Reviewerly/exHarmony huggingface dataset:
Online sequential extreme learning machine with the increased classes

In this paper, we investigate the online learning problem in a specific open environment, i.e., the number of classes can be dynamically increased. Online sequential extreme learning machine (OS-ELM) is extended to address the problem of the increased classes. Specifically, two different increased classes scenarios are considered. The first scenario is that the new classes, which haven't appeared in the previous instances, emerge in the new received data. The other scenario is that in data stream, an old class is split into several new subclasses due to some specific reasons. For the first kind of scenario, OS-ELM is inserted an alternative output node which can be extended whenever the new class instances are received. While for the second kind of scenario, we adopt a hierarchical structure to adapt the new split classes. We adopt the simple experiments to show the effectiveness and feasibility of the proposed models.
Pervasive Computing Paradigm: A Survey

The number of devices connected to a network within a modern home is growing day by day. These connected devices, such as, light control, security and alarm control, window controller, environment monitoring, door control, motion sensors, have turned modern homes into pervasive computing paradigm. This research focuses on characteristics and architecture of pervasive computing. The goal is to serve as a guideline for researchers who are new to pervasive computing and to provide further research directions. It also presents some research developments in this area along with open issues and challenges.
Approximate Volume of Sand Materials Stockpile Based on Structure From Motion (SFM)

Information can be obtained through image processing. One of the information that can be obtained is the Volume of a 3-dimensional object. A digital image is 2dimensional data obtained from shooting an object using a camera. A stockpile is a place to store material in an open area. This material is piled up and then taken, processed, marketed, or used later. Various materials can be categorized as a stockpile, one of which is sand material. Calculating the Volume of stockpiles in the mining world is important. Stockpile volume can be measured using image processing. The method used in this research is 3D reconstruction. This image processing uses the Structure from Motion method. The calculation is continued by determining centroid and radius distance using the Euclidean Distance algorithm. The goal of this research is to measure the estimated volume of sand material stockpile based on the results of SFM using Euclidean Distance. Testing experiment processed using a miniature small sand material stockpile. The dataset in this research are images of sand material taken sequentially. Data is obtained using a digital camera. There are nine images used in each test data. The testing data used were 25 pieces of material sand. The average accuracy value is 74.21%, and the RMSE value is 14.31.
Multi-Dimensional Contract-Matching for Federated Learning in UAV-Enabled Internet of Vehicles

Beyond ground data sources, Unmanned Aerial Vehicles (UAVs) based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), is increasingly popular in the Internet of Vehicles (IoV) applications in recent years. However, the stringent regulations governing data privacy potentially impedes data sharing across independently owned UAVs. To this end, we propose the adoption of a Federated Learning (FL) based approach to enable privacy-preserving collaborative Machine Learning for the development of IoV applications, e.g., for traffic prediction and car park occupancy management. Given the information asymmetry and incentive mismatches between the UAVs and model owner, we leverage on the self-revealing properties of a multi-dimensional contract to ensure truthful reporting of the UAV types, while accounting for the multiple sources of heterogeneity, e.g., in sensing and transmission costs. Then, we adopt the Gale-Shapley algorithm to match the lowest cost UAV to each subregion. The simulation results validate the incentive compatibility of our contract design and shows the efficiency of our matching.
Forensic analytics using cluster analysis: Detecting anomalies in data

Abstract Cluster analysis is a data analytics technique that can help forensic accountants effectively detect anomalies in complex financial datasets. This article provides a description of clustering analysis, discusses how it can be implemented to detect anomalies in data, and illustrates its use through a worked example using the Tableau software.
Challenges of Credit Reference Based on Big Data Technology in China

Abstract Big data-based credit reference system gradually attracts wide attention due to its ad-vantages in remedying the shortages of traditional credit reference and dealing with new challenges arising from financial credit management. Nevertheless, this new method is also adapted through different studies and experiments to be problematic with island of credit information and information security. Some researchers begin exploring the possibility of applying blockchain technology to the individual credit reference field. The business links in the individual credit reference can be innovated through the blockchain mechanism so that credit data from different industries get collected through peering points, secure communication and anonymous protection on the basis of such techniques as distributed storage, point-to-point transmission, consensus mechanism and encryption algorithm. In this way, it is feasible to solve island of information and enhance the protection of user information security. A promising future can be expected about the big data-based credit reference, but there are also many problems with blockchain-based credit reference in China.
Performance Characteristics of Virtualized GPUs for Deep Learning

As deep learning techniques and algorithms become more and more common in scientific workflows, HPC centers are grappling with how best to provide GPU resources and support deep learning workloads. One novel method of deployment is to virtualize GPU resources allowing for multiple VM instances to have logically distinct virtual GPUs (vGPUs) on a shared physical GPU. However, there are many operational and performance implications to consider before deploying a vGPU service in an HPC center. In this paper, we investigate the performance characteristics of vGPUs for both traditional HPC workloads and for deep learning training and inference workloads. Using NVIDIA's vDWS virtualization software, we perform a series of HPC and deep learning benchmarks on both non-virtualized (bare metal) and vGPUs of various sizes and configurations. We report on several of the challenges we discovered in deploying and operating a variety of virtualized instance sizes and configurations. We find that the overhead of virtualization on HPC workloads is generally <; 10%, and can vary considerably for deep learning, depending on the task.
Abstract Classification Using Support Vector Machine Algorithm (Case Study: Abstract in a Computer Science Journal)

Abstract Jurnal Komputasi is an online journal written by researchers and published by the Department of Computer Science, University of Lampung. Specific scientific information contained in journals is difficult to find because journals have not been structured and are classified into more specialized categories of computer science. Text mining can convert the shape of a journal into structured by homogeneous data form in it. 144 journal abstracts are collected into one corpus document in CSV format used as a research dataset. Journal abstract classification is done using one of the supervised machine learning methods, namely Support Vector Machine (SVM) so that the classification process is faster than the manual method. The TF-IDF technique is used to transform sentences in the abstract into vector so that they can be modelled with SVM. The classification model will be validated by applying the 10-fold cross validation technique. From these classifications a calculation of the resulting performance will be calculated based on the confusion matrix calculation of the resulting performance will be calculated based on the confusion matrix calculation and the use of 3 SVM kernels. The conclusion based on this research is that there are two factors that affect classification accuracy, that is the number of members between scientific classes that are not balanced and the number of features generated from text mining. The highest accuracy of testing result obtained on the use of 205 features and SVM Linear kernel with a value of 58,3%.
Three‐level quantum satellite communication framework and its applications

Summary Quantum cryptography uses quantum natures to realize unconditional security without computational hard problems, which can achieve lots of significant applications of secure communication, multiparty secure computation, E‐commerce, and so on. Even it can get the free‐space optical communication with quantum channel. In this paper, we firstly proposed a three‐level quantum satellite communication framework that covers dozens of common solutions. Among the solutions, we select and give a quantum semi‐honest and semi‐quantum mutual authenticated key agreement scheme with three‐particle entangled states, which can provide secure service for cross‐realm traditional clients with quantum satellite and super nodes. More important, by the quantum satellite's helping, the quantum satellite and the two super nodes can authenticate each other, but the three nodes cannot get any information of the session key between the cross‐realm clients. The proposed scheme has the properties of completeness, information theoretic security, nonrepudiation, and unforgeability. Its information‐theoretic security is ensured by quantum indistinguishability mechanics. On the other hand, our scheme is more efficient than the similar schemes.
Image Retrieval System-An Integrated Approach

Abstract With the development of multimedia technology, the usage of large image database becomes possible and is rapidly increasing. These can be used for the purpose of retrieving files, which works on the mechanism of image search. Different databases are available on different websites like Instagram, Facebook, Twitter, Flickr, and Picasa. This paper shows the advantage of content-based image retrieval system, as well as its key technologies. Comparing to the shortcoming, only certain feature are used in the traditional system. This paper presents a review on different techniques of image retrieval techniques which are based on color, texture and shape of images. It also focuses on the feature extraction and representation, several commonly used algorithms and different methods used for matching of images.
Digital Competence in higher education:A case study of teachers’ perception of working with technologies

With the development of technological tools and the changing world situation, digital competence is becoming increasingly important to our current knowledge society and it has played an important role in the learning process. However, the measurement of digital competencies is a challenge in further understanding their development in practice. This study attempts to analyze the teachers’ perception of using ICT tools in educational activities and their digital competence in the context of higher education. A self-report questionnaire and an interview were used to collect data. The survey was completed by a sample of 536 teachers who worked in different faculties in Gansu Agricultural University in China. The results described the profile of participants' digital competence and revealed their thought about using ICT tools in class. We found that in general, teachers considered themselves as positive in several sections of digital competence, but they also expressed their weak point in digital content creation. Moreover, teachers expressed using ICT tool in class was very important with mentioned training that deficient digital competence related training may lead difficulties in the process of teaching.
Fast variational alignment of non-flat 1D displacements for applications in neuroimaging

In the context of signal analysis and pattern matching, alignment of 1D signals for the comparison of signal morphologies is an important problem. For image processing and computer vision, 2D optical flow (OF) methods find wide application for motion analysis and image registration and variational OF methods have been continuously improved over the past decades. We propose a variational method for the alignment and displacement estimation of 1D signals. We pose the estimation of non-flat displacements as an optimization problem with a similarity and smoothness term similar to variational OF estimation. To this end, we can make use of efficient optimization strategies that allow real-time applications on consumer grade hardware. We apply our method to two applications from functional neuroimaging: The alignment of 2-photon imaging line scan recordings and the denoising of evoked and event-related potentials in single trial matrices. We can report state of the art results in terms of alignment quality and computing speeds. Existing methods for 1D alignment target mostly constant displacements, do not allow native subsample precision or precise control over regularization or are slower than the proposed method. Our method is implemented as a MATLAB toolbox and is online available. It is suitable for 1D alignment problems, where high accuracy and high speed is needed and non-constant displacements occur.
Hybrid deep neural networks to infer state models of black-box systems

Inferring behavior model of a running software system is quite useful for several automated software engineering tasks, such as program comprehension, anomaly detection, and testing. Most existing dynamic model inference techniques are white-box, i.e., they require source code to be instrumented to get run-time traces. However, in many systems, instrumenting the entire source code is not possible (e.g., when using black-box third-party libraries) or might be very costly. Unfortunately, most black-box techniques that detect states over time are either univariate, or make assumptions on the data distribution, or have limited power for learning over a long period of past behavior. To overcome the above issues, in this paper, we propose a hybrid deep neural network that accepts as input a set of time series, one per input/output signal of the system, and applies a set of convolutional and recurrent layers to learn the non-linear correlations between signals and the patterns, over time. We have applied our approach on a real UAV auto-pilot solution from our industry partner with half a million lines of C code. We ran 888 random recent system-level test cases and inferred states, over time. Our comparison with several traditional time series change point detection techniques showed that our approach improves their performance by up to 102%, in terms of finding state change points, measured by F1 score. We also showed that our state classification algorithm provides on average 90.45% F1 score, which improves traditional classification algorithms by up to 17%.
Re-examining the Pioneering Studies on Eye Movements in Aviation: Connecting the Past to the Present

Objective: To reduce the gap between old and new eye-tracking studies in aviation by raising the interest of the scientific community in some of the pioneering works. We present two emblematic cases: the misattributed origin of the use of eye-tracking techniques in aviation to Paul M. Fitts and his collaborators, and the forgotten (and often reinvented) oculometer training tape technique.Background: Over the last century, military and civilian researchers have used eye-tracking techniques to solve many challenges faced by the aviation industry, from assessing new graphical displays to testing procedural trainings. Yet, these techniques have been always classified as merely promising. The difficulty of using eye trackers outside of a laboratory environment, and the labor-intensive data extraction and interpretation procedures have long been considered a barrier to implementing eye-tracking techniques in aviation settings.Method: We revised original scientific articles as well as military and civilian technical reports on the use of eye-tracking techniques in aviation settings from the beginning of Aviation Psychology.Results: A systematic failure in recognizing and learning from the pioneering works might be a concomitant explanation for classifying the use of eye-tracking techniques as merely promising.Conclusion: Taking together past and present findings, it would not be over-optimistic to state that eye-tracking finally went from being a “promising” technique in aviation to a “proven” one.
A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation

Background Patient data is considered particularly sensitive personal data. Privacy regulations strictly govern the use of patient data and restrict their exchange. However, medical research can benefit from multicentric studies in which patient data from different institutions are pooled and evaluated together. Thus, the goals of data utilization and data protection are in conflict. Secure multiparty computation (SMPC) solves this conflict because it allows direct computation on distributed proprietary data—held by different data owners—in a secure way without exchanging private data. Objective The objective of this work was to provide a proof-of-principle of secure and privacy-preserving multicentric computation by SMPC with real-patient data over the free internet. A privacy-preserving log-rank test for the Kaplan-Meier estimator was implemented and tested in both an experimental setting and a real-world setting between two university hospitals. Methods The domain of survival analysis is particularly relevant in clinical research. For the Kaplan-Meier estimator, we provided a secure version of the log-rank test. It was based on the SMPC realization SPDZ and implemented via the FRESCO framework in Java. The complexity of the algorithm was explored both for synthetic data and for real-patient data in a proof-of-principle over the internet between two clinical institutions located in Munich and Berlin, Germany. Results We obtained a functional realization of an SMPC-based log-rank evaluation. This implementation was assessed with respect to performance and scaling behavior. We showed that network latency strongly influences execution time of our solution. Furthermore, we identified a lower bound of 2 Mbit/s for the transmission rate that has to be fulfilled for unimpeded communication. In contrast, performance of the participating parties have comparatively low influence on execution speed, since the peer-side processing is parallelized and the computational time only constitutes 30% to 50% even with optimal network settings. In the real-world setting, our computation between three parties over the internet, processing 100 items each, took approximately 20 minutes. Conclusions We showed that SMPC is applicable in the medical domain. A secure version of commonly used evaluation methods for clinical studies is possible with current implementations of SMPC. Furthermore, we infer that its application is practically feasible in terms of execution time.
Image search optimization with web scraping, text processing and cosine similarity algorithms

The process of searching for image data in cyberspace is only limited to text keywords in the form of file names of images entered on search engines such as Google, Yahoo and Bing so that the results obtained are many variations of the image. With the development of information retrieval technology and text processing, it is hoped that it can help the image search process to be more specific according to the text keywords that are input. The web scrap process can help to dig more detailed information down to metadata from an image source on the website. The text data generated in the web scrap process is further processed using text processing and cosine similarity algorithms to produce information relevant to the image being sought. The results obtained reach 90% accuracy for general image data with a search image limit of up to 20 images. For specific images, it only reaches 25% accuracy for a limit of 20 images. There are 2 things that affect the accuracy value of image search, namely a very large image limit and a very specific query or image name so that there are less relevant images generated by search engines. With an image search method like this, it is expected to be able to find and download images that are truly relevant and of high quality to be used as training data in the image classification process.
Accuracy Improvement of Age Prediction Model Based on AutoML

Abstract Recommendations from websites are sometimes not suitable enough to fit the needs of different groups of people. One element in considering the recommended algorithm is the age range of users. To approach the goal of improving recommended algorithms in general, age prediction based on machine learning became the target of this research. We started by using AutoML from Google Cloud as the training tool and proportionally choosing 4,908 facial images of Asian people, which were categorized into six groups: images of the age below 10; images of the age from 10 to 20; images of the age from 20 to 30; images of the age from 30 to 40; images of the age from 40 to 50; images of the age from 50 to 60; and images of the age above 60. From the first training, we demonstrated 64.89 percentage of precision with the confidence threshold value of 0.5. After changing the threshold to 0.71, we optimized the result to 69.83 percentage of precision. By observing the result given by the deployment from Cloud training to an Android device, we found that the model differentiated Asian people aged from 10 to 40 most precisely, while it was relatively weak to distinguish people of the age from 50 to 60 and people of the age above 60. These results show an intelligent work of the prediction model on sifting people by ages and the strong relationship between the precision and the quantity of corresponding data. Its focus on distinguishing young people is suitable for our goal as young people are the main force of Internet users. By adjusting the composition of the dataset, we are looking for broader usage in other fields.
Ban, Boom, and Echo! Entrepreneurship and Initial Coin Offerings

Regulatory spillovers occur when regulation in one country affects either the expected regulatory approach and/or entrepreneurial finance markets in other countries. Drawing on institutional theory, we investigate the global implications of a regulatory spillover on entrepreneurship. We argue that regulatory spillovers have both short- and long-term effects on the number and quality of entrepreneurial finance initiatives such as Initial Coin Offerings (ICOs). Based on a sample of 3,838 ICOs in 108 countries, we find that a regulatory ban of ICOs in one country causes a short-term increase in the number of low-quality ICOs in other countries and a long-term drop in the number of ICOs albeit with a higher average quality. Overall, a restrictive regulation triggered a process of increased market selection.