Reviewerly/exHarmony huggingface dataset:
Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models

Novel architectures have recently improved generative image synthesis leading to excellent visual quality in various tasks. Of particular note is the field of ``AI-Art'', which has seen unprecedented growth with the emergence of powerful multimodal models such as CLIP. By combining speech and image synthesis models, so-called ``prompt-engineering'' has become established, in which carefully selected and composed sentences are used to achieve a certain visual style in the synthesized image. In this note, we present an alternative approach based on retrieval-augmented diffusion models (RDMs). In RDMs, a set of nearest neighbors is retrieved from an external database during training for each training instance, and the diffusion model is conditioned on these informative samples. During inference (sampling), we replace the retrieval database with a more specialized database that contains, for example, only images of a particular visual style. This provides a novel way to prompt a general trained model after training and thereby specify a particular visual style. As shown by our experiments, this approach is superior to specifying the visual style within the text prompt. We open-source code and model weights at https://github.com/CompVis/latent-diffusion .
Universal Adversarial Triggers for Attacking and Analyzing NLP

Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset. We propose a gradient-guided search over tokens which finds short trigger sequences (e.g., one word for classification and four words for language modeling) that successfully trigger the target prediction. For example, triggers cause SNLI entailment accuracy to drop from 89.94% to 0.55%, 72% of "why" questions in SQuAD to be answered "to kill american people", and the GPT-2 language model to spew racist output even when conditioned on non-racial contexts. Furthermore, although the triggers are optimized using white-box access to a specific model, they transfer to other models for all tasks we consider. Finally, since triggers are input-agnostic, they provide an analysis of global model behavior. For instance, they confirm that SNLI models exploit dataset biases and help to diagnose heuristics learned by reading comprehension models.
The Hadoop Security in Big Data A Technological Viewpoint and Analysis

At present, the world is going to become more digital.As every person using the internet today, a huge amount of data gets generated day-to-day.The data are very essential with respect to carrying out their day-to-day activities, and also helping the business management to achieve their aims and make the best decisions on the basis of the information extracted from them.The big data phenomenon is a direct consequence of the digitization and "datafication" of nearly every activity in public, private, and commercial life.Despite that, big data originated new matter related not only to the volume or the diversity of the data, but also to data security.There is need to endow security of such data.The Apache Hadoop platform is used to handle, store, manage, and distribute big data across many server nodes.Here are different tools, which research on the top of Apache Hadoop stack to provide security for data.In order to obtain a full perspective of the problem, we decided to carry out examine with the objective of existing security methods for Apache Hadoop security in big data.
Federated Learning for Emoji Prediction in a Mobile Keyboard

We show that a word-level recurrent neural network can predict emoji from text typed on a mobile keyboard. We demonstrate the usefulness of transfer learning for predicting emoji by pretraining the model using a language modeling task. We also propose mechanisms to trigger emoji and tune the diversity of candidates. The model is trained using a distributed on-device learning framework called federated learning. The federated model is shown to achieve better performance than a server-trained model. This work demonstrates the feasibility of using federated learning to train production-quality models for natural language understanding tasks while keeping users' data on their devices.
Racial Bias in Hate Speech and Abusive Language Detection Datasets

Technologies for abusive language detection are being developed and applied with little consideration of their potential biases. We examine racial bias in five different sets of Twitter data annotated for hate speech and abusive language. We train classifiers on these datasets and compare the predictions of these classifiers on tweets written in African-American English with those written in Standard American English. The results show evidence of systematic racial bias in all datasets, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at substantially higher rates. If these abusive language detection systems are used in the field they will therefore have a disproportionate negative impact on African-American social media users. Consequently, these systems may discriminate against the groups who are often the targets of the abuse we are trying to detect.
An enhanced YOLOv5 based on Color Harmony Algorithm for object detection in Unmanned Aerial Vehicle captured Images

Abstract Recent improvements in robotics and computer vision enable new camera-equipped drone applications. Aerial Object Detection (OD) is one. Despite recent advances, computer vision OD remains difficult. Due to UAVs' fast speed, different views, and fluctuating altitudes, objects in Unmanned Aerial Vehicle (UAV) photos are heterogeneous, fluctuate in size, and are dense, making OD challenging with existing algorithms. Existing object recognition algorithms perform worse on UAV images because OD in aerial images is more difficult than in ground-taken images. If a generic object detection technique is used to drone-captured images, its performance will be drastically degraded owing to the fact that varied surroundings with complicated background and the size of objects are at the core of this phenomena. In this work, an object detection model CHA-YOLOv5 is proposed. In the proposed system the detection module is optimized using the Color Harmony Algorithm, as it determines the prediction head from the available three prediction heads. CHA-YOLOv5 accurately predicts several bounding boxes per grid cell. The proposed model has been trained using several images from challenging contexts. The experimental results show that the objects are identified accurately using the proposed improved network model in this study. It achieved precision 98.675%, pecall 97.8023%, mAP 97.23%, F1 score 98.50%. Our system outperformed the YOLOV5, ResNet50, VGG19, and InceptionNet V3.
Development of Interactive E-Books containing Virtual Laboratory to Improve Students’ Motivation Learning

This study aims to analyze the characteristics, validity, and ability of Interactive E-Book media containing Virtual Laboratory in fostering learning motivation and to find out how the cognitive learning outcomes of students. The design of this research is Research and Development, using modified steps according to Sugiyono. Analysis of potential problems seen from the low motivation to learn of students, which is supported by the lack of innovation in learning media on global warming at SMP N 4 Semarang. In this study, an interactive learning media on global warming material was developed in the form of an E-Book with an application format so that it could be accessed flexibly via mobile phones. The Interactive E-Book was declared very valid with an average validation of 92.94% from media validators and 89.87% from material validators. Then a trial was conducted to determine the readability of the media. The results of the media readability research from students obtained an average of 88.92% with very good criteria. The ability of Interactive E-Books to increase students' learning motivation was measured using a learning motivation questionnaire, there was an increase in the high category. In addition, the increase in learning motivation is also supported by the cognitive learning, average pretest score was 51.78 while the posttest score was 85.56. Based on the description, it is concluded that the Interactive E-Book is valid to be used as a medium in learning activities and is able to improve students' learning motivation.
The Effect of Digital Material Preparation Training on Technological Pedagogical Content Knowledge Self-Confidence of Pre-service Social Studies Teachers

This study examines whether digital material preparation training provided to pre-service teachers’ using instructional technologies affects their levels of Technological Pedagogical Content Knowledge (TPACK) Self-Confidence. The research is conducted using the Embedded Design, one of the Mixed Methods Research Designs. Study group consists of 24 pre-service social studies teachers. Data is collected using TPACK Self-Confidence Scale (TPACK-SCS) and Semi-Structured Questionnaire. Pre-service teachers are trained on different Web 2.0 tools throughout the implementation phase and thereafter were asked to prepare different digital materials specific to social studies. Quantitative data collected were analyzed using descriptive and predictive statistics. Content analysis of qualitative data was performed using MaxQda 2020 qualitative data analysis software. Research findings revealed a statistically significant difference between pre-test and post-test scores of pre-service teachers’ TPACK self-confidence levels. Pre-service teachers’ fear of failure, presumed failure and reluctance for being a part of the process turned into a perception of success and self-efficacy following the training and their learned helplessness disappeared. Teachers should be supported with a practice-based training that will allow to integrate technological, pedagogical and content knowledge for developing TPACK, as a comprehensive answer to 21st century teaching competencies.
An Exploratory Content and Sentiment Analysis of The Guardian Metaverse Articles Using Leximancer and Natural Language Processing

Abstract The metaverse has become one of the most popular concepts of recent times. Companies and entrepreneurs are fiercely competing to invest and take part in this virtual world. Millions of people globally are anticipated to spend much of their time in the metaverse, regardless of their age, gender, ethnicity, or culture. There are few comprehensive studies on the positive/negative sentiment and effect of the newly identified, but not well defined, metaverse concept that is already fast evolving the digital landscape. Thereby, this study aimed to better understand the metaverse concept, by, firstly, identifying the positive and negative sentiment characteristics and, secondly, by revealing the associations between the metaverse concept and other related concepts. To do so, this study used Natural Language Processing (NLP) methods, specifically Artificial Intelligence (AI) with computational qualitative analysis. The data comprised metaverse articles from 2021–2022 published on The Guardian website, a key global mainstream media outlet. To perform thematic content analysis of the qualitative data, this research used the Leximancer software, and the The Natural Language Toolkit (NLTK) from NLP libraries were used to identify sentiment. Further, an AI-based Monkeylearn API was used to make sectoral classifications of the main topics that emerged in the Leximancer analysis. The key themes which emerged in the Leximancer analysis, included "metaverse", "Facebook", "games" and "platforms". The sentiment analysis revealed that of all articles published in the period of 2021–2022 about the metaverse, 61% (n = 622) were positive, 30% (n = 311) were negative, and 9% (n = 90) were neutral. Positive discourses about the metaverse were found to concern key innovations that the virtual experiences brought to users and companies with the support of the technological infrastructure of blockchain, algorithms, NFTs, led by the gaming world. Negative discourse was found to evidence various problems (misinformation, harmful content, algorithms, data, and equipment) that occur during the use of Facebook and other social media platforms, and that individuals encountered harm in the metaverse or that the metaverse produces new problems. Monkeylearn findings revealed “marketing/advertising/PR” role, “Recreational” business, “Science &amp; Technology” events as the key content topics. This study’s contribution is twofold: first, it showcases a novel way to triangulate qualitative data analysis of large unstructured textual data as a method in exploring the metaverse concept; and second, the study reveals the characteristics of the metaverse as a concept, as well as its association with other related concepts. Given that the topic of the metaverse is new, this is the first study, to our knowledge, to do both.
GANs for generation of synthetic ultrasound images from small datasets

Abstract The task of medical image classification is increasingly supported by algorithms. Deep learning methods like convolutional neural networks (CNNs) show superior performance in medical image analysis but need a high-quality training dataset with a large number of annotated samples. Particularly in the medical domain, the availability of such datasets is rare due to data privacy or the lack of data sharing practices among institutes. Generative adversarial networks (GANs) are able to generate high quality synthetic images. This work investigates the capabilities of different state-of-the-art GAN architectures in generating realistic breast ultrasound images if only a small amount of training data is available. In a second step, these synthetic images are used to augment the real ultrasound image dataset utilized for training CNNs. The training of both GANs and CNNs is conducted with systematically reduced dataset sizes. The GAN architectures are capable of generating realistic ultrasound images. GANs using data augmentation techniques outperform the baseline Style- GAN2 with respect to the Frechet Inception distance by up to 64.2%. CNN models trained with additional synthetic data outperform the baseline CNN model using only real data for training by up to 15.3% with respect to the F1 score, especially for datasets containing less than 100 images. As a conclusion, GANs can successfully be used to generate synthetic ultrasound images of high quality and diversity, improve classification performance of CNNs and thus provide a benefit to computer-aided diagnostics.
Amalgamation of 3-Dimensions in Education field using Augmented Reality Technology

Augmented reality (AR) is a method that overlays a user’s perspective of the real world with a computer-generated image. In order to build best AR applications that helped us to accept augmented reality related technologies and systems, one of the key foundations is to understand and determine the user’s expectations. This paper examines existing research on user expectations for augmented reality in education to assist in the future endeavors. AR in education can generate creative experiences and improve problem-solving skills to prepare pupils for the future. Gaming engines like Unity and Unreal are used to develop AR based digital applications. In the present paper, mechanism being AR applications and Unity 3D software is discussed. A digital application is also mentioned in the paper which is developed by exploring Unity 3D software. In addition, Unity 3D software is also explored and digital applications are developed using the same
A Novel Signature and Authentication Cryptosystem for Hyperspectral Image by Using Triangular Association Encryption Algorithm in Gyrator Domains

A novel optical signature and authentication cryptosystem is proposed by applying triangular association encryption algorithm (TAEA) and 3D Arnold transform in Gyrator domains. Firstly, a triangular association encryption algorithm (TAEA) is designed, which makes it possible to turn the diffusion of pixel values within bands into the diffusion within and between bands. Besides, the image signature function is considered and utilized in the proposed cryptosystem. Without the image signature, the original image cannot be restored even if all of the keys are obtained. Moreover, the image integrity authentication function is provided to prevent pixel values from being tampered with. Through the numerical simulation of various types of attacks, the effectiveness and capability of the proposed hyperspectral data signature and authentication cryptosystem is verified.
Seeing through Wavy Water–Air Interface: A Restoration Model for Instantaneous Images Distorted by Surface Waves

Imaging through a wavy water–air interface is challenging since light rays are bent by unknown amounts, leading to complex geometric distortions. Considering the restoration of instantaneous distorted images, this paper proposes an image recovery model via structured light projection. The algorithm is composed of two separate parts. In the first part, an algorithm for the determination of the instantaneous shape of the water surface via structured light projection is developed. Then, we synchronously recover the distorted airborne scene image through reverse ray tracing in the second part. The experimental results show that, compared with the state-of-the-art methods, the proposed method not only can overcome the influence of changes in natural illumination conditions for WAI reconstruction, but also can significantly reduce the distortion and achieve better performance.
Asymmetric double image encryption, compression and watermarking scheme based on orthogonal-triangular decomposition with column pivoting

A novel asymmetric scheme for double image encryption, compression and watermarking based on QR decomposition in the Fresnel domain has been presented. The QR decomposition provides a permutation matrix as a ciphertext and the product of orthogonal and triangular matrix as a key. The ciphertext obtained through this process is a sparse matrix that is compressed by the CSR method to give compressed encrypted data, which, when combined with a host image, gives a watermarked image. Thus, a cryptosystem that involves compression and watermarking is proposed. The proposed scheme is validated for grayscale images. To check the efficacy of the proposed scheme, histograms, statistical parameters, and key sensitivity are analyzed. The scheme is also tested against various attacks. Numerical simulations are performed to validate the security of the scheme.
Research on Normal Behavior Models for Status Monitoring and Fault Early Warning of Pitch Motors

Nowadays, pitch motors play an important role in many manufacturing plants. To ensure the other components run normally, it is urgent to automatically monitor the running state of pitch motors and early warning faults to avoid huge losses at a later period. Based on the normal behavior modeling technique, this paper studies the status monitoring of the pitch motors. Based on the fact that the state of the motor varies with time, we propose to train an echo state network with the SCADA data to predict the temperature of the pitch motor. Subsequently, the EWMA (exponentially weighted moving average) technique is used to set the alarm limit lines of each parameter. By employing some real data collected in a wind farm in China to conduct experiments, the results show that in comparison with several other methods, the proposed method can more effectively identify and early warn the faults of the pitch motor.
Analysis and Possibilities of Innovation of the Business Model Called Central Regulation Using Blockchain Technology

Purpose: This paper clearly describes the possibilities of digitising a purchasing group’s processes using blockchain technology. The main focus lies on its core business, financial service called central regulation and other services. Methodology/Approach: After reviewing the literature, the most prominent blockchain projects in recent years in large companies were depicted to identify successful adoptions. The processes of purchasing group were described with the extension of innovative blockchain technology. A summary of potential advantages is presented in a table. Findings: The conclusions could potentially be of a benefit to the management of purchasing groups as evidence suggests an increase in transparency in the whole process of the supply chain and, at the same time, bring effectiveness in payment processing. Combining permissioned and permissionless blockchain could be a viable solution for a purchasing group which uses a central payment system. Research Limitation/Implication: The main limitation is the impossibility of testing the usage of blockchain technology in real-life conditions on a full scale. However, a sample of processes could be modelled further to test and develop the improvements and measure various parameters. Originality/Value of paper: This approach is the first of its kind as there is no literature or publication on the usage of blockchain technology in the central regulation system. This paper can help with the decision-making process at the management level.
On the Road to Perfection? Evaluating Leela Chess Zero Against Endgame Tablebases

Board game research has pursued two distinct but linked objectives: solving games, and strong play using heuristics. In our case study in the game of chess, we analyze how current AlphaZero type architectures learn and play late chess endgames, for which perfect play tablebases are available. We study the open source program Leela Chess Zero in three and four piece chess endgames. We quantify the program’s move decision errors for both an intermediate and a strong version, and for both the raw policy network and the full MCTS-based player. We discuss a number of interesting types of errors by using examples, explain how they come about, and present evidence-based conjectures on the types of positions that still cause problems for these impressive engines.