Reviewerly/exHarmony huggingface dataset:
Streaming Data Classification using Hybrid Classifiers to tackle Stability-Plasticity Dilemma and Concept Drift

In recent days because of the availability of large number of sensors there is a possibility of generating huge streams of data. Performing data mining tasks such as outlier detection, classification and regression in these streaming data is difficult but it is very much necessary. The main challenges faced by these tasks include online model parameter adaptation, concept drift, the stability-plasticity dilemma, efficient memory models, model benchmarking, adaptive model complexity and meta-parameters. Out of these challenges the adaptation of concept drift with respect to classification task in streaming data and stability-plasticity dilemma has been addressed here. A new framework using offline GMM and incremental GMM has been proposed for adapting to concept drift and to tackle stability-plasticity dilemma. Experiments are carried out using Phasor Measurement Unit data and the results prove that the proposed framework is efficient in terms of adaption of concept drift.
Exploration of Spatial and Temporal Modeling Alternatives for HOI

Human-Object Interaction detection from a video clip can be considered as a special case of video-based Visual-Relationship Detection wherein the subject must be a human. Specifically, it involves detecting the humans and objects in the clip as well as the interactions between them. Conventionally, the problem has been formulated as a space-time graph inference problem over the video clip features. In this work, we explore alternate spatial approaches for detecting Human-Object Interactions. We consider a hierarchical setup that decouples spatial and temporal aspects of the problem and analyse the impacts of a variety of design choices for the spatial networks. Particularly, to capture spatial relationships in the scene, we analyze the effectiveness of the traditionally used Graph Convolutional Networks against Convolutional Networks and Capsule Networks. Unlike current approaches, we avoid using ground truth data like depth maps or 3D human pose during inference, thus increasing generalization across non-RGBD datasets as well. We demonstrate a comprehensive analysis of the exploration, both quantitatively and qualitatively, while achieving state-of-the-art results in human-object interaction detection (88.9% and 92.6%) and anticipation tasks of CAD-120 and competitive results on image based HOI detection in V-COCO dataset, setting a new benchmark for visual features based approaches.
Smart Data Agent for Preserving Location Privacy

A novel agent model is proposed that preserves the privacy of location information by using smart data i.e., data that protect themselves in a manner appropriate to the needs of the user. The model protects personal data by wrapping in a “cloak of intelligence”. This requires development of an intelligent agent that acts as the user's virtual proxy in cyberspace controlling the release of the user's information in accordance with the its preferences, context of information and/or nature of situation. The presented work focuses on developing a location data agent for smartphone users. Many applications request access to the user's location data in order to provide their services, but some may take advantage of this opportunity to continuously record location data even when they are not being used. This continuous tracking of the user's location can reveal extensive personal information. The aim of this work is to develop a neural network based intelligent agent that learns the user's privacy preferences and estimates the preferred privacy levels for future interactions. Agent in the proposed model behaves as an advanced data location manager and interacts with the apps instead of letting them directly access the location data. Apart from deciding the amount of distortion, the agent also works to prevent remove spatial and temporal correlations by adding perturbations to actual access history so as to prevent a third party from making an kind of prediction on personal data.
Photovoltaic output prediction of regional energy Internet based on LSTM algorithm

Abstract By the huge development of large scale and modular photovoltaic power generation, accurate photovoltaic (PV) output prediction can help PV power station, scheduling department and power system operate safely and economically. In the process of PV output prediction, the data density is large, and the output data is relatively regular. Therefore, this paper considers the use of long-termed and short-termed memory neural network algorithm to optimize the problem of algorithm gradient vanishment in recurrent neural network, and complete the output prediction of PV power in the regional energy Internet on the basis of historical output data. In this paper, LSTM algorithm is used to analyze the historical output data of PV stations in an industrial zone of a certain city. It can be found that LSTM algorithm has good adaptability for short-term PV output prediction, which can meet the needs of application.
Adjusting Indonesian Multiword Expression Annotation to the Penn Treebank Format

Multiword Expression (MWE) has been a pain in the neck, especially in determining its word-classes in syntactic treebank. Previous work had proposed annotation guidelines for Indonesian MWEs that align to the Penn Treebank (PTB) format. However, we think that their proposed annotation still needs improvements. Therefore, this study proposes a new annotation guideline in labeling Indonesian MWE that conforms to PTB format. Moreover, we also revised the MWE annotation of an existing Indonesian constituency treebank consisting of 1030 sentences to conform to the new guidelines. To evaluate the revised treebank's quality, we built an Indonesian constituency parser model using the revised treebank and Stanford parser. The experiments show that the resulting parser has an F1-score of 69.97%.
Formalising and implementing Boost POSIX regular expression matching

Whereas Perl-compatible regular expression matchers typically exhibit some variation of leftmost-greedy semantics, those conforming to the posix standard are prescribed leftmost-longest semantics. However, the posix standard leaves some room for interpretation, and Fowler and Kuklewicz have done experimental work to confirm differences between various posix matchers. The Boost library has an interesting take on the posix standard, where it maximises the leftmost match not with respect to subexpressions of the regular expression pattern, but rather, with respect to capturing groups. In our work, we provide the first formalisation of Boost semantics, analyze the complexity of regular expression matching when using Boost semantics, and provide efficient algorithms for both online and multipass matching.
About Open Science and Autonomy of Science

This article invites us to a concise walk through the past, offering insights defined by the major challenges science encountered during the centuries. Some lessons for today and tomorrow are enumerated in the three sections of the article, and they go beyond the relatively few perspectives offered by today's Data Science: Open Science (OS) is what has always happened and is nothing new, because science has always sought to be open. Esthetical values played a relevant role in the past. Former scientists recognized the intrinsic relation between the way they opened science and the way they followed the principles of beauty and the sense of esthetic. Their groundbreaking heritage still inspires us in being ready to open new ways in science. Whereas Latin was the original lingua franca of European science, and English is the recent lingua franca, the new lingua franca is software. Pieces of software are the filter, which connect researchers to the world, through layers of data. They assist in observing, in choosing, and in selecting. Open scientists should be aware of the fact that their autonomy in science depends on the quality of these pieces. Another lesson is that ethics—regarded as a source of innovative activities—must be a core component of innovative processes in OS, because society needs a responsible use of data and algorithms in corresponding practices that serve OS.
Anomaly Detection using Variational Autoencoder with Spectrum Analysis for Time Series Data

Uncertainty is an ever present challenge in life. To meet this challenge in data analysis, we propose a method for detecting anomalies in data. This method, based in part on Variational Autoencoder, identifies spiking raw data by means of spectrum analysis. Time series data are examined in the frequency domain to enhance the detection of anomalies. In this paper, we have used the standard data sets to validate the proposed method. Experimental results show that the comparison of the frequency domain with the original data for anomaly detection can improve validity and accuracy on all criteria. Therefore, analysis of time series data by combining Variational Autoencoder and frequency domain spectrum methods can effectively detect anomalies. Contribution- We have proposed an anomaly detection method based on the time series data analysis by combining Variational Autoencoder and Spectrum analysis, and have benchmarked the method with reference to recent related research.
Bayesian Perceptron: Towards fully Bayesian Neural Networks

Artificial neural networks (NNs) have become the de facto standard in machine learning. They allow learning highly nonlinear transformations in a plethora of applications. However, NNs usually only provide point estimates without systematically quantifying corresponding uncertainties. In this paper a novel approach towards fully Bayesian NNs is proposed, where training and predictions of a perceptron are performed within the Bayesian inference framework in closed-form. The weights and the predictions of the perceptron are considered Gaussian random variables. Analytical expressions for predicting the perceptron's output and for learning the weights are provided for commonly used activation functions like sigmoid or ReLU. This approach requires no computationally expensive gradient calculations and further allows sequential learning.
Mutual Information based Feature Selection for Nurse Care Activity Recognition

Human activity recognition is a challenging task as performing the activities varies from person to person. For the last few years, many complex methods have been proposed to identify human activities from sensor readings. To date, several studies have been conducted successfully to identify simple activities and many commercial applications have also been developed. However, reliable recognition of complex activities is still an active research area. The nurse care activity dataset can be treated as a complex activity recognition dataset. Researchers have proposed many solutions to identify nurse care activity by extracting numerous handcrafted features or using the Spatio-temporal graph convolution method. However, some of these features may be noisy, redundant, or even distract the classifier performance. In this paper, we propose a feature selection strategy to select important features from the handcrafted features. We claim that the simple classifier can provide satisfactory performance once the important features are selected and the noisy ones are eliminated. Experiments demonstrate that our proposed approach achieves 87.93% accuracy and 87.97% f1-score in test data. This is a significant improvement over state-of-the-art approaches on this benchmark dataset and thereby establishing our claim.
GPQ: Greedy Partial Quantization of Convolutional Neural Networks Inspired by Submodular Optimization

Recent work has revealed that the effects of neural network quantization on inference accuracy are different for each layer. Therefore, partial quantization and mixed precision quantization have been studied for neural network accelerators with multi-precision designs. However, these quantization methods generally require network training that entails a high computational cost or exhibit a significant loss of inference accuracy. In this paper, we propose a greedy search algorithm for partial quantization that can derive optimal combinations of quantization layers; notably, the proposed method exhibits a low computational complexity, O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) (N denotes the number of layers). The proposed Greedy Partial Quantization (GPQ) achieved 4.2 × model size compression with only -0.03% accuracy loss in ResNet50 and 2.5× compression with +0.015% accuracy gain in Xception. The computational cost of GPQ is only 2.5 GPU-hours in the case of EfficientNet-B0 8-bit quantization for ImageNet classification.
WITHDRAWN: Cost efficient tool for cloud storage services

Cloud computing gives an efficient model for the use of large industries for managing applications. The cloud providers give an advantage of paying the money only when we use the storage. That is, the time when we get difficulty to get both the important features damagingly. Our objective here is a cost-conscious, elastic cloud provider designed to provide elastic cloud infrastructure for cost-effective analysis. The system analyses the corresponding cost-driven Cloud SaaS optimization problem and provides an affordable optimization approach. The proposed system ensures the service level Agreement and the heterogeneous services which are made over reservations and became reliable and provide an appropriate online cost maintenance tool for two-tier SAAS clouds. The result of the analysis provides users with more information on the frequency of use of cloud storage.
Robust constrained kalman filter algorithm considering time registration for GNSS/Acoustic joint positioning

Currently, the precise positions of seafloor control points are very important for expanding marine navigation activities. Equipped with a variety of sensors, a survey vessel measures and integrates heterogeneous data to obtain the coordinates of seafloor control points. However, the existence of time error factors such as inconsistent sampling intervals or signal transmission delays will cause time mismatches in multisource time series data. This will greatly reduce the positioning accuracy of seafloor control points. Previous studies have generally ignored the problem of time registration. To address this issue, this paper first briefly introduces the basic principle of positioning a single seafloor control point by using multiple types of sensors. The influences of various time errors on the resolution are analyzed through formula derivations and simulation experiments. Then, a robust constrained Kalman filter (RCKF) algorithm considering time registration is proposed. Based on the Kalman filter algorithm, the RCKF method takes the transmission delay error as a parameter and introduces priori constraints and the robust estimation. So the time registration and the determination of the seafloor control point coordinates are carried out in real time. The performance of the RCKF method in the designed simulation experiment and a testing experiment near Jiaozhou Bayou is discussed. The positioning accuracy is 3.07 cm in simulation when adopting the RCKF method, which is better than other comparison methods. The positioning accuracy of the testing results can be greatly improved from 1.348 m in the common method to 0.12 m based on the RCKF method. The results show that the newly proposed method ensures the consistency of multisource time series and has good stability and accuracy in positioning a single seafloor control point.
Securing Medical Images via a Texture and Chaotic Key Framework

Healthcare information framework is a critical component in any information technology framework as patient-specific information like initial diagnosis, response to drugs, extent of recovery, etc., always resides over these systems. From the perspective of a healthcare professional, the focus is always on the best utilization of this critical data. This may be achieved by sharing such data over a network to enhance chances of better interpretation. However, this data may get compromised in terms of its integrity, confidentiality and authenticity over such an insecure network. On the other hand, Health Insurance Portability and Accountability Act (HIPAA) guidelines must be adhered to once such data flows an insecure network. Therefore, considering the importance of medical data transmission for better data interpretation and consequently better diagnosis and prognosis, we propose a texture edge map and multilevel chaotic map-driven encryption framework for medical images. The proposed technique employs texture maps obtained from Gabor filters in tandem with multiple chaotic maps: quadratic, cubic and logistic maps to enhance the key space, robustness and security of medical images over a vulnerable channel. The security and reliability of the proposed methodology are illustrated through key sensitivity analysis, statistical and performance analysis. The proposed methodology ensures large key length and pixel diffusion. Security analysis illustrates one of the prime properties of key sensitivity to initial conditions to withstand differential attacks. In addition to withstanding the differential attacks, the proposed technique has a large enough key space to see off brute force attacks. Considering the criticality of the transmission of medical images for use in the state-of-the-art e-healthcare systems like picture archiving and communication systems, the proposed technique is a potential candidate for addressing security vulnerabilities of medical images over the communication networks.
Noise as a Resource for Learning in Knowledge Distillation

While noise is commonly considered a nuisance in computing systems, a number of studies in neuroscience have shown several benefits of noise in the nervous system from enabling the brain to carry out computations such as probabilistic inference as well as carrying additional information about the stimuli. Similarly, noise has been shown to improve the performance of deep neural networks. In this study, we further investigate the effect of adding noise in the knowledge distillation framework because of its resemblance to collaborative subnetworks in the brain regions. We empirically show that injecting constructive noise at different levels in the collaborative learning framework enables us to train the model effectively and distill desirable characteristics in the student model. In doing so, we propose three different methods that target the common challenges in deep neural networks: minimizing the performance gap between a compact model and large model (Fickle Teacher), training high performance compact adversarially robust models (Soft Randomization), and training models efficiently under label noise (Messy Collaboration). Our findings motivate further study in the role of noise as a resource for learning in a collaborative learning framework.
Information Fusion as an Autonomy enabler for UAV Traffic Management

Autonomy proliferates air and space traffic management with the National Aeronautics and Space Administration (NASA) initiative on Unmanned Aircraft System Traffic Management (UTM). New endeavors such as electric vertical take-off and landing (eVTOL) and COVID19 are challenging every aspect of the NextGEN rollout. Hence, to develop a safe and secure UTM, there is need for knowledge management. Knowledge management comes from awareness about the environment and awareness is based on assessment. The information fusion community has long developed methods for data fusion (e.g., statistical analysis), sensor fusion (e.g., navigation and tracking), information fusion (e.g., Notice to Airman and air tracks), as well intelligence fusion (e.g., response to malicious attacks) for knowledge assessment. Each of these techniques has opportunities to enable UTM autonomy, joint all-domain command and control, and surveillance. This paper explores the various uses of fusion available to support the autonomy. For example, three types of autonomy have been proposed: autonomy at rest (e.g., flight plans, radar positions), autonomy in motion (e.g., dynamic tracking with automatic dependent surveillance-broadcast (ADS-B) and weather), as well as autonomy in use (e.g., getting the right data at the correct time). A use case is presented for UTM that utilizes data fusion from ADS-B, radar, LiDAR, and visual data to provide effective positioning in response to various cyber attacks on the ADS-B data.
Research on auxiliary methods of swimming training virtual simulation technology based on embedded computer✰

Virtual Reality (VR) is a partnership between an independent hardware and software integrated system that ships it into a nearly simulated environment. Studies have shown that technology modifies variables related to an individual's visual and motor skills. Therefore, there are many possible explanations for the positive effects of encounters in sports that need to be considered using virtual reality. Virtual Reality Optimization Technique (VROT) can also trigger motor learning in the brain responsible for motor activity, thus using motor imaging (MI) technology, even when no action is occurring. This application is used to train swimming. This provides the criteria for evaluation, and the evaluated group will receive the experimental group. Swimming is one of the most popular games to train students and perform better results using the virtual reality technique. Solving the swimming lesson system is improving the ecological validity of the study, maintaining the great potential for cognitive and behavioral research, and separating cognitive behavior. Perform a comparative analysis of the student's correct and incorrect swimming practice. The proposed system analyzes the result using virtual reality technique based on the FPGA controller and getting better performance results.
Recommender system for an academic supervisor with a matrix normalization approach

This article proposes a recommendation system for choosing an academic supervisor, based on an assessment of the similarity of student interests and the scientific achievements of the possible mentor from the university faculty. We used a new approach to calculate similarity with no creating co-authorship networks but using Scopus quality metrics. Each scientist is presented as a combination of his achievements in each field of science. As a normalization method, we used the cumulative distribution function of the logarithm of the weighted impacts of professors in the field. We compared different similarity measures and performed clustering to assess their adequacy and thus assess the quality of the system due to the impossibility of comparing the received recommendations with the data of the past years.