Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Supervised Machine Learning Algorithms for Detection of Cyberbullying in Twitter

These days, the use of social media is inevitable. Social media is beneficial in several means, but there are severe terrible influences. A crucial difficulty that needs to be addressed is cyberbullying. Social media, especially Twitter, advances numerous concerns due to a misunderstanding concerning the notion of freedom of speaking. One of those problems is cyberbullying, which influences both man or woman victims as well as the societies. Harassment by way of cyberbullies is a big issue on social media. Cyberbullying affects both in terms of the mental and expressive manner of someone. So there is a need to plan a technique to locate and inhibit cyberbullying in social networks. To conquer this condition of cyberbullying, numerous methods have been devised. This paper would help to comprehend the methods and procedures like logistic regression (LR ), naïve Bayes (NB ), support vector machine (SVM), and term frequency—inverse document frequency (TF-IDF) which are used by numerous social media web sites, especially Twitter. In this paper, we have worked on the accuracy of the SVM, LR, and NB algorithms to detect cyberbullying. We observed that the SVM outperforms the others.
IoT architectures: from data to smart systems

SPECIALTY GRAND CHALLENGE article Front. Internet Things, 22 August 2022Sec. IoT Architectures Volume 1 - 2022 | https://doi.org/10.3389/friot.2022.959268
Tabu search exploiting local optimality in binary optimization

A variety of strategies have been proposed for overcoming local optimality in metaheuristic search. This paper examines characteristics of moves that can be exploited to make good decisions about steps that lead away from a local optimum and then lead toward a new local optimum. We introduce strategies to identify and take advantage of useful features of solution history with an adaptive memory metaheuristic, to provide rules for selecting moves that offer promise for discovering improved local optima. Our approach uses a new type of adaptive memory based on a construction called exponential extrapolation. The memory operates by means of threshold inequalities that ensure selected moves will not lead to a specified number of most recently encountered local optima. Associated thresholds are embodied in choice rule strategies that further exploit the exponential extrapolation concept and open a variety of research possibilities for exploration. The considerations treated in this study are illustrated in an implementation to solve the Quadratic Unconstrained Binary Optimization (QUBO) problem. We show that the AA algorithm obtains an average objective gap of 0.0315% to the best known values for the 21 largest Palubeckis instances. This solution quality is considered to be quite attractive because less than 20 s on average are taken by AA, which is 1 to 2 orders of magnitude less than the time required by most algorithms reporting the best known results.
Exploring the collective process of classroom dialogue using sequential pattern mining technique

Classroom dialogue is a widely used method for teaching and teacher's skills in managing dialogue matters closely to the productiveness of lessons. This study explores the sequential patterns of classroom dialogue and reveals how contributions are built collectively. There were commonalities in the dialogic sequences of the three subjects although they maintained their own characteristics. Talk concerning previously learnt knowledge and analysis tended to be initiations or follow-ups of other kinds of dialogue in the three subjects. Prominent long sequences in science lessons revealed high levels of cognition, which tended to start from elaboration, then triggered comparison and a summary of content, and finally gave rise to discussion about how to apply knowledge to solve novel problems.
Quantifying the impact of context on the quality of manual hate speech annotation

Abstract The quality of annotations in manually annotated hate speech datasets is crucial for automatic hate speech detection. This contribution focuses on the positive effects of manually annotating online comments for hate speech within the context in which the comments occur. We quantify the impact of context availability by meticulously designing an experiment: Two annotation rounds are performed, one in-context and one out-of-context, on the same English YouTube data (more than 10,000 comments), by using the same annotation schema and platform, the same highly trained annotators, and quantifying annotation quality through inter-annotator agreement. Our results show that the presence of context has a significant positive impact on the quality of the manual annotations. This positive impact is more noticeable among replies than among comments, although the former is harder to consistently annotate overall. Previous research reporting that out-of-context annotations favour assigning non-hate-speech labels is also corroborated, showing further that this tendency is especially present among comments inciting violence, a highly relevant category for hate speech research and society overall. We believe that this work will improve future annotation campaigns even beyond hate speech and motivate further research on the highly relevant questions of data annotation methodology in natural language processing, especially in the light of the current expansion of its scope of application.
An Object Localization-based Dense Image Captioning Framework in Hindi

Dense image captioning is a task that requires generating localized captions in natural language for multiple regions of an image. This task leverages its functionalities from both computer vision for recognizing regions in an image and natural language processing for generating captions. Numerous works have been carried out on dense image captioning for resource-rich languages like English; however, resource-poor languages like Hindi are not explored. Hindi is one of India’s official languages and is the third most spoken language in the world. This article proposes a dense image captioning model to describe different segments of an image by generating more than one caption in the Hindi language. For localized image recognition and language modeling, we employ Faster R-CNN and Long Short-Term Memory (LSTM), respectively. Apart from this, we conduct various experiments using gated recurrent units (GRUs) and attention mechanism. By manually translating the well-known Visual Genome dataset from English to Hindi, a dataset has been created for dense image captioning in Hindi. The experiments conducted on the newly constructed Hindi dense image captioning dataset illustrate the efficacy of the proposed method over the state-of-the-art methods.
Nonclassicality and teleportation fidelity of coherent photon-subtracted two-mode squeezed thermal states

Abstract The coherent photon-subtracted two-mode squeezed thermal states (CPS-TMSTS) are theoretically introduced by successively performing coherent photon subtraction operation on the two-mode squeezed thermal states (TMSTS). Based upon the normalization factor which is related to bivariate Hermite polynomials, the nonclassicality and non-Gaussianity are analyzed according to the Wigner function. It is shown that the coherent photon subtraction operation is an effective manner in generating nonclassicality and non-Gaussianity. Furthermore, the fidelity in teleporting coherent states using CPS-TMSTS as entangled resources is quantified both analytically and numerically. It is shown that the quantities of fidelity are clearly larger than that in the original TMSTS, and it can be further improved by modulating control parameters effectively. The CPS-TMSTS, as the ideal entangled resources with higher quantum teleportation fidelity, shall have potential applications in quantum optics tasks.
TCSD: Triple Complementary Streams Detector for Comprehensive Deepfake Detection

Advancements in computer vision and deep learning have made it difficult to distinguish deepfake visual media. While existing detection frameworks have achieved significant performance on challenging deepfake datasets, these approaches consider only a single perspective. More importantly, in urban scenes, neither complex scenarios can be covered by a single view nor can the correlation between multiple datasets of information be well utilized. In this article, to mine the new view for deepfake detection and utilize the correlation of multi-view information contained in images, we propose a novel triple complementary streams detector (TCSD). First, a novel depth estimator is designed to extract depth information (DI), which has not been used in previous methods. Then, to supplement depth information for obtaining comprehensive forgery clues, we consider the incoherence between image foreground and background information (FBI) and the inconsistency between local and global information (LGI). In addition, we designed an attention-based multi-scale feature extraction (MsFE) module to extract more complementary features from DI, FBI, and LGI. Finally, two attention-based feature fusion modules are proposed to adaptively fuse information. Extensive experiment results show that the proposed approach achieves state-of-the-art performance on detecting deepfakes.
General spiking neural network framework for the learning trajectory from a noisy mmWave radar

Abstract Emerging usages for millimeter wave (mmWave) radar have drawn extensive attention and inspired the exploration of learning mmWave radar data. To be effective, instead of using conventional approaches, recent works have employed modern neural network models to process mmWave radar data. However, due to some inevitable obstacles, e.g., noise and sparsity issues in data, the existing approaches are generally customized for specific scenarios. In this paper, we propose a general neuromorphic framework, termed mm-SNN, to process mmWave radar data with spiking neural networks (SNNs), leveraging the intrinsic advantages of SNNs in processing noisy and sparse data. Specifically, we first present the overall design of mm-SNN, which is adaptive and easily expanded for multi-sensor systems. Second, we introduce general and straightforward attention-based improvements into the mm-SNN to enhance the data representation, helping promote performance. Moreover, we conduct explorative experiments to certify the robustness and effectiveness of the mm-SNN. To the best of our knowledge, mm-SNN is the first SNN-based framework that processes mmWave radar data without using extra modules to alleviate the noise and sparsity issues, and at the same time, achieve considerable performance in the task of trajectory estimation.
Overview of LiDAR point cloud target detection methods based on deep learning

Purpose The purpose of this paper is to summarize the existing point cloud target detection algorithms based on deep learning, and provide reference for researchers in related fields. In recent years, with its outstanding performance in target detection of 2D images, deep learning technology has been applied in light detection and ranging (LiDAR) point cloud data to improve the automation and intelligence level of target detection. However, there are still some difficulties and room for improvement in target detection from the 3D point cloud. In this paper, the vehicle LiDAR target detection method is chosen as the research subject. Design/methodology/approach Firstly, the challenges of applying deep learning to point cloud target detection are described; secondly, solutions in relevant research are combed in response to the above challenges. The currently popular target detection methods are classified, among which some are compared with illustrate advantages and disadvantages. Moreover, approaches to improve the accuracy of network target detection are introduced. Findings Finally, this paper also summarizes the shortcomings of existing methods and signals the prospective development trend. Originality/value This paper introduces some existing point cloud target detection methods based on deep learning, which can be applied to a driverless, digital map, traffic monitoring and other fields, and provides a reference for researchers in related fields.
Pop Materialising: Layers and topological space in digital pop music

This article argues that pop music’s increasing assimilation of hip-hop and EDM (electronic dance music) practices combine with computational automation and this has substantial consequences for musical space. Traditional ‘space-makers’ such as reverb or delay are subject to other functions such as frequency filters and compression that interrelate processual layers of textures. Instead of an active-listener-orientated sonic space with distinct source-bonded entities, it is based on a particular sonic materiality. With a new media theoretical approach, I consequently argue that this new type of space can better be understood as a mediatised topological materiality.
Long-term object search using incremental scene graph updating

Abstract Effective searching for target objects in indoor scenes is essential for household robots to perform daily tasks. With the establishment of a precise map, the robot can navigate to a fixed static target. However, it is difficult for mobile robots to find movable objects like cups. To address this problem, we establish an object search framework that combines navigation map, semantic map, and scene graph. The robot updates the scene graph to achieve a long-term target search. Considering the different start positions of the robots, we weigh the distance the robot walks and the probability of finding objects to achieve global path planning. The robot can continuously update the scene graph in a dynamic environment to memorize the position relation of objects in the scene. This method has been realized in both simulation and real-world environments. The experimental results show the feasibility and effectiveness of this method.
Spectral analysis of a surface occlusion model for image-based rendering sampling

Occlusion is an important and challenging problem in light field rendering technology. We present a signal-processing framework to study the occlusion phenomenon for object surfaces. Our framework combines the occlusion phenomenon with object surface construction information. Based on the geometry of a given scene, we establish an accurate closed-form expression describing object surface occlusion for light field rendering. In this manner, scene occlusion and spectral support of the light field are analyzed in the frequency domain. Light field rendering predictions rooted in the frequency domain then serve as the controlling factors for scene sampling and rendering. This analysis extends previous work on light field sampling theory in which no occlusion in scenes were considered, and it offers a comprehensive treatment of case with self-occlusion and no occlusion. Extensive experimental evaluations of synthetic and actual scenes demonstrate that our light field sampling approach notably outperforms competing algorithms.
Time series change detection using reservoir computing networks for remote sensing data

In this study, a multivariate time series forecasting model applicable to high-dimensional short-term forecasting is proposed. The Reservoir Computing Network (RCN) state reduction method is investigated to address the dimensionality arising from high-dimensional data. To enable the states to express very distant dependencies in time so that the RCN satisfies the echo state property, a bidirectional RCN model is investigated to capture the dependencies of the data forward and backward in time. To solve the short-term data prediction, the spatiotemporal transformation theory is combined with the RCN to exploit the conjugate property between delayed and nondelayed embeddings of dynamical systems, and the correlation between variables in high-dimensional systems is used to compensate for the shortcomings of short-term series, design the method of the RCN training, and complete the prediction of future states. In the experiments, four existing models based on two different data sets were used for comparison, and the results showed that our method has better prediction performance.
Extract Aspect-based Financial Opinion Using Natural Language Inference

The emergence of transformer-based pre-trained language models (PTLMs) has bought new and improved techniques to natural language processing (NLP). Traditional rule-based NLP, for instance, is known for its deficiency of creating context-aware representations of words and sentences. Natural language inference (NLI) addresses this deficiency by using PTLMs to create context-sensitive embedding for contextual reasoning. This paper outlines a system design that uses traditional rule-based NLP and deep learning to extract aspect-based financial opinion from financial commentaries written using colloquial Cantonese, a dialect of the Chinese language used in Hong Kong. We need to confront the issue that existing off-the-shelf PTLMs, such as BERT and Roberta, are not pre-trained to understand the language semantics of colloquial Cantonese, let alone the slang, jargon, and codeword that people in Hong Kong use to articulate opinions. As a result, we approached the opinion extraction problem differently from the mainstream approaches, which use model-based named entity recognition (NER) to detect and extract opinion aspects as named entities and named entity relations. Because there is no PTLM for our specific language and problem domain, we solve the opinion extraction problem using rule-based NLP and deep learning techniques. We report our experience of creating a lexicon and identifying candidate opinion aspects in the input text using rule-based NLP. We discuss how to improve BERT's linguistic knowledge of colloquial Cantonese through a fine-tuning procedure. We illustrate how to prepare the input text for contextual reasoning and demonstrate how to use NLI to confirm candidate opinion aspects as extractable.
Integrating corpus-based and NLP approach to extract terminology and domain-oriented information: an example of US military corpus

Within the modern information, communication and technology (ICT), seeking high efficient and accurate corpus-based approaches to process natural language data (NLD) is critical. Traditional corpus-based approaches for processing corpus (i.e. the collected NLD) mainly focused on quantifying and ranking words for assisting human in extracting keywords. However, traditional corpus-based approaches cannot identify the meanings behind the words to properly extract terminologies nor their information. To address this issue, the main objective of this paper is to propose an integrated linguistic analysis approach that combines two corpus-based approaches and a rule-based natural language processing (NLP) approach to extract and identify terminologies and create the text database for extracting deeper domain-oriented information by using the terminologies as channels to retrieve core information from the target corpus. Military domain is an uncommon research field and often classified as confidential data, which caused little researches to focus on. Nevertheless, military information is vital to national security and should not be ignored. Hence, to verify the proposed approach in extracting terminologies and information of the terminologies, the researchers adopt the US Army field manual (FM) 8-10-6 as the target corpus and empirical case. Compared with AntConc 3.5.8 and Tongpoon-Patanasorn’s hybrid approach, the results indicate that from the perspectives of terminology identification, texts database creation, domain knowledge extraction, only the proposed approach can handle all these issues.
Contradiction of modern and social-humanitarian artificial intelligence

Purpose The purpose of the paper is to propose an effective approach of artificial intelligence (AI) addressing social-humanitarian reality comprising non-formalizable representation. The new task is to describe processes of integration of AI and humans in the hybrid systems framework. Design/methodology/approach Social-humanitarian dynamics contradict traditional characteristics of AI. Suggested methodology embraces formalized and non-formalized parts as a whole. Holonic and special convergent approaches are combined to ensure purposefulness and sustainability of collective decision-making. Inverse problem solving on topology spaces, control thermodynamics and non-formalizable (considering quantum and relativistic) semantics include observers of eigenforms of reality. Findings Collective decision-making cannot be represented only by formal means. Thus, this paper suggests the equation of hybrid reality (HyR), which integrates formalizable and non-formalizable parts conveying and coalescing holonic approaches, thermodynamic theory, cognitive modeling and inverse problem solving. The special convergent approach makes the solution of this equation purposeful and sustainable. Research limitations/implications The suggested approach is far reaching with respect of current state-of-the-art technology; medium-term limitations are expected in the creation of cognitive semantics. Practical implications Social-humanitarian events embrace all phenomena connected with individual and collective human behavior and decision-making. The paper will impact deeply networked experts, groups of crowds, rescue teams, researchers, professional communities, society and environment. Originality/value New possibilities for advanced AI to enable purposeful and sustainable social-humanitarian subjects. The special convergent information structuring during collective decision-making creates necessary conditions toward the goals.
Optimal two-qubit gates in recurrence protocols of entanglement purification

We propose and investigate a method to optimize recurrence entanglement purification protocols. The approach is based on a numerical search in the whole set of $\text{SU}(4)$ matrices with the aid of a quasi-Newton algorithm. Our method evaluates average concurrences where the probabilistic occurrence of mixed entangled states is also taken into account. We show for certain families of states that optimal protocols are not necessarily achieved by bilaterally applied controlled-not gates. As we discover several optimal solutions, the proposed method offers some flexibility in experimental implementations of entanglement purification protocols and interesting perspectives in quantum information processing.
On Addressability, or What Even Is Computation?

This article argues against the conflation of digital and computational that ails contemporary critical discourse. Searching for a common logic among the three modes of computing (namely analog, digital, and quantum), it ends up finding an answer in the individuating backbone that runs through histories of postal, civic, and technological addresses. Borrowing the concept of addressability from computer science and adapting it to explain procedures of identificatory mapping at large, the article theorizes addressability as a cultural technique that can be traced back to the origins of the modern disciplinary state. In doing so, it not only posits addressability as one of the core operational logics for all computation and an alternative analytic to the digital but also suggests a longer arc of computation that considers the ubiquitous digitality as a mere subset of modern state apparatuses and their urban infrastructures. The article concludes by reflecting on this computational addressability and its relationship with ideological interpellation, proposing computation as a multiscalar assemblage of political techniken.