Reviewerly/exHarmony huggingface dataset:
Feature Contrastive Learning for No-Reference Segmentation Quality Evaluation

No-reference segmentation quality evaluation aims to evaluate the quality of image segmentation without any reference image during the application process. It usually depends on certain quality criteria to describe a good segmentation with some prior knowledge. Therefore, there is a need for a precise description of the objects in the segmentation and an integration of the representation in the evaluation process. In this paper, from the perspective of understanding the semantic relationship between the original image and the segmentation results, we propose a feature contrastive learning method. This method can enhance the performance of no-reference segmentation quality evaluations and be applied in semantic segmentation scenarios. By learning the pixel-level similarity between the original image and the segmentation result, a contrastive learning step is performed in the feature space. In addition, a class activation map (CAM) is used to guide the evaluation, making the score more consistent with the human visual judgement. Experiments were conducted on the PASCAL VOC2012 dataset, with segmentation results obtained by state-of-the-art (SoA) segmentation methods. We adopted two meta-measure criteria to validate the efficiency of the proposed method. Compared with other no-reference evaluation methods, our method achieves a higher accuracy which is comparable to the supervised evaluation methods and partly even exceeds them.
Policing third-world countries through a system of lending: The socioeconomic and political implications for Nigeria

One common economic phenomenon of third-world countries is the high tendency not to be able to financially sustain themselves. According to the Central Intelligence Agency (2022), a plethora of these countries like Nigeria, Ghana, Togo, Congo, Sierra Leone, and Equatorial Guinea, among others, tend to continually borrow funds from countries with developed economies and other global financial institutions. In other words, many African countries continue to depend on the Western powers for survival (Mhango, 2017). This has been a growing trend since many of the developing countries attained the status of political independence some four to six decades ago. There are realities around the core inability of the governments of developing countries to finance many state-owned projects, including recurrent expenditures. Since the genesis of Nigeria’s public debt in the late 1970s and early 1980s, the country has been through economic hardship with poverty and stagnation to show for the humongous loans taken year-in, year-out. Oftentimes, going by the Western powers’ analysis, fingers have been pointed at the country’s corrupt leadership, while feasible lessons from the soil have indicated colonialism, neo-colonialism, and imperialism of Western domination. Isiani et al. (2021) maintained that the most pressing position has been Nigeria’s economic linkage with the International Monetary Fund (IMF) and other instrumentalities of external loans that have over the years enmeshed the country in deeper debts through their imposed conditionality, and in addition submitted the example that the Structural Adjustment Program of IMF loans in the 1990s cemented Nigeria’s economic doom until today.
iDehaze: Supervised Underwater Image Enhancement and Dehazing via Physically Accurate Photorealistic Simulations

Underwater image enhancement and turbidity removal (dehazing) is a very challenging problem, not only due to the sheer variety of environments where it is applicable, but also due to the lack of high-resolution, labelled image data. In this paper, we present a novel, two-step deep learning approach for underwater image dehazing and colour correction. In iDehaze, we leverage computer graphics to physically model light propagation in underwater conditions. Specifically, we construct a three-dimensional, photorealistic simulation of underwater environments, and use them to gather a large supervised training dataset. We then train a deep convolutional neural network to remove the haze in these images, then train a second network to transform the colour space of the dehazed images onto a target domain. Experiments demonstrate that our two-step iDehaze method is substantially more effective at producing high-quality underwater images, achieving state-of-the-art performance on multiple datasets. Code, data and benchmarks will be open sourced.
WITHDRAWN: RAMPVIS: A visualization and visual analytics infrastructure for COVID-19 data

The COVID-19 pandemic generated large amounts of diverse data, including testing, treatments, vaccine trials, data from modeling, etc. To support epidemiologists and modeling scientists in their efforts to understand and respond to the pandemic, there arose a need for web visualization and visual analytics (VIS) applications to provide insights and support decision-making. In this paper, we present RAMPVIS, an infrastructure designed to support a range of observational, analytical, model-developmental, and dissemination tasks. One of the main features of the system is the ability to "propagate" a visualization designed for one data source to similar ones, this allows a user to quickly visualize large amounts of data. In addition to the COVID pandemic, the RAMPVIS software may be adapted and used with different data to provide rapid visualization support for other emergency responses.
Multiscale Attention Fusion for Depth Map Super-Resolution Generative Adversarial Networks

Color images have long been used as an important supplementary information to guide the super-resolution of depth maps. However, how to quantitatively measure the guiding effect of color images on depth maps has always been a neglected issue. To solve this problem, inspired by the recent excellent results achieved in color image super-resolution by generative adversarial networks, we propose a depth map super-resolution framework with generative adversarial networks using multiscale attention fusion. Fusion of the color features and depth features at the same scale under the hierarchical fusion attention module effectively measure the guiding effect of the color image on the depth map. The fusion of joint color-depth features at different scales balances the impact of different scale features on the super-resolution of the depth map. The loss function of a generator composed of content loss, adversarial loss, and edge loss helps restore clearer edges of the depth map. Experimental results on different types of benchmark depth map datasets show that the proposed multiscale attention fusion based depth map super-resolution framework has significant subjective and objective improvements over the latest algorithms, verifying the validity and generalization ability of the model.
A Joint-Learning-Based Dynamic Graph Learning Framework for Structured Prediction

Graph neural networks (GNNs) have achieved remarkable success in structured prediction, owing to the GNNs’ powerful ability in learning expressive graph representations. However, most of these works learn graph representations based on a static graph constructed by an existing parser, suffering from two drawbacks: (1) the static graph might be error-prone, and the errors introduced in the static graph cannot be corrected and might accumulate in later stages, and (2) the graph construction stage and graph representation learning stage are disjoined, which negatively affects the model’s running speed. In this paper, we propose a joint-learning-based dynamic graph learning framework and apply it to two typical structured prediction tasks: syntactic dependency parsing, which aims to predict a labeled tree, and semantic dependency parsing, which aims to predict a labeled graph, for jointly learning the graph structure and graph representations. Experiments are conducted on four datasets: the Universal Dependencies 2.2, the Chinese Treebank 5.1, the English Penn Treebank 3.0 in 13 languages for syntactic dependency parsing, and the SemEval-2015 Task 18 dataset in three languages for semantic dependency parsing. The experimental results show that our best-performing model achieves a new state-of-the-art performance on most language sets of syntactic dependency and semantic dependency parsing. In addition, our model also has an advantage in running speed over the static graph-based learning model. The outstanding performance demonstrates the effectiveness of the proposed framework in structured prediction.
Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition

As an important research direction in image and video processing, set-based video recognition requires speed and accuracy. However, the existing static modeling methods focus on computational speed but ignore accuracy, whereas the dynamic modeling methods are higher-accuracy but ignore the computational speed. Combining these two types of methods to obtain fast and accurate recognition results remains a challenging problem. Motivated by this, in this study, a novel Manifolds-based Low-Rank Dictionary Pair Learning (MbLRDPL) method was developed for a set-based video recognition/image set classification task. Specifically, each video or image set was first modeled as a covariance matrix or linear subspace, which can be seen as a point on a Riemannian manifold. Second, the proposed MbLRDPL learned discriminative class-specific synthesis and analysis dictionaries by clearly imposing the nuclear norm on the synthesis dictionaries. The experimental results show that our method achieved the best classification accuracy (100%, 72.16%, 95%) on three datasets with the fastest computing time, reducing the errors of state-of-the-art methods (JMLC, DML, CEBSR) by 0.96–75.69%.
The Role of Information Technology in Raising the Efficiency of Amman Stock Exchange Mediated by the Behavior of the Stock Prices

The study aimed to explore the role of information technology in raising the efficiency of the Amman Stock Exchange, mediated by the behavior of the stock prices. The study chose a sample consisting of 24 companies that are listed on the Amman Stock Exchange. The study used the average of the abnormal return of the stocks gained by companies through information technology applications. The study carried out a multiple regression analysis to explore the degree to which the independent variable affected the dependent one. The study results found that the abnormal return of the stocks gained by companies through information technology applications is low. The study also found that there is a significant relationship between using IT applications and the efficiency of the Amman Stock Exchange, mediated by the behavior of the stock prices. Therefore, the study recommends expanding the scope of using IT in emerging stock markets, including the Amman Stock Exchange, with the aim of raising the operational efficiency of such markets.
Virtual Reality Collision Detection Based on Improved Ant Colony Algorithm

In order to improve the performance in terms of detecting objects colliding in virtual reality, the ant colony algorithm was used to detect collisions. In the preliminary detection stage, the OBB bounding box and the spherical bounding box were used to detect the collision of objects, and the objects that may collide were selected. In the accurate detection stage, the model was sampled, and the feature pairs were used as the set to be detected for detecting collisions, the collision detection problem of the three-dimensional model was transformed into a nonlinear optimization problem of the distance between the feature pairs in the two-dimensional discrete space. The ant colony algorithm was introduced to solve the problem, and the pheromone concentration and update rules of the ant colony algorithm were optimized to improve the efficiency of the algorithm. The simulation results showed that, compared with the commonly used collision detection algorithms, our algorithm had high accuracy in detecting collisions and was less time-consuming.
Vulnerability, susceptibility and community preparedness for flood disaster in Banyumas, Indonesia

Abstract Banyumas Subdistrict, Banyumas Regency, which is an area prone to flooding, it is proven that five villages were flooded and dozens of people were evacuated due to the overflowing of the Serayu River so which had an impact on socio-economic conditions. Information on flood hazard and vulnerability data is still in numerical form (table) so it has not been mapped spatially. The objectives of this study were: (1) To analyze the level of flood vulnerability in Banyumas District, Banyumas Regency (2) To analyze the level of flood susceptibility in Banyumas District, Banyumas Regency (3) To analyze the level of community preparedness in dealing with flood disasters in Banyumas District, Banyumas Regency. The research method uses a descriptive approach. The sampling technique used was the saturated sample method and the simple random sample method. Data collection techniques using field observations, interviews, questionnaires, and documentation. The data analysis technique used to determine the level of vulnerability and susceptibility to flooding uses spatial analysis and scoring, to determine the level of community preparedness in dealing with flood disasters using descriptive analysis. The results of the study are: (1) The level of flood vulnerability in Banyumas District is dominated by a high level of vulnerability as many as 18 land units with an area of 1.668,53 Ha (39%). (2) The level of flood susceptibility in Banyumas District is dominated by a moderate level of susceptibility in as many as 10 villages with an area of 2.684,72 Ha (83%) and high susceptibility in as many as 2 villages with an area of 1.342,86 Ha (17%). (3) The level of community preparedness in dealing with flood disasters in Kedunguter Village is ready to face flood disasters.
Objective Prediction of Human Visual Acuity Using Image Quality Metrics

This work addresses the objective prediction of human uncorrected decimal visual acuity, an unsolved challenge due to the contribution of both physical and neural factors. An alternative approach to assess the image quality of the human visual system can be addressed from the image and video processing perspective. Human tolerance to image degradation is quantified by mean opinion scores, and several image quality assessment algorithms are used to maintain, control, and improve the quality of processed images. The aberration map of the eye is used to obtain the degraded theoretical image from a set of natural images. The amount of distortion added by the eye to the natural image was quantified using different image processing metrics, and the correlation between the result of each metric and subjective visual acuity was assessed. The correlation obtained for a model based on a linear combination of the normalized mean square error metric and the feature similarity index metric was very good. It was concluded that the proposed method could be an objective way to determine subjects’ monocular and uncorrected decimal visual acuity with low uncertainty.
A Blockchain-Based Certificate System with Credit Self-Adjustment

Currently, digital certificate systems based on blockchain have been extensively developed and adopted. However, most of them do not take into account the certificate quality. To evaluate the credibility of certificates issued by educational institutions, we propose a novel blockchain-based system with credit self-adjustment (BC-CS). In BC-CS, employers can provide feedback according to the performances of their employees (i.e., students) holding different certificates. Based on the feedback, BC-CS automatically adjusts the certificate credits by using our proposed credit self-adjustment algorithm. To verify the feasibility of our proposed system, a decentralized application prototype has been developed on an Ethereum network. Experimental results demonstrate that the proposed system can fully support multi-step accreditation and automatic adjustment for certificate credit.
Digital Literacy and Digital Self-Efficacy of Australian Technology Teachers

Agriculture is being increasingly transformed into a technological industry and calls for a greater need for digitally literate employees. To ensure school students are best placed for this requirement, the development of teacher digital literacy, self-efficacy, and the awareness of agricultural technology is essential. The current study explores the digital literacy and self-efficacy of Australian Technology Mandatory teachers who were participants in a one-day workshop (n = 185). The workshop introduced participants to the GPS Cows module, a complete teaching resource specifically designed to cover agricultural aspects of the Technology Mandatory syllabus. Data were collected by way of classroom ‘clickers’ during the workshop and by a post-workshop survey. Teachers were found to have reasonable basic digital literacy but lacked the confidence to conduct more detailed analytics. There was also some evidence that a teacher’s own digital literacy may also impact their perception of their students’ skills. Professional development workshops, such as the GPS Cows workshop, can improve teacher digital literacy and self-efficacy through hands-on learning in a collaborative, team environment.
De-Identification Technique with Facial Deformation in Head CT Images

Head CT, which includes the facial region, can visualize faces using 3D reconstruction, raising concern that individuals may be identified. We developed a new de-identification technique that distorts the faces of head CT images. Head CT images that were distorted were labeled as "original images" and the others as "reference images." Reconstructed face models of both were created, with 400 control points on the facial surfaces. All voxel positions in the original image were moved and deformed according to the deformation vectors required to move to corresponding control points on the reference image. Three face detection and identification programs were used to determine face detection rates and match confidence scores. Intracranial volume equivalence tests were performed before and after deformation, and correlation coefficients between intracranial pixel value histograms were calculated. Output accuracy of the deep learning model for intracranial segmentation was determined using Dice Similarity Coefficient before and after deformation. The face detection rate was 100%, and match confidence scores were < 90. Equivalence testing of the intracranial volume revealed statistical equivalence before and after deformation. The median correlation coefficient between intracranial pixel value histograms before and after deformation was 0.9965, indicating high similarity. Dice Similarity Coefficient values of original and deformed images were statistically equivalent. We developed a technique to de-identify head CT images while maintaining the accuracy of deep-learning models. The technique involves deforming images to prevent face identification, with minimal changes to the original information.
Pengaruh Gaya Kepemimpinan Terhadap Kinerja Pegawai di Museum Siginjai Kota Jambi

Organization and employees are two things that need each other. Improving employee performance will bring progress for the institution or organization to be able to continue to carry out the goals to be achieved. Leadership style is a process of directing, influencing, and motivating members to achieve organizational goals to help create a more productive work atmosphere at work. The leader's behavior affects the understanding, psychological condition, and performance of members. The Siginjei Museum in Jambi City is one of the is a management implementing unit which is a parliamentary institution that serves the interests of the community and its progress does not seek profit but seeks to collect, maintain, research, exhibit, and communicate material objects of humans and the environment for the purposes of study, education, and enjoyment. This study aims to describe and analyze the leadership and performance of employees at the Siginjei Museum, Jambi City. Research data obtained from respondents through questionnaires, focus groups, and panels, or also data from interviews with informants. The analytical method used is descriptive analysis, verification, and simple regression analysis. Statistical analysis was carried out using the SPSS 20 program. Based on the results of interviews and questionnaires, it shows that the leadership style and performance of employees at the Siginjei Museum in Jambi City are good. The regression model obtained is: Y= 25,265 + 0,535 X+ e. If the leadership style increases by 1 point or temporary unit, then the employee performance variable will increase by 0,535. Based on the t-test shows that leadership style has a positive and significant effect on employee performance because the significance value is 0,001 &lt; 0,05. So it can be said based on the results of hypothesis testing that leadership style affects employee performance by 24.3% and the rest is influenced by other variables.
A Practical Analysis of Techniques for Minting Genetic Information as NFTs in Blockchain Technology

In this paper we perform a practical analysis of techniques for minting genetic information, in particular genomic data, as non-fungible tokens (NFTs) in supporting blockchains, and perform a wide-range analysis of the best and most efficient tools to ensure such data’s privacy, non-repudiation and storage efficiency. We analyze the demands of the NFT-driven blockchain ecosystem today, and discuss how we can apply common approaches in storing and accessing genomic data, such as compression methods and encryption techniques for it, to the NFT world. We perform a practical experiment of our assessment, and compare the publicly available tools and libraries for achieving the aforementioned goal, in order to determine which one provides the best results in terms of speed and storage efficiency, and draw relevant conclusions as to which approach is more beneficial to NFT-driven ecosystems where genomic data could be safely preserved and actively traded.
Cloud Based Attendance Automation System With Analytics And Reporting

Managing attendance has always been tedious for the organizations as it is time consuming and repetitive activity. Fake attendance is a major issue to be addressed. Existing bio-metric system is a decent solution but they have limited functionality and have many vulnerabilities. Our objective is to provide an efficient and scalable cloud-based solution with facial recognition which can detect fake attendance and provide alerts to the management in case of irregular attendance. Our solution follows a verification process to avoid fake attendance. Initially, students have to upload a photo with their ID cards into our application. Next, the teacher or supervisor of the class will take a group photo of all the students and upload them into cloud through a mobile application, which will act as a user interface of our solution. The cloud architecture deployed in our solution combines various services like Amazon Rekognition, Amazon Simple Storage Service, Amazon Relational Database Service, Amazon Simple Notification Service, Amazon Lambda, Amazon Textract and Amazon Amplify in the most efficient manner in terms of storage and cost by regular deletion of unwanted data. We can then use the required cloud services to compare the faces of the student’s photo saved in our database with the group photo uploaded by the teacher to detect the students who are present. The proposed solution also analyzes the historical data within the database to prepare an aggregated report that consists of various fields like frequency of irregularity in attending classes, subject balance score and average attendance. While the back-end of the application is deployed in cloud, the front-end will be handled using Android as 70% of the general populace are dependent on this platform. Our solution uses Model-View-View-Model (MVVM) architecture. Execution time in MVVM applications is faster due to it supporting data binding with an average difference of 126.21ms.