Reviewerly/exHarmony huggingface dataset:
Research on image algorithm of road slope dangerous rock based on fusion template

There are many mountainous areas in my country, and the geological disasters caused by the sliding and collapse of slopes threaten road traffic safety at all times, and seriously hinder social and economic development. This research is designed to realize image processing technology and slope sliding monitoring system. It proposes a fusion template corner detection algorithm based on fusion template, improves Harris corner detection algorithm, and solves the problem of traditional algorithm's large amount of calculation and dependence on response threshold setting. In addition, there are shortcomings such as corner clustering, and then experiments are used to verify the effectiveness of the improved algorithm in the extraction of feature corners of slopes and mountains.
TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers

Leveraging the characteristics of convolutional layers, neural networks are extremely effective for pattern recognition tasks. However in some cases, their decisions are based on unintended information leading to high performance on standard benchmarks but also to a lack of generalization to challenging testing conditions and unintuitive failures. Recent work has termed this "shortcut learning" and addressed its presence in multiple domains. In text recognition, we reveal another such shortcut, whereby recognizers overly depend on local image statistics. Motivated by this, we suggest an approach to regulate the reliance on local statistics that improves text recognition performance. Our method, termed TextAdaIN, creates local distortions in the feature map which prevent the network from overfitting to local statistics. It does so by viewing each feature map as a sequence of elements and deliberately mismatching fine-grained feature statistics between elements in a mini-batch. Despite TextAdaIN's simplicity, extensive experiments show its effectiveness compared to other, more complicated methods. TextAdaIN achieves state-of-the-art results on standard handwritten text recognition benchmarks. It generalizes to multiple architectures and to the domain of scene text recognition. Furthermore, we demonstrate that integrating TextAdaIN improves robustness towards more challenging testing conditions.
Cross-Game Generalization Approaches for General Video Game Playing using Deep Reinforcement Learning

This paper presents a number of novel approaches to help facilitate Deep Reinforcement Learning (DRL) for Neural Networks based agents in the domain of General Video Game Playing (GVGP). Using common processing methods, the NN can retain a fixed predetermined input and output shape while having the flexibility to play a variety of games. Training can also be made more efficient via reward normalization. Our results show that these modifications negligibly impact the learning time and performance of the model when testedupon games in the GVGAI framework. Furthermore analysis of these approaches within the parameters of the GVGAI learning competition was also performed. This research work attempts to use the adaptations presented to train an agent thatwill compete in the CEC2019 GVGAI Learning Competition.
Spatio‐temporal feature fusion based correlative binary relevance for visual object detection

Abstract Object detection in video is an emerging and challenging research topic in Computer Vision. Recently, deep learning based approaches achieves greater results in object detection. The major issues in detecting multiple objects from the videos are exploiting low‐level visual concepts and temporal information and also exploring the correlation between the objects in the videos. Therefore, to address the above mentioned issues, the spatio‐temporal feature fusion based correlative binary relevance (STFF‐CBR) classifier is proposed to generate a rich vector representation and exploit the label correlation for the object detection in videos. In this article, first, the spatio‐temporal feature fusion (STFF) is proposed to exploit the low‐level visual concepts and temporal information in the videos which significantly improves the object detection performance. Second, correlative binary relevance (CBR) classification approach is proposed to exploit the dependencies between the labels in the video using the nearest neighbor based label dependency graph (LDG). Additionally, feed forward neural network (FFNN) classifier is utilized to increase the classification accuracy of the CBR method. Experimental evaluation shows that the STFF‐CBR classifier model achieves better performance for object detection problem in video against the state‐of‐the‐art methods.
A Conceptual Framework to Map Responses to Hate Crime, Hate Incidents and Hate Speech: The Case of Australia

Responses to hate crimes, hate incidents and hate speech are characterised by an exceptional fragmentation in terminology and lack of coordination among governmental and non-governmental organisations. This article proposes a new conceptual framework to map the diversity of responses to hate crime, hate incidents and hate speech, with the aim of assessing gaps and needs in this important policy area. Using Australia as a case study, we create and analyse a database of 222 organisations running activities focusing on tackling hate against different target groups. The results highlight an uneven distribution of efforts across different geographical areas, types of activities and target groups. The majority of anti-hate efforts, especially by government organisations, focus on awareness raising and education rather than victim support and data collection. Racial and religious hate are the main foci of anti-hate efforts, compared to other forms of hate, such as anti-LGBTIQ+ and disablist hate.
A Phased BOC Signal Detection Algotithm Based on Cyclic Stationary Characteristics

Aiming at the problem that BOC (Binary Offset Carrier) signal is difficult to be detected by traditional means under low SNR, this paper proposes a phased BOC signal detection algorithm based on cyclic stationary feature. Firstly, this paper introduces BOC signal model, proposes an improved cyclic stationary feature detection method, analyzes the difference of gray images under binary hypothesis, and utilizes this difference to extract features by putting the two types of images into the convolutional neural network, and then uses the trained network for detection. Furthermore, in order to detect signals more efficiently, energy detection is carried out at first. If no signal is detected, the improved cyclostationary detection is adopted in the second stage. Simulation results show that the detection performance of the proposed method is significantly better than that of the traditional cycliostationary detection and energy detection when SNR is less than −7db.
Creating a Wiring Framework for Embedded GUI Programming Course

This paper presents the results of implementing the Wiring unit for Embedded Wizard, which offers the possibility of using functions familiar from Arduino projects inside a high level GUI building and prototyping environment. The unit allows students for easier microcontroller interfacing of their embedded GUI applications, so they can focus on studying UI and UX design concepts and doing online or offline prototyping of their applications without the need for extensive hardware testing.During the development of the Wiring-like framework also a large-scale study was performed about the usage of the framework in Arduino projects. Almost 200,000 Arduino source code files were analyzed from public GitHub repositories, and the findings about the topmost used functions and libraries are also presented in the paper.
An Agent Based Trust Mechanism for Selection of an Effective Data Center

As the demand for cloud computing has grown and especially at the time of pandemic, efficient handling of cloud marketplace has become more complicated. The cloud market is highly dependent on data centers. There are numerous options for selecting a data center that can actually consider various factors like effective use of resources, low cost, increased number of infrastructures. Therefore, selecting an efficient data center is a current issue in cloud computing. To ensure this, a mechanism has been proposed for its appropriate selection of data center. A trust agent has been used that can actually help in selecting data center based on credible values of trust calculated by analyzing reputation feedback of data center. The proposed framework considers various parameters such as response time, performance time and service response time. Finally, the proposed mechanism has been compared with other mechanism by considering more than one data centers and performance validation has been done to analyze the generated results.
Energy Efficiency Through a Wearable Device for the Elderly Based on the Integrated Smart Neighbourhood Framework of Malaysia 5.0 Model

In reinventing a nation, Malaysia is moving from a social paradigm shift of an Agricultural-Industrial to a Digital one, moving towards the development of Malaysia 5.0. This human-centric concept portrays the citizens to be smart in using the right technology to help them conduct their daily activities. A Study conducted on dimensions of the concept of Malaysia 5.0 which is used in the design of the Integrated Smart Neighbourhood Framework (ISNF) of the Malaysia 5.0 model, comprised twelve dimensions: smart mobility/transportation, smart finance, smart economy, smart education, smart social & health, smart environment, smart governance, smart industry integration, culture, human capital, Information and Communications Technology (ICT) and spirituality. This paper highlights the integration between the smart social & health with the smart finance dimensions, through the use of a wearable device designed for the elderly for social and health, as well as financial purposes through energy efficiency. Based on an ambient interface, the tangible user interface (TUI), used in the design of the wearable device allows the elderly to control electrical and electronic appliances such as air conditioning, lights, lamp, fans, computer, as well their telephones for social and emergency calls, through the control system built in their homes. The wearable device was validated and its results have been published previously in another publication and will not be discussed in this paper. The energy efficiency which will be reported in this paper was measured based on the financial savings made for a span of three months when the experiments were conducted. Based on the study the findings showed that using the wearable device to control the use of electrical appliances in their homes, the elderly made financial savings of between 20%–25% of their normal electricity bill compared with the previous three months when they were not using the wearable device to control the electricity appliances in their homes. Thus, the efficient use of electricity or ‘energy efficiency’ function of the wearable device had resulted in financial savings for the elderly.
Enhanced and secured random number generation for eUASBP

Authentication is needed for vital applications around the world. Applications that deal with sensitive information need to be kept secret through anyone type of authentication mode. Password-based smart card authentication is a generic model of authentication. Many password-based smart card authentication protocols have been proposed by Sangeetha et al., Giri et al., Jia et al. , Das et al. and so on. Those protocols provide protection against many type of attacks such as offline password guessing attack, server masquerading attack, user impersonation attack, privileged insider attack, but they are open to Crypt analytic attack, Cloning attack, and Reverse engineering attack. This paper proposes an authentication protocol that is termed as hrngRO- hybrid RNG by RO-PUF and SHA-256 for eUASBP simulated using AVISPA tool. It provides Ring Oscillator PUF based random number generator using the SHA-256 hash function. This hybrid combination of RNG makes hrngRO more secure against Crypt analytic attack, Cloning attack, Reverse engineering attack and makes the system even faster. Pre Early Wrong Password Detection is supported by hrngRO using bloom filters. hrngRO provides protection against many types of attacks like other authentication protocols and it also provides additional security by avoiding the above-mentioned attacks. Security of hrngRO is tested using AVISPA tool.
Risk Prediction Model of Sudden Public Safety Incidents Based on Neural Network

Taking natural disasters as an example, this paper collects the data of natural disasters in China from 2014 to 2020, and makes a statistical analysis on the temporal and spatial distribution of natural disasters in China. Four prediction models including Bayesian regression, KNN, support vector machine and neural network are established. The natural disaster risk prediction indexes are constructed by using the direct economic loss of natural disasters and the affected population, and the regression fitting and prediction experiments are carried out. The fitting and prediction performance of each model are compared by using four quantitative evaluation indexes: mean absolute error, r2score, mean square error and adjusted R-square. The results show that natural disasters in China show obvious temporal and spatial distribution law, and the prediction performance of neural network model is the best.
An Improved Cuckoo Search Clustering Method for Line Loss Data of Transformer District with DGs

Abstract For the low-voltage transformer district with distributed generations (DGs), the traditional theoretical calculation method of line loss is not applicable. This paper presents a novel clustering method for line loss data of transformer district with DGs, which combined an improved Cuckoo Search algorithm and K-Means clustering algorithm. Firstly, the influence factors of line loss are screened based on the maximum information coefficient, and the line loss index system is established. Secondly, an improved cuckoo search clustering algorithm is proposed to cluster the sample data set to reduce the dependence on the initial clustering center. Finally, the simulation results of 410 samples from a certain area with photovoltaic power supply show the accuracy and effectiveness of the proposed method. The simulation results show that the proposed method is accurate and effective.
Mind the Gap: Aligning Software Engineering Education and Industry in Namibia

Software Engineering (SE) professionals design, implement, and maintain modern business applications. From the software industry’s perspective, SE graduates must possess the necessary competencies required by industry. In this paper, we present results from a focus group arranged as a webinar conducted amongst SE industry supervisors, a graduate programmer, and academics from three computing departments in different universities in Namibia. Our results identified crucial SE skills expected by the SE industry in Namibia. Furthermore, our results highlighted the importance of enhancing university-industry collaboration in educating, preparing, and nurturing SE graduates with employable skills. We present our results from the focus group discussions, and based on the results, we suggest competencies that academia should consider incorporating in their curriculum development to ensure that SE graduates possess the necessary skills required in their careers.
Unearthing the Cause of Slow Seismic Waves in Subduction Zones

Researchers look to the fossil rock record to unearth the driving forces for variable seismic speed through subduction zones.
Generating Pool of Classifiers with Hyper-Parameter Optimization for Ensemble

Ensemble is an effective method to integrate diverse classifiers and take advantage of their individual strengths. Given a test sample, ensemble selects the most competent classifiers from a pool of candidates and predicts the result by combining the outputs of the selected classifiers. One of the key factors that affects the performance of ensemble is the criterion defined to evaluate the competence of classifier. The commonly used criterion is the prediction accuracy in the local region around the test sample. However, if the competence of all classifiers in the pool are not qualified, the prediction of the ensemble will be inaccurate. In this paper, we propose a hyper-parameter optimization based strategy to generate high quality classifiers. In order to automatically identify the optimal hyper-parameters, we explored six optimization algorithms that are based on Gaussian Process and Evolutionary theory. The experiment results show that Bayesian Optimization is the best algorithm for hyper-parameters optimization in terms of time consumption and classification accuracy. Furthermore, cooperating with the pool of classifiers that are configured with the optimized hyper-parameters, Kullback-Leibler Divergence and Randomized Reference Classifier are the two outstanding dynamic ensemble methods that can provide high classification accuracy.
Identifying the Presence of Cyberbullying in Tamil-English Phonetic Words Using Browser Plugin

While social media provides excellent communication opportunities, it also exposes people to potentially threatening situations online. The rising popularity of various social media platforms has enabled people worldwide to freely exchange views, ideas, and interests. But that does not come without consequences, including an increase in cyberbullying. Early identification of cyberbullying has been proven to be beneficial in preventing it from spreading. We have tested a web browser plugin over few social media platforms to identify offensive comments or posts. Furthermore, research on cyberbullying identification has been done in many languages, but none has been done on Tamil and English phonetic words until the time of conducting this study. Therefore, this study attempts to elicit keywords or phrases relating to cyberbullying incidents in both Tamil and English phonetic words. The study results might be helpful to the research community to create further tools and prepare a training dataset for cyberbullying identification.
Day-Ahead Solar Power Forecasting with Pattern Analysis and State Transition

This study focuses on the day-ahead forecasting of the power generation of the grid-connected photovoltaic (PV) system. In the literature, most PV power forecasting models depends on the information from the numerical weather prediction (NWP). The errors of the weather prediction might cause the performance of the PV power forecasting model to decrease. Therefore, in this paper, the proposed day-ahead PV power forecasting model only utilizes the historical PV power generations as the input data. The proposed forecasting model is composed of two parts, pattern analysis and the state transition. The pattern analysis evaluates the similarity of the time series of the daily PV power generations based on Euclidean distance and group the time series with similar patterns by k-means time series clustering algorithm. Each cluster is regarded as one state. Then, the probabilities of the state transitions between two consecutive days are calculated. Finally, the expected PV power pattern of tomorrow is obtained as the forecasted PV power outputs. In this study, the proposed model is compared with the traditional feed-forward neural network (FFNN). The results of the cast study demonstrate the proposed model leads to smaller errors in 10 sets of the testing data.
Design of a Computational Offloading Platform for Immersive Experiences in Digital Culture

Recent advances in three-dimensional (3D) media delivery technologies, such as Augmented and Mixed Reality, have paved the way towards the realization of high-quality immersive experiences in digital culture applications. Nowadays, these services play a crucial role in cultural heritage dissemination, transforming visitors from passive spectators into proactive culture caretakers and content creators. Nevertheless, they demand significant computational resources and are subject to low delay tolerances, that are generally difficult to acquire via standard mobile devices. Under this scope, the current paper deals with the preliminary design of a hybrid cloud/edge computing platform for the support of digital culture applications. The goal is to recognize computationally intensive services that are to be offloaded based on their resource and time criticality to the cloud or edge respectively, and offer constant guarantees regarding the visitors’ Quality of Experience. The use case of Byzantine Monuments and Churches is also reported to better contextualize the approach and present the platform’s layering and data flow.
DataWords: Getting Contrarian with Text, Structured Data and Explanations

Our goal is to build classification models using a combination of free-text and structured data. To do this, we represent structured data by text sentences, DataWords, so that similar data items are mapped into the same sentence. This permits modeling a mixture of text and structured data by using only text-modeling algorithms. Several examples illustrate that it is possible to improve text classification performance by first running extraction tools (named entity recognition), then converting the output to DataWords, and adding the DataWords to the original text – before model building and classification. This approach also allows us to produce explanations for inferences in terms of both free text and structured data.
Measuring and Evaluation of the Results of UI-Re-Engineering in the Nursing Field

Since all systems and services are ultimately aimed at human users, it is essential to address the interaction between humans and machines. The acceptance of a system, i.e. the question whether a system is interpreted by a user as good or bad, is a complex construct of perception and evaluation processes. A disproportionately growing number of care recipients and the associated avalanche of data in all areas of modern care continuously increases the need for more efficient, higher-quality and at the same time more cost-saving options for care and its networking of relatives, doctors and nurses. As part of a project to digitize dementia residential communities, an information platform called “INFODOQ” was developed. The system serves as a transparent information, coordination and communication platform for various dementia residential communities to optimize the daily care and nursing routine. After completion, the system underwent several analytical and empirical evaluations in which nursing teams were involved. The subject of the work presented here is the presentation of the evaluation results, the subsequent reengineering process of nursing documentation and the presentation of a specially developed analysis tool. New insights into the interaction behavior of caregivers in digital nursing documentation and the optimal interface development are the results of this work.