Reviewerly/exHarmony huggingface dataset:
Pengembangan Media Pembelajaran Berbasis Kearifan Lokal Permainan Mpa’a Gopa Untuk Keterampilan Berbahasa Indonesia Siswa Kelas IV Tahun Ajaran 20121/2022

The purpose of this study was to develop an effective and valid local wisdom game Mpa'a Gopa used by schools in the learning process. This study uses a Research and Development (R&amp;D) approach. The development model used in this research is the ADDIE development model. The ADDIE development model consists of five stages which include: 1) analysis, 2) design, 3) development, 4) implementation, 5) evaluation. The results of research on the development of the Mpa'a Gopa Game: a) Assessment of all aspects by material expert validators with an average total score of 77 aspects so that it is included in the appropriate category with an average score of 4.52. Assessment of all aspects by media expert validators with an average total score of 39 aspects assessed, so that it is included in the appropriate category with an average score of 4.33. b) Local wisdom based on the Mpa'a Gopa game is suitable for use in the context of thematic learning, especially Indonesian language material with stories in class IV SDN 1 DENA. This is based on the overall assessment of the student response aspects with an average total score of 42.5 which is included in the very feasible category with an average score of 2.82. c) The effectiveness of local wisdom products based on the Mpa'a Gopa game is effectively used. This is based on more than half of the students, namely 16 out of 30 students experienced an increase in language skills.
The Impact of the Defensive Strategy on the Level of Organizational Performance: A Field Study at Yemeni Banks

The study aimed to identify the impact of the defensive strategy on the level of organizational performance, according to the balanced scorecard model covering all of its dimensions of finance, clients, operations and learning and growth at Yemeni banks. The study adopted the descriptive analytical method, and used a questionnaire for data collection. The study population was (1525) employees in supervisory positions at Yemeni banks and the study sample consisted of (322) employees, representing the study population. In light of this, data were collected and analyzed, and all hypotheses were tested using SPSS. The study results revealed that the defensive strategy has a positive impact on the level of organizational performance achieved at Yemeni banks. The study recommends paying attention to the strategies used in the organization as a basic and important element in all stages of the administrative process, within the available facilities, because this will contribute to improving the organizational performance of the Yemeni banks under study.
Information System Web Based for Group Randomization of State Defense Education Class using Linear Congruential Method

One of the actual forms of state defense education is through outbound activities. In this context, education is interpreted as a process to humanize humans to become fully human adults. However, the world was shocked by the outbreak of a disease caused by a virus called corona or known as covid-19. Transmission through social contact between humans is difficult to predict and also cannot be avoided so its spread is also very rapid. Therefore, it is necessary to change the outbound state defense education method from conventional activities to offline activities based on the web and automatically computed. The system will replace the conventional process of supervising outbound activities where activities that are usually carried out in groups and simultaneously, will be carried out independently by sending evidence of activities that have been recorded through document files or videos. The results of this study are the Outbound State Defense Group Randomization Information System (SIOBEL) using the Linear Congruent Method (LCM) which helps in creating random groups of students and with the Graph Query Language (Graph-QL) system architecture which can facilitate interface and system developers to exchange data.
A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning

Proxy-based Deep Metric Learning (DML) learns deep representations by embedding images close to their class representatives (proxies), commonly with respect to the angle between them. However, this disregards the embedding norm, which can carry additional beneficial context such as class- or image-intrinsic uncertainty. In addition, proxy-based DML struggles to learn class-internal structures. To address both issues at once, we introduce non-isotropic probabilistic proxy-based DML. We model images as directional von Mises-Fisher (vMF) distributions on the hypersphere that can reflect image-intrinsic uncertainties. Further, we derive non-isotropic von Mises-Fisher (nivMF) distributions for class proxies to better represent complex class-specific variances. To measure the proxy-to-image distance between these models, we develop and investigate multiple distribution-to-point and distribution-to-distribution metrics. Each framework choice is motivated by a set of ablational studies, which showcase beneficial properties of our probabilistic approach to proxy-based DML, such as uncertainty-awareness, better-behaved gradients during training, and overall improved generalization performance. The latter is especially reflected in the competitive performance on the standard DML benchmarks, where our approach compares favorably, suggesting that existing proxy-based DML can significantly benefit from a more probabilistic treatment. Code is available at github.com/ExplainableML/Probabilistic_Deep_Metric_Learning.
Historical Transformations of the Higher Library Education System in Poland

The aim of the study is to determine the features of the cognitive and organisational components’ formation of the Polish higher library and information education model in the main historical periods of its development. The article highlights the history and current status of Poland’s higher library education system and clarifies its place within the European system of university training of library and information professionals. The research methodology is based on heuristic frameworks of systematic and historical approaches, source and comparative methods, and content analysis of bachelor’s and master’s curricula in Library Studies. The scientific novelty of the research is to prove common and different in the systems of higher library education in Poland and Ukraine, to introduce the results of the curriculum in library content and structure analysis of leading Polish universities scientifically. It is noted that Poland, before other post-Soviet European countries, has successfully implemented all the basic principles of the Bologna Process into educational practice. It helped to improve the quality of librarians’ training and their competitiveness in the European labour market. Conclusions are made on the main periods of the Polish model of the higher library education institutionalisation, with its formation being influenced by various socio-cultural, geographical and political factors. It is determined the specifics of the Bachelor, Master and Doctor of Philosophy Degrees’ training in the “Library and Information Studies” speciality in leading Polish universities, with the profiles and content of their educational programmes been characterised. The study determines the organisational features of the library and information staff’s training at different academic levels and gives characteristics to the key competencies that students acquire during their mastery. Emphasis is placed on promising for Ukraine achievements and innovations of the Polish model of training library and information specialists, which should be implemented within a single integrated programme subject area 029 “Information, Library and Archival Studies”.
A law of adversarial risk, interpolation, and label noise

In supervised learning, it has been shown that label noise in the data can be interpolated without penalties on test accuracy. We show that interpolating label noise induces adversarial vulnerability, and prove the first theorem showing the relationship between label noise and adversarial risk for any data distribution. Our results are almost tight if we do not make any assumptions on the inductive bias of the learning algorithm. We then investigate how different components of this problem affect this result, including properties of the distribution. We also discuss non-uniform label noise distributions; and prove a new theorem showing uniform label noise induces nearly as large an adversarial risk as the worst poisoning with the same noise rate. Then, we provide theoretical and empirical evidence that uniform label noise is more harmful than typical real-world label noise. Finally, we show how inductive biases amplify the effect of label noise and argue the need for future work in this direction.
Online Food Ordering System

Abstract: Online food ordering is a feature of our suggested system, which makes it convenient for customers. It eliminates the drawbacks of the conventional queuing mechanism. Our system is an easy way to order food from restaurants and get a mess service online. This system enhances the process of taking consumer orders. Customers can easily place orders as they like using the online meal ordering system, which sets up a food menu online. Additionally, clients can simply follow orders if there is a food menu. Additionally, this system has a feedback feature that allows users to rank the food products. Additionally, the suggested system can suggest restaurants and hotels based on the ratings provided by the user. The hotel personnel will also be advised of any quality and improvement issues. Both online and pay-on-delivery payment methods are available. By giving each user a unique ID and password, separate accounts are maintained for each user for more secure ordering
Calling Rosetta Xiomë

An Introduction to the Open Knowledge Reference Model (OKRM). This presentation outlines the traceable derivation of the OKRM and Worldwide Mosaic Web concepts. It showcases a selection of OKRM assets and demonstrates their application to the Sustainability Emergency.
Bernstein Vazirani deep neural perceptron based certificateless signcryption for secured data communication in cloud

Present day evolutions in providing genuine security in the Cloud Computing Network havebeen made Certificateless Signcryption model exceedingly acceptable for several applications. However, security and privacy are the paramount area of interests in safeguarding data or data packet as they are said to be highly sensitive.Moreover, with conventional Certificateless Signcryption process found to be computationally laborious and with the discrete operation minimizing the advantages gained from smaller key size, data security is said to be compromised. To address on this issue, in this work, a novel method called, Bernstein Vazirani Deep Neural Perceptron-based Certificateless Signcryption (BVDNP-CS) for secured data communication in cloud computing network is proposed with multiple layers. The proposed BVDNP-CS method consists of one input layer, three hidden layers and one output layer. The BVDNP-CS method in the first layer obtains the data from personal cloud dataset. In the first hidden layer, with security parameterobtained as input, system parameters and master key are generated by means of Multi-linear mapping. In the second hidden layer, three keys, i.e., partial private key, private key and public key are generated by utilizing Bernstein–Vazirani Key Generation, therefore reducing the overhead incurred.
The Impact of Governance on Financial Institution and Financial Market Development: Empirical Evidence from Emerging Markets

Abstract The overall objective of the study is to investigate the impact of governance on financial development in Sub-Saharan African countries. To achieve the stated objective, the study employed balanced data of 43 Sub-Saharan African countries during the year 2002 to 2018. To analyze the data, the study used both the fixed and random effect estimation approaches and explored the relationship between the three dimensions of governance and three pillars of financial development in Sub-Saharan African countries. The study also applied the Principal Component Analysis (PCA) to create indexes for the political, economic, and institutional dimensions of governance taking the six world governance indicators. The overall findings of the study indicate that the political, economic, and overall governance composite index has a positive and significant impact on the overall financial developments of sub-Saharan African countries. The development of financial institutions in the region is influenced significantly and positively by political, institutional, and overall governance. In addition, the economic dimension of governance has had a significant and positive impact on the development of the financial markets in sub-Saharan Africa. Furthermore, trade openness, real interest rate, inflation, real GDP, and access to electricity are all major macroeconomic predictors of financial development, according to the study. As a result, all aspects of governance quality in the Sub-Saharan African countries must be improved. This can be achieved by policies aimed at strengthening voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, corruption control, and overall macroeconomic reform.
VS-Neuron Cell

Humans and some animals have logical thinking and feelings, but today, AI machines don’t. This Artificial Neuron named VS-Neuron has the capability of such deeds. Neural Network of VS-Neuron can lead active brain.Which have the ability as the human brain has.
Post-COVID Library: Educating Children to Media and Information Literacy through UNESCO’s Memory of the World Programme

The COVID-19 pandemic has severely disrupted children's education in schools, and reflects the urgent demands of children and their educators for diverse learning channels and materials. Memory institutions such as libraries, archives and museums, as repositories of reliable sources of information and knowledge service provider, have a responsibility to contribute to the cultural and media literacy education of children. Therefore, memory institutions should explore innovative ways to safeguard and utilize documentary collections in media and information literacy education for people and especially children, and share and promote good practices. These efforts will also inspire younger generations to participate in the preservation and transmission of historical records.
Pala'wan "Sudsugid" Stories and their Viability in Developing Story Books for Indigenous People Education (IPED)

This study documents “sudusugid” oral literature in a community in Sitio Tagbituka, Brgy. Tarusan, Bataraza. The researchers accomplished it by using a qualitative approach in the form of narrative research. Community elders served as key informants for this study. During the site visits from April 26 to May 20, 2022, ten (10) "sudsugid" stories were told by the key informants but mentioned that not all of them are appropriate to be developed into storybooks for elementary learners as some of them have inappropriate themes. From the ten stories presented by the key informants, they have suggested two stories that could be developed as storybooks. Results also indicate how each individual story reflects the Pala'wan culture, such as their cosmology and value system.
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning experiments and open-source training environments are typically limited in scale, supporting tens or sometimes up to hundreds of interacting agents. In this paper we demonstrate the use of Vogue, a high performance agent based model (ABM) framework. Vogue serves as a multi-agent training environment, supporting thousands to tens of thousands of interacting agents while maintaining high training throughput by running both the environment and reinforcement learning (RL) agents on the GPU. High performance multi-agent environments at this scale have the potential to enable the learning of robust and flexible policies for use in ABMs and simulations of complex systems. We demonstrate training performance with two newly developed, large scale multi-agent training environments. Moreover, we show that these environments can train shared RL policies on time-scales of minutes and hours.
GCN-based Multi-task Representation Learning for Anomaly Detection in Attributed Networks

Anomaly detection in attributed networks has received a considerable attention in recent years due to its applications in a wide range of domains such as finance, network security, and medicine. Traditional approaches cannot be adopted on attributed networks' settings to solve the problem of anomaly detection. The main limitation of such approaches is that they inherently ignore the relational information between data features. With a rapid explosion in deep learning- and graph neural networks-based techniques, spotting rare objects on attributed networks has significantly stepped forward owing to the potentials of deep techniques in extracting complex relationships. In this paper, we propose a new architecture on anomaly detection. The main goal of designing such an architecture is to utilize multi-task learning which would enhance the detection performance. Multi-task learning-based anomaly detection is still in its infancy and only a few studies in the existing literature have catered to the same. We incorporate both community detection and multi-view representation learning techniques for extracting distinct and complementary information from attributed networks and subsequently fuse the captured information for achieving a better detection result. The mutual collaboration between two main components employed in this architecture, i.e., community-specific learning and multi-view representation learning, exhibits a promising solution to reach more effective results.
Spatio-temporal error concealment in video by denoised temporal extrapolation refinement

In video communication, the concealment of distortions caused by transmission errors is important for allowing for a pleasant visual quality and for reducing error propagation. In this article, Denoised Temporal Extrapolation Refinement is introduced as a novel spatiotemporal error concealment algorithm. The algorithm operates in two steps. First, temporal error concealment is used for obtaining an initial estimate. Afterwards, a spatial denoising algorithm is used for reducing the imperfectness of the temporal extrapolation. For this, Non-Local Means denoising is used which is extended by a spiral scan processing order and is improved by an adaptation step for taking the preliminary temporal extrapolation into account. In doing so, a spatio-temporal error concealment results. By making use of the refinement, a visually noticeable average gain of 1 dB over pure temporal error concealment is possible. With this, the algorithm also is able to clearly outperform other spatio-temporal error concealment algorithms.
A Review of Quantum Cybersecurity: Threats, Risks and Opportunities

The promise of quantum computing is not speeding up conventional computing rather delivering an exponential advantage for certain classes of problems, with profound implications for cybersecurity for instance. With the advent and development of quantum computers, cyberspace security can surely become the most critical problem for the Internet in near future. On contrary, prosaic quantum technology can be promising to transform cybersecurity. This research aims to synthesize basic and fundamental studies concerning quantum cybersecurity that can be emerged both as a threat and solution to critical cybersecurity issues based on a systematic study. We provide a comprehensive, illustrative description of the current state-of-the-art quantum computing and cybersecurity and present the proposed approaches to date. Findings in quantum computing cybersecurity suggest that quantum computing can be adopted for the betterment of cybersecurity threats while it poses the most unexpected threats to cybersecurity. The focus and depth of this systematic survey not only provide quantum and cybersecurity practitioners and researchers with a consolidated body of knowledge about current trends in this area but also underpins a starting point for further research in this field.
Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection

RGB-thermal salient object detection (RGB-T SOD) aims to locate the common prominent objects of an aligned visible and thermal infrared image pair and accurately segment all the pixels belonging to those objects. It is promising in challenging scenes such as nighttime and complex backgrounds due to the insensitivity to lighting conditions of thermal images. Thus, the key problem of RGB-T SOD is to make the features from the two modalities complement and adjust each other flexibly, since it is inevitable that any modalities of RGB-T image pairs failure due to challenging scenes such as extreme light conditions and thermal crossover. In this paper, we propose a novel mirror complementary Transformer network (MCNet) for RGB-T SOD. Specifically, we introduce a Transformer-based feature extraction module to effective extract hierarchical features of RGB and thermal images. Then, through the attention-based feature interaction and serial multiscale dilated convolution (SDC) based feature fusion modules, the proposed model achieves the complementary interaction of low-level features and the semantic fusion of deep features. Finally, based on the mirror complementary structure, the salient regions of the two modalities can be accurately extracted even one modality is invalid. To demonstrate the robustness of the proposed model under challenging scenes in real world, we build a novel RGB-T SOD dataset VT723 based on a large public semantic segmentation RGB-T dataset used in the autonomous driving domain. Expensive experiments on benchmark and VT723 datasets show that the proposed method outperforms state-of-the-art approaches, including CNN-based and Transformer-based methods. The code and dataset will be released later at https://github.com/jxr326/SwinMCNet.
A Computer-Aided Diagnosis (CAD) System for Automatic Counting of Ki67 Cells in Meningioma

Meningioma is a type of primary brain tumour where this tumour arises in the three thin layers of tissues, called meninges. Tumour grading is usually used to describe tumour cells' characteristics and behaviours and how they look under a microscope. There were many techniques used for determining the grade of the tumour. Ki67 was the most common proliferation marker used to measure cell proliferation activity. Currently, pathologists used the manual counting technique to count the Ki67 cells before determining tumour grading. However, this technique was time-consuming, tiring and the counting results are often not accurate. Besides that, manual counting has poor reproducibility and discordant between counting values’ among the pathologist. Therefore, this study aimed to develop a Computer-Aided Design (CAD) software that automatically counts the Ki67 cells for determining tumour grading. The purpose of developing this software is to alleviate pathologists’ workload associated with counting Ki67 cells and scoring the Ki67 index. The CAD software was developed through seven stages. Based on Pearson Correlation Coefficient results, there was a good positive correlation between the proposed technique with the manual counting technique in counting positive and negative Ki67 cells with a correlation of 0.99 and 0.72 respectively. The proposed CAD system also showed promising results in computing the Ki67 labeling index with a low percentage absolute error of 1.85%.