Reviewerly/exHarmony huggingface dataset:
SentiNeg

Sentiment analysis is the process of identifying and categorizing opinions computationally to determine the attitude expressed in the spoken or written text as positive, negative, or neutral. Negation analysis is the task of analyzing the negative opinions by identifying the scope of negation within a sentence and applying linguistic or grammatical rules of the language. In this paper, the rules for identifying the scope of negation within a sentence and the rules applicable to different negation categories are defined. An algorithm by the name SentiNeg has been proposed for processing negations at the sentence level. SentiNeg algorithm filters non-opinionated sentences from the data to avoid unnecessary processing. For opiniated sentences, the algorithm applies different linguistic or grammatical rules of the language to identify negative opinions. SentiNeg algorithm takes opinionated sentences as input and provides a detailed aspect-based summary of negative opinions that are expressed on the entity under analysis.
Joint-Based Action Progress Prediction

Action understanding is a fundamental computer vision branch for several applications, ranging from surveillance to robotics. Most works deal with localizing and recognizing the action in both time and space, without providing a characterization of its evolution. Recent works have addressed the prediction of action progress, which is an estimate of how far the action has advanced as it is performed. In this paper, we propose to predict action progress using a different modality compared to previous methods: body joints. Human body joints carry very precise information about human poses, which we believe are a much more lightweight and effective way of characterizing actions and therefore their execution. Estimating action progress can in fact be determined based on the understanding of how key poses follow each other during the development of an activity. We show how an action progress prediction model can exploit body joints and integrate it with modules providing keypoint and action information in order to be run directly from raw pixels. The proposed method is experimentally validated on the Penn Action Dataset.
NegoSim: A Modular and Extendable Automated Negotiation Simulation Platform Considering EUBOA

In recent years, the research community has become increasingly interested in automated negotiation. It has been used in real-world systems such as autonomous vehicle transportation systems, smart grids, and e-commerce. Considering the broad range of applications, automated negotiation is of great use and interest to engineers, developers, and industry-focused researchers. Therefore, some researchers have developed platforms for automated negotiation. However, these systems do not provide complete control over the development of various protocols or analytic tools. This paper introduces NegoSim, a new negotiation simulator that introduces a new negotiation framework called EUBOA, though it also works on the well-known BOA framework. NegoSim is completely modular, with different APIs allowing users full control over developing and modifying automated negotiation agents, different assessments, protocols, and utility spaces, as well as the ability to create or modify different GUIs. NegoSim also includes predefined agents, protocols, utility space, and analytic tools, making it an ideal platform for researchers.
An Introduction to The Impact of India's Use of Central Bank Digital Currency on the International Financial System

In the course of global research and development of Central Bank Digital Currency (CBDC), researchers are increasingly aware of the importance of CBDC. India, one of the important economies in the world, has obtained many fruitful results in the research process of CBDC. This study draws on data and findings from previous studies by exploring the good policies and possible risks faced in the process of introducing CBDC in India. Based on the research data, it explores the impact of the introduction of CBDC in India on international monetary policy and the implications for other countries. The study shows that the introduction of CBDC may receive some hindrance due to the inadequate infrastructure and the difference in the quality of the population's education levelIn the course of global research and development of Central Bank Digital Currency (CBDC), researchers are increasingly aware of the importance of CBDC. India, one of the important economies in the world, has obtained many fruitful results in the research process of CBDC. This study draws on data and findings from previous studies by exploring the good policies and possible risks faced in the process of introducing CBDC in India. Based on the research data, it explores the impact of the introduction of CBDC in India on international monetary policy and the implications for other countries. The study shows that the introduction of CBDC may receive some hindrance due to the inadequate infrastructure and the difference in the quality of the population's education level.
CNN-Based Temporal Video Segmentation Using a Nonlinear Hyperbolic PDE-Based Multi-Scale Analysis

An automatic temporal video segmentation framework is introduced in this article. The proposed cut detection technique performs a high-level feature extraction on the video frames, by applying a multi-scale image analysis approach combining nonlinear partial differential equations (PDE) to convolutional neural networks (CNN). A nonlinear second-order hyperbolic PDE model is proposed and its well-posedness is then investigated rigorously here. Its weak and unique solution is determined numerically applying a finite difference method-based numerical approximation algorithm that quickly converges to it. A scale-space representation is then created using that iterative discretization scheme. A CNN-based feature extraction is performed at each scale and the feature vectors obtained at multiple scales are concatenated into a final frame descriptor. The feature vector distance values between any two successive frames are then determined and the video transitions are identified next, by applying an automatic clustering scheme on these values. The proposed PDE model, its mathematical investigation and discretization, and the multi-scale analysis based on it represent the major contributions of this work. Some temporal segmentation experiments and method comparisons that illustrate the effectiveness of the proposed framework are finally described in this research paper.
Deep Learning-Based Sign Language Recognition for Hearing and Speaking Impaired People

Sign language is mainly utilized in communication with people who have hearing disabilities. Sign language is used to communicate with people having developmental impairments who have some or no interaction skills. The interaction via Sign language becomes a fruitful means of communication for hearing and speech impaired persons. A Hand gesture recognition system finds helpful for deaf and dumb people by making use of human computer interface (HCI) and convolutional neural networks (CNN) for identifying the static indications of Indian Sign Language (ISL). This study introduces a shark smell optimization with deep learning based automated sign language recognition (SSODL-ASLR) model for hearing and speaking impaired people. The presented SSODL-ASLR technique majorly concentrates on the recognition and classification of sign language provided by deaf and dumb people. The presented SSODL-ASLR model encompasses a two stage process namely sign language detection and sign language classification. In the first stage, the Mask Region based Convolution Neural Network (Mask RCNN) model is exploited for sign language recognition. Secondly, SSO algorithm with soft margin support vector machine (SM-SVM) model can be utilized for sign language classification. To assure the enhanced classification performance of the SSODL-ASLR model, a brief set of simulations was carried out. The extensive results portrayed the supremacy of the SSODL-ASLR model over other techniques.
Systematicity of students' independent work in operating systems classes

The research investigates the systematicity of students' learning activity as a parameter of the student's model, as well as the influence of systematicity on learning outcomes in the course "Operating Systems." As a theoretical framework, the importance of equipping the student himself as the subject of the educational process with the abilities and proper pedagogical forecasting tools for independent choice of the right variation of educational activity is demonstrated. Model parameters in such a pedagogical diagnostics system are proposed and analyzed. Empirical work has been completed on the basis of the learning management system Moodle and allows for the analysis of the association between students' timeliness in completing learning tasks and their educational achievements, as well as the structure of students' time planning at homework. Recommendations to improve the educational process have been suggested.
Multi-sensor tracking with partly overlapping FoV using detection field of probability modeling and the GLMB filter

Abstract In this paper, we consider multi-sensor with partly overlapping field of view (FoV) in the labeled random finite set (L-RFS) framework. This is different from most existing multi-sensor tracking algorithms, where the sensors are assumed to have the same FoV. We describe the partly overlapping FoV by modeling probability field of detection for individual sensors in whole observation area and can be seen as the same range of FoV. We consider all these using generalized labeled multi-Bernoulli filter in labeled RFS framework. Besides, we also propose a measurement-driven target birth model. Finally, the effectiveness of the proposed algorithm is verified by experiments.
The role of citizens in platform governance: A case study on public consultations regarding online content regulation in the European Union

This article proposes to start considering the role that citizens play in platform governance as a way of critically reflecting on issues of inclusivity in and effectiveness of current decision-making processes. This article attempts to apply the above suggestion by studying citizens’ discourse in recent European efforts to regulate online content. It does so by employing an experimental methodology, namely, a computationally assisted Critical Discourse Analysis on textual data derived from citizens’ contributions to the European Commission’s Public Consultations on three crucial regulatory texts: the Code of Practice on Disinformation, the Recommendation on Tackling Illegal Content Online and the Digital Services Act. The present analysis suggests that the EU’s strategy to advance participatory governance through public consultations seems to ignore citizens’ qualitative input and, thus, the feedback received can be severely limited. Concluding, the article maintains that scholarship should adopt a more encompassing scope when studying platform governance, especially concerning citizen and user participation, beyond the traditional frame of participation through civil society representation, while critically scrutinising existing ostensibly participatory structures.
Reconceptualising nomadic architecture: from the body to the space creation

Abstract Nomadic architecture from the past until now has been adapted to the spirit of the age. The research aims to search the nomadic lifestyle that has become a phenomenon of local and global movements worldwide. This paper investigates the meaning of nomadic architecture using historical and bibliographical methods. Our exploration provides a better understanding of nomadic architecture and reconceptualises the idea of future nomadic architecture with the attention of history period; (1) Hunter-gatherer; (2) Nomadic Pastoralism; (3) Dom; (4) Neo-Nomadic Society; (5) Contemporary Nomad. In conclusion, this approach reflects a strong connection between the human body, activity, and nomadic architecture creation. The research findings are a transformation of space in nomadism that implies the hybridisation of technology and new materials towards integration with future city development.
Learning Reliable Neural Networks with Distributed Architecture Representations

Neural architecture search (NAS) has shown the strong performance of learning neural models automatically in recent years. But most NAS systems are unreliable due to the architecture gap brought by discrete representations of atomic architectures. In this article, we improve the performance and robustness of NAS via narrowing the gap between architecture representations. More specifically, we apply a general contraction mapping to model neural networks with distributed representations (Neural Architecture Search with Distributed Architecture Representations (ArchDAR)). Moreover, for a better search result, we present a joint learning approach to integrating distributed representations with advanced architecture search methods. We implement our ArchDAR in a differentiable architecture search model and test learned architectures on the language modeling task. On the Penn Treebank data, it outperforms a strong baseline significantly by 1.8 perplexity scores. Also, the search process with distributed representations is more stable, which yields a faster structural convergence when it works with the differentiable architecture search model.
(Retracted) Transfer learning-based YOLOv3 model for road dense object detection

The Editor-in-Chief and the publisher have retracted this article, which was submitted as part of a guest-edited special section. An investigation uncovered evidence of systematic manipulation of the publication process, including compromised peer review. The Editor and publisher no longer have confidence in the results and conclusions of the article.CZ, JL, and FZ either did not respond directly or could not be reached.
Missing Recovery: Single Image Reflection Removal Based on Auxiliary Prior Learning

Photographs taken through a glass window are susceptible to disturbances due to reflection. Therefore, single image reflection removal is crucial to image quality enhancement. In this paper, a novel learning architecture that can address this ill-posed problem is proposed. First, a novel reflection removal pipeline was designed to reconstruct the missing information caused by the camera imaging process using the proposed missing recovery network. Second, to address the issues in existing reflection removal strategies, we revisit several auxiliary priors and integrate them by defining an energy function. To solve the energy function, a convolutional neural network-based optimization scheme was proposed. Finally, we investigated the dark channel responses of reflection and clean images and found an interesting way to distinguish between these two types of images. We prove this property mathematically and propose a novel loss function called dark channel loss to improve performance. Experiments show that the proposed method outperforms state-of-the-art reflection removal methods both quantitatively and qualitatively.
Thermal UAV Image Super-Resolution Guided by Multiple Visible Cues

Unmanned aerial vehicle (UAV) thermal-imaging has received much attention, but the insufficient image resolution caused by thermal imaging systems is still a crucial problem that limits the understanding of thermal UAV images. However, high-resolution visible images are relatively easy to access, and it is thus valuable for exploring useful information from visible image to assist thermal UAV image super-resolution (SR). In this article, we propose a novel multiconditioned guidance network (MGNet) to effectively mine the information of visible images for thermal UAV image SR. High-resolution visible UAV images usually contain salient appearance, semantic, and edge information, which plays a critical role in boosting the performance of thermal UAV image SR. Therefore, we design an effective multicue guidance module (MGM) to leverage the appearance, edge, and semantic cues from visible images to guide thermal UAV image SR. In addition, we build the first benchmark dataset for the task of thermal UAV image SR guided by visible images. It is collected by a multimodal UAV platform and composes of 1025 pairs of manually aligned visible and thermal images. Extensive experiments on the built dataset show that our MGNet can effectively leverage useful information from visible images to improve the performance of thermal UAV image SR and perform well against several state-of-the-art methods. The dataset is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mmic-lcl/Datasets-and-benchmark-code</uri> .
An Integrated Framework Implementation For Cloud Forensics Investigation Using Logging Tool

Today, straightforward computer and network forensics techniques cannon satisfactory performance for cloud forensics in conventional cloud based systems. The complexity of cloud computing as well as crime incident reconstruction, cloud instance isolation, and data provenance, is the reason behind this. This study aims is to support investigators and Cloud Service Providers (CSP) in understanding how to accomplish cyber forensic investigations in the cloud environment. In this research, a digital investigation procedure is recommended for use in analyzing large volumes of cloud data logs to support the reconstruction of criminal events' timelines in the cloud instance. A forensic framework is suggested and implemented using Apache Spark to perform the analysis process of log data. As such, data from cloud based committed during a particular time can be analyzed and extracted to help digital investigators in their investigations. The framework was also evaluated interactively using custom-built scenarios.
Booth Encoding-Based Energy Efficient Multipliers for Deep Learning Systems

Artificial intelligence on edge is a growing research field. In this brief, we propose a novel re-encoding scheme for reducing the size of the weights of deep neural networks (DNNs). The proposed re-encoding scheme exploits the Booth encoding scheme and the power-of-two (PO2) quantization to allow for very low energy computations during the inference of the neural networks with minimal loss in classification accuracy. We demonstrate the advantages of the proposed re-encoding scheme by computing a convolutional neural network (CNN) and a linear neural network on the proposed Extended Exact Multiplier and the proposed PO2 Multiplier. Our proposed PO2 quantization and re-encoding method reduce the model size for the CNN by 30.77% and the model size of the linear neural network by 49.86%. Furthermore, our multipliers reduce the inference energy for CNN by 50.6% and for the linear neural network by 90.1%. The PO2 Multiplier is proposed for the sensor-end computation of the linear neural network with a 77.32% reduction in the area relative to an exact Booth multiplier and it reduces the inference energy consumption of the linear neural network by 93.2% when compared to the unmodified exact multiplier. Our proposed scheme can be used to improve the energy consumption during inference for most Booth multipliers with minor modifications to the re-encoding signal arrangements. We also demonstrate that the proposed re-encoding scheme paired with the proposed multipliers outperforms all the existing designs in terms of resource utilization with a minimal impact on the inference accuracy of the neural networks.
Universal Multimodal Representation for Language Understanding

Representation learning is the foundation of natural language processing (NLP). This work presents new methods to employ visual information as assistant signals to general NLP tasks. For each sentence, we first retrieve a flexible number of images either from a light topic-image lookup table extracted over the existing sentence-image pairs or a shared cross-modal embedding space that is pre-trained on out-of-shelf text-image pairs. Then, the text and images are encoded by a Transformer encoder and convolutional neural network, respectively. The two sequences of representations are further fused by an attention layer for the interaction of the two modalities. In this study, the retrieval process is controllable and flexible. The universal visual representation overcomes the lack of large-scale bilingual sentence-image pairs. Our method can be easily applied to text-only tasks without manually annotated multimodal parallel corpora. We apply the proposed method to a wide range of natural language generation and understanding tasks, including neural machine translation, natural language inference, and semantic similarity. Experimental results show that our method is generally effective for different tasks and languages. Analysis indicates that the visual signals enrich textual representations of content words, provide fine-grained grounding information about the relationship between concepts and events, and potentially conduce to disambiguation.
Piecewise Weighted Smoothing Regularization in Tight Framelet Domain for Hyperspectral Image Restoration

Hyperspectral images captured by remote-sensing satellites are easily corrupted by various types of noise. Generally, hyperspectral signatures appear to be scattered in spatial-spectral domain, as well as noise. In transform domain, however, the principal components of a image are often centralized in the low-frequency band, while noise and some details are mainly contained in high-frequency components. The traditional transformation domain based smoothing methods take no account of the respective information distribution carried by different frequency bands. Hyperspectral image restoration aims to remove mixed noise for clean data, which usually amounts to an ill-posed inverse problem. Moreover, the matrix-decomposition-based model needs to reshape hyperspectral datacube into matrix form, which will lead to certain loss of spatial structure information. In order to address these issues, this paper incorporates piecewise weighted smoothing regularization in tight framelet domain to reformulate a novel convex model for hyperspectal image restoration, which maintains image signature to a great extent. Based on the noise-perturbed degradation model, the smoothing regularizer in tight framelet domain is imposed on abundance signature, in which the transformation matrix, as well as the weighted coefficient, is well designed for different frequency bands. As a surrogate of sparsity, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{q}$ </tex-math></inline-formula> -norm is employed to denoise for the enhancement of hyperspectral signatures. To the end, an efficient solver is carefully designed to derive the closed-form solutions by proximal alternating optimization. The experimental results on several synthetic and real datasets, not only demonstrate that the performance of proposed method is better than that of the current state-of-the-art approaches, but also verify the validation of regularization terms for hyperspectral image restoration.