. Detected Faces can be recognised using Support Vector Machine (SVM) and Convolutional Neural Network (CNN) which were widely used in monitoring systems and other face recognition applications. In this work, the features that are common within family members are detected by computing the threshold dynamically. Structural Similarity Index (SSIM) is a method for comparing two images with the perceptual metric. Using SSIM we can compare the extracted features to predict the dominant feature among the family members. Certain features that are similar between persons cannot be determined even through naked eyes. Hence this application comes into play that could determine the similar and dominant feature with adequate accuracy from the detected and recognized face. Age progression is most often used as a forensics tool by law enforcement. Using the inherited features extracted from the family members helps in predicting age progression of an individual from that family. By dynamically computing the threshold, the dominant feature of a family has been computed which improves the precision of the system. To reach maximum accuracy in age progression dominant features along with inherited features extracted from the image are used in combination with existing age progression technique. For the high performance of the system, dynamic variation technique in fixing the threshold and feature extraction have been developed.
Unmanned Vehicles: Real Time Problems in Drone Receivers

We are working on a (re) configurable general-purpose drone development. In this context, we are setting the basis for replacing the proprietary and traditional RC (Radio Control) receivers by other wireless communication devices. Thus, the communication device could be specifically defined for each application. We expect to be able to interact with standard drone flight controls (FC) as well as our own one/s. We show in this paper the general drone receiver proposal, the signals it handles, and a proof-of-concept Wi-Fi implementation. Furthermore, interacting with standard FC lets us to demonstrate that our proposal is not "tailored" to our own proprietary or specific FC.
Auto-scaling Resources for Cloud Applications using Reinforcement learning

Elasticity is an attractive feature of cloud computing, that enables increasing or decreasing the resources allocated to an application in order to adapt to changes in the workload. To efficiently utilize elasticity of clouds, the decisions on resource allocation need to be made algorithmically, adaptively and in real-time. The resource provisioning algorithm must also consider the performance requirements of the application as specified in the Service Level Agreement between the cloud provider and the client. In this paper, we present a reinforcement learning based algorithm that addresses the issues of slow convergence and lack of scalability in classical approaches such as Q-learning. We use the technique of adaptive tile coding and workload forecasting to ensure efficient utilization of resources. The effectiveness of the proposed method as compared to static, threshold-based and other reinforcement learning based allocation schemes is established with experiments on the Cloudsim platform.
Applicability of Agile Methodology in Technology Migration Projects: A Thematic Overview

Ever since, inception of IT services on global level, lot of SDLC methodologies were developed to ensure proper balance between Time, Cost and Scope factors of the Projects. Over period of time, it was noticed that IT services using waterfall SDLC were gradually unable to deliver best requirements, despite having extensive experience in handling such large-scale IT projects. To overcome the shortcoming of the Waterfall SDLC model in early 2001, Agile SDLC methodology was introduced, which is even till today treated as de facto best industry standards of any IT projects across the globe as it successfully curtailed the demerits of earlier model such as dangers of traditional, front-end planning methods that often lead to downstream development pathologies. In this research paper, an attempt is made by the authors to understand how Agile SDLC methodology helps in improving efficacy of software development and various challenges faced during the development process. Limited research is done to highlight the efficacy of same across different variants of IT Projects. It is worthwhile to mentioned based on the past experiences of the authors that in case of Technology Migration IT Projects, bare implementation of Agile Methodology will not provide any addition advantage and success rate is extremely low. Authors has made an attempt to decipher the root causes which influences the efficacy of Agile SDLC methodology on Technology Migration Projects and suggest new SDLC methodology with list of KPIs to monitor the success rate of the project on real time basis.
High Load Function Prediction Model Based on Decision Tree

Cloud computing platform application for adaptive non-downtime upgrade has become one of the current hot research issues. However, the system self-adapting upgrade of high-load function will bring some risks, so it is necessary to upgrade the high-load function under the supervision of the relevant technicians as far as possible. Many of the load values of cloud computing platform applications are dynamic and related to many factors. Current functional awareness can only determine the load of the current function, and can not predict the load when upgrading. Therefore, an algorithm is urgently needed to determine the load level of upgrade according to a variety of factors. In this paper, a decision tree based prediction model is proposed, which can predict whether the load is high when a particular function is upgraded by the state value of the current system.