Reviewerly/exHarmony huggingface dataset:
Content Analysis in the Research Field of Incivility and Hate Speech in Online Communication

Abstract The origins of research on incivility and hate speech can be traced back to the question of what qualities public communication should have in order to establish and maintain a democratic society. Democracy and public sphere theorists have presented different answers to this question and accordingly developed different concepts of civility. Incivility is a controversial concept associated with a wide spectrum of behaviors. Based on the different theoretical concepts, different indicators of incivility have been used. This chapter summarizes previous theoretical approaches and provides an overview of existing content analytic studies of incivility in online user-generated communication.
Learning Language Intersections

We study active automata learning, where the target language is given as the intersection of n regular languages. We assume membership oracles for the individual languages, and various forms of equivalence queries, implemented as usual via conformance testing. These oracles can be used by several different learning strategies. In this paper, we propose these strategies and compare them experimentally.
State Identification and Verification with Satisfaction

We use SAT-solving to construct adaptive distinguishing sequences and unique input/output sequences for finite state machines in the flavour of Mealy machines. These sequences solve the state identification and state verification problems respectively. Preliminary experiments evaluate our implementation and show that this approach via SAT-solving works well and is able to find many short sequences.
Optimal Parent Hamiltonians for Many-Body States

After presenting the main features and challenges of the quantum inverse problem, we illustrate numerous results of recent literature, with a particular focus on the different Parent Hamiltonian reconstruction methods and on the scaling of their complexity with the system size.
Natural Language Processing and Functioning Ontological Solver with Visualization in an Integrated System

In this article, we consider the issue of artificial intelligence integrated systems. This issue was studied within the experimental system of automatic solving geometry problems. The system comprises a linguistic processor, domain ontology, solver, and interactive visualization. The cognitive schemes are considered to create drawing in accordance with the text of geometric problems written in Russian natural language. System’s explanatory means are based on the synthesis of a natural language description of semantic representation. The research results, including the processing of elliptical sentences and possibilities for cognitive systems to support the formation of problem semantic representations and their drawing are described. The applied value of the research for creating learning systems in education have taken into account.
Adaptive Keyword Extraction Service for Turkish

Keyword extraction is one of the important tasks of NLP. In this study, we have implemented a fast and competitive keyword extraction model for Turkish. We have benchmarked our model with all available online keyword extraction models such as RAKE, YAKE, and TF/IDF. We have used fastText for word embeddings which produced well-distributed clusters. For obtaining keywords, we have developed a dynamic cluster selection mechanism that can vary through input text. The keyword selection mechanism is based on word votes on input text which detects target cluster(s) to keyword set. Every word has a unique vote according to its IDF score. We also established a penalty function for weakening verbs. Our model enforces the constraint that common verbs in Turkish should not be selected as a keyword candidate. Our model can infer keywords in a millisecond timeframe. It does not require GPU or TPU for inference. It is a cost-effective, online, self-learning, and fast keyword extraction model.
Quality Assurance for Speech Synthesis with ASR

Autoregressive TTS models are still widely used. Due to their stochastic nature, the output may vary from very good to completely unusable from one inference to another. In this publication, we propose to use the percentage of completely correct transcribed sentences (PCTS) of an ASR system as a new objective quality measure for TTS inferences. PCTS is easy to measure and represents the intelligibility dimensions of a typical subjective evaluation with mean opinion score (MOS). We show that PCTS leads to similar results as subjective MOS evaluation. A more detailed, semi-automatic error analysis of the differences between ASR transcripts of TTS speech and the text used for generating the TTS speech can help identifying problems in the TTS training data that are harder to find with other methods.
Neural Networks Training on Graphics Processing Unit (GPU) Using Dynamic Parallelism (DP)

Artificial Neural Networks (ANN) are a crucial foundation for deep learning and many machine learning algorithms. Training an ANN is computationally intensive and inherently parallel, thus may be accelerated by a Graphics Processing Unit (GPU). Due to the dependency across different ANN layers, which is created by the nature of Back Propagation (BP) algorithm, it is quite challenging to design a highly efficient ANN training algorithm on GPU. In this work, we investigate and demonstrate the technology, Dynamic Parallelism (DP) and will further speed up an ANN training task on GPU. We implemented a generic ANN framework on GPU that consists of an arbitrary number of layers and an arbitrary number of nodes in each layer. In two sets of experiments, we trained the generic ANN on GPU for handwritten digit recognition with DP enabled and disabled. We observed that training ANNs on GPU with DP enabled achieved up to 12.7x performance gain, compared with that with DP disabled on GPU. After being trained on GPU, our neural network achieved an accuracy rate of 96% in handwritten digit recognition.
Adversarial Attack and Defense Based Hydrangea Classification via Deep Learning: Autoencoder and MobileNet

Hydrangea, a pervasive flower that grew throughout the world, has proven to be harmful in ways as an invasive species. To tackle this widespread issue, our experiment aimed to prevent adversarial attacks, which can result in real-life effects. One minor but vital step of our process was preprocessing, including normalization and train-test splitting. Furthermore, an autoencoder (AE) was implemented to help distinguish noise. A total of 7 algorithms were then applied to test how successful our program was. As demonstrated in the paper, images with noise returned a significantly lower accuracy. Two algorithms, the normal model and MobileNet, were tested once more with the addition of an AE. The established hypothesis was that it would result in higher accuracies compared to those with noise since that would verify the benefit of implementing AEs. For reference, the accuracy of MobileNet was 67.4% with noise, 88.5% without noise, and 100% with both noise and an autoencoder. In both scenarios, the autoencoder notably boosted the accuracy in the presence of noise. Though hydrangea was the specialized field of study in this research, our methodologies can be implemented elsewhere, as long as the required data is available.
Zero-Shot Visual Emotion Recognition by Exploiting BERT

The explosive growth of multimedia has attracted many people to express their opinions through social media like Flickr and Facebook. As a result, social media has become the rich source of data for analyzing human emotions. Many earlier studies have been conducted to automatically assess human emotions due to their wide range of applications such as education, advertisement, and entertainment. Recently, many researchers have been focusing on visual contents to find out clues for evoking emotions. In literature, this type of study is called visual sentiment analysis. Although a great performance has been achieved by many earlier studies on visual emotion analysis, most of them are limited to classification tasks with pre-determined emotion categories. In this paper, we aim to recognize emotion classes that do not exist in the training set. The proposed model is trained by mapping the visual features to the emotional semantic representation embedded by the BERT language model. By evaluating the model on a cross-domain affective dataset, we achieved 66% accuracy for predicting the unseen emotions not included in the training set.
Using Segmentation to Improve Machine Learning Performance in Human-in-the-Loop Systems

The expectations of Machine Learning systems are becoming increasingly demanding, namely in what concerns the diversity of applications, the expected accuracy, and the pressure for results. However, there are cases in which Human experts are needed to label the data, which may have a significant cost in terms of human resources and time. In these cases, it is often best to learn on-the-fly, without expecting for the whole data to be labeled. Often, it is desirable to guide the Human annotators into focusing on the more relevant instances: this constitutes the so-called active learning. In this paper we propose an approach in which a clustering algorithm is used to find groups of similar instances. Then, the procedure is guided with the objective of favoring the annotation of the groups that are under-represented in the labeled dataset. Results show that this approach leads to models that are, over time, more accurate and reliable.
A Deep Learning Framework to Optimize Underground Wireless Sensors Network Performance in Oil and Gas Reservoirs Environment

Fracture Robots (FracBots) technology is a game-changing technology that has been developed to revolutionize upstream operations. FracBots are magnetic induction (MI)-based wireless sensor nodes that have the inter-node wireless communication, sensing and localization estimation capabilities. FracBots are miniature devices that can operate as wireless underground sensor networks (WUSNs) inside hydraulic fractures to collect and communicate important data and generate real-time mapping. A large number of FracBots are deployed to establish FracBot-to-FracBot connectivity, making the technology the first IoT (Internet of Things) to generate and exchange data inside the reservoir without human intervention. In addition, a novel artificial intelligence (AI) framework is designed in order to optimize the sensor selection in real-time. This assists in determining pressure and temperature in the reservoir, and characterizes the formation. The framework consists of an artificial intelligence technique that estimates sensor data uncertainty, and subsequently optimizes the selection of the sensors. The optimal selection ensures that the reservoir is sufficiently monitored. The optimization results demonstrate that a limited number of sensors can properly monitor the fractures within the reservoir.
A Deep Learning Approach to UML Class Diagrams Discovery from Textual Specifications of Software Systems

Software engineering has developed tools to streamline developing software systems. Among these, the model-driven architecture proposes going from specifications written in natural language to the application code via two intermediate models: a platform-independent model and a platform-specific model. Since the models and code are written in a semi-formal language, it is possible to switch between them automatically, making the process automatable over three-quarters of its length. The first step, which creates a platform-independent model based on specifications, cannot be automated in the current state because of the complexity of natural language. We hypothesize that deep learning techniques developed within the natural language processing framework allow us to consider the automation of this step and produce a UML class diagram. We believe that entity detection can help us identify key concepts of the class diagram, that relation classification can help us identify links between concepts, and that coreference resolution can help us gather all the information relating to a concept. That’s why we propose an architecture that jointly resolves these three tasks and constructs the class diagram. We also provide an annotated dataset to solve these tasks. Our architecture, composed of a BERT-type encoding layer and three feed-forward-neural-network-type decoding layers, makes it possible to produce a simple class diagram that is imperfect but consistent. Therefore, the architecture validates the approach and presents the first encouraging results.
Generating Algorithmic Patterns from Semi-structured Input Using a Transition-Based Neural Network

Synthesizing program code from natural language is a challenging task as natural language utterances tend to be ambiguous and require substantial prior knowledge to interpret. Recent solutions approach these difficulties in different ways. Some models do not constrain their inputs and operate with a large variety of sentences, but tend to be less accurate. Others limit the space of possible inputs, requiring them to meet a fixed structure which makes them more similar to code than language. This paper offers a middle ground between these approaches. We train a transition-based neural network on descriptions of programming tasks generated by using context-free grammar and templates. We show that the model is able to generalize and can solve synthesis problems described in natural language.
ANNExR: Efficient Anytime Inference in DNNs via Adaptive Intermediate Decision Points

In DNNs, anytime inference refers to obtaining the goal output from any of the layers before the final layer, without having to recalculate the input. Models that allow for this type of inference at any time have become essential in the field of embedded AI, particularly for tasks that require near-real-time inference for a seamless user experience. While several techniques exist that permit anytime inference by adding intermediate decision points in DNNs, the number and location of such decision points are either based on heuristics or simply placed after each layer, which may or may not work. As a result, a novel framework is offered for designing customized decision points that are best suited for a particular job and model architecture. The choice points are placed using the ANNExR Optimal Exit Selector algorithm, which uses representative metrics like likelihood and uncertainty of prediction, as well as user preference, to achieve optimal performance. Additionally, for seamless execution, the framework performs automatic DNN modularization (based on the derived optimal decision points). The empirical results show how adaptive decision/exit points reduce average latency while maintaining the classification model’s accuracy.
Airplane Detection and Classification Based on Mask R-CNN and YOLO with Feature Engineering

Deep learning algorithms achieve good performance in object detection and image classification. In this paper, we apply two algorithms, Mask R-CNN and YOLOv3, to the Rareplane dataset for airplane detection and classification. To achieve better performance in the fine grain classification problem, we propose a multi-step algorithm: Mask R-CNN is used to obtain bounding box, an edge extraction algorithm is used to get a more precise mask, the obtained masks are standardized, and their features are extracted. Using this algorithm, the mask type in the mask library with the most similar features is identified as the type of aircraft. Preliminary test results demonstrate that this algorithm is effective in fine grain classification, with an overall precision rate of 89.6% for the Airbus A300 and 88.6% for the Airbus A319.
Synthesizing Pokémon Trading Cards Using Nvidia StyleGAN2 from Home

Artificial intelligence plays a large role in the way that we consume content acting as a director behind the scenes controlling what and how we interact with digital mediums daily. Only within the last decade has AI software and hardware advanced to a point where it can also help contribute directly to content creation with the use of technologies such as generative adversarial networks (GANs). In this paper, we will examine some modern GAN solutions as well as the viability of an at-home model training experience in 2021 and into 2022. Specifically, the task being examined is the image synthesis of Pokémon Trading Cards. This dataset was chosen for its familiarity, large number of uniform samples, accessibility, and to examine the effects of training on datasets that share many consistent elements (card layout, formatting, color schemes). Some implications of being able to generate images in such a way are faster prototyping, a more streamlined workflow, or near-instantaneous content creation for creative professionals and illustrators.
Towards Augmented Reality Interactions Driven by Universal Dynamic Procedural Browser Actions in the 4.0 Contexts

In the last decade, Augmented Reality is a technology that has been advancing strongly revolutionizing information technologies, particularly in the context of 4.0, it has managed to expand to different fields of application such as tourism, entertainment, industry, health, marketing, and others. This development led to the need to establish standardized data access to achieve a universal experience regardless of the application that generates it. Different proposals emerged that integrated semantic web technologies to achieve this purpose, so new challenges emerge to ensure that these virtual contents are used by the user in a dynamic interaction directed by procedures composed of a set of actions to be performed in a physical context enriched by augmented reality technologies. In this order, this article presents the AR interaction structured by the ontology used in a framework that assists in the creation, exploitation, and publication of these procedures to be consumed by applications that use these technologies.
Combining Edge Recombination Crossover with Ant Colony Optimisation for Improved Routing

Meta-heuristics are commonly applied to difficult permutation type routing problems such as the Traveling Salesman Problem (TSP). Genetic Algorithms (GA) and Ant Colony Optimisation (ACO) are two of the most successful. However, GAs require specialist crossover operators for permutation problems to avoid repetition. Crossover operators such as Edge Recombination (ER) focus on preservation of parental edges but do not account for edge quality. However, ACO involves ants selecting edges based on pheromone and quality. Consequently, this paper proposes combining GAs and ACO via ER crossover (ER-ACO) whereby ants ensure the quality of preserved parental edges. Applied to a range of TSP instances the ER-ACO crossover method demonstrates considerable improvements over standard ER highlighting the importance of edge quality. Moreover, ER-ACO also demonstrates inserting high quality non-parental edges is highly beneficial. Indeed, ER-ACO crossover is able to achieve solutions within 1–2% of optimal for TSP instances of several thousand cities.
Secure System of Continuous User Authentication Using Mouse Dynamics

Now a days, as there is a noteworthy increase in the usage of computer systems and internet, resulting in the tremendous increase in the cases of unauthorized access. Due to this, user authentication based on biometrics especially behavioural biometrics are gaining popularity. Mouse dynamics is one of the emerging behavioral trait for authentication of a user. In mouse dynamics, the genuinity of a user can be verified by observing the mouse usage behaviour of the user. In this paper, the proposed method focuses on the mouse dynamics to provide continuous user authentication in a system. The proposed system is designed for an open scenario, where any person can access the computer. So no prior registration of the user is required. One-class Support Vector Machine (SVM) was used to identify the deviation of current behaviour from recorded patterns, and thus verifying genuine user from intruder.
An Automatic Feature Selection Model for Running Sprint Reorganization Using Wrist Accelerometers

For the existing motion recognition tasks, few researchers have studied running sprint, that is, the sprint part of the running segment is discriminated. In order to monitor the running situation of sportsman, we proposed an Automatic Feature Selection Mode (AFSM) for running sprint reorganization using wrist accelerometers. First, information such as acceleration signal is collected through wearable devices. After pre-processing and feature extraction, an automatic feature selection algorithm is used to select important features. We also propose two key features, heart rate and acceleration derivatives, for experiment. The experimental results show that AFSM can automatically screen useful features to achieve better classification accuracy, effectively identify the sprint period in the running segment, and the recognition accuracy rate reaches 83.7%; Heart rate and acceleration derivatives are the key information for sprint recognition, which can significantly improve the classification effect, improve the accuracy rate by 1.77 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , and achieve more accurate recognition.
An Overview of Face Image Forgery Detection

: With the development of face forgery techniques, the spread and malicious abuse of forged images have become a thought-provoking problem, and the face forgery detection technique has also attracted people's attention. Academia has carried out in-depth research and discussion on detection techniques. This review discussed different face forgery methods and detection techniques. Four categories of detection methods are introduced: 1) detection method based on spatial domain, 2) detection method based on the frequency domain, 3) detection method based on biological information, and 4) detection method of multiple feature domains. This paper discussed each detection method's evolution and development in recent years. We paid special attention to the detection method of multiple feature domains and focused on the progress that has been made and the challenges it faced. In addition, this paper discussed open issues and future development trends that should be paid attention to in this field.
Improved butterfly algorithm based on population expansion and adaptive parameters

To address the problems of slow convergence process and low accuracy of final convergence results in butterfly optimization algorithm (BOA), a symmetric augmentation optimized population and parameter dynamic adaptive butterfly algorithm (PEAPBOA) is proposed, which optimizes the initial population by symmetric augmentation combined with dominant population method to improve the speed of algorithm convergence, and then crosses and mutates the individuals with poor adaptation. The algorithm further introduces dynamically changing sensory modality c, power exponent a, dynamic switching probability p and position updating dynamic weights w1 and w2, so that the algorithm focuses on global search in the early stage and local search in the later stage, which improves the breadth of global search and the depth of local random search of the algorithm, thus improving the convergence speed and convergence accuracy of the algorithm. The test experiment verifies the effectiveness and superiority of the improved butterfly algorithm proposed in this paper.