Reviewerly/exHarmony huggingface dataset:
Performance of dynamic algorithms on the dynamic distance minimization problem

In the area of multi-objective optimization, a special challenge is dynamic optimization problems. These problems change their optimal values or optimal configurations of input variables over time, making it harder for metaheuristic algorithms to track these changes and find the new optima. To test the search ability of such dynamic multi-objective algorithms, a dynamic benchmark called the Dynamic Distance Minimization Problem (dDMP) was proposed in the literature. The dDMP implements multiple changes, not only in location and fitness values of the Pareto-optimal sets, but also in the complexity of the problem. This work aims to test the performance of two well-known dynamic multi-objective algorithms on different instances of the dDMP with varying complexity. This involves changes in the number of objectives and changes of the distance metric at runtime, which has not been done before with this problem in the literature. The results show that both algorithms struggled to recover after the number of objectives was reduced and then increased again. When the distance metric was changed over time both algorithms performed reasonable well. However, there were gaps in the found Pareto fronts when switching between the Euclidean and the Manhattan distance metrics.
Transfer Regression with Data-Augmented Ensemble Learning Framework

In biological measuring instruments design, it is time-consuming and expensive to acquire sufficient samples for calibration process. Transfer learning has provided an efficient approach for this problem by leveraging the labeled samples from calibration channels, considered as source domain, to annotate the target domain which has few labels under actual working conditions. In this paper, we design a general transfer regression framework improved by data-augmented ensemble learning called DA-TRTs. First, we modify two-stage TrAdaBoost.R2 to TrAdaBoost. RT (TRT) which concentrates more on hard examples during transfer boosting. Second, model stacking framework is introduced to improve the performance of regression, calling TRT as a base learner. Then, we introduce a simple data augmentation routine, based on label smoothing assumption. Data augmentation improves the strength in diversity of base learners and implicitly controls model complexity. Finally, to illustrate the performance of DA-TRTs, we conduct experiments on synthetic datasets.
Research on Segmentation and Recognition of Printed Chinese Characters

Abstract The printed character recognition (PCR) is an efficient and automatic information entry method, which will become the important function interface of the computer in the future. A good character segmentation method can greatly improve the speed and accuracy of character recognition. This paper proposes a Chinese character segmentation algorithm based on OpenCV open source library and parallel processing, which can segment Chinese characters efficiently and accurately. And then, using the Tesseract-OCR character recognition engine to identify Chinese characters, the experimental results show that the average recognition time of a single Chinese character is 0.07s, and the recognition rate of Chinese characters is 96.2%, which proves the feasibility of the method.
Advertisement Prediction in Social Media Environment Using Big Data Framework

With the development of mobile technologies and IOT devices, the world has stepped into the era of big data and social media as well. Having collected data from social media, business companies can easily understand behavior and buying patterns of the individual customers. The data is being collected via machine learning algorithms and social media platforms. A prediction mechanism is needed to process these larger data. Based on the results generated by big data framework, business companies can directly target individuals for sending advertises. In this chapter, an advertisement prediction framework has been proposed that uses prediction approaches on big data platforms. In addition, social media platforms are used to collect data that is based on user interest. The experiments has been performed on real-time data that is collected from social media platforms. The introduced framework can be served as a benchmark for business companies to send appropriate advertisement to the individuals.
Segmentation of Kidney Stones in Medical Ultrasound Images

The computer-aided diagnostic system has become an important issue in clinical diagnosis. Development of new technologies and use of various imaging modalities have raised more challenging issues. The major issue is processing and analyzing a significantly large volume of image data, to generate qualitative information for diagnosis and treatment of diseases. Medical imaging, particularly ultrasound imaging is one of the commonly used diagnostic tool by medical experts. Segmenting a region of interest in medical ultrasound image is a difficult task because of variation in object shape, orientation and image quality. In the present study, initially preprocessing of kidney ultrasound images is performed using contourlet transform and contrast enhancement using histogram equalization. The proposed method focuses on segmentation of kidney stones in preprocessed medical ultrasound images using level set method. The developed method shows better performance in segmenting renal calculi in medical ultrasound images of the kidney. The experimental results demonstrate the effectiveness of the developed software module.
Visual Tracking with Autoencoder-Based Maximum A Posteriori Data Fusion

In this paper, a novel method for tracker fusion is proposed and evaluated for vision-based object tracking. This work combines three distinct popular techniques into a recursive Bayesian estimation algorithm. First, a semi-supervised learning approach is used to train deep neural networks capable of detecting anomalous visual tracking behavior. Next, the network output is used to compute maximum a posteriori scores. Finally, these scores are integrated into the observation weighing mechanism of an existing data fusion algorithm. We evaluated the proposed algorithm on the OTB-100 benchmark dataset and compared its performance to the performance of the baseline fusion approach.
A performance study on different data load methods in relational databases

Alongside with new cloud system emerging, legacy systems inside organizations are being migrated. With them, databases, and all stored data, which might variate from some GB to large amounts of TB. These systems migrations pose considerable problems - data export method, import method, consumed time, consistency, and so on - the so-called legacy system migration opens a new research topic, concerning how to migrate data timely efficient. The same problem, loading data, can be applied to ETL processes, with particular focus to the Load phase, which needs to be performed as fast as possible. This paper provides a brief review of different relational databases load methods and compares their performance. Experimental results show that despite the different available methods to efficiently load data (without losing information), performance is severely affected, presenting variations that can go from seconds to hours/days depending on the used strategy.
Efficient and Secure k-Nearest Neighbor Search Over Encrypted Data in Public Cloud

Cloud computing has become an important and popular infrastructure for data storage and sharing. Typically, data owners outsource their massive data to a public cloud that will provide search services to authorized data users. With privacy concerns, the valuable outsourced data cannot be exposed directly, and should be encrypted before outsourcing to the public cloud. In this paper, we focus on k-Nearest Neighbor (k-NN) search over encrypted data. We propose efficient and secure k-NN search schemes based on matrix similarity to achieve efficient and secure query services in public cloud. In our basic scheme, we construct the traces of two diagonal multiplication matrices to denote the Euclidean distance of two data points, and perform secure k-NN search by comparing traces of corresponding similar matrices. In our enhanced scheme, we strengthen the security property by decomposing matrices based on our basic scheme. Security analysis shows that our schemes protect the data privacy and query privacy under attacking with different levels of background knowledge. Experimental evaluations show that both schemes are efficient in terms of computation complexity as well as computational cost.
Push

tutorial Share on Push: genetic programming for finite algebras Authors: Lee Spector UMass UMassView Profile , Nicholas Freitag McPhee University of Minnesota Morris University of Minnesota MorrisView Profile , Lee Spector Hampshire College Hampshire CollegeView Profile , David M. Clark SUNY New Paltz SUNY New PaltzView Profile , Ian Lindsay Hampshire College Hampshire CollegeView Profile , Bradford Barr Hampshire College Hampshire CollegeView Profile , Jon Klein Hampshire College Hampshire CollegeView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 1175–1196https://doi.org/10.1145/3319619.3323392Published:13 July 2019Publication History 1citation81DownloadsMetricsTotal Citations1Total Downloads81Last 12 Months10Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
SPL-AT Gherkin: A Gherkin Extension for Feature Oriented Testing of Software Product Lines

As cloud platforms turn into software product lines (SPLs), testing products composed of customer selected features becomes more and more important. In this paper, we propose a feature-oriented testing approach for platform-based SPLs through a novel extension to Gherkin called SPL-AT Gherkin and a novel automatic test method generation technique, which utilizes TestNG framework. We demonstrate the applicability of the proposed approach by a case study.
Searching for Community and Safety: Evaluating Common Information Shared in Online Ex-Vaxxer Communities

This study examines a collection of artifacts passed on from some closed Facebook groups of anti-vaxxers. The study conducted a thematic analysis to determine whether or not the group is a community of practice, evaluate and categorize the types of information shared in these groups, and determine the sources of over 1,100 links across two compiled documents to address a series of questions related to claims of ex-vaxxers when compared to anti-vaxxers and the types of data commonly referenced. Findings indicated that ex-vaxxers and anti-vaxxers have separate and distinct claims, abstracts are the most commonly shared scholarly document, and select information is most often taken out of context. This data set can be analyzed for valence and language use in future studies. The purpose of this study is to evaluate information shared in among anti- and ex-vaxxer parents. This study does not seek to validate a specific position or point of view, nor does the researcher want to explore or determine correctness of beliefs.
PAAA

Now we are in the information age, and distance learning is becoming more and more popular. For subjects related to computer science, programming assignments can't be avoided. But it's hard for human to review massive answers. Therefore, on the basis of former study, we put forward a new method, which is on the level of source code comprehension. In this paper, we revealed a method of programming assignment automatic assessing. Our project, Programming Assignments Automatic Assessing (PAAA), imitates human reviewers' action, and take such strategy to look through the source code. Furthermore, with the help of general grammar analyze tool ANTLR, it is able to read source code written in different languages, and build a language-independent program directed graph. Through a lot of tests, PAAA can be up to a lot of grading jobs of programming assignments assessment. And the result shows that PAAA's result is very close to human's marking.
A Place to Discover, Imagine, and Change: Smart Learning with Local Places

Following the results of a study focusing on the unique affordances of mobile technologies that support their informed integration in learning, a novel pedagogical model has been developed. This model was tried out within the context of geography and cultural heritage in four different cases. The first part of this paper briefly presents the main findings of the study and sketches the emergent uniqueness profile of mobile apps for learning. The second and main part outlines the DICE model (Discover, Imagine, ChangE) that was developed for designing place-based learning activities with the assistance of mobile apps, and presents its main characteristics through data gathered from the four cases. Through participating in DICE activities, learners uncover the invisible in familiar places and become more aware of their surroundings, while the place becomes a powerful object to learn with.
Regional Computer Graphics Competition As A Tool Of Influence On The Profession Choice: Experience Of Sumy Region Of Ukraine

Recently, the IT-industry has become increasingly popular in Ukraine. Young people are aware of the prospects of their own realisation as programmers, system administrators, web designers, etc. At the same time, the prestige of teachers' work decreases. Pedagogical universities face the problem of small recruitment for pedagogical specialties, where the specialty of a computer science teacher is not an exception. The article describes the experience of the regional computer graphics competition “Colour Your Life” as one of the solutions to the problem of professional orientation of young people in the computer science teaching profession in Sumy region of Ukraine. The authors studied the following problems: 1) confirmation of the competition status as a regional level competition by determining the geography of the participants; 2) determining the most popular graphic editors among youth; 3) determining the preferences of the competition participants for the future profession choice; 4) confirmation of the relationship between the number of participants and the number of applications for the specialty “014 Secondary Education. Computer science” (results are illustrated with infographics). Based on correlation analysis, a direct relationship between the total number of participants and the number of applicants to Makarenko Sumy State Pedagogical University was confirmed.
Division polynomial‐based elliptic curve scalar multiplication revisited

Here, the authors provide a derivation of an improvement to Kanayama's elliptic curve scalar multiplication algorithm using division polynomials. In addition, they also provide experimental results and show that the improvement is useful when the elliptic curve under consideration is defined over large prime fields.
An approach based on classifier combination for online handwritten text and non-text classification in Devanagari script

In this article, a method of analysing features of elliptical regions and combining outcomes of classifiers using Dempster–Shafer Theory (DST) is presented to classify online handwritten text and non-text data of any online handwritten document in the most popular Indic script—Devanagari. Although a few works exist in this regard in different non-Indic scripts, to our knowledge, no study is available to classify handwritten text and non-text document in online mode in any Indic script. The present method uses various structural and directional features analysed in elliptical regions to extract feature values from strokes of text and non-text data. The features are then studied separately in classification platforms based on Support Vector Machine (SVM) and Hidden Markov Model (HMM). The probabilistic outcomes of these two classification platforms are then combined using DST to improve the system performance. The efficiency of the present system has been measured on a self-generated dataset and it provides promising result.
Cooking Video Summarization Guided By Matching with Step-By-Step Recipe Photos

Cooking videos shared on social media enable users to easily understand the cooking process. However, it is difficult to create such summarization videos, as it requires video editing expertise to create high-quality content. In this paper we propose a semi-automatic system for summarizing cooking videos from a full video of the cooking process and a number of step-by-step recipe images. The system is designed to allow anyone to create shareable cooking videos. We focus on online recipe websites because their recipe images provide good descriptions of the food preparation procedures. Our system first searches for scenes similar to each recipe image using features of an Inception Net, fine-tuned to recipe images. These matches are used as key frame candidates, from which users can then select their favorite scenes. Finally, our system automatically creates a summarized video using editing techniques such as speeding up. User studies showed high satisfaction rates of both editors and viewers, demonstrating the benefits of the proposed system.
Research on Pedestrian Attribute Recognition Based on Semantic Segmentation in Natural Scene

Smart city is a new term given to society by technology, and cameras are important infrastructure for building a smart city. How to use camera information efficiently and effectively plays an important role in people’s daily life and maintain social order. Pedestrian information accounts for a large proportion of camera information, so we hope to make good use of pedestrian information. Previous works use traditional machine learning methods and neural network to identify pedestrian attributes, mainly judge the existence of pedestrian attributes in natural scenes. However, it’s not enough to judge whether an attribute exist or not, getting the position of an attribute often gives you more information. In this paper, we propose to use semantic segmentation to obtain the position information of pedestrian attributes. We first propose pedestrian attribute semantic dataset in natural scene called PASD (Pedestrian attribute semantic dataset), which select 27 visualized pedestrian attributes. Deeplabv3+ is used to perform experiments on PASD, which obtain the mIoU (mean intersection over union) baseline of 27 pedestrian attributes. For getting useful conclusion, we conduct data analysis about mIoU from three aspects: attribute distribution, accuracy and resolution.
An Auto-Correction Teleoperation Method for a Mobile Manipulator Using Gaze Tracking and Hand Motion Detection

Situational awareness in remote environments is crucial for human operators to teleoperate mobile manipulators confidently and reliably. Visual feedback is the most common way for environment perception, providing rich information to human operators. This paper proposes an intuitive teleoperation method by combining gaze tracking and hand motion detection to teleoperate a mobile manipulator. A camera is fixed on the end-effector of the mobile robot’s arm to provide visual feedback, acting as the eye of the teleoperator. Rather than direct remote control of the robot, an adaptive auto-correction mechanism is introduced for helping human operators to achieve better hand-eye coordination of the teleoperation experiences. The mobile manipulator can adjust its behaviours, such as speed, while gaze and hand movements of the operator are in different states. The experiments carried out demonstrated the effectiveness of the proposed algorithm and the survey evaluation verified the practical application value of the system.
Ncryptr: A symmetric and asymmetric encryption application

Each person carries digital devices that communicate with other person's devices and may transmit sensitive data. When the transmitted data is sensitive, it becomes necessary to implement secure mechanisms to hide the information and thus avoid it from being analyzed by third parties. This paper aims to present Ncryptr, a web-based application that allows its users to exchange encrypted instant messages in real time. Outside parties are unable to extract the contents of the messages exchanged between two or more users. Ncryptr allows its users to choose the type of encryption employed, being this a major distinction with others already existing instant messaging applications. The performance analysis is made by comparing the efficiency and latency time of different encryption methods, showing the pros and cons of using each one.
A Framework for Measurement of Interactivity of Digital Learning Resources

The Digital Education Action plan, adopted by the European Commission is setting out a strategy to help EU Member States meet the challenges and opportunities of education in the digital age. Action 9, which covers the third priority of the plan is intended to measure the progress in the use of digital technologies in school education. Measuring the progress itself requires establishment of measurable criteria. When we consider digital education, the digital learning content becomes one of the crucial components of the educational process. At the same time the growth of the interactive technologies transforms the digital generation to a generation demanding a highly interactive experience in the classroom. The research presented in this paper identifies a necessity to be established a detailed specification of criteria measuring the interactivity of digital learning content in order to assure a learning process adequate to the learners needs, attitudes and expectations. An abstract concept of learner-content interaction in a digital context is provided and a framework of identifiers, measuring the interactivity of various formats of digital learning content is proposed.
Proper Usage of the Group Signature Scheme in ISO/IEC 20008-2

In ISO/IEC 20008-2, several anonymous digital signature schemes are specified. Among these, the scheme denoted as Mechanism 6, is the only plain group signature scheme that does not aim at providing additional functionalities. The Intel Enhanced Privacy Identification (EPID) scheme, which has many applications in connection with Intel Software Guard Extensions (Intel SGX), is in practice derived from Mechanism 6. In this paper, we firstly show that Mechanism 6 does not satisfy anonymity in the standard security model, i.e., the Bellare-Shi-Zhang model [CT-RSA 2005]. We then provide a detailed analysis of the security properties offered by Mechanism 6 and characterize the conditions under which its anonymity is preserved. Consequently, it is seen that Mechanism 6 is secure under the condition that the issuer, who generates user signing keys, does not join the attack. We also derive a simple patch for Mechanism~6 from the analysis.