Reviewerly/exHarmony huggingface dataset:
Challenges in Generating Juice Effects for Automatically Designed Games

Automated game design research is usually most concerned with the mechanics and systems of a game, while aesthetics and effects are left to a minimum, if they are considered at all. In this project we integrate 'Squeezer', a tool for generating visual and audio effects (sometimes called 'juice') for games, with 'Puck', an automated game designer. The resulting hybrid system can design games and then generate appropriate sets of effects, making it the first automated game design system that directly engages with 'juiciness' in design. We support this work with a user study, measuring player responses to games with simple animations, effects automatically designed and arranged by Puck, and effects designed and arranged by an expert human designer. We then dissect the engineering challenges presented by integrating the two systems, and the new research questions raised by applying juice through automated game design systems.
Technology Integration Traditions, Transitions, and Best Practices in Philippine Higher STEAM Education

The literature’s focus on technology integration (henceforth as TI) in the country has been noteworthy and extensive in the use of ICT in the teaching and learning (TL) process. However, studies on TI’s aspects such as accessibility, teacher training, tools and equipment, and digital literacy remain underexplored. Through a five-instrument classroom observation protocol, this study explores the traditions, transitions, and best practices in TI of 85 tertiary teachers of STEAM (science, technology, engineering, agri-fisheries, mathematics) disciplines in the Philippines. The findings indicated that TI practices are clustered as conventional, web/ software-based, and electronic/computer-based. For instance, the majority of the teachers prefer conventional technology and practice a low level of engagement to web and learning applications in the context of pedagogy and content/discipline, and learners. Specifically, the transition to advocating higher engagement to technology and blending such to pedagogy and content are evident in science, mathematics, engineering, and technology. The analysis also revealed that TI practices, which exhibit fusion of the technological pedagogical content knowledge (TPCK) system also matched the Philippine Professional Standards for Teachers (PPST) domains specific to pedagogy and content, assessment and reporting, and diversity of learners and learning environment. The study further showed that the best practices of TI in terms of eight teacher technological characters emphasize their sustainability literacy skills such as future thinking, values thinking, systemic thinking, and strategic thinking. Correspondingly, Philippine universities and colleges may explore professional development programs for STEAM teachers in preparation for 4IR (fourth industrial revolution).
Specific Emitter Identification of Frequency Hopping Signals Based on Feature Extraction and Deep Residual Network

In the modern war with complex and changeable electromagnetic environment, Specific Emitter Identification (SEI) is an important and difficult problem, which is of great significance in obtaining intelligence information, identifying ourselves or foe, and specifying combat plans. In order to solve the problems of low accuracy, poor robustness and difficulty in extracting effective individual features of frequency hopping (FH) radio set, this paper studies the generation of PSK signal constellation to proposes a radio frequency fingerprint(RFF) based on constellation. We also improved the existing RFF extraction method, combined with the deep learning recognition method based on the Deep Residual Network (ResNet), to achieve effective feature fusion. By comparing different input methods, we found that the recognition accuracy is improved, and reaches 96.16% in the outfield experiments of 15 FH radio sets. In addition, we also designed a ResNet structure to compare the recognition accuracy under different signal-to-noise ratio(SNR), different network structure, different number of individuals, different modulation methods and different recognition algorithms, which proved the superiority of our method.
Learning Model During Post Covid-19 Pandemic to Improve Early Childhood Language Development

Bold learning during Covid-19 causes a decrease in the quality of linguistic abilities in children because teachers cannot convey material contextually. This study aimed to analyze learning models that can be used to develop post-pandemic early childhood language skills or through face-to-face learning. This type of research is qualitative research, in which the study is carried out to understand events regarding what happened to a research subject. This phenomenological research approach seeks to let reality naturally in the research place at Nyai Ahmad Dahlan Kindergarten, Yogyakarta. The study results indicate a learning model to develop early childhood language by departing from changes in early childhood behavior in learning. Behavioral changes that lead to learning with an activity-based learning approach and multi-media-multi-method are the solutions offered in this study to improve children's language development. It contributes to the knowledge of the learning model in the post-Covid-19 pandemic toward early childhood language development.
How International Aid Significantly Influence Ghana’s Trade Balance

Purpose: The study's goal was to ascertain the impact of foreign aid on Ghana's trade balance, the impact of the country's exchange rate on trade balance, and the causal relationship between foreign aid and trade balance in Ghana. &#x0D; Methodology: Based on vector autoregressive (VAR) methods, descriptive and econometric analysis of time series data from 1982 to 2012 were conducted for the study. The existence of a link between foreign assistance and trade balance was examined using cointegration and causality tests. To ascertain how quickly economic equilibrium will shift from the short run to the long run, the Vector Error Correction Model (VECM) was used.&#x0D; Findings: The analysis found that the exchange rate has a favorable impact on Ghana's trade balance and that foreign aid has a negative impact on it. The study also showed a long-term connection between trade balance, exchange rate, and foreign aid as well as a bidirectional causation between foreign aid and Ghana's trade balance. According to the test for VECM, the transition from short-run to long-run equilibrium happened more quickly, at 1.43 or 143 percent.&#x0D; Recommendation: The study recommended the Republic of Ghana to undertake an import substitution program in order to decrease the volume of imports that have been contributing to the long-term trade imbalance. Instead of depending on unreliable foreign aid, export promotion policy should be a priority in order to generate sufficient foreign revenues. To accomplish these goals, the Ghanaian government should be intelligently prepared to remove all trade restrictions that Ghanaian exporters faced on the international market.
Role of KDD in quality of experience driven detection in wireless network

Wireless Network is operator based, QoE Design focus on improving the use of networks and provides and propose a variety of network resource simulation strategies based on the data collected during the surveillance and measuring procedure to maximize QoE. The analysis suggests how the corrupt and inefficient eNodeBs serve can be effectively uncovered using algorithms in a heterogeneous network created with an NS-3 simulation model to forecast user QoE. In future high-density and environmentally friendly wireless services which should be self-organizational and personality, this framework can play a key role.The configuration parameters for the Simulation Analysis included User Numbers, eNodeB Number, eNodeB Broadband, Convey useable eNB power, transmit disorganized eNB power and software tool. NetAnim Set up, node priority - node id, node position (Node X and Y), Node color - Red, Green, Blue, Alpha, Node size, node resolution, IPv4 Address, Mac Addresses. Access point between that firewall and connected with database, server1, server2, etc., Node 3 - mobility model (ns3)L constant position mobility model, network device- type ns3 loopback net device, IPv4 Address and MAC Address, Mobile Netdevice 1: tpe ns3, IPv4 address and MAC Address.
A developed cryptographic model based on AES cryptosystem

AES as a block cipher considers as one of the most important security applications that provides confidentiality through encryption. However, AES cryptosystem is under analyzing by cryptanalysts using the computing technologies that have rapidly progressed. Therefore, the possibility of breaking AES algorithm might be accelerated in anytime. Consequently, this paper improves the AES security against a possible future attack. It proposes a model that has the ability to encrypt data with block size 256-bit and a key size of 256-bits. In addition, it also uses S-box of the serpent algorithm to be replaced the original S-box of the AES cryptosystem. The implementation of the proposed idea shows that the time requirements is acceptable even if the shifting algorithm is applied in two directions on row and column. Moreover, the randomness of the modified algorithm remains high.
Spanning Tree Protocol (STP) Based Computer Network Performance Analysis on BPDU Config Attacks and Take Over Root Bridge Using the Linear Regression Method

Spanning Tree Protocol (STP) is used in manageable switch devices that apply more than one path to the connection between switches. This study aims to assist engineer staff in improving STP network security. Furthermore, the benefit is to improve the STP network security system by using the Spanning Tree Protocol and Virtual Local Area Network (VLAN) trunking mitigation techniques. The results of testing data before STP is attacked, after STP attacked, and anticipatory data. Then a simple linear regression analysis is carried out by the result of that there is not a significant relationship between time and size in the DoS attack, which 48.6% of the time variable is influenced by the size of variable, while the remaining 51.4% by other variables. Root attack is 43.8%-time variable is influenced by size variable, the remaining 56.2% by other variables. Correlation between Karl Pearson DoS and root, there is a significant relationship between time and size, with the DoS correlation coefficient (-0.697) in contrast root (-0.662), and paired sample t-test (paired sample t-test) can be concluded the anticipation which is done by using BPDU guard and root guard mitigation.
Web-Based Employee Attendance System Development Using Waterfall Method

Employees are one of the important elements in carrying out the duties and functions of the Pemalang Regency Communication and Information Department (Diskominfo). However, the management of employee data for recording the attendance of Non Pegawai Negeri Sipil (Non PNS) employees at Diskominfo Pemalang Regency is still done manually so it is not effective and efficient. Therefore, it is necessary to create an Employee Presence System that can manage employee attendance data. The goal is to facilitate the process of managing employee attendance data and make attendance data more accurate. The data collection method used in making the system is an interview. In the interview, consultations and direct questions and answers were conducted with the field supervisor regarding the system requirements to be made. Making this system using the waterfall method. The waterfall method is carried out sequentially from one phase to another. There are five phases in the waterfall method, namely requirements analysis, system design, implementation, system testing, and operation and maintenance. From making a system with this method, an employee attendance system is produced that can manage employee attendance data at the Communication and Information Department of Pemalang Regency.
Implementación de la Educación Remota de Emergencia. El caso de la Escuela Intercultural de la Universidad Nacional Intercultural de la Selva Central Juan Santos Atahualpa en pandemia

The Covid-19 health emergency at the international level led to the interruption of face-to-face classes, since then theintercultural public university has faced various challenges in mediating the continuation of the educational servicein the application of ‘Emergency Remote Education’ (ERE) . In this sense, the article highlights the bibliographicreview around the state of the matter, the implementation of the ERE from the higher university regulations ina pandemic, the characteristics of the phenomenon, its phases, as well as the implications in the ProfessionalSchool of Bilingual Intercultural Education. of the Juan Santos Atahualpa National Intercultural University of theCentral Jungle (UNISCJSA) during the semesters 2020-1 and 2020-2. The method used is qualitative case studyimmersion and documentary analysis. Among the main conclusions we can affirm that the ERE that was given inthe UNISCJSA was in the modality of Remote Urgent Not Adapted and that this type of teaching was due to theabsence of institutional resources not provided to teachers and the meager expertise in technological management.of the teaching staff which were exhibited in the development of their classes.
Ground-Vehicle Relative Position Estimation with UWB Ranges and a Vehicle Dynamics Model

An accurate relative position vector (RPV) solution between local vehicles is critical in numerous autonomous vehicle applications from cooperative cruise control (CACC) to commercial truck platooning. Relative GPS solutions can deliver sufficient accuracy for such applications, but resiliency and availability can suffer in cluttered environments; thus, relative position solutions independent of GPS are necessary. Ultra-wideband (UWB) radios can provide accurate and robust range, and have gathered attention in recent decades for use in navigation. Prior work has shown that relative position estimation of dynamic vehicles is possible without a GPS reference through UWB-ranging and a constant-velocity Extended Kalman Filter (EKF). Through a kinematic bicycle model, this work modifies the state vector to include direct estimates of the lead vehicle's longitudinal speed and effective steer angle. This improves estimation quality and robustness in the presence of UWB measurement errors, unfavorable relative geometry, and dynamic maneuvers. The algorithm has been compared to previous methods in simulation and experimental testing environments and exhibits both superior accuracy as well as improved robustness during periods of difficult relative geometry.
Geographical landmark recognition: Using CNN-Based approach to improve accuracy

Landmarks in a digital image are the visibly distinguishable objects that can be properly classified by human brain. In the field of computer vision, landmark recognition is an important topic since it involves the basic ability of machines to identify objects in an image. Training a machine to identify all the things in an image will involve training it on the most basic landmarks first. But landmark recognition is not an easy task to accomplish. The widely available datasets in the field of landmark recognition are not only very large to adversely impact the training, but they are also highly noisy and complex. Even the best of pictures clicked tend to contain a lot of noise in the processed data. To tackle the problem, we trained a Convolutional Neural Network (CNN) on a publicly available geographical landmarks dataset and found that both, training and test accuracies of CNNs (98% and 73% respectively),were higher than the classical machine learning techniques. Also, it was seen that overfitting of models was lesser in CNNs.
Structure and Texture Preserving Network for Real-World Image Super-Resolution

Real-world image super-resolution (Real-SR) is a challenging task due to the unknown complex image degradation. Recent research on Real-SR has achieved remarkable progress by degradation process modeling; however, there are still undesired structural distortions and over-smoothed textures in the recovered images. In this letter, we propose a structure and texture preserving network, towards reducing the structural distortions while refining the perceptual-pleasant textures. Specifically, we propose a structure tensor (ST) branch to guide the restoration of high-resolution images by extracting channel-aggregated structural information. To further adaptively optimize different local texture, we replace the global discriminator with a global-local discriminator. By “local,” we mean that the discriminator loss, imposed on local areas randomly selected from the generated SR image, is minimized to generate textures with great visual perception in the selected local areas. Experimental results on five real-world datasets demonstrate the superiority of our methods in restoring structures, generating visually realistic SR images, as well as handling images of different degradation levels.
Existential execution and professional motivation in subjects of helping professions

Рецензируемые научные журналы МГОУ. Журналы МГОУ входящие в перечень ВАК: География, История, Международные отношения, Политология, Лингвистика, Педагогика, Психология, Филология, Физика-математика, Философия, Экономика, Юриспруденция
Mapping Synthesis for Hyperproperties

In system design, high-level system models typically need to be mapped to an execution platform (e.g., hardware, environment, compiler, etc). The platform may naturally strengthen some constraints or weaken some others, but it is expected that the low-level implementation on the platform should preserve all the functional and extra-functional properties of the model, including the ones for information-flow security. It is, however, well known that simple notions of refinement do not preserve information-flow security properties. In this paper, we propose a novel automated mapping synthesis approach that preserves hyperproperties expressed in the temporal logic HyperLTL. The significance of our technique is that it can handle formulas with quantifier alternations, which is typically the source of difficulty in refinement for information-flow security policies. We reduce the mapping synthesis problem to HyperLTL model checking and leverage recent efforts in bounded model checking for hyperproperties. We demonstrate how mapping synthesis can be used in various applications, including enforcing non-interference and automating secrecy-preserving refinement mapping. We also evaluate our approach using the battleship game and password validation use cases.
Systematic Mapping of Testing Smart Contracts for Blockchain Applications

In the last few years, the technological future becoming apparent by the introduction of smart contracts into mainstream technology, specifically in the development of Web3 and the metaverse. Smart contracts will play a vital role in the decentralization and autonomy of the day-to-day tasks that must be completed. Several literature reviews, considered secondary sources, highlight the current state of testing methods for smart contracts made for Blockchain applications. In this paper, we present the results from a systematic mapping study to give structure to the information found from primary sources. Systematic mapping is a well-known method to identify and categorize research papers in a field with an increasing amount of literature. For this systematic mapping, we searched for studies between 2017 and present-day (March 2022) and were able to find 303 results, from which 47 were selected, by specific inclusion and exclusion criteria, to be relevant to this study. A concept map was created from the information gathered from primary sources to the attributes such as research type, contribution type, blockchain network, smart contract language, development process, testing methods, and testing environment. We also categorized the trends and demographics found in the selected papers based on publication year, author’s country, and more. The results of this systematic mapping showed that this field is very new and quickly increasing with new research. The researchers that are interested in this field could use the results found to create opportunities for their future work.
FedGAN: A Federated Semi-supervised Learning from Non-IID Data

Federated Learning (FL) lately has shown much promise in improving sharing model and preserving data privacy. However, these existing methods are only of limited utility in the Internet of Things (IoT) scenarios, as they either heavily depend on high-quality labeled data or only perform well under idealized distribution conditions, which typically cannot be found in practical applications. In this work, we propose FedGAN, a Generative Adversarial Network (GAN) based federated learning method for semi-supervised image classification. In IoT scenarios, a big challenge is that decentralized data among multiple clients are normally non-independent and identically distributed (non-IID), leading to performance degradation. To address this issue, we further propose a dynamic aggregation mechanism that can adaptively adjust client weights in aggregation. Extensive experiments on three benchmarks demonstrate that FedGAN outperforms related federated semi-supervised learning methods, including a 55.36% accuracy on CIFAR-10 with 2k labels and 70.65% accuracy on SVHN with 1k labels - just 100 labels per class. Moreover, we carry out an extensive ablation and robust study to tease apart the experimental factors that are important to FedGAN’s improvement.
Knowledge Distillation of Russian Language Models with Reduction of Vocabulary

Today, transformer language models serve as a core component for majority of natural language processing tasks. Industrial application of such models requires minimization of computation time and memory footprint. Knowledge distillation is one of approaches to address this goal. Existing methods in this field are mainly focused on reducing the number of layers or dimension of embeddings/hidden representations. Alternative option is to reduce the number of tokens in vocabulary and therefore the embeddings matrix of the student model. The main problem with vocabulary minimization is mismatch between input sequences and output class distributions of a teacher and a student models. As a result, it is impossible to directly apply KL-based knowledge distillation. We propose two simple yet effective alignment techniques to make knowledge distillation to the students with reduced vocabulary. Evaluation of distilled models on a number of common benchmarks for Russian such as Russian SuperGLUE, SberQuAD, RuSentiment, ParaPhaser, Collection-3 demonstrated that our techniques allow to achieve compression from 17× to 49×, while maintaining quality of 1.7× compressed student with the full-sized vocabulary, but reduced number of Transformer layers only. We make our code and distilled models available.
PENERAPAN TEKNOLOGI SMART FARMING BERBASIS INTERNET OF THINGS BAGI MASYARAKAT PETANI JERUK SIAM

South Kalimantan Province is one of the areas of agricultural land suitable for Siamese citrus. But several problems can affect the decline in Siam Banjar citrus production and must be prevented immediately so that production rates remain stable and can continue to increase, one of which is farmers in Indonesia who still depend on the rainy season to grow crops and will experience drought problems during the dry season. Come. That's why, along with the development of current technology, one of which is in the agricultural sector, with the help of Internet of Things (IoT) technology, a tool is made, namely automatic watering using the Fuzzy Logic Control method. There are several advantages when using the help of the Internet of Things (IoT), such as water that will be real-time, comprehensive water intake, and following the state of the plant at that time. It is hoped that with the help of this device, Siamese orange farmers can get better harvests in the future