Reviewerly/exHarmony huggingface dataset:
Constructions of linear codes with small hulls from association schemes

&lt;p style='text-indent:20px;'&gt;The intersection of a linear code and its dual is called the hull of this code. The code is a linear complementary dual (LCD) code if the dimension of its hull is zero. In this paper, we develop a method to construct LCD codes and linear codes with one-dimensional hull by association schemes. One of constructions in this paper generalizes that of linear codes associated with Gauss periods given in [&lt;xref ref-type="bibr" rid="b5"&gt;5&lt;/xref&gt;]. In addition, we present a generalized construction of linear codes, which can provide more LCD codes and linear codes with one-dimensional hull. We also present some examples of LCD MDS, LCD almost MDS codes, and MDS, almost MDS codes with one-dimensional hull from our constructions.&lt;/p&gt;
DynTriPy: A Python Package for Detecting Dynamic Earthquake Triggering Signals

Abstract Long-term and large-scale observations of dynamic earthquake triggering are urgently needed to understand the mechanism of earthquake interaction and assess seismic hazards. We developed a robust Python package termed DynTriPy to automatically detect dynamic triggering signals by distinguishing anomalous seismicity after the arrival of remote earthquakes. This package is an efficient implementation of the high-frequency power integral ratio algorithm, which is suitable for processing big data independent of earthquake catalogs or subjective judgments and can suppress the influence of noise and variations in the background seismicity. Finally, a confidence level of dynamic triggering (0–1) is statistically yielded. DynTriPy is designed to process data from multiple stations in parallel, taking advantage of rapidly expanding seismic arrays to monitor triggering on a global scale. Various data formats are supported, such as Seismic Analysis Code, mini Standard for Exchange of Earthquake Data (miniSEED), and SEED. To tune parameters more conveniently, we build a function to generate a database that stores power integrals in different time and frequency segments. All calculation functions possess a high-level parallel architecture, thoroughly capitalizing on available computational resources. We output and store the results of each function for continuous operation in the event of an unexpected interruption. The deployment of DynTriPy to data centers for real-time monitoring and investigating the sudden activation of any signal within a certain frequency scope has broad application prospects.
M-Learning Boost Students' Motivation in Learning Arabic Language Proficiency for Elementary Level

The language acquisition is one of the most important criteria that contribute to the measurement of language proficiency for students and the objectives of teaching of Arabic in Malaysia are currently based on the Standard Arabic Curriculum, which focuses on mastery of the four language skills.However, the inability to master the four language skills is still very significant.This occurs due to the fact that students only learn Arabic to pass the exam and that learning strategies applied to the students are less interactive and effective which led to demotivate the student to continue the learning process.Therefore, the primary purpose of this study is to examine the impact of mobile learning towards motivation in learning Arabic language proficiency.The research focuses on analysing the participants' motivation regarding the education of Arabic language in particular, via mobile learning.The attention, relevance, confidence, and satisfaction (ARCS) model shaped the comprehension of the effect of education through mobile app towards the motivation of the respondents.A total of 273 participants in Malaysians Higher Institutions used mobile learning for Arabic language proficiency during teaching and learning process.After this, they will complete the motivation questionnaire.The results showed that attention, relevance, satisfaction, and confidence motivational factors had a significant impact on student's motivation to learn Arabic through mobile app.Hence, this study contributes to the significant empirical results on the effectiveness of the integration of ARCS model in mobile app for learning Arabic language.
Disentangling effect size heterogeneity in meta-analysis: A latent mixture approach.

An important task of meta-analysis is to observe, quantify, and explain the heterogeneity across the reported effect sizes of primary studies. A primary issue that challenges this task is the myriad of subtle factors that could have contributed to the observed heterogeneity. We leveraged the recent advances in theoretical machine learning to develop a novel latent mixture-based method for disentangling effect-size heterogeneity in meta-analysis. Mathematical analysis and simulation studies were carried out to demonstrate that, when the observed heterogeneity stems from more than 1 factor, our method can attain a substantially higher statistical power than the traditional methods for moderator analysis without requiring researchers to make judgment calls on which factors to consider or correct for in analyzing the observed heterogeneity. We also conducted a case study with real-world data to show how our method may be used to address long-standing inconsistencies in the literature. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Quality of images showing medication packaging from individuals with vision impairments: Implications for the design of visual question answering applications

Abstract Mobile phone technology empowers individuals with vision impairment to submit images with questions and promptly receive answers from remote humans through visual question answering applications. This study aimed (a) identify challenges for visually impaired users of VizWiz, a visual question answering (VQA) application, to obtain information about medication packaging and (b) recommend design guidelines for better supporting this population to receive visual assistance. Using our four‐category coding scheme, we analyzed the quality of 265 images (131 with questions; 134 without) of medication packages from VizWiz. We found that 46% of the images were legible, 40% contained clear indicators for what information was sought, 40% had minimum background clutter, and only 5% total contained sufficient information to definitively answer the users' questions. We also conducted a thematic analysis to identify major challenges in having their questions answered. Major challenges we identified include: image legibility, incomplete display of key information, and a lack of clarity on what information is being sought. We recommend designers pay more attention to how the technology can facilitate training of users on best practices and provide additional tools to aid visually impaired users in capturing good quality images that can be used to answer their questions.
Tribes: A New Turn-Based Strategy Game for AI Research

This paper introduces Tribes, a new turn-based strategy game framework. Tribes is a multi-player, multi-agent, stochastic and partially observable game that involves strategic and tactical combat decisions. A good playing strategy requires the management of a technology tree, build orders and economy. The framework provides a Forward Model, which can be used by Statistical Forward Planning methods. This paper describes the framework and the opportunities for Game AI research it brings. We further provide an analysis on the action space of this game, as well as benchmarking a series of agents (rule based, one step look-ahead, Monte Carlo, Monte Carlo Tree Search, and Rolling Horizon Evolution) to study their relative playing strength. Results show that although some of these agents can play at a decent level, they are still far from human playing strength.
Automatic Garbage Scattered Area Detection with Data Augmentation and Transfer Learning in SUAV Low-Altitude Remote Sensing Images

Cleaning up the garbage timely plays an important role in protecting the ecological environment of nature reserves. The traditional approach adopts manual patrol and centralized cleaning to clean up garbage, which is inefficient. In order to protect the ecological environment of nature reserves, this paper proposes an automatic garbage scattered area detection (GSAD) model based on the state-of-the-art deep learning EfficientDet method, transfer learning, data augmentation, and image blocking. The main contributions of this paper are (1) we build a garbage sample dataset based on small unmanned aerial vehicle (SUAV) low-altitude remote sensing and (2) we propose a novel data augmentation approach based on garbage scattered area detection and (3) this paper establishes a model (GSAD) for garbage scattered area detection based on data augmentation, transfer learning, and image blocking and gives future research directions. Experimental results show that the GSAD model can achieve the F1-score of 95.11% and average detection time of 1.096 s.
Housing Design In Planet Green Tambora Using Augmented Reality For Promotion Media

One way of organizing the town to make it look elegant and tidy is to create a housing estate. Planet Green Tambora Housing is a housing estate located in the Tambakboyo area close to the center of Lamongan City. However, in the promotion process, it is still not very attractive, so the demand is still lacking. In this study, the authors use Augmented Reality as an attractive medium for marketing housing products on Planet Green Tambora. This application displays the design of each type of house in a tangible 3-dimensional visual form. Where this housing has 2 types of houses namely types 27 and 42. In addition to displaying a 3-dimensional visual form, it also displays house plans for each type of house. From the results of this Augmented Reality (AR) promotion making research, the results obtained that the response of respondents very interested in this application with a percentage of 97.25%. In addition, we testing too using light and edge detection methods, both showed very well.
Pedagogical Data Analysis Via Federated Learning Toward Education 4.0

Pedagogical data analysis has been recognized as one of the most important features in pursuing Education 4.0. The recent rapid development of ICT technologies benefits and revolutionizes pedagogical data analysis via the provisioning of many advanced technologies such as big data analysis and machine learning. Meanwhile, the privacy of the students become another concern and this makes the educational institutions reluctant to share their students' data, forming isolated data islands and hindering the realization of big educational data analysis. To tackle such challenge, in this paper, we propose a federated learning based education data analysis framework FEEDAN, via which education data analysis federations can be formed by a number of institutions. None of them needs to direct exchange their students' data with each other and they always keep the data in their own place to guarantee their students' privacy. We apply our framework to analyze two real education datasets via two different federated learning paradigms. The experiment results show that it not only guarantees the students' privacy but also indeed breaks the borders of data island by achieving a higher analysis quality. Our framework can much approach the performance of centralized analysis which needs to collect the data in a common place with the risk of privacy exposure.
Towards Traffic Saturation Detection Based on the Hough Transform Method

The principal aim of this paper is to solve the problem of traffic saturation, in using only GPS on board of vehicles and servers in a station for the control, without deploying camera or more materials on roads. GPS takes a large region into account. The challenge is to reduce materials effectively deployed on board of vehicules and on roads. This paper deals with the application of the Hough transform method to the automatic detection of traffic saturation and monitoring in order to prevent drivers for traffic saturation. The Hough Transform method establishes a relation between an image space and a parameter space in 2D. In a map, Some main roads have been identified by their coordinates in the accumulator. The Hough Transform method is applied to the Cartesian coordinates of GPS coordinates for vehicles to determine the number of votes around the coordinates of main roads in the accumulator. The number of votes around main road coordinates shows saturation roads and drivers will receive a voice message to change directions. Forward, the method could be an alternative to elaborate a solution to fight against Covid-19 to detect the presence of the crowd in a street based on GPS.
Offline Signature Identification and Verification Based on Capsule Representations

Abstract Offline signature is one of the frequently used biometric traits in daily life and yet skilled forgeries are posing a great challenge for offline signature verification. To differentiate forgeries, a variety of research has been conducted on hand-crafted feature extraction methods until now. However, these methods have recently been set aside for automatic feature extraction methods such as Convolutional Neural Networks (CNN). Although these CNN-based algorithms often achieve satisfying results, they require either many samples in training or pre-trained network weights. Recently, Capsule Network has been proposed to model with fewer data by using the advantage of convolutional layers for automatic feature extraction. Moreover, feature representations are obtained as vectors instead of scalar activation values in CNN to keep orientation information. Since signature samples per user are limited and feature orientations in signature samples are highly informative, this paper first aims to evaluate the capability of Capsule Network for signature identification tasks on three benchmark databases. Capsule Network achieves 97 96, 94 89, 95 and 91% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 and 32×32 resolutions, which are lower than usual, respectively. The second aim of the paper is to generalize the capability of Capsule Network concerning the verification task. Capsule Network achieves average 91, 86, and 89% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 resolutions, respectively. Through this evaluation, the capability of Capsule Network is shown for offline verification and identification tasks.
Learning Permutation Invariant Representations Using Memory Networks.

Many real-world tasks such as classification of digital histopathology images and 3D object detection involve learning from a set of instances. In these cases, only a group of instances or a set, collectively, contains meaningful information and therefore only the sets have labels, and not individual data instances. In this work, we present a permutation invariant neural network called Memory-based Exchangeable Model (MEM) for learning set functions. The MEM model consists of memory units that embed an input sequence to high-level features enabling the model to learn inter-dependencies among instances through a self-attention mechanism. We evaluated the learning ability of MEM on various toy datasets, point cloud classification, and classification of lung whole slide images (WSIs) into two subtypes of lung cancer---Lung Adenocarcinoma, and Lung Squamous Cell Carcinoma. We systematically extracted patches from lung WSIs downloaded from The Cancer Genome Atlas~(TCGA) dataset, the largest public repository of WSIs, achieving a competitive accuracy of 84.84\% for classification of two sub-types of lung cancer. The results on other datasets are promising as well, and demonstrate the efficacy of our model.
A Joint Embedding Method of Relations and Attributes for Entity Alignment

Entity alignment is to link the entities that point to same objects in the real world among different knowledge graphs (KGs).Existing kn10owledge-embedding-based entity alignment methods mostly regard KG as relation triples, while ignoring attributes and attribute values in KG.However, attribute information provides a valid information supplement for relation triple, alleviates relation triple's relation universality problem and information incompleteness problem, and improves accuracy of entity alignment task.In this paper, we make the first attempt towards combing relation and attribute triples for entity alignment.We divide a KG into relation triples and attribute triples, use parameter sharing (PS) joint method and translation-based knowledge embedding methods to embed them jointly.In addition, we design two strategies: direct accumulation and weight assignment strategy, to explore the effect of relation and attribute triple's embedding on experiment performance.The experimental results show that our method has significantly improved Hits@1, Hits@10 and Mean Rank metrics compared to baseline, and is the state of the arts on entity alignment task.The source code for this paper is available from https://github.com/ChengRui536/RAKRL.
Facial Mask Detection Using Stacked CNN Model

The coronavirus outbreak has affected the whole world critically. Amongst all other things, wearing a mask nowadays is mandatory to avoid the spread of the virus according to the World Health Organization. All the people in the country prefer to live a salubrious life by wearing a mask in public gatherings to avoid contracting the deadly virus. Recognizing faces wearing a mask is often a tedious job as there are no substantial datasets available comprising of masked as well as unmasked images. In this paper, we propose a stacked Conv2D model that is highly efficient for the detection of facial masks. Such convolutional neural networks work effectively as they can deduce even minute pixels of the images. The proposed model is a stack of 2-D convolutional layers with relu activations as well as Max Pooling and we implemented this model by using Gradient Descent for training and binary cross-entropy as a loss function. We trained our model on an amalgam of two datasets that are RMFD (Real World Masked Face Dataset) and Kaggle Datasets. Overall, we achieved a validation/testing accuracy of 95% and a training accuracy of 97%. In addition to this, we also developed an email notification system that sends an email whenever a person is entering without a mask and it will also prompt the user to wear the mask before entering into the system. Such a system is beneficial to large multinational companies and can be deployed there as the spread of viruses there is high because employees are from different regions.
NSA-Net: A NetFlow Sequence Attention Network for Virtual Private Network Traffic Detection

With the increasing attention on communication security, Virtual private network(VPN) technology is widely used to meet different security requirements. VPN traffic detection and classification have become an increasingly important and practical task in network security management. Although a lot of efforts have been made for VPN detection, existing methods mostly extract or learn features from the raw traffic manually. Manual-designed features are often complicated, costly, and time-consuming. And, handling the raw traffic throughout the communication process may lead to the compromise of user privacy. In this paper, we apply bidirectional LSTM network with attention mechanism to the VPN traffic detection problem and propose a model named NetFlow Sequence Attention Network (NSA-Net). The NSA-Net model learns representative features from the NetFlow sequences rather than the raw traffic to ensure the user privacy. Moreover, we adopt the attention mechanism, which can automatically focus on the information that has a decisive effect on detection. We verify our NSA-Net model on the NetFlow data generated from the public ISCXVPN2016 traffic dataset. And the experiment results indicate that our model can detect VPN from non-VPN traffic accurately, and achieve about 98.7% TPR. Furthermore, we analyze the performance of our model in the presence of sampling and our model still achieves over 90% TPR and Accuracy at low sampling rates.
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning

A key question in reinforcement learning is how an intelligent agent can generalize knowledge across different inputs. By generalizing across different inputs, information learned for one input can be immediately reused for improving predictions for another input. Reusing information allows an agent to compute an optimal decision-making strategy using less data. State representation is a key element of the generalization process, compressing a high-dimensional input space into a low-dimensional latent state space. This article analyzes properties of different latent state spaces, leading to new connections between model-based and model-free reinforcement learning. Successor features, which predict frequencies of future observations, form a link between model-based and model-free learning: Learning to predict future expected reward outcomes, a key characteristic of model-based agents, is equivalent to learning successor features. Learning successor features is a form of temporal difference learning and is equivalent to learning to predict a single policy's utility, which is a characteristic of model-free agents. Drawing on the connection between model-based reinforcement learning and successor features, we demonstrate that representations that are predictive of future reward outcomes generalize across variations in both transitions and rewards. This result extends previous work on successor features, which is constrained to fixed transitions and assumes re-learning of the transferred state representation.
A Location-Based Approach for Web Service QoS Prediction via Multivariate Time Series Forecast

With the wide use of service-oriented architecture (SOA) in academia and industry, the method of recommending services based on the Quality-of-Service (QoS) has become increasingly important. To obtain the QoS attributes of Web services, many QoS prediction methods have been proposed. However, existing methods rarely consider the QoS variance caused by the location information and the mutual influence between QoS attributes at the same time. To tackle this problem, this study proposes a novel approach called Mul-TSFL (Multivariate Time Series Forecast based on Location), which combines both collaborative filtering method and time series model to achieve more accurate QoS prediction results. The experiments based on the real-world QoS data set WS-Dream have been conducted to verify the effectiveness of the proposed method. Experimental results show that our method outperforms the related arts.
An Image Recognition Algorithm of Bolt Loss in Underground Pipelines Based on Local Binary Pattern Operator

This paper mainly designs an image recognition algorithm of bolt loss in underground pipelines. Firstly, the local binary pattern (LBP) operator was improved to optimize the information content of eigenvectors and enhance the discriminability. Next, the patterns were selected through weighting and ranking, thereby optimizing the original features in each channel of the image. Meanwhile, the main patterns of each channel were classified and identified with the support vector machine (SVM) classifier. The radial basis function (RBF) was taken as the kernel function for the SVM, and the teaching-learning-based optimization (TLBO) algorithm was improved to optimize the SVM parameters. Finally, the improved SVM classifier assigns suitable weights to the predicted class tags of different channels, facilitating the recognition of bolt loss. The research results shed new light on the application of swarm intelligence in image recognition.