Reviewerly/exHarmony huggingface dataset:
Attention-Based Vandalism Detection in OpenStreetMap

OpenStreetMap (OSM), a collaborative, crowdsourced Web map, is a unique source of openly available worldwide map data, increasingly adopted in Web applications. Vandalism detection is a critical task to support trust and maintain OSM transparency. This task is remarkably challenging due to the large scale of the dataset, the sheer number of contributors, various vandalism forms, and the lack of annotated data. This paper presents Ovid - a novel attention-based method for vandalism detection in OSM. Ovid relies on a novel neural architecture that adopts a multi-head attention mechanism to summarize information indicating vandalism from OSM changesets effectively. To facilitate automated vandalism detection, we introduce a set of original features that capture changeset, user, and edit information. Furthermore, we extract a dataset of real-world vandalism incidents from the OSM edit history for the first time and provide this dataset as open data. Our evaluation conducted on real-world vandalism data demonstrates the effectiveness of Ovid.
A Study on the Impacts of Slot Types and Training Data on Joint Natural Language Understanding in a Spanish Medication Management Assistant Scenario

This study evaluates the impacts of slot tagging and training data length on joint natural language understanding (NLU) models for medication management scenarios using chatbots in Spanish. In this study, we define the intents (purposes of the sentences) for medication management scenarios and two types of slot tags. For training the model, we generated four datasets, combining long/short sentences with long/short slots, while for testing, we collect the data from real interactions of users with a chatbot. For the comparative analysis, we chose six joint NLU models (SlotRefine, stack-propagation framework, SF-ID network, capsule-NLU, slot-gated modeling, and a joint SLU-LM model) from the literature. The results show that the best performance (with a sentence-level semantic accuracy of 68.6%, an F1-score of 76.4% for slot filling, and an accuracy of 79.3% for intent detection) is achieved using short sentences and short slots. Our results suggest that joint NLU models trained with short slots yield better results than those trained with long slots for the slot filling task. The results also indicate that short slots could be a better choice for the dialog system because of their simplicity. Importantly, the work demonstrates that the performance of the joint NLU models can be improved by selecting the correct slot configuration according to the usage scenario.
The Pattern is in the Details: An Evaluation of Interaction Techniques for Locating, Searching, and Contextualizing Details in Multivariate Matrix Visualizations

Matrix visualizations are widely used to display large-scale network, tabular, set, or sequential data. They typically only encode a single value per cell, e.g., through color. However, this can greatly limit the visualizations' utility when exploring multivariate data, where each cell represents a data point with multiple values (referred to as details). Three well-established interaction approaches can be applicable in multivariate matrix visualizations (or MMV): focus+context, pan&zoom, and overview+detail. However, there is little empirical knowledge of how these approaches compare in exploring MMV. We report on two studies comparing them for locating, searching, and contextualizing details in MMV. We first compared four focus+context techniques and found that the fisheye lens overall outperformed the others. We then compared the fisheye lens, to pan&zoom and overview+detail. We found that pan&zoom was faster in locating and searching details, and as good as overview+detail in contextualizing details.
Power‐VR: Interactive 3D virtual environment to increase motivation levels of powerlifters during training sessions

Abstract Motivation is one of the important factors that increase the success of athletes, especially who lift weights. It increases not only the success of the athletes but also influences other notions such as increasing self‐confidence, decreasing anxiety, and improving endurance. Although motivation has a key role in the performance of athletes, most of the athletes may be fully motivated only in competition environments. However, being motivated only in competition environments is not enough for athletes since they spend most of their time in training. If they are motivated in training environments as motivated as in competition environments, they can increase their performance, however, being motivated during training sessions is not easy since there are not enough motivational factors, such as spectators, in training environments. In order to solve this problem, this study aims to increase the motivation levels of powerlifters during training sessions by developing a virtual competition environment. In this environment, the athletes experience a virtual competition environment by using HTC Vive. To understand the efficiency of the virtual environment, it was tested with 32 professional athletes. The findings illustrated that using VR technology was beneficial to increase the level of motivation of powerlifters during training sessions.
Implementation of single-qubit measurement-based t-designs using IBM processors

Random unitary matrices sampled from the uniform Haar ensemble have a number of important applications both in cryptography and in the simulation of a variety of fundamental physical systems. Since the Haar ensemble is very expensive to sample, pseudorandom ensembles in the form of t-designs are frequently used as an efficient substitute, and are sufficient for most applications. We investigate t-designs generated using a measurement-based approach on superconducting quantum computers. In particular, we implemented an exact single-qubit 3-design on IBM quantum processors by performing measurements on a 6-qubit graph state. By analysing channel tomography results, we were able to show that the ensemble of unitaries realised was a 1-design, but not a 2-design or a 3-design under the test conditions set, which we show to be a result of depolarising noise during the measurement-based process. We obtained improved results for the 2-design test by implementing an approximate 2-design, in which measurements were performed on a smaller 5-qubit graph state, but the test still did not pass for all states. This suggests that the practical realisation of measurement-based t-designs on superconducting quantum computers will require further work on the reduction of depolarising noise in these devices.
The Impact of Optical and Geometrical Thickness on Perceived Translucency Differences

In this work we study the perception of suprathreshold translucency differences to expand the knowledge about material appearance perception in imaging and computer graphics, and 3D printing applications.Translucency is one of the most considerable appearance attributes that significantly affects the look of objects and materials.However, the knowledge about translucency perception remains limited.Even less is known about the perception of translucency differences between materials.We hypothesize that humans are more sensitive to small changes in absorption and scattering coefficients when optically thin materials are examined and when objects have geometrically thin parts.To test these hypotheses, we generated images of objects with different shapes and subsurface scattering properties and conducted psychophysical experiments with these visual stimuli.The analysis of the experimental data supports these hypotheses and based on post experiment comments made by the observers, we argue that the results could be a demonstration of a fundamental difference between translucency perception mechanisms in see-through and non-see-through objects and materials.
PENERAPAN MODEL PEMBELAJARAN DISCOVERY LEARNING UNTUK MENINGKATKAN HASIL BELAJAR EKONOMI MATERI INFLASI PADA SISWA (KELAS XI IPS 2 SMAN 1 CARIU KABUPATEN BOGOR TAHUN PELAJARAN 2018/2019)

This classroom action research aims to improve the economics learning outcomes of Inflation material in class XI IPS-2 SMAN 1 Cariu Bogor Regency through the application of discovery learning learning models. The research method used is classroom action research with four basic interrelated and continuous stages, namely Planning (Plan), Action (Action), Observation (observation), Reflection (reflection) which is designed in 2 cycles. The subjects of this study were students of class XI IPS 2 for the 2018/2019 academic year at SMAN 1 Cariu, Bogor Regency. The number of research subjects was 29 students consisting of 10 sons and 19 daughters. The results of the study concluded 1) There was an increase in economic learning outcomes on inflation material after being given a discovery learning model to class XI IPS-2 students of SMAN 1 Cariu, Bogor Regency. This can be proven by the acquisition of an average pre-cycle score of 68.8 with 44.8% completeness, increasing to 75.2 in the first cycle with 65.5% completeness, and increasing again in the second cycle with an average value of 78.4 and learning completeness 93.1%. Thus the research has been achieved or has fulfilled the completeness of learning. 2) There is an increase in student learning activities. This is evidenced by the level of student learning activity which includes aspects of activeness, attention, discipline and assignments, which have a fairly more dominant score than a score of less so that student learning activity in the first cycle is sufficient category, then in Cycle II it is known that the level of student learning activity who has a good score is more dominant compared to other scores so that student learning activity in cycle II is in good category.
Parallel Approach of Adaptive Image Thresholding Algorithm on GPU

Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated using a specific algorithm instead of a pre-defined value obtained from observation or experiment. However, the algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold that is costly for high-resolution images. In this research, parallel implementations on GPU for three adaptive image thresholding methods, namely Otsu, ISODATA, and minimum cross-entropy, were proposed to optimize their computational times to deal with high-resolution images. The approach involves parallel reduction and parallel prefix sum (scan) techniques to optimize the calculation. The proposed approach was tested on various sizes of grayscale images. The result shows that the parallel implementation of three adaptive image thresholding methods on GPU achieves 4-6 speeds up compared to the CPU implementation, reducing the computational time significantly and effectively dealing with high-resolution images.
SoK

Within the last few years, indirect network-level covert channels have experienced a renaissance with new ideas and evolving concepts. Logical network separation may now be crossed and the sending and receiving activities can be performed with temporal distance between sending and receiving operations. Despite these new developments, all indirect network covert channels share certain basic principles that allow a categorization. So far, the concepts of indirect network-level covert channels have never been systematized. In this paper, we introduce a taxonomy containing indirect covert channel patterns that allow a differentiated analysis of all known indirect network-level covert channels. We introduce additional definitions to unify the understanding of the domain and further identify crucial features of indirect covert channels to make them comparable and describable. We further discuss application scenarios as well as potential and already evaluated countermeasures against indirect covert channels. Further, we discuss observable trends and anticipated future developments in the research area of indirect network-level covert channels.
FINANCIAL ACCESS AND VALUE ADDED IN SUB‐SAHARAN AFRICA:

This research assesses the importance of financial access on value added in three economic sectors in 25 countries in Sub‐Saharan Africa using data for the period 1980–2014. The empirical evidence is based on the Generalized Method of Moments. Financial access is measured with private domestic credit, while the three outcome variables are: value added in the agricultural, manufacturing, and service sectors, respectively. Enhancing financial access does not significantly improve value added in the agricultural and manufacturing sectors, while enhancing financial access improves value added in the service sector. An extended analysis shows that, in order for the positive net incidence of enhancing credit access on value added to the service sector to be maintained, complementary policies are required when domestic credit to the private sector is between 77.50 percent and 98.50 percent of GDP. Policy implications are discussed .
Fusion, Propagation, and Structuring in Belief Networks

chapter Share on Fusion, Propagation, and Structuring in Belief Networks Author: Judea Pearl Search about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 139–188https://doi.org/10.1145/3501714.3501728Online:04 March 2022Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks1 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A trapdoor one-way function for verifiable secret sharing

This paper proposes a (t,n)–threshold verifiable secret sharing scheme with changeable parameters based on a trapdoor one-way function. This scheme consists of a generation phase, a distribution phase, an encoding phase and a reconstruction phase. The generation and distribution phases are, respectively, based on Shamir’s and Feldman’s approaches, while the encoding phase is based on a novel trapdoor one-way function. In the reconstruction phase, the shares and reconstructed secret are validated using a cryptographic hash function. In comparison with existing schemes, the proposed scheme leaks no direct information about the secret from public information. Furthermore, unlike some existing schemes, the generation and distribution phases of the proposed scheme are both independent of the secret. This feature leads to a number of advantages over existing approaches such as the dealer’s ability to perform the following modifications without updating the shares (i) modify the secret and (ii) adjust the threshold parameters of the scheme. Furthermore, each participant receives a single share, and designated participants can be given the privilege of choosing their own shares for reconstructing a secret S. Moreover, the proposed scheme possesses a high level of security which is inherited from the schemes of Shamir and Feldman, in addition to the trapdoor one-way function and the employed cryptographic hash function.
Field-programmable gate array design of image encryption and decryption using Chua’s chaotic masking

&lt;span&gt;This article presents a simple and efficient masking technique based on Chua chaotic system synchronization. It includes feeding the masked signal back to the master system and using it to drive the slave system for synchronization purposes. The proposed system is implemented in a field programmable gate array (FPGA) device using the Xilinx system generator tool. To achieve synchronization, the Pecora-Carroll identical cascading synchronization approach was used. The transmitted signal should be mixed or masked with a chaotic carrier and can be processed by the receiver without any distortion or loss. For different images, the security analysis is performed using the histogram, correlation coefficient, and entropy. In addition, FPGA hardware co-simulation based Xilinx Artix7 xc7a100t-1csg324 was used to check the reality of the encryption and decryption of the images.&lt;/span&gt;
Reproducible Research Publication Workflow: A Canonical Workflow Framework and FAIR Digital Object Approach to Quality Research Output

Abstract In this paper we present the Reproducible Research Publication Workflow (RRPW) as an example of how generic canonical workflows can be applied to a specific context. The RRPW includes essential steps between submission and final publication of the manuscript and the research artefacts (i.e., data, code, etc.) that underlie the scholarly claims in the manuscript. A key aspect of the RRPW is the inclusion of artefact review and metadata creation as part of the publication workflow. The paper discusses a formalized technical structure around a set of canonical steps which helps codify and standardize the process for researchers, curators, and publishers. The proposed application of canonical workflows can help achieve the goals of improved transparency and reproducibility, increase FAIR compliance of all research artefacts at all steps, and facilitate better exchange of annotated and machine-readable metadata.
Novel absolute phase measurement method with few-patterns

The temporal phase unwrapping method can obtain the absolute phase of an object with large discontinuities and isolated objects in phase measuring profilometry. But it needs many frames fringe patterns, which may consume a significant amount of time in 3D surface measurement. In the light of this problem, we proposed a novel absolute phase measurement method with few-patterns. The proposed method combines an object reflectivity correction and a half-period gray-coded phase unwrapping algorithm. One group of precoded three-step phase-shifting fringe patterns are used to calculate the wrapped phase, divide the whole measurement region into many sub-regions, correct the surface reflectivity of the tested object. A frame of half-period gray-coded pattern and the index number of the sub-region are used to determine the fringe order. The proposed method can obtain a large number of codewords for fringe order without decreasing the intensity level for each stair. To verify the effectiveness of the proposed method, we measured two isolated masks and compared the result with Chen's method [1]. The result shows that the accuracy of our method is similar to Chen's method, but our method has two images less than Chen's method, which improves the measurement speed. Then we measured two complex isolated sculptures. The result shows that our method is also effective for complex surfaces.
Heuristic RRT fusion A* for 3D path planning of UAV

Path planning is the key to the autonomous flight of unmanned aerial vehicle (UAV). Aiming at the problems of blind search, long and zigzag path in the path planning of three-dimensional space environment, A heuristic bidirectional target RRT fusion A* algorithm was proposed. Firstly, the algorithm combines heuristic probability and bias expansion strategy to improve the target orientation of sampling. After obtaining the complete path point, the shortest path is obtained by fusing redundant nodes in the complete path of A* algorithm. Finally, B-spline algorithm is used to smooth the path, which obtains a smooth feasible path. Experimental results show that compared with the traditional B-RRT algorithm, the improved B-RRT algorithm improves the node utilization by 9.6 times, reduces the extension nodes by 86.9%, shorts the search time by 21.46%, and shorts the path by 7.9%.
Federated Learning convergence on IID features via optimized local model parameters

Federated learning enables collaborative training of global models without sharing local training data. In this paper, we propose a method to enhance the convergence of federated learning algorithm by modifying the training method at the client's side, such as we have deployed two different methods at local models, i) filter out batch normalization parameters and remove them from weight decay, and ii) zero initialize the last batch normalization in each residual branch (in case of resnet) so that the residual branch starts with zeros and each residual block behaves as identity. The following two methods were previously proposed for distributed training. In this paper, we propose to deploy the method in federated learning to enhance the convergence of the federated aggregation method. The proposed method is implemented at local model training and has shown significant improvements in the convergence, keeping the same rounds of federated learning. We have rigorously evaluated the efficacy of the proposed algorithm using a range of image classification models such as Resnet (-18, -50) and MobileNetv2 under the CIFAR-10 dataset. For all experiments, we have used an independent and identically distributed (IID) configuration for distributing the data among contributing clients and fedavg as a federated aggregation algorithm. We have deployed our code based on the open-source library “Flower” for federated learning and communication. We have presented detailed results on various performance metrices such convergence under pretrained networks and without pretrained networks with different number of federated rounds, hyperparameter optimization, impact on different models to provide generability. The proposed algorithm outperforms state-of-the art algorithms in terms of convergence for federated learning by increasing up to 1.5% accuracy in limited federated rounds.
Network security analysis for cloud computing environment

Information technology services for businesses and consumers can be delivered via the Internet using cloud computing (CC) because it is agile, cost-effective, and time-tested. For many real-world applications, the data are kept in the cloud by a third-party service and accessible through the Internet as needed through CC approaches. Risks associated with CC involve the data security and network security account for real-time systems. This paper discusses different security threats in CC and suggests a solution by designing a network security analysis scheme with machine learning (NSA-ML). The ML classifier predicts the network vulnerabilities and prevents insecure communication in a CC environment. The proposed NSA-ML presents a data authentication scheme with a novel encryption methodology to ensure data security. The experimental results show that the proposed NSA-ML outperforms the existing cloud security approaches by gaining an efficiency of 95.4%.
Recommendation of Micro Teaching Video Resources Based on Topic Mining and Sentiment Analysis

Video learning resources are preferred by many students, owing to their intuitiveness and attractiveness. It is of practical significance to study the recommendation methods of video learning resources. Most of the existing research methods treat the scoring matrix as the main element, failing to consider video contents and learner interests. As a result, few of them can realize precise recommendation of videos. To solve the problem, this paper explores the recommendation of micro teaching video resources based on topic mining and sentiment analysis. Firstly, the dialog text features of English dialog videos and learner interest features were mined based on the deep word vector, and a topic mining model was established to achieve similarity-based resource recommendation. Next, the micro teaching videos with text information were subjected to sentiment analysis, improving the pushing accuracy of micro teaching videos. Finally, the scientific nature of our algorithm was demonstrated through experiments.