Reviewerly/exHarmony huggingface dataset:
Development of nitrogen fertilization dose prediction on rice field based on leaf color chart

Nitrogen (N) is one of the main nutrients for plant growth, as a major component of chlorophyll and proteins closely related to leaf color, growth, and crop yield. The efficiency of nitrogen fertilization is needed to increase rice productivity. Adequacy of N nutrient can be observed in the leaf’s color. The use of Leaf Color Chart (LCC) is one way to analyze the need for nitrogen fertilizer in the rice field. This research proposed the development tool to determine the greenish scale of rice leaves automatically and to give advice on the appropriate and optimal dose of nitrogen fertilizer to the rice field. In this study, Arduino Uno is functioned as a Processing Unit and integrated with TCS3200 color sensor as an input component to detect the greenish level of rice leaf color. By using this tool, the prediction of the needs of nitrogen in the rice field can be determined more effectively, not depending on one’s perception of the leaf’s color scale or different light intensity. The measurements show that the system has an average scale misidentification of 2%, therefore this system can be used as an alternative to determining the dose of nitrogen fertilizer according to the conditions of the plants and the desired harvest target can be achieved optimally.
Occluded Video Instance Segmentation with Set Prediction Approach

Occluded Video Instance Segmentation (OVIS) is a multi-task problem performing detection, segmentation, and tracking simultaneously under severe occlusions. We propose an extended model for the OVIS task based on the real-time one-stage instance segmentation method. The proposed model was applied to the OVIS dataset hold by the ICCV 2021 - Occluded Video Instance Segmentation Workshop 2021. We also show that the occlusions can be handled efficiently through one-stage approaches.
Template for Building Manageable Data Mining Autonomous Process with RapidMiner Studio

This article describes architecture, configuration, features and application of a template for building simple autonomous process for data mining and analysis with RapidMiner Studio. The core idea is to provide a general mini-framework in a form of a template for making manageable file-based long running process, suitable for working in restricted environments, like hardened systems. The template is decomposing the whole process into a generic main process, which is available ready and few sub-processes that have to be prepared for concrete specific use cases.
FF-GAT: Feature Fusion Using Graph Attention Networks

Convolutional neural networks (CNNs) have accomplished magnificent performance on object classification tasks. This work introduces a novel image classification approach based on feature vector fusion of two CNN architectures using graph attention networks (GAT). In the proposed method we extract feature maps from shallow and deep layers of two CNN architectures. These extracted feature vectors are represented as nodes in a graph, and edges between nodes are constructed based on similarities between the feature vectors. The GAT is used to aggregate and fuse connected nodes based on their importance and relevance to the classification task. We believe that this approach compensates for convolution defects during feature processing when using a single CNN. This paper attempts to show that graph-based deep learning can be used to fuse two CNN architectures and not to push the state-of-the-art of image classification accuracy. Our experimental results prove that GAT can be used to fuse feature vectors resulting from CNN architectures.
Particle filter implemented as a hardware accelerator in Cortex-M core periphery

Particle filters are a group of filtering methods based on recursive Bayesian filters which can be used to estimate the state of an observed dynamical system. The main advantage of the particle filters when compared to other similar filtering methods is that it can handle a nonlinear system which includes non-Gaussian noise sources. A hardware accelerator represents a digital module which is specifically made to perform some function. The module is adapted exclusively to the designed function which the module executes more efficiently and faster than it is possible using a software implementation on a general-purpose processing core. This paper analyses a hardware accelerator which implements a particle filter used in the inertial measurement unit (IMU) measurements. The accelerator is used in periphery of a low power and low gate count Cortex-M1 core. The accelerator is evaluated with respect to the area utilization and execution performance and it is compared to a software implementation of an equivalent particle filter executed on the Cortex-M1 core. The results show that the hardware accelerator has a 30% smaller cell count than the accompanying Cortex-M1 core. The hardware implementation of the particle filter has a two-orders of magnitude shorter execution time than the reference software implementation running on the Cortex-M1 core.
VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answering

We present VQA-MHUG – a novel 49-participant dataset of multimodal human gaze on both images and questions during visual question answering (VQA) collected using a high-speed eye tracker. We use our dataset to analyze the similarity between human and neural attentive strategies learned by five state-of-the-art VQA models: Modular Co-Attention Network (MCAN) with either grid or region features, Pythia, Bilinear Attention Network (BAN), and the Multimodal Factorized Bilinear Pooling Network (MFB). While prior work has focused on studying the image modality, our analyses show – for the first time – that for all models, higher correlation with human attention on text is a significant predictor of VQA performance. This finding points at a potential for improving VQA performance and, at the same time, calls for further research on neural text attention mechanisms and their integration into architectures for vision and language tasks, including but potentially also beyond VQA.
Surviving Mars, Haemimont Games (2018)

Review of: Surviving Mars , Haemimont Games (2018) Stockholm: Paradox Interactive PC download, US$29.99
The Reform and Exploration of Intelligent PYTHON Language Teaching

with the continuous improvement of social productivity, various fields also develop rapidly. All levels of practitioners also need to improve their own comprehensive quality and ability. In the field of artificial intelligence and high-end computer industry, the Python programming language gradually shows its field advantages and is favored by professionals, thus accelerating the development of scientific research and high-tech products. Teachers or trainers should understand and teach PYTHON courses at a deeper level and reform PYTHON language teaching based on this. With the maturity of artificial intelligence technology and the continuous development of programming languages, Internet companies have an increasing demand for programming talents. Therefore, how to cultivate excellent PYTHON programmers has drawn close attention from all fields of society. In the era of intelligence, it is one of the main tasks of schools to train PYTHON programming talents to meet the needs of the society. The traditional PYTHON language teaching model can no longer meet the needs of society. This paper proposes to build a kind of intelligent online PYTHON teaching system, provide students with personalized teaching resources and systematic practical programming problems, and then cultivate students&#x2019; independent programming thinking, and train practical talents who master PYTHON programming technology for Internet enterprises. Promote the education with practice, not only can improve students&#x2019; learning enthusiasm and motivation, increase employment opportunities for students, at the same time satisfy the social demand for talents, thus promoting the schools&#x2019; curriculum reform.
A Low-Cost In-Line Encryption System for SCADA Applications

Encryption is an essential security mechanism to achieve data confidentiality. However, some organizations may choose not to encrypt their critical traffic due to the high cost or difficulties of setting up an encryption system between the various devices of their networks. This paper presents a system that provides a protected communication between devices with a reasonable cost and an easy-to-use interface. The system can be easily added in-line to communication links to minimize the disturbance to existing infrastructure. The system has a central management unit that is used to configure and update encryption devices that are placed at the endpoints of the communication links that must be secured. Each encryption device is a Linux-based mini-computer that encrypts the traffic sent by the end device and decrypts the incoming traffic. The proposed system had been successfully implemented and extensively tested to ensure that it works correctly.
Phishing as a Service Campaign using IDN Homograph Attack

The aim of this paper is to demonstrate the method for implementation of a Phishing as a Service (PhaaS) architecture and an internationalized domain name (IDN) homograph attack (using a similar top-level domain) in a real phishing campaign. The official campaign targeted students from "Transilvania" University of Brasov in an official phishing campaign approved by the information technology department, as part of a cybersecurity informative campaign. The paper describes the implemented platform, tools and methodologies used for checking the level of awareness for this type of attack, evaluated by the number of users that will submit their confidential data that could compromise one or more systems.
Good-Enough Example Extrapolation

This paper asks whether extrapolating the hidden space distribution of text examples from one class onto another is a valid inductive bias for data augmentation. To operationalize this question, I propose a simple data augmentation protocol called "good-enough example extrapolation" (GE3). GE3 is lightweight and has no hyperparameters. Applied to three text classification datasets for various data imbalance scenarios, GE3 improves performance more than upsampling and other hidden-space data augmentation methods.
A project review under the focus of ‘complexities’ on the example of exploreAT!

Abstract This article provides insights into dealing with complexities in the Digital Humanities project exploreAT!. By exploring a non-standard language collection for cultural insights, a three-fold approach is presented looking into concrete realizations and solutions of tackling challenges in terms of Open Innovation infrastructure, technology and the topic of choice, food. Methods and processes applied and developed in the project are aimed to serve as examples for future projects with similar data sets.
Comparing Head and AR Glasses Pose Estimation

In this paper, we compare AR glasses and head pose estimation performance. We train different pose estimation approaches for head pose estimation with the generated head pose labels to compare them to their AR glasses estimation accuracy. These include the state-of-art GlassPoseRN and P2P networks, as well as our novel CapsPose algorithm. We show that estimating the AR glasses pose is more accurate than the head pose in general. In a first analysis, we show the general regression performance of the models when the AR glasses and faces are both known to the network during training. We then analyze the driver generalization performance, where all glasses are known, but part of the drivers are unknown to the Neural Networks. There, the estimation of AR glasses pose again exceeds the head pose. Only in our third analysis, head pose estimation performs better than AR glasses pose estimation. In this case, a new glasses model is added, which was unknown to the Neural Network yet. In addition, we introduce a novel pose estimation network called CapsPose, which is the first network deploying Capsule Networks for 6-DoF pose estimation. We outperform the current state-of-the- art method GlassPoseRN on the HMDPose dataset by reducing the error by 46% for orientation and 51% for translation.
Using Synthetic Datasets to Hone Intuitions Within an Adaptive Learning Environment

Adaptive learning typically focuses on the challenge of adjusting the presentation of instructional material based on an automated assessment of real-time student performance. The goal is to move away from a one-size-fits-all approach to learning toward a more personalized experience. While we support this goal, in this paper we focus on a related but logically prior challenge, namely, the development of an adaptive environment that helps the instructional designer build better learning materials in the first place. Rather than focus on the automated assessment of student performance, we have instead focused on mining data generated by the instructional designers as they developed new course materials. Working on the assumption that the discovery of patterns in past design decisions will inform better future design decisions, we have applied an off-the-shelf machine learning technique to explore associations between learning contexts and the selection of specific learning activities. Although machine learning techniques have become commodities, practical guidance regarding their expected performance is harder to come by. We have filled this gap by systematically generating our own synthetic data sets that represent notional histories of user interactions to hone our own intuitions about the performance of the Bayesian network that underpins our adaptive environment. Our intent is not to add yet one more benchmark data set to promote Bayesian approaches but, rather, to describe by way of example a method for generating such data sets that will help the practitioner understand what an otherwise black box is doing.
Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning

Automatic modulation classification (AMC), whose main purpose is to recognize the received signal modulation mode under multi-signal environment and noise interference, provides the foundation for subsequent signal processing. In our paper, we propose a novel robust and real-time AMC implementation method based on deep learning (DL) and cognitive radio (CR), which can get faster recognition and higher accuracy. In this paper, it only needs 79.76us to recognize a set of data, and the accuracy is great than 90% at low signal-to-noise ratio.
Bag of Groups of Convolutional Features Model for Visual Object Recognition

Deep convolutional neural networks (CNNs) are only equivariant to translation. Recently, equivariant CNNs are proposed for the task of image classification which are not only equivariant to translation but also to other affine geometric transformations. Moreover, CNNs and equivariant CNNs require a large amount of labeled training data to generalize its parameters which also limit their application areas. We propose a bag of groups of convolutional features (BoGCFs) model for the CNNs and group-equivariant CNNs (G-CNNs)[1], which preserves the fundamental property of equivariance of G-CNNs and generate the global invariant features by dividing the convolutional feature maps of the deeper layers of the network into groups. The proposed model for CNNs and G-CNNs, referred as CNN-BoGCFs and G-CNN-BoGCFs, performs significantly high when trained on a small amount of labeled data for image classification. The proposed method is evaluated using rotated MNIST, SIMPLIcity and Oxford flower 17 datasets.
Exploring the Effect of Conversion on the Distribution of Inflectional Suffixes: A Multivariate Corpus Study

Abstract Lexical ambiguity in the English language is abundant. Word-class ambiguity is even inherently tied to the productive process of conversion. Most lexemes are rather flexible when it comes to word class, which is facilitated by the minimal morphology that English has preserved. This study takes a multivariate quantitative approach to examine potential patterns that arise in a lexicon where verb-noun and noun-verb conversion are pervasive. The distributions of three inflectional suffixes, verbal - s , nominal - s , and - ed are explored for their interaction with degrees of verb-noun conversion. In order to achieve that, the lexical dispersion, context-dependency, and lexical similarity between the inflected and bare forms were taken into consideration and controlled for in a Generalized Additive Models for Location, Scale and Shape (GAMLSS; Stasinopoulos, M. D., R. A. Rigby, and F. De Bastiani. 2018. “GAMLSS: A Distributional Regression Approach.” Statistical Modelling 18 (3–4): 248–73). The results of a series of zero-one-inflated beta models suggest that there is a clear “uncanny” valley of lexemes that show similar proportions of verbal and nominal uses. Such lexemes have a lower proportion of inflectional uses when textual dispersion and context-dependency are controlled for. Furthermore, as soon as there is some degree of conversion, the probability that a lexeme is always encountered without inflection sharply rises. Disambiguation by means of inflection is unlikely to play a uniform role depending on the inflectional distribution of a lexeme.
The Current State of Prescriptions and Potential Enhancements Using Blockchain

E-prescription is an essential need to address fallacies of paper-based prescription. E-prescription system could benefit from blockchain technology immensely. Blockchains' decentralized architecture promotes greater reach and economy of implementation. This paper attempt to understand the potential of blockchain in enhancing e-prescription systems and identify required improvements for a potential e-prescription design. In this paper, the existing issues of the prescription system are identified. A basic understanding of e-prescriptions and blockchain technology with relevant applications is provided. Recent works on blockchain-based e-prescriptions are analysed for identifying the required enhancements needed. A potential e-prescription system design is feasible based on the identified improvements mitigating existing prescription issues.
MNIST Image: Color to Sound Conversion and Classification Using SVM

Sound is a longitude wave. Light, transverse one. Frequency is the common term in between these two. Using the visible light spectrum and audible sound ranges, the color (light) in an image is converted into the sound for that specific frequency. MNIST dataset containing the handwritten images of digits from zero to nine is used for the research and those images are converted from image (light) to sound (audio). The image is mapped to piano musical audio notes. Then the audio is classified as belonging to disease category image or not, using SVM. The accuracy we got is nearly 83.35% using audio classification.
PHP Development of Tourism Culture Website under the background of Modern Computer Network Technology

The vigorous development of the Internet has created an era of intelligence. With the continuous promotion and innovation of information technology, the amount of information on the Internet is explosive growth. At present, there are a variety of text information on the Internet, including politics, economy, culture, life, geography and other aspects. This paper expounds the implementation process of tourism culture management platform from the aspects of development technology overview, demand analysis, system architecture design, system implementation, etc. The core module of the system, namely the advertising management module, is designed and implemented in detail, and shows the functions of plan creation, material creation, data report, directional content package management, etc.
Complex-InversE: Improving bilinear knowledge graph embeddings by mapping into complex space

Knowledge graph link prediction uses known fact links to infer the missing link information in the knowledge graph, which is of great significance to the completion of the knowledge graph. Generating low-dimensional embeddings of entities and relations which are used to make inferences is a popular way for such link prediction problems. This paper proposes a knowledge graph link prediction method called Complex-InversE in the complex space, which maps entities and relations into the complex space. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. The Complex-InversE effectively captures the antisymmetric relations and introduces Dropout and Early-Stopping technologies into deal with the problem of small numbers of relationships and entities, thus effectively alleviates the model’s overfitting. The results of comparison experiment on the public knowledge graph datasets show that the Complex-InversE achieves good results on multiple benchmark evaluation indicators and outperforms previous methods. Complex-InversE’s code is available on GitHub at https://github.com/ZeyuMiao97/Complex-InversE.
Covid-19 Related Topic Detection in Catering Industry with Online Review and Embedding

The catering industry is one of the industries most seriously affected by Covid-19, it is significant for us to understand the customers' concerns and find the epidemic prevention loopholes in the catering industry by analyzing the pandemic related topics through online reviews. However, topic detection of short texts such as online reviews has always been a difficult problem in this field. In addition, the interference of background topics related to diet makes this study more difficult. This paper proposes a topic detection method based on topic model and feature embedding. The proposed method called ER-BTM solves the long tail problem of online text by embedding and topic estimation uses feature-pairs instead of features to optimize the process of parameter estimation. The experimental results show that the proposed method can effectively identify epidemic related topics on word-of-mouth platforms such as Yelp. Its performance is also better than control methods on public datasets, it is a powerful tool in online topic detection and epidemic prevention.
Cascaded Convolutional Generator Networks For Solving Imaging Inverse Problems

Image restoration or generation covers a range of important image inverse problems that aim to enhance a degraded image to obtain a restored image dataset. Several techniques based on deep learning have been developed for solving inverse problems. However, these methods require large training data sets to obtain a model with a good recovery performance. Recently, deep image prior (DIP) has emerged as a network-based approach that exploits the image representation power of convolutional neural networks (CNN) without resorting to the training stage but requiring the knowledge of the degradation models that describe the degraded observations. In this work, we propose a cascaded convolutional generator network that estimates the target image from degraded observations at an intermediate network stage. Furthermore, the proposed network architecture learns the degradation model by downscaling the recovered image adding relevant information in the restoration process. This approach was implemented to solve three imaging inverse problems: inpainting, deblurring, and super-resolution. The experimental results demonstrate the remarkable performance of the proposed approach, improving the DIP recovery under different scenarios.