Reviewerly/exHarmony huggingface dataset:
DGST: Efficient and scalable suffix tree construction on distributed data-parallel platforms

The suffix tree is a fundamental data structure for string processing. It is widely used in many important scenarios such as text processing, information retrieval, and bioinformatics. With the rapid growth of data volume, constructing the suffix tree for large-scale datasets is very time-consuming. To solve this problem, a number of MPI-based parallel algorithms were proposed, but they have limitations in fault tolerance and scalability for large-scale datasets. Recently, there are ever-increasing application demands on efficient algorithms for constructing the suffix tree for large-scale datasets on distributed data-parallel platforms, such as Hadoop and Spark. In this paper, we present DGST, which is an efficient and scalable algorithm for generalized suffix tree construction on distributed data-parallel platforms. DGST consists of two major stages: parallel sub-tree partitioning and parallel sub-tree construction. We first design a novel data partitioning strategy for both two stages in the data-parallel paradigm. Then, we propose an efficient sub-tree partitioning algorithm based on parallel frequency counting. To improve the load balance and amortize the disk I/O costs, we propose an efficient Bin-Packing and Number-Partitioning based task allocation strategy for the sub-tree construction. At the sub-tree construction stage, we further propose a novel data structure LCP-Range and a multi-way LCP-Merge sorting algorithm for parallel LCP array construction. The experimental results on Apache Spark reveal that DGST outperforms the state-of-the-art ERa algorithm with approximately 3 times speedup on both the DNA and English text datasets. Furthermore, DGST achieves near-linear data and node scalability.
Ship Segmentation and Orientation Estimation Using Keypoints Detection and Voting Mechanism in Remote Sensing Images

Ship detection in remote sensing images is an important and challenging task in civil fields. However, the various types of ships with different scale and ratio and the complex scenarios are the main bottlenecks for ship detection and orientation estimation of the ship. In this paper, we propose a new method based on Mask R-CNN, which can perform ship segmentation and direction estimation on ships at the same time by simultaneously output the binary mask and the bow and sterns keypoints locations. We can achieve keypoints detection of the ship without significantly losing the accuracy of the mask. Finally, we regress the coordinates of the ship's bow and sterns to four quadrants and use the voting mechanism to determine which quadrant the bow keypoint locates. Then we combine the quadrant of bow keypoint with the minimum bounding box of the mask to determine the final orientation of the ship. Experiments on the datasets have achieved effective performance.
A Deep Neural Network Method for Automatic Modulation Recognition in OFDM with Index Modulation

Automatic modulation recognition (AMR) plays an indispensable role in many fields, such as cognitive radio, spectrum sensing, non-cooperative link adaptation, and other civilian and military fields. OFDM-IM is an innovative OFDM-based scheme which has better bit error performance than classical OFDM scheme especially in high mobility cases. Different from OFDM, the modulation parameters include both M-ary signal constellations and indices of the subcarriers in OFDM- IM scheme. In this paper, we studied a deep neural network (DNN) based AMR method in orthogonal frequency division multiplexing with index modulation (OFDM-IM) scheme.
Adaptive Learning Emotion Identification Method of Short Texts for Online Medical Knowledge Sharing Community

The medical knowledge sharing community provides users with an open platform for accessing medical resources and sharing medical knowledge, treatment experience, and emotions. Compared with the recipients of general commodities, the recipients in the medical knowledge sharing community pay more attention to the intensity or overall evaluation of emotional vocabularies in the comments, such as treatment effects, prices, service attitudes, and other aspects. Therefore, the overall evaluation is not a key factor in medical service comments, but the semantics of the emotional polarity is the key to affect recipients of the medical information. In this paper, we propose an adaptive learning emotion identification method (ALEIM) based on mutual information feature weight, which captures the correlation and redundancy of features. In order to evaluate the proposed method's effectiveness, we use four basic corpus libraries crawled from the Haodf's online platform and employ Taiwan University NTUSD Simplified Chinese Emotion Dictionary for emotion classification. The experimental results show that our proposed ALEIM method has a better performance for the identification of the low-frequency words' redundant features in comments of the online medical knowledge sharing community.
An empirical study of configuration changes and adoption in Android apps

Android platform is evolving rapidly. Therefore, evolution and maintenance of Android apps are major concerns among developers. One of the essential components of each app is an Android manifest file, which is a configuration file used to declare various key attributes of apps. This paper presents an empirical study to understand app evolution through configuration changes. The results of this study will help developers in identifying change-proneness attributes, including change patterns and the reason behind the change, understanding the adoption of different attributes introduced in different versions of the Android platform, and understanding effort distribution pattern in configuration changes and taking proactive measures to reduce the effort. In this paper, we use a data mining approach. We analyze commit histories of Android manifest files of 908 apps to understand the app evolution. The results of this study show that most of the apps extend core functionalities and improve user interface over time, configuration changes are mostly influenced by functionalities extension, platform evolution, and bug reports, very few numbers of existing apps adopt new attributes introduced by the platform, apps are generally slow in adopting new attributes, and significant effort is wasted in changing configuration and then reverting back the change.
A combinatorial approach to determine earthquake magnitude distributions on a variable slip-rate fault

SUMMARY Combinatorial methods are used to determine the spatial distribution of earthquake magnitudes on a fault whose slip rate varies along strike. Input to the problem is a finite sample of earthquake magnitudes that span 5 kyr drawn from a truncated Pareto distribution. The primary constraints to the problem are maximum and minimum values around the target slip-rate function indicating where feasible solutions can occur. Two methods are used to determine the spatial distribution of earthquakes: integer programming and the greedy-sequential algorithm. For the integer-programming method, the binary decision vector includes all possible locations along the fault where each earthquake can occur. Once a set of solutions that satisfy the constraints is found, the cumulative slip misfit on the fault is globally minimized relative to the target slip-rate function. The greedy algorithm sequentially places earthquakes to locally optimize slip accumulation. As a case study, we calculate how earthquakes are distributed along the megathrust of the Nankai subduction zone, in which the slip rate varies significantly along strike. For both methods, the spatial distribution of magnitudes depends on slip rate, except for the largest magnitude earthquakes that span multiple sections of the fault. The greedy-sequential algorithm, previously applied to this fault (Parsons et al., 2012), tends to produce smoother spatial distributions and fewer lower magnitude earthquakes in the low slip-rate section of the fault compared to the integer-programming method. Differences in results from the two methods relate to how much emphasis is placed on minimizing the misfit to the target slip rate (integer programming) compared to finding a solution within the slip-rate constraints (greedy sequential). Specifics of the spatial distribution of magnitudes also depend on the shape of the target slip-rate function: that is, stepped at the section boundaries versus a smooth function. This study isolates the effects of slip-rate variation along a single fault in determining the spatial distribution of earthquake magnitudes, helping to better interpret results from more complex, interconnected fault systems.
Security Evaluation of Wordpress Backup Plugins

Protecting the web applications is an important task for every organization. Wordpress is the most preferred content management system by organizations. In this paper we discuss why Wordpress is an important web application and the necessity of securing it. Another analyzed aspect regards the security of backup plugins created for Wordpress from the perspective of sensitive data leak, number of vulnerable modules, causes of vulnerability, most common mistakes and the impact of such vulnerabilities. Based on an experimental plan, the paper also emphasizes the destructive potential of those vulnerable backup plugins on the most relevant websites among the top ten million worldwide websites.
Towards Agent-Oriented Blockchains: Autonomous Smart Contracts

Features of blockchain technology (BCT) such as decentralisation, trust, fault tolerance, and accountability, are of paramount importance for multi-agent systems (MAS). In this paper we argue that a principled approach to MAS-BCT integration cannot overlook the foundational character of agency—that is, autonomy. Accordingly, we present a custom BCT implementation where autonomy is placed in smart contracts (SC) interpreted as software agents. We show how agency can enhance SC expressiveness with autonomy, situatedness, sociality, and intelligence, and highlight the limitations of state-of-art BCT in supporting MAS design and implementation.
The Effect of Scaffolding Approach Assisted by PhET Simulation on Students’ Conceptual Understanding and Students’ Learning Independence in Physics

Abstract The aim of this study was to investigate the effect of learning with scaffolding approach assissted by PhET simulation on students’ conceptual understanding and students’ learning independence. Research design using experimental design with one group pretest posttest study. The sample was 27 grade 10 th senior high school students. Sample was chosen by purposive sampling technique. A test utilized as the pretest and posttest to collect data students’ conceptual understanding. Students also observed during the interventions to know students learning independence. Data analysis utilized quantitative and qualitative analysis techniques. The results of this study indicated that there is difference between students’ conceptual learning before and after learning in the scaffolding approach assisted by PhET simulation. However, this difference was not have a significant effect on students’ conceptual understanding. It also revealed that learning in the scaffolding approach assisted PhET simulation has effect on students’ learning independence. Students’ learning independence were in good enough and good category. Besides, students activities were increased during learning. Students became more active in seeking knowledge and interaction among students were increased. It provides communication and collaboration activities. The scaffolding and simulation were appropriate with the Indonesian curriculum and 21st century learning. These results suggest that within high school, scaffolding approach with PhET simulation can be use to improve students’ learning independence.
Testing in a DevOps Era: Perceptions of Testers in Norwegian Organisations

To better understand the challenges encountered by testers in DevOps development, we have performed an empirical investigation of what are the trends and challenges for the testers in the DevOps environment. We have discussed the quality assurance in the difference focus areas of DevOps: Social Aspects, automation, leanness, sharing, measurement. The results were then themed in five different topics of concern to testers: collaboration, roles and responsibilities, types of tests, automation and monitoring and infrastructure. In Testing, there has been a change on the roles and responsibilities of testers, where there is much more focus on the responsibilities for testing across the teams, instead of a sole responsibility of the tester. Testers are also forced to collaborate more with other stakeholders as operations and business. Testing is brought to another level of automation in DevOps but there is still need for manual tests, that have to be much more risk-based than before. And finally, testing transparency is a must in this process and should involve not only development team but also operations and customers. This paper contributes to the body of knowledge on what are the areas we need to focus for improvement in testing for the DevOps environment. This paper also contributes to practitioners to improve their testing focusing on specific areas that needs attention.
A Survey on Accessible Context-Aware Systems

At the present time, 15% of the growing world population is estimated to have disabilities and special needs. Disabilities can seriously limit participation in regular life activities, such as controlling home facilities, using transportation services, joining social events, accessing educational contents, to name but a few. With the advancement in ubiquitous and pervasive computing, context-aware systems (CAS) are gaining much attention and demonstrating a stronger association with applications for people with disability. Modern CAS tend to minimize user interactions with the system and provide seamless services, automated awareness, and ambient intelligence and monitoring. CAS for people with disability can detect the surrounding environment, identify an appropriate user interface, interact, and service the user depending on the situation. Nevertheless, a large number of investigations on CAS for people with disability are presented in the literature, limited systems are practically available in the market. In this paper, we survey the literature to thoroughly analyze, evaluate, and critique state-of-the-art research in accessible CAS. Systems are classified according to the type of disability; besides, many interaction models are examined and strategies for making CAS accessible are identified. The investigation confirms the need for frameworks that enable improving security aspects, better exploiting modern hardware systems, performing reliable verification, and further supporting system customization and adaptation.
Item Recommendation by Combining Relative and Absolute Feedback Data

User preferences in the form of absolute feedback, s.a., ratings, are widely exploited in Recommender Systems (RSs). Recent research has explored the usage of preferences expressed with pairwise comparisons, which signal relative feedback. It has been shown that pairwise comparisons can be effectively combined with ratings, but, it is important to fine tune the technique that leverages both types of feedback. Previous approaches train a single model by converting ratings into pairwise comparisons, and then use only that type of data. However, we claim that these two types of preferences reveal different information about users interests and should be exploited differently. Hence, in this work, we develop a ranking technique that separately exploits absolute and relative preferences in a hybrid model. In particular, we propose a joint loss function which is computed on both absolute and relative preferences of users. Our proposed ranking model uses pairwise comparisons data to predict the user's preference order between pairs of items and uses ratings to push high rated (relevant) items to the top of the ranking. Experimental results on three different data sets demonstrate that the proposed technique outperforms competitive baseline algorithms on popular ranking-oriented evaluation metrics.
Learning when to garbage collect with random forests

Generational garbage collectors are one of the most common types of automatic memory management. We can minimize the costs they incur by carefully choosing the points in a program's execution at which they run. However, this decision is generally based on simple, crude heuristics. Instead, we train random forest classifiers to decide when to collect based on features gathered from a running program. This reduces the total cost of collection in both time and space. We demonstrate useful generalization of learned policies to unseen traces of the same program, showing this approach may be fruitful for further investigation.
A Content-Aware Image Retargeting Quality Assessment Method Using Foreground and Global Measurement

Image retargeting methods aim to minimize the perceptual loss while changing sizes and aspect ratios of images.Since optimal retargeting methods for different images are generally not the same, the image retargeting quality assessment (IRQA) becomes a meaningful task.This paper proposes a content-aware image retargeting quality assessment method using foreground and global measurement to achieve better performance.In our proposed method, images are first divided into two categories according to the foreground object detection result, and then different corresponding measurements are designed for them.For those with obvious foreground object, both foreground and global measurement are applied.For others, only global measurement is conducted.Foreground measurement includes two complementary features: the high-level semantic similarity feature and the low-level size ratio feature.Global measurement includes another two features: an improved aspect ratio similarity (ARS) feature and edge group similarity (EGS) feature.Two public databases, i.e., the RetargetMe and CUHK, have been evaluated, and experimental results demonstrate that our method is quite effective, and it also provides state-of-the-art performance in the IRQA.a INDEX TERMS Image retargeting quality assessment, image quality assessment, foreground object quality measurement, semantic similarity feature.
Predicting Co-Changes between Functionality Specifications and Source Code in Behavior Driven Development

Behavior Driven Development (BDD) is an agile approach that uses. feature files to describe the functionalities of a software system using natural language constructs (English-like phrases). Because of the English-like structure of. feature files, BDD specifications become an evolving documentation that helps all (even non-technical) stakeholders to understand and contribute to a software project. After specifying a. feature files, developers can use a BDD tool (e.g., Cucumber) to automatically generate test cases and implement the code of the specified functionality. However, maintaining traceability between. feature files and source code requires human efforts. Therefore,. feature files can be out-of-date, reducing the advantages of using BDD. Furthermore, existing research do not attempt to improve the traceability between. feature files and source code files. In this paper, we study the co-changes between. feature files and source code files to improve the traceability between. feature files and source code files. Due to the English-like syntax of. feature files, we use natural language processing to identify co-changes, with an accuracy of 79%. We study the characteristics of BDD co-changes and build random forest models to predict when a. feature files should be modified before committing a code change. The random forest model obtains an AUC of 0.77. The model can assist developers in identifying when a. feature files should be modified in code commits. Once the traceability is up-to-date, BDD developers can write test code more efficiently and keep the software documentation up-to-date.
An efficient frequent pattern mining algorithm using a highly compressed prefix tree

The identification of frequent patterns plays a key role in mining association rules. FP-growth is a fundamental algorithm for frequent pattern mining. It employs a prefix tree structure (FP-Tree) and a recursive mining process to discover frequent p
Verification of Normalization Effects Through Comparison of CNN Models

Recently, there has been an increase in the interest in and necessity of image data analysis. Many studies have proposed algorithms for analyzing image data. Accordingly, image data algorithms have kept advancing as a result of new structures or data processing techniques. Ultimately, the level of image processing technology has exceeded human recognition. Nevertheless, neural networks cannot be perfectly applied to image analysis in every field. Many attempts are being made to overcome this limitation. Normalization is one of those attempts. Normalization is one of the methods used for improving the network performance, and it is adopted for several models. The performance of local response normalization is still under dispute. This type of normalization has been insufficiently studied. This study verifies the real effects of local response normalization by using various datasets and conducts an experiment to identify its impact on learning time. The result of this study is expected to demonstrate the effect of local response normalization on image classification.
Physics driven behavioural clustering of free-falling paper shapes

Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour.
Geometric Reconstruction and Measurement of Long Steel Products Using 3-D Sensors in Real Time

Calibration, registration, reconstruction, and measurement are the fundamental tasks required for the inspection of dimensions in long steel products. Calibration is performed offline. The rest of the tasks are performed repeatedly while the long steel product is moved under 3-D reconstruction sensors. This study proposes robust methods for the reconstruction of long steel products. In addition, measurement procedures for some representative dimensions are presented. Three different reconstruction procedures are proposed-reconstruction based on geometric primitives in the model of the product, reconstruction based on local fitting, and reconstruction based on piecewise linear approximation. Tests on synthetic on real data indicate excellent performance in terms of computational costs and measurement accuracy. Conclusions also provide recommendations for the application of the proposed reconstruction procedures depending on whether a model is available or not, and the type of features that need to be calculated for the long steel products.