Reviewerly/exHarmony huggingface dataset:
Implementation of Algorithm Rabin-Karp for Thematic Determination of Thesis

Thesis / scientific work is a science composition that presents facts and is written according to a good and correct writing methodology. But to find out what the theme discussed should read the abstract of the essay. Along with the development of information technology that offers convenience for human labor in simplification of his work. Difficulty in determining the theme in the thesis can be aided with applications that are able to set themes based on abstract. To make the application of thesis thematic determination can use string matching algorithm, one of string matching algorithm is Rabin-Karp algorithm by doing string matching based on the hash value on text and hash value in a pattern. The Rabin-Karp algorithm is often used to detect plagiarism text document, comparing the overall hash value of k-gram contained in all text documents, then deciding the degree of similarity. Then it can be concluded that Rabin-Karp is able to string a text as a whole, from this the researcher is interested in proving what if the Rabin-Karp algorithm is implemented in the classification field wherein in this study it is used to string matching each word or keyword refers to the theme of an Indonesian essay or thesis abstract. The application is built using web programming language PHP and MySQL database. The confusion matrix is used to determine accuracy, obtained 88% and 12% for misclassification rates.
Vision-Based Quality Control Testing of Elastomeric Bridge Bearings

Elastomeric bridge bearings are widely used in bridges in order to allow deformations caused by temperature variations, traffic loading, shrinkage due to prestress or aging, and construction misalignments. However, manufacturing imperfections in these bearings (such as deviations in rubber layer thickness and non-parallel steel plates) affect their structural performance. Currently, quality control of these bearings involves destructive testing, which is costly and time consuming. This paper presents an alternative, vision-based approach for assessing the internal structure of elastomeric bridge bearings, which eliminates the need to destroy them. Three-dimensional digital image correlation (3D-DIC) is used to estimate the thickness of different rubber layers and any shim rotation or distortion based on deformation patterns on the surfaces of the bearings during compression testing. 3D-DIC provides full-field measurements of displacement and strain on the surface by tracking the distribution of grayscale values of pixel-based subsets in the sequence of stereo images. The presented approach enables engineers to efficiently evaluate bearings for quality control purposes.
Sampling a two dimensional matrix

A new sampling design for populations whose units can be arranged as an N×M matrix is proposed. The sample must satisfy some constraints: row and column sample sizes are set in advance. The proposed sampling method gives the same selection probability to all the sample matrices that satisfy the constraints. Three algorithms to select a sample uniformly in the feasible set are presented: an exact algorithm based on the multivariate hypergeometric distribution, an MCMC algorithm, and the cube method. Their performances are evaluated using Monte Carlo simulations. The designs for sampling elements in a given row or a given column are investigated and the single inclusion and joint selection probabilities under the proposed design are evaluated. Several variance estimators are proposed for the Horvitz–Thompson estimator of the population mean of the survey variable y and their performances are compared in a Monte Carlo study. A numerical example dealing with a creel survey of fishermen found at 9 sites over 36 days is presented.
Bounds on relative generalised Hamming weight

The rth relative generalised Hamming weight (RGHW) of an linear code C and an subcode , a generalisation of generalised Hamming weight (GHW), characterises code performances of wiretap channel of type II, secure network coding, linear ramp secret sharing scheme, trellis complexity etc. In this study, the authors investigate non-asymptotic and asymptotic bounds on RGHW. In the non-asymptotic setting, they present a new proof of the Griesmer bound on RGHW by residue code and introduce the new concept of relative chain condition. They show that code pairs meeting the Griesmer, Singleton, and weak Plotkin bounds satisfy this condition. The notion of relative chain condition and these results provide a new perspective on researches including evaluating trellis complexity and determining the weight hierarchy of code pairs etc. In the asymptotic setting, they improve previous work by introducing two new metrics, respectively, for the cases r is fixed and r is proportionally increasing with n. They show the asymptotic Singleton, Plotkin, and Gilbert–Varshamov bounds on the first metric and determine the value of the second metric, which is helpful for characterising the optimal asymptotic performances of applications and constructing the optimal coding schemes.
LESSQL: Dealing with Database Schema Changes in Continuous Deployment

The adoption of Continuous Deployment (CD) aims at allowing software systems to quickly evolve to accommodate new features. However, structural changes to the database schema are frequent and may incur in systems' services downtime. This encompasses the proper maintenance of both schema and source code, including rewrites of all outdated queries that use the same database. Previous solutions try to mitigate the burdening task of manually rewriting outdated queries. Unfortunately, a software team must still interact with some tools to properly fix the affected queries. Moreover, the team still has to locate and modify all the impacted code, which are often error-prone tasks. Thus, a project may not experience CD benefits when changes impact various code regions. In this paper we present an alternative approach, called LESSQL, whose goal is to improve queries' stability in the presence of structural schema changes over time. LESSQL supports queries that are less dependent on the database schema since they do not include the FROM clause. An underlying framework intercepts each LESSQL query and generates a corresponding SQL query for the current schema. It also locates the query attributes in the current schema and generates proper expressions to join the required tables. LESSQL supports unsupervised, supervised and hybrid configurations to process mappings of attributes to a newer schema version. We conducted experiments in the context of a popular open-source project, which experienced many diverse structural schema changes. Experiments outcomes indicate that our approach is effective in significantly reducing the modifications required for applying schema changes, allowing to better reap the benefits of CD. While supervised and hybrid configurations achieved a success rate higher than 95% with a minor query generation overhead, the unsupervised configuration was also successful for certain types of structural schema changes. These results show that LESSQL effectively favours CD and keeps queries running after database schema changes without services interruption.
IDF Curve Patterns for Flood Control of Air Lakitan river of Musi Rawas Regency

Abstract The problem of flooding in a Watershed (Basin) or region is caused by natural conditions, such as relatively high rainfall, sloping topographic conditions and tidal influences. Human behaviour is often also the cause of flooding problems, such as the location of settlements that are in the river borders or industries that dump waste directly into river bodies. Besides the problem of flooding can also be caused by a not well-organized river/drainage system or lack of maintenance of an existing river/drainage system. Furthermore, social problems include increasing demands for the availability of new and good residential environmental infrastructure. To overcome the problems as described above, before an activity such as build in a left/right inspection road planning, is need analysis rainfall data and make patterns of Intensity Duration Frequency (IDF) Curve to an analysis of hydraulics in determining flood discharge plans. The results showed that the discharge of river capacity of Air Lakitan river was 482.74 m 3 /seconds, so the river is unable to hold water flow.
An exploratory study on extract method <i>floss-refactoring</i>

As a software evolves its code requires constant updating. In this sense, refactoring edits aim at improving structural aspects of a code without changing its external behavior. However, studies show that developers tend to combine in a single commit refactorings and behavior-changing edits (extra edits) - floss-refactoring. Floss-refactorings can be error-prone and require careful handling. However, little has been done to understand how refactorings and extra edits relate in practice. In this work, we propose a strategy for extracting floss-refactoring data. Moreover, we mine code repositories of 16 open-source projects and analyse commits with floss refactoring related to Extract Method. Our results show that developers often combine Extract Method with inner method extra edits (e.g., statement insert), with an expected increase of 8-16% of extra edits by each Extract Method. Moreover, some statements are more likely to be changed depending on the extra edit performed.
Privacy-Preserving Constrained Quadratic Optimization With Fisher Information

Noisy (stochastic) gradient descent is used to develop privacy-preserving algorithms for solving constrained quadratic optimization problems. The variance of the error of an adversary's estimate of the parameters of the quadratic cost function based on iterates of the algorithm is related to the Fisher information of the noise using the Cramér-Rao bound. This motivates using the Fisher information as a measure of privacy. Noting that the performance degradation in noisy gradient descent is proportional to the variance of the noise, a measure of utility is defined to be equal to the variance of the noise. Trade-off between privacy and utility is balanced by minimizing the Fisher information subject to a constraint on the variance of the noise. The optimal privacy-preserving noise is proved to be Gaussian, which implies that the developed privacy-preserving optimization algorithm also guarantees differential privacy.
Improved YOLO Algorithm for Object Detection in Traffic Video

The detection of moving targets in complex traffic scenes is the most basic and important technical means in video surveillance. In order to balance the speed and accuracy of object detection, this paper chooses You Only Look Once(YOLO) algorithm to extract foreground targets in video frames. Meanwhile, some steps are used to improve this algorithm. First, data is augmented during the pre-processing phase to ameliorate the imbalance of sample category. Then, re-clustering our own data set before training to get the corresponding anchor box size to enhance the accuracy of the final training model. In the training process, the focus loss is used instead of the binary cross entropy loss to further solve the problem of slow convergence rate and poor training effect caused by the imbalance of sample category. The improved YOLO algorithm is used to compare the training results with the original YOLO algorithm, and they are comprehensively analyzed by the model evaluation index. It can be verified that the improved YOLO algorithm maintains a faster training speed while it also improves the accuracy of training.
Development of android-based assessment to improve student’s concept acquisition on vector topics

Efforts to achieve learning goals are often constrained by students’ initial concepts of misunderstanding. Misunderstanding concepts will influence other concepts of understanding. Moreover, the vector is a basic concept needed to be understood in physics. This study aims to develop an android-based assessment to improve the student concepts understanding vector topics. The android-based assessment contains conceptual questions with three levels of true-false questions accompanied by vector learning materials in the form of videos. The difficulty level of the questions increases from level 1 to level 3. Each level consists of three questions with the same subtopic and developed in application (.apk) format. Research and development are carried out using the ADDIE model which consists of Analysis, Design, Development, Implementation, Evaluation. Android-based assessments developed on vector topics are categorized as very feasible with a score of 93.0%. The results of limited trials indicate that the android-based assessment application is categorized as very practical with a percentage of 81.6%. This shows that the development of the android-based assessment can be used without revision. Microsoft Power-Point selection in the development of Android-Based assessment was expected to motivate teachers to be able to develop Android-based assessment independently.
An adaptive, fault tolerant, flow-level routing scheme for data center networks

Recent Data center network topologies put forward multiple paths between the end hosts to provide high bisection bandwidth. For the sake of utilizing the multiple paths efficiently many load-balancing techniques are proposed in literature. These load-balancing proposals mainly deal with distribution of traffic among multiple paths. Further, a good load-balancing technique scales down the latency of flows. While balancing the traffic among multiple paths, few proposals also take care of congestion. Flow-level solutions avoid flow splitting to prevent the packet re-ordering at the destination. Such adaptive solutions try to re-route (change the path) flows dynamically based on the feedback received from the network. In case of link failures, re-routing of the flow based on the queue size of the remote switch alone may be sub-optimal. The path to be chosen among multiple paths for re-routing, also poses a valid question. This paper proposes FlowFurl, an adaptive, distributed, end host driven and flow-level load-balancing technique. This adaptive routing technique supports dynamic flow-to-path assignment. This flow-to-path assignment is performed not only based on the congestion at the switch, but also the status of the links along the path. FlowFurl is the first technique to combine link failure and congestion information parameter for re-routing of flows. The proposed technique is deployment friendly, however it incurs small amount of overhead (small amount of memory at each switch to maintain the link state). We show that our proposed scheme FlowFurl performs better than considered schemes in terms of average flow latency, percentage of flows that achieve deadline and average flow throughput, by utilizing local information such as connectivity information with congestion information.
Designing a recommender system for board games

Interest in board games has grown dramatically in the recent years, and so has the number of releases per year. Consumers can find it hard for themselves to choose the next board game to delve into, and often rely on curated lists and expert recommendations. At the same time, the broad availability of qualitative and quantitative data about board games makes this domain ripe for the application of automated recommendations. In this paper, we employ existing and novel techniques for recommending board games.
A structure of optical see-through head mounted display

We proposed an optical structure of the optical see-through head mounted display. Our approach is built on the principles of glasses-type transparent projection optics and pinhole array for better mobility, lightweight, lower burden on skin, and wider field of view. We used a ray tracing software to model the head mounted display and performed optical simulation to review overall performance.
Optimisation of both classifier and fusion based feature set for static American sign language recognition

Sign language recognition becomes a popular research field in human–computer interaction. Attention on hand signal analysis helps to make easy communication among computer and human for information sharing. Major focus of the gesture recognition system is to identify and recognise various gestures, by a computer. This study introduces optimisation of both classifier and feature set for static American sign language recognition. Initially, the hand part is segmented from other parts of the image through effective edge and skin colour detection. Thereafter, robust features are obtained using discrete cosine transform, Zernike moment, scale-invariant feature transform, speeded-up robust features, histogram of oriented gradients and binary object features from the segmented hand image. From these extracted features, an optimal feature set is selected by social ski driver optimisation algorithm. Deep Elman recurrent neural network classifier is then introduced for recognition purpose. Optimisation is performed on feature sets, derived by fusion of features obtained from the above methods, based on precision, accuracy, F-measure and recall. Finally, optimised feature set and best classifier are used to recognise the hand gesture for classification purpose. The performance of this proposed method is evaluated and compared with existing literature.
Cyber Resiliency Automation Using Blockchain

Protecting organization assets from malicious attack is key to an organization business continuity goal. Most of the organizations are aware of security threats and have invested time and effort in defining a security strategy. The security strategy identifies the technology, policies, people and the processes required to deal with security breaches. However cyber incidents continue to rise and often attackers find new ways to cause damage. Since its difficult to prevent cyberattacks the focus should be more on how to respond and recover quickly from attacks. The cyber resilient approach must be based on the latest threats and needs continuous improvement. Often organizations feel that they have a cyber resilience process that works but during an attack they struggle to recover. This is mainly due to the fact that the process has many pitfalls and is not up to date. This paper briefly discusses some of the drawbacks in the processes and suggests a process utilizing blockchain which provides better security and ability to recover.
Blockchain real estate relational value survey

Relational capital for companies has been theorized and modeled in the past, but so far, no real Web application has tried to bring its value to light, especially concerning the real estate application domain. In this paper, we present the results of our surveys on how the types of relations who have contributed to a product impact price premium and what real estate agencies think about real estate relational value, especially as implemented on our Web marketplace connected to Facebook social network and NEO public blockchain for products certifications transparency.
The Effects of Length and Orientation on Numerical Representation in Flow Maps

Flow maps are a common type of geographic information visualization in which lines that symbolize flow are typically varied in width to represent differences in the magnitude of the flow. An accurate perception of thickness is critical to numerical representation in flow maps. Previous studies have identified some of the factors, such as horizontal–vertical visual illusions and color size effects, that affect the perceived size of objects. However, the question of whether multiple visual variables that encode flow lines, such as length, orientation, and shape, interfere with their perceived thicknesses, remains unanswered. In this study, we performed a user study to determine the effect of length and orientation on thickness perception. The result indicates that the horizontal orientation is perceived to be thicker than the vertical orientation, and a short length is perceived to be thicker than a long length. Furthermore, we report and discuss other results (e.g., on adjustment direction) that are consistent with previous work. Although this study constitutes basic research, accumulating evidence on thickness perception is essential to this field of science. This study may contribute to our understanding of the factors that influence the perception of the thickness of lines on a flow map. We provide some concrete guidelines for the design of flow maps that may be beneficial to map designers.
Understanding the Security Implication of Aborting Virtual Machine Live Migration

Live migration of Virtual machines (VMs) has become a regular tool for edge and cloud operators to facilitate system maintenance, fault tolerance, and load balancing, with little impact on running instances. However, the potential security risks of live migration of VMs are still obscure. In this article, we expose a new vulnerability in the existing VM live migration approaches, especially the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">post-copy</i> approach. The entire live migration mechanism relies upon reliable TCP connectivity for the transfer of the VM state. We demonstrate that, if the host server is vulnerable to off-path TCP attacks, the loss of TCP reliability leads to VM live migration failure. We demonstrate that, by intentionally aborting the TCP connection, attackers can cause unrecoverable memory inconsistency for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">post-copy</i> , leading to a significant increase in downtime and performance degradation of the running VM. Additionally, we present detailed techniques to reset the migration connection under heavy networking traffic. We also propose effective defenses to secure the VM live migration. Our experimental results demonstrate that memory inconsistencies could be devastating to some applications, and it only takes a few minutes to reset a heavy migration connection.
Linear Twin Quadratic Surface Support Vector Regression

Twin support vector regression (TSVR) generates two nonparallel hyperplanes by solving a pair of smaller-sized problems instead of a single larger-sized problem in the standard SVR. Due to its efficiency, TSVR is frequently applied in various areas. In this paper, we propose a totally new version of TSVR named Linear Twin Quadratic Surface Support Vector Regression (LTQSSVR), which directly uses two quadratic surfaces in the original space for regression. It is worth noting that our new approach not only avoids the notoriously difficult and time-consuming task for searching a suitable kernel function and its corresponding parameters in the traditional SVR-based method but also achieves a better generalization performance. Besides, in order to make further improvement on the efficiency and robustness of the model, we introduce the 1-norm to measure the error. The linear programming structure of the new model skips the matrix inverse operation and makes it solvable for those huge-sized problems. As we know, the capability of handling large-sized problem is very important in this big data era. In addition, to verify the effectiveness and efficiency of our model, we compare it with some well-known methods. The numerical experiments on 2 artificial data sets and 12 benchmark data sets demonstrate the validity and applicability of our proposed method.