Reviewerly/exHarmony huggingface dataset:
Sediment-carrying capacity prediction using extreme learning machine

Abstract Sediment-carrying capacity prediction is an important part in the river sediments movement simulation. In this paper, a novel machine learning approach was proposed to predict the sediment-carrying capacity by using the Extreme Learning Machine (ELM). The evaluation indexes and the average errors of these prediction models were analysed in detail. The result shows that the ELM model has a good generalization performance for the sediment-carrying capacity prediction, and the ELM approach could be an effective tool for the sediment movement simulation.
Adaptive Weight Based Sparse Block Aggregation Algorithm for Stereo Matching

To overcome the low performance in efficiency and accuracy in stereo matching, a stereo matching method that combines adaptive weight with sparse block aggregation is presented to handle the region where the depth is not continuous. Specifically, an improved matching cost function is derived from incorporating intra-color difference of pixel points into the adaptive weight to improve the success matching rate. Furthermore, a sparse mode of the interlaced columns and the center point cross direction is adopted at the cost aggregation stage, which can boost the calculation procedure. The left-right consistency detection and median filtering are utilized to rectify the mismatch points. Experimental results show that the average mismatch rate of the optimized method reduces by 5.66% in the region where the depth is not continuous and the running time of the optimized method is 15 times shorter than the ASW algorithm. Moreover, it is ultra-robust to illumination interference.
Imbalanced Data Optimization Combining K-Means and SMOTE

With the wide application of imbalanced data processing in various fields, such as credit card fraud identification, network intrusion detection, cancer detection, commodity recommendation, software defect prediction, and customer churn prediction, imbalanced data has become one of the current research hotspots.When classifying imbalanced data sets, aiming at the problems of low classification accuracy of negative class samples in the random forest algorithm and marginalization for selecting new samples in the SMOTE algorithm, a new algorithm, KMS_SMOTE, is proposed to deal with imbalanced data sets.In order to avoid the problem of marginalization of new samples, the K-Means algorithm is used to classify the negative class samples to obtain the centroid of the negative class samples, and then the new data set is obtained by selecting the samples near the centroid.Finally, in order to verify the effect of the KMS_SMOTE algorithm, it is compared with the SMOTE algorithm on the data sets of UCI machine learning.The experimental results show that the KMS_SMOTE algorithm effectively improves the classification performance of the random forest algorithm on the imbalanced data set.
An Adaptive Multi-Features Aware Correlation Filter for Visual Tracking

In recent years, correlation filter (CF) based tracking methods have attracted more attention due to its low computational complexity and excellent performance. Most CF based tracking methods adopt CNN features of multiple layers to train the tracker for better performance. These methods fuse CNN features of multiple layers directly, and cannot make full use of the valuable information contained in the CNN features. In this paper, an adaptive multi-features aware correlation filter method is proposed. By extracting several basic features, different combinations of CNN features are formed. The proposed method can select an optimal feature combination for tracking adaptively according to the object appearance at the current frame. Experimental results show that the proposed method can track different challenging sequences robustly. By evaluating on the OTB-100 dataset, it can be found that the proposed method is advantageous compared with the state-of-the-art methods.
Identification of Heuristics for Assessing the Usability of Websites of Public Administration Units

A very important aspect of modern websites is their usability.Thanks to modern, and constantly developing technologies it is possible to create user-friendly services for each user.The usefulness of online services may be considered in terms of their functionality, clarity, and accessibility.It is particularly important that these criteria are met by public administration websites.The aim of this paper is to present the most common usability errors identified on the websites of public administration units as well as to indicate the links between particular types of problems and traditional heuristics of Jakob Nielsen.The survey was conducted by evaluating the websites of the Public Information Bulletin in Poland (BIP, which stands for "Biuletyn Informacji Publicznej"), which are supposed to provide universal access to public information by the citizens of the country.A heuristic method (based on J. Nielsen's heuristics) was used to evaluate 60 websites.The errors obtained were grouped into 14 categories.Each of the error groups was assessed by an expert in terms of its importance for the overall assessment of the website's usefulness.The analysis of relations between the identified categories of errors and heuristics of Jakob Nielsen indicates a need for specifying heuristics in the context of evaluating the usability and availability of public administration websites.
Efficient Certificate-Based Signature with Short Key and Signature Sizes from Lattices

Certificate-based cryptography (CB-PKC) is an attractive public key setting, which reduces the complexity of public key infrastructure in traditional public key settings and resolves the key escrow problem in ID-based public key settings.In the past, a large number of certificate-based signature and encryption schemes were proposed.Nevertheless, the security assumptions of these schemes are mainly relied on the difficulties of the discrete logarithm and factorization problems.Unfortunately, both problems will be resolved when quantum computers come true in the future.Public key cryptography from lattices is one of the important candidates for post-quantum cryptography.However, there is little work on certificate-based cryptography from lattices.In the paper, we propose a new and efficient certificate-based signature (CBS) scheme from lattices.Under the short integer solution (SIS) assumption from lattices, the proposed CBS scheme is shown to be existential unforgeability against adaptive chosen message attacks.Performance comparisons are made to demonstrate that the proposed CBS scheme from lattices is better than the previous lattice-based CBS scheme in terms of private key size and signature size.
An expert study on hierarchy comparison methods applied to biological taxonomies curation

Comparison of hierarchies aims at identifying differences and similarities between two or more hierarchical structures. In the biological taxonomy domain, comparison is indispensable for the reconciliation of alternative versions of a taxonomic classification. Biological taxonomies are knowledge structures that may include large amounts of nodes (taxa), which are typically maintained manually. We present the results of a user study with taxonomy experts that evaluates four well-known methods for the comparison of two hierarchies, namely, edge drawing , matrix representation , animation and agglomeration . Each of these methods is evaluated with respect to seven typical biological taxonomy curation tasks. To this end, we designed an interactive software environment through which expert taxonomists performed exercises representative of the considered tasks. We evaluated participants’ effectiveness and level of satisfaction from both quantitative and qualitative perspectives. Overall quantitative results evidence that participants were less effective with agglomeration whereas they were more satisfied with edge drawing . Qualitative findings reveal a greater preference among participants for the edge drawing method. In addition, from the qualitative analysis, we obtained insights that contribute to explain the differences between the methods and provide directions for future research.
Research on Formation Reconfiguration of UAVs Based on RRT Algorithm

To adapt the complexity and flexibility of battlefield environment, a method of formation reconfiguration based on Rapidly-exploring Random Tree (RRT) algorithm is proposed, which shows the advantage of formation of Unmanned Aerial Vehicles (UAVs). Firstly, the kinematic model for UAVs is built, and the feasibility of combination of traditional RRT algorithm and formation reconfiguration of UAVs is analyzed. Secondly, the strategies of trajectory correction comprising node removal and transition trajectory are adopted. Then the dynamic and collision avoidance constraints are discussed respectively, which are essential for exploring the process of RRT algorithm as well as adjusting the trajectory of UAVs. Finally, the simulation and flight experiment are carried out to verify the effectiveness of the proposed method. The results show that the reconfiguration method is able to achieve the formation reconfiguration rapidly and safely. Moreove, the planed trajectory can satisfy the tracing requirement, which is of significance for flight of UAVs in the battlefield environment.
Smart Urban Objects to Enhance Safe Participation in Major Events for the Elderly

IoT increasingly permeates the public area, e.g., in traffic control and public transport.We propose to equip conventional urban objects with IoT technology to transform them into Smart Urban Objects (SUO's).While there exists some research exploring the potentials, specific solutions to enhance safety for the elderly outdoors are still lacking.The elderly's safety is threatened due to declining physical conditions.As a consequence, the elderly may be excluded from outdoor activities such as participating in major events.Against this backdrop, we design SUOs for adaptive indications of urban hazards, barrierfree passages and for smart reservation of seats to enhance resting possibilities.We report on our solution using Bluetooth technology for remote sensing of older pedestrians serving as input for the objects' adaptive capacities.The SUOs have been installed for test purposes on a major event in a larger German city.
Expanding graphs of the Extremal Graph Theory and expanded platforms of Post Quantum Cryptography

Explicit constructions in Extremal Graph Theory give appropriate lower bounds for Turan type problems.In the case of prohibited cycles, the explicit constructions can be used for various problems of Information Security.We observe recent applications of algebraic constructions of regular graphs of large girth and graphs with large cycle indicator to Coding Theory and Cryptography.In particular, we present a new multivariate platforms of postquantum Non-commutative Cryptography defined in graph theoretical terms.
Preliminary Citation and Topic Analysis of International Conference on Agile Software Development Papers (2002-2018)

This study utilizes citation analysis and automated topic analysis of papers published in International Conference on Agile Software Development (XP) from 2002 to 2018.We collected data from Scopus database, finding 789 XP papers.We performed topic and trend analysis with R/RStudio utilizing the text mining approach, and used MS Excel for the quantitative analysis of the data.The results show that the first five years of XP conference cover nearly 40% of papers published until now and almost 62% of the XP papers are cited at least once.Mining of XP conference paper titles and abstracts result in these hot research topics: "Coordination", "Technical Debt", "Teamwork", "Startups" and "Agile Practices", thus strongly focusing on practical issues.The results also highlight the most influential researchers and institutions.The approach applied in this study can be extended to other software engineering venues and applied to large-scale studies.
Project Management Tasks in Agile Projects: A Quantitative Study

Recent studies have confirmed the efficacy of agile methodologies in project success.However, can projects skip several project management tasks and still deliver the expected results?How are traditional project managers engaged in agile projects?The results from this study quantify subjective and theoretical speculation on who performs the project management tasks in agile projects.Project managers are engaged in agile projects and the team, the product owner, and project sponsor are significantly involved in project management tasks.The agile coach is not a substitute for the project manager.The study identifies that agile and traditional methodologies should be updated to clarify team, product owner, and agile coach responsibilities.
Correlation-based spike sorting of multivariate data

Abstract Automated classification of waveforms is an important method of data processing used in various fields of science, such as neuroscience, biomedical engineering, etc. This work shows the possibility of sorting special waveforms i.e. spikes recorded with multichannel electrode arrays by using principles of correlation and data-driven reference. A new method to estimate the number of k-means clusters by using a Monte Carlo method is introduced. To demonstrate the performance of the algorithm, generated signals were used, which are created to mimic multichannel recording of the extra-cellular neuronal signals.
Identifying Tags Describing Image Contents

On many photo-sharing social media, e.g., Instagram, a user posting a photo can add tags, i.e., words describing it. Tags are used for keyword-based image search. Some tags, however, describe not image contents but some metadata, e.g., camera names. We propose a method of determining which tag is more likely to describe the associated image contents. We determine it based on pairwise comparisons of tags. Given a pair of tags A and B, we compare three sets of images: (1) those associated with A, (2) those associated with B, and (3) those associated with both A and B. If (3) is more similar to (1) than to (2), A is more likely to describe image contents. Our experiment with Instagram data shows that our method is effective.
Research on Stereo Matching Technology Based on Binocular Vision

With the rapid development of machine vision, binocular stereo vision based on the principle of parallax has gradually become the core of scientific research.This paper briefly presents the background and research significance, elaborates the research status of binocular vision robot at home and abroad and studies the checkerboard calibration method, and uses Matlab to complete binocular camera calibration.Stereo matching technology is the core and most difficult part of binocular stereoscopic 3D reconstruction research.Firstly, the image acquired after calibration is enhanced by gray scale transformation to make the image clearness optimal, and then use NCC (normalization cross-compilation).The algorithm performs the matching of left and right image pairs in the Matlab environment to generate an optimal matching disparity map.
Adaptive Grid Decomposition Algorithm based on Standard Deviation Circle Radius

The differential privacy preservation model based on spatial dataset meshing has been widely concerned, but the distribution characteristics of the dataset and user's query granularity are often ignored or not fully considered in the partitioning of the dataset.Aiming at deficiencies in existing mesh-based algorithms, a standard deviation circle radius adaptive grid decomposition (SDCAG) algorithm is proposed.Firstly, the standard deviation circle radius is introduced to quantitatively represent the distribution characteristics of datasets in order to calculate privacy preservation requirements.Secondly, filtering and bucketing are used to reduce the noise error.Finally, the improved query precision is implemented based on the post-processing.Experiments on the NYC dataset, the Beijing dataset, and the Checkin dataset show that the SDCAG algorithm is superior to similar algorithms in terms of query performance.
Data Mining Techniques for Software Quality Prediction in Open Source Software

Software quality monitoring and analysis are among the most productive topics in software engineering research. Their results may be effectively employed by engineers during software development life cycle. Open source software constitutes a valid test case for the assessment of software characteristics. The data mining approach has been proposed in literature to extract software characteristics from software engineering data. This paper aims at comparing diverse data mining techniques (e.g., derived from machine learning) for developing effective software quality prediction models. To achieve this goal, we tackled various issues, such as the collection of software metrics from open source repositories, the assessment of prediction models to detect software issues and the adoption of statistical methods to evaluate data mining techniques. The results of this study aspire to identify the data mining techniques that perform better amongst all the ones used in this paper for software quality prediction models.
Crime Scene Reconstruction with RGB-D Sensors

Photographic surveying, a fundamental procedure in crime investigation, is typically performed using 2D cameras.Although useful, such cameras remain limited due to the lack of depth information.In this work, we propose a 3D reconstruction solution that leverages the advantages of cheap RGB-D sensors to create a 3D model of the crime scene and to provide the investigator with an interactive crime scenario simulation environment.A structure from motion approach is proposed in order to align the captured point clouds on each other using 3D key points.An iterative refinement and a global optimization algorithm are later adapted for the optimization of the registered 3D model, which is then triangulated before the underlying surface is reconstructed.The resulting model is used for interactive crime investigation and object dynamics simulation.The obtained results show the effectiveness of our solution with a visually appealing rendering, an accurate simulation and a quantitative error of less than 18cm for the 4m×4m indoor scene.An accompanying video is provided in order to illustrate the processing pipeline 1 .
Mutex-Based De-anonymization of an Anonymous Read/Write Memory

Anonymous shared memory is a memory in which processes use different names for the same shared read/write register. As an example, a shared register named A by a process p and a shared register named B by another process q can correspond to the very same register X, and similarly for the names B at p and A at q which can correspond to the same register $$Y\ne X$$ . Hence, there is a permanent disagreement on the register names among the processes. This new notion of anonymity was recently introduced by G. Taubenfeld (PODC 2017), who presented several memory-anonymous algorithms and impossibility results. This paper introduces a new problem, that consists in "de-anonymizing" an anonymous shared memory. To this end, it presents an algorithm that, starting with a shared memory made up of m anonymous read/write atomic registers (i.e., there is no a priori agreement on their names), allows each process to compute a local addressing mapping, such that all the processes agree on the names of each register. The proposed construction is based on an underlying deadlock-free mutex algorithm for $$n\ge 2$$ processes (recently proposed in a paper co-authored by some of the authors of this paper), and consequently inherits its necessary and sufficient condition on the size m of the anonymous memory, namely m must belong to the set $$M(n)=\{m:~ \text{ such } \text{ that } \forall ~ \ell : 1<\ell \le n:~ \mathsf{{gcd}}(\ell ,m)=1\}\setminus \{1\}$$ . This algorithm, which is also symmetric in the sense process identities can only be compared by equality, requires the participation of all the processes; hence it can be part of the system initialization. Last but not least, the proposed algorithm has a noteworthy first-class property, namely, its simplicity.
The Vertical Structure of Neolithic Finds in the Fills of Archaeological Features

This article examines the distribution of Neolithic finds in the fillings of features with different functions (settlement pits, rondel ditches, enclosure) in the Bylany 4 microarea, Kutná Hora district, Czech Republic.We investigate the nature of their extinction or the emergence of fills.The vertical structure of archaeological finds (pottery and non-pottery) deposited in the sediments of various features was monitored in terms of five attributes.Based on the different structures of the fills of archaeological features, it is evident that the structure of the horizontal or vertical distribution of findings in their fills can yield information about the creation of the finding assemblages.It also reflects the dynamics of the development of anthropogenic activities in the vicinity of the features.In terms of methodology, the work follows up on the general theory of formative processes, while their natural and cultural transformations are discussed at a more detailed level of specific forms.It is necessary to consistently distinguish between the behaviour of clay sediments and the actual artefacts stored therein.It is further necessary to individually evaluate the specific natural conditions of deposition at a specific site as well as the variable cultural roles of individual features.
Information System Risk Assessment And Management (Study Case at XYZ University)

Information system risk assessment case studies based on information assets that need to be mapped in detail, this case study has been conducted at XYZ University in the directorate of online learning. There is a need for the application of a risk management information system for information asset security and has to be designed to identify risks and problems that exist at the online learning especially. Information system risk assessment must be watched out for because it can threaten the integrity, confidentiality, and availability of online learning activity and through this qualitative research which uses the OCTAVE ALLEGRO framework, critical assets can be identified together with the proposed prevention approach.