Reviewerly/exHarmony huggingface dataset:
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks

Anomaly detection is a critical task that involves the identification of data points that deviate from a predefined pattern, useful for fraud detection and related activities. Various techniques are employed for anomaly detection, but recent research indicates that deep learning methods, with their ability to discern intricate data patterns, are well-suited for this task. This study explores the use of Generative Adversarial Networks (GANs) for anomaly detection in power generation plants. The dataset used in this investigation comprises fuel consumption records obtained from power generation plants operated by a telecommunications company. The data was initially collected to identify irregularities in the fuel consumption patterns of the generating sets located at the company's base stations. The dataset was subsequently divided into two categories: anomalous and normal data points, based on specific variables. Approximately 64.88% of the data was classified as normal, while 35.12% was classified as anomalous. An examination of feature importance, using the random forest classifier, revealed that the variable “Running Time Per Day” exhibited the highest relative importance. To enhance performance, a Generative Adversarial Network (GANs) model was trained and fine-tuned, both with and without data augmentation, in order to increase the dataset's size. The generator model consisted of five dense layers with the tanh activation function, while the discriminator included six dense layers, each incorporating a dropout layer to prevent overfitting. Following data augmentation, the model achieved an accuracy rate of 98.99%, in contrast to 66.45% without augmentation. This demonstrates that the model almost perfectly classified data points into normal and anomalous categories, with the augmented data significantly improving the GANs' performance in anomaly detection.
An Incoherent Simultaneous Codeword Optimization Algorithm for Dictionary Learning

In recent years., dictionary learning has attracted great interest in the field of sparse representation. To improve the sparse representation performance of signals., this paper proposes an incoherent dictionary learning algorithm based on the simulta-neous codeword optimization (SimCO) algorithm. The proposed algorithm is achieved by embedding a coherence penalty for the framework of the SimCO algorithm to avoid performance degradation caused by similar atoms appearing in the learned dictionary. Simulation results show that the proposed algorithm has a competitive sparse representation performance compared to existing algorithms.
From Base to Conversational: Japanese Instruction Dataset and Tuning Large Language Models

Instruction tuning is essential for large language models (LLMs) to become interactive. While many instruction tuning datasets exist in English, there is a noticeable lack in other languages. Also, their effectiveness has not been well verified in non-English languages. We construct a Japanese instruction dataset by expanding and filtering existing datasets and apply the dataset to a Japanese pre-trained base model. We performed Low-Rank Adaptation (LoRA) tuning on both Japanese and English existing models using our instruction dataset. We evaluated these models from both quantitative and qualitative perspectives. As a result, the effectiveness of Japanese instruction datasets is confirmed. The results also indicate that even with relatively small LLMs, performances in downstream tasks would be improved through instruction tuning. Our instruction dataset, tuned models, and implementation are publicly available online.
A Control Room Monitored Software Engineering Robot Working Fully Autonomously at High Speed

The author describes an already productive robot that is responsible for the complete anonymization of the data of around five million people. The scientific community considers the complete anonymization of data as very complex. Principally, data anonymization is not difficult, but the complete anonymization of huge amount of data is hard. In this project, more than 1000 clerks continuously change large databases. Therefore, the clerks can enter at any time sensitive data into database fields that an anonymization tool does not consider yet. Software engineers often cannot recognize these changes timely and thus cannot guarantee complete anonymization. Therefore, the author created a robot that works in the project and successfully completes this task. To do this, the robot uses an expert system to identify and close the new gaps in the anonymization tool at high speed. The author had this new idea. Traditional expert systems advise people. So far, no one has suggested the development of an expert system as an advisor for a software engineering robot. With this new idea, the author has succeeded in developing the world's first robot of this type. The author's new idea allows the robot to maintain the anonymization tool completely autonomously.
Formation patterns of schoolchildren’s scientific linguistic world picture at Russian language lessons in the Russian Federation

The relevance of the research is due to the importance of forming a scientific linguistic picture of the world in the Russian language lessons as a cognitive component of the students’ linguistic worldview and to the need of developing linguistic-methodological foundations to achieve this aim. The aim of the study is to identify and describe the patterns of forming the scientific linguistic world picture in the process of teaching Russian in secondary schools. Theoretical analysis of scientific literature, observation, pedagogical experiment were used as scientific methods. The scientific publications on cultural linguistics, cognitive linguistics, theory and methodology of teaching the Russian language were the research materials. The definition of the concept “scientific linguistic picture of the world” is given in relation to the tasks of teaching the Russian language. The authors revealed that the formation of the scientific linguistic picture of the world is formed through learning new words and concepts as a result of observation, analysis and interpretation of language phenomena from linguistic positions during categorization and conceptualization primarily at the lexical level. The formation of scientific linguistic world picture is based on the knowledge, skills and abilities acquired at Russian language lessons and, to a certain extent, is set in the guidelines of the textbook authors. The main didactic means of forming the scientific linguistic world picture is the scientific linguistic text. The leading method of forming the scientific linguistic world picture is cognitive linguistic tasks, including comparative ones. Prospects for further study of the problem lie in developing a methodology for forming a scientific linguistic world picture at Russian language lessons on the basis of various sections of linguistics.
Improvement of Exemplar Based Inpainting by Enhancement of Patch Prior

Image Inpainting is the technique of filling out a photo with missing details. The purpose of inpainting is to visualize the realistic reconstruction of lost areas in a way that looks to the human eye natural. We present a novel algorithm in this paper that strengthens, enlarges, and fasters to the previous approach algorithm. A post-processing of the inpainted image is done by enhancement to get better quality of the picture. An exemplar-based approach for image inpainting is applied where Pixel values are copied from the source region and pasted to the target region after the best matching patch for the target region in the picture has been found. The choices of various patch matching criteria, patch size, and filling sequence significantly impact the outcomes. We dynamically choose the source and destination patch sizes. To preserve the consistency of the structures and textures, we search for several source patches globally using the angle-aware rotation technique rather than only choosing one. We have easily produced high-quality results. Our solution. can be prevented somewhat and restoration of the effect of improved inpainting process
Investigating the Efficacy of a Newly Proposed Activation Function on Deep Neural Networks

The use of an AF is crucial in DNNs (DNNs) since it serves to incorporate non-linearity into the model. Although numerous AFs have been put forth in the literature, a more potent AF is still required in order to enhance DNN performance. In this research, we suggest a brand-new AF termed “CustomMade” and assess its effectiveness on DNNs. The Tanh function, which is frequently used in DNNs, has been changed to become the CustomMade function. On the CIFAR-10 dataset, we assess the CustomMade function's performance using cutting-edge DNN architectures like ResNet. Our tests demonstrate that, in terms of accuracy and convergence time, the CustomMade function surpasses both cutting-edge AFs and other frequently employed AFs, such as Leaky ReLU and ELU. Additionally, we perform ablation studies to examine the effects of various CustomMade function hyperparameters on DNN performance. Our findings demonstrate that the CustomMade function may deliver consistent performance across various architectures and datasets while being resilient to hyperparameter modification. Overall, our results point to the CustomMade function as a potential replacement for the often employed AFs in DNNs, and they also show that it may result in improved performance and quicker convergence,
Comparative Study of Implementation of Very Deep Super Resolution Neural Network and Bicubic Interpolation for Single Image Super Resolution Quality Enhancement

To improve the resolution and quality of a low level image, single image super resolution (SISR) is a challenging endeavor in the turf of computer vision. It is essential to many applications, including image editing, security systems, medical imaging, and others. In order to increase the spatial quality of a low level image, SISR attempts to approximate the high frequency details that are missing from the image. The field was formerly based on manually created features and interpolation techniques, but current developments in deep learning have completely changed it. The suggested work compares the effectiveness of bicubic interpolation and neural network based very deep super resolution for single image super resolution image quality improvement. Utilizing blind and complete reference picture quality measures, the effectiveness of the two approaches is evaluated. The effectiveness of the very deep super resolution for single image is clearly demonstrated by the experimental results.
Volumetric Kombat: a Case Study on Developing a VR Game with Volumetric Video

This paper presents a case study on the development of a Virtual Reality (VR) game using Volumetric Video (VV) for character animation. We delve into the potential of VV, a technology that fuses video and depth sensor data, which has progressively matured since its initial introduction in 1995. Despite its potential to deliver unmatched realism and dynamic 4D sequences, VV applications are predominantly used in non-interactive scenarios. We explore the barriers to entry such as high costs associated with large-scale VV capture systems and the lack of tools optimized for VV in modern game engines. By actively using VV to develop a VR game, we examine and overcome these constraints developing a set of tools that address these challenges. Drawing lessons from past games, we propose an open-source data processing workflow for future VV games. This case study provides insights into the opportunities and challenges of VV in game development and contributes towards making VV more accessible for creators and researchers.
Digital Preservation of Experiences in Virtual Worlds: Using Metaverse Worlds to Preserve VR Research Worlds

Over 20 years after Second Life's debut, marking the first metaverse concept, the term has evolved beyond a buzzword. Many virtual worlds now have mature APIs for various applications, imbuing the term with real meaning as more than just a marketing term for online virtual worlds. During this same period, the use of 3D worlds for academic research has become much more prevalent. These worlds tend to have very ethereal existences, exploring one area of human-computer interaction and not engineered in any way for longevity due to being built. This paper is part of a trilogy of short papers exploring this topic, aimed at different audiences. This middle paper is aimed at game developers and academics to start a discussion of how it could be possible to preserve research within metaverse platforms such as VRChat or built from the ground up for these platforms. Metaverse platforms, of course, can be just as ethereal, but in the future, if such platforms became mainstream, they would potentially still offer a longer period of accessibility. The paper reports on the recreation of a VR experience designed to explore the effect of immersive experiences on changing people's opinions on climate change, and a second VR experience was written just for this paper to explore the conversion process starting with an application. These examples are used to illustrate the process, and a performance test is conducted to show the impact of using the VRChat API, which from these tests, demonstrates it is not a significant bottleneck in terms of performance, could limit some UI interactions options for the researcher, and ultimately does create applications that could be easier to share with the community.
Factors Affecting Software Quality Improvements in South African Software Development Environments

So far, producing quality software is a persistent challenge facing the software industry and research. The continued reporting of failed software and the associated costs are typical indicators of software quality challenges. Research focus on software quality improvement has been on the technical aspects, including processes, standards, methods, tools and techniques, amongst others, with less emphasis on human-related factors. To understand some of the human factors affecting software quality, we have surveyed 103 practitioners in South Africa on testing skills, time allocated, reliance on external resources, requirements evolution and use of technology in quality activities. Practitioners reported to have sufficient personnel and skills for testing software and rely less on external testers. However, as in previous research, lack of time and evolving requirements remain a challenge. The use of technology is considered prevalent in software development environments. The study findings show that sufficient human capabilities and the use of technology can improve attention to quality, whilst time limitations and evolving requirements are a challenge to giving the required attention to software quality.
Medical Image Processing Using CNN

Malaria detection takes time. The sole approach that provides confirmation is blood sample analysis. A variety of computational techniques are currently being used to speed it up. Our proposed methodology employs the usage of image enhancement filters along with the Convolutional Neural Network (CNN) idea to reduce the time complexity of malaria identification. The standard model employs several deep-learning approaches while confirming stability on the same dataset. The model employs a self-built CNN architecture with multiple filters. The Gaussian filter, along with our dataset, produced the best accuracy of 98.13%. While the model with no filter, achieves 95% accuracy.
VR Rhythm Game Featuring Audience Participation

This study presents a virtual reality (VR) game with a mechanic that enables audience participation. It has been previously shown that VR games offer better immersion, as well as improved enjoyment for the player. By introducing audience participation we aim to investigate the extent to which an audience becomes emotionally invested while observing and providing support to a VR game player. To achieve that, a preliminary experiment was conducted to measure the enjoyment of the player and the audiences, and to evaluate social interaction among the audience members. The results show the potential of the audience participation mechanic in a VR game for the enjoyment of the player and the audiences. In addition, the results show that the proposed mechanic effectively promotes the social interactivity among the audience members.
3DK-Reate: Create Your Own 3D Key for Distributed Authentication in the Metaverse

The Metaverse is becoming a social and economic ecosystem in which users can meet each other, exchange personal data, and make transactions. In this respect, authentication can be easily forecasted to be one of the key aspects for the development of social, immersive Virtual Reality (VR) environments. Notwithstanding, there is still a lack of techniques that can authenticate the users while protecting their sensitive data and leveraging the intrinsic characteristics of VR experiences. In this scenario, the present paper introduces a novel authentication schema based on asymmetric cryptography. More specifically, the proposed approach supports the creation of breakout rooms in which only mutually authenticated users can access. Due to the adoption of the asymmetric cryptography schema, the users are requested to create and insert their personal and secret private keys. To this aim, the current paper proposes and investigates four different interfaces for creating and inserting the keys, leveraging the three-dimensionality of the immersive environment.
A Framework For Presence Maintenance: Evoking Synesthesia and Empathy in Cinematic Virtual Reality

As VR films have evolved from traditional 360-degree films into 6DoF cinematic virtual reality (CVR) storytelling experiences, CVR authors are now striving to pursue engaging content with a narrative arc that elevates emotional engagement. However, in most CVR content, the audience's positioning has not been properly addressed, reducing them to invisible observers and resulting in a failure to maintain presence throughout the movie. Inspired by Shin's two-tiered process of immersion model, this paper presents a presence maintenance framework in the production of the VR cinema The Midnight Rendezvous. The framework incorporates elements of mise-en-scene and the mechanism of synesthesia to accentuate narrative and technical immersion, leverages the viewer's head movements to evoke proprioceptive involvement, and arouse emotional responses toward the protagonists. We believe that if a viewer is able to vicariously engage in the character's role, their feelings of presence can be sustained, which is the point we'll verify in future experiments.
Student Motivation in Augmented Reality-Enhanced Gamified STEM Learning Settings

Against the background of the research-based assumption that student motivation is a key factor for successful teaching and learning processes, the following paper presents results from a study evaluating the role of student motivation in Augmented Reality (AR)-enhanced gamified STEM learning settings. In the study, an international sample of 1988 primary school students and 91 teachers used an AR-enhanced gamified STEM learning app (intervention group) or traditional teaching and learning approaches (control group). Student motivation and performance were assessed in a pre and post test design and teachers' opinions were recorded using online surveys. The results indicate that students and teachers in the intervention group considered the motivational impact of the AR-enhanced gamified app significantly higher, compared to the ratings for alternative media in the control group. The product-moment correlation between motivation and further variables, such as student attitudes and self-efficacy, showed significant, medium strong results which are comparably high in intervention and control group. Only in the intervention group, motivation and learning achievement were correlated. Students and teachers in the intervention group tended to agree on the motivational impact of the study phase while a correlation between the opinions of teachers and students in the control group could not be confirmed. Overall, the study provides evidence of the pedagogical usefulness of AR-enhanced gamified STEM learning applications in terms of supporting student motivation, based on findings from a large international sample.
The Data Systems Grammar: Self-designing Systems for the era of AI

Data systems are everywhere. A data system is a collection of data structures and algorithms working together to achieve complex data processing tasks. For example, with data systems that utilize the correct data structure design for the problem at hand, we can reduce the monthly bill of large-scale data applications on the cloud by hundreds of thousands of dollars. We can accelerate data science tasks by dramatically speeding up the computation of statistics over large amounts of data. We can train drastically more neural networks within a given time budget, improving accuracy. However, knowing the right data system design for any given scenario is a notoriously hard problem; there is a massive space of possible designs, while no single design is perfect across all data, queries, and hardware contexts. In addition, building a new system may take several years for any given (fixed) design. As a result modern data-driven applications incur massive cloud costs and development details.We will discuss our quest for the first principles of data system design. We will show that it is possible to reason about this massive design space. This allows us to automatically create self-designing data systems that can take drastically different shapes to optimize for the workload, hardware, and available cloud budget using a grammar for data systems. These shapes include data structure, algorithms, and overall system designs which are discovered automatically and do not (always) exist in the literature or industry, yet they can be more than 10x faster. We will show performance examples for up to 1000x faster NoSQL processing and up to 10x faster neural network training.
Factoid Question Answering System using Knowledge Graph

Here we discusses various topics related to natural language processing and question answering systems. It explains the difference between reading comprehension QA and Knowledge Graph Question Answering (KGQA) and highlights the importance of world knowledge for dialogue systems. The text then goes on to describe a simple question answering system and the limitations of such systems in handling complex questions. Finally, the concept of knowledge graphs is introduced and their role in NLP applications is discussed. Overall, the text provides an overview of key concepts and techniques used in question answering systems and their potential applications in various fields.
On Overcoming HPC Challenges of Trillion-Scale Real-World Graph Datasets

Progress in High-Performance Computing in general, and High-Performance Graph Processing in particular, is highly dependent on the availability of publicly-accessible, relevant, and realistic data sets. To ensure continuation of this progress, we (i) investigate and optimize the process of generating large sequence similarity graphs as an HPC challenge and (ii) demonstrate this process in creating MS-BioGraphs, a new family of publicly available real-world edge-weighted graph datasets with up to 2.5 trillion edges, that is, 6.6 times greater than the largest graph published recently. The largest graph is created by matching (i.e., all-toall similarity aligning) 1.7 billion protein sequences. The MSBioGraphs family includes also seven subgraphs with different sizes and direction types. We describe two main challenges we faced in generating large graph datasets and our solutions, that are, (i) optimizing data structures and algorithms for this multi-step process and (ii) WebGraph parallel compression technique. The datasets are available online on https://blogs.qub.ac.uk/ DIPSA/MS-BioGraphs.