Reviewerly/exHarmony huggingface dataset:
Transformer Text Classification Model for Arabic Dialects That Utilizes Inductive Transfer

In the realm of the five-category classification endeavor, there has been limited exploration of applied techniques for classifying Arabic text. These methods have primarily leaned on single-task learning, incorporating manually crafted features that lack robust sentence representations. Recently, the Transformer paradigm has emerged as a highly promising alternative. However, when these models are trained using single-task learning, they often face challenges in achieving outstanding performance and generating robust latent feature representations, especially when dealing with small datasets. This issue is particularly pronounced in the context of the Arabic dialect, which has a scarcity of available resources. Given these constraints, this study introduces an innovative approach to dissecting sentiment in Arabic text. This approach combines Inductive Transfer (INT) with the Transformer paradigm to augment the adaptability of the model and refine the representation of sentences. By employing self-attention SE-A and feed-forward sub-layers as a shared Transformer encoder for both the five-category and three-category Arabic text classification tasks, this proposed model adeptly discerns sentiment in Arabic dialect sentences. The empirical findings underscore the commendable performance of the proposed model, as demonstrated in assessments of the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.
NeuSyRE: Neuro-symbolic visual understanding and reasoning framework based on scene graph enrichment

Exploring the potential of neuro-symbolic hybrid approaches offers promising avenues for seamless high-level understanding and reasoning about visual scenes. Scene Graph Generation (SGG) is a symbolic image representation approach based on deep neural networks (DNN) that involves predicting objects, their attributes, and pairwise visual relationships in images to create scene graphs, which are utilized in downstream visual reasoning. The crowdsourced training datasets used in SGG are highly imbalanced, which results in biased SGG results. The vast number of possible triplets makes it challenging to collect sufficient training samples for every visual concept or relationship. To address these challenges, we propose augmenting the typical data-driven SGG approach with common sense knowledge to enhance the expressiveness and autonomy of visual understanding and reasoning. We present a loosely-coupled neuro-symbolic visual understanding and reasoning framework that employs a DNN-based pipeline for object detection and multi-modal pairwise relationship prediction for scene graph generation and leverages common sense knowledge in heterogenous knowledge graphs to enrich scene graphs for improved downstream reasoning. A comprehensive evaluation is performed on multiple standard datasets, including Visual Genome and Microsoft COCO, in which the proposed approach outperformed the state-of-the-art SGG methods in terms of relationship recall scores, i.e. Recall@K and mean Recall@K, as well as the state-of-the-art scene graph-based image captioning methods in terms of SPICE and CIDEr scores with comparable BLEU, ROGUE and METEOR scores. As a result of enrichment, the qualitative results showed improved expressiveness of scene graphs, resulting in more intuitive and meaningful caption generation using scene graphs. Our results validate the effectiveness of enriching scene graphs with common sense knowledge using heterogeneous knowledge graphs. This work provides a baseline for future research in knowledge-enhanced visual understanding and reasoning. The source code is available at https://github.com/jaleedkhan/neusire.
VOS

In this chapter, we elaborate on the task of video object segmentation (VOS), which aims at dividing pixels of a video into disjoint subsets where each subset usually represents either a target object or the background. The VOS task has different problem settings given different input or output requirements.
Design and Implementation of Power Big Data Platform

Abstract In recent years, with the advancement of the national construction of smart grid and the development of power grid enterprise integration system, the traditional power data platform has some defect, such as insufficient scalability, repeated implementation of two sets of logic of off-line data warehouse and real-time data warehouse, highly correlated storage of data warehouse during computation, and difficulty in platform migration, which restrict the deeper mining and analysis of power big data. Therefore, this paper adopts a technical architecture incorporating distributed calculating, micro-service, flow-batch integration and lake-warehouse integration, combining the current popular Web front-end and back-end technologies such as Vue, SpringCloud and Flask, and utilizing big data components such as Hadoop, Flink, Hudi and Kafka as well as Docker containerization technology. We explore and build a power grid system operation oriented big data platform that assembles real-time computing, multi-source heterogeneous data storage, and develop APIs for authority management, information management, data analysis and visualization, machine learning and other data supporting services that assist power enterprises in managing electrical equipment and users’ power consumption.With the help of this platform, power enterprises can intelligently track the equipment status, view the offline and real-time visual charts to analyze the power consumption by users, so as to have better basis in the power dispatching, maintenance and scheme adjustment of electrical market.
Booster-Driven Consistency Training Framework for Semi-Supervised Learning

Abstract The paper proposes an inductive semi-supervised learning method, called Booster-Driven Consistency Training (BDCT). In our work, we extend consistency training by designing a “booster” module for aggregating multi-view information, preventing the networks from collapsing into each other, and constructing better targets in parallel with the existing consistency training method. By booster, we mean that the one network is treated as the booster to help the training of the other two co-trained and superior networks, “spacecraft”. Furthermore, BDCT integrates smoothness enforcing into the designed fast feedback loop to ensure the effectiveness and the robustness of training. Meanwhile, adversarial examples are exploited to maintain the diversity among the networks that are learned to be smooth on the low dimensional manifold. BDCT demonstrates satisfactory performance in comparison with state-of-the-art semi-supervised deep learning methods and extensive experiments validate the effectiveness of the “booster” module.
Analyzing Most Popular Object Detection Models for Deep Neural Networks

With the rapid evolution of deep convolutional neural networks (CNNs), major breakthroughs have been achieved in object detection in the field of computer vision. However, the majority of state-of-the-art detectors, in both one-stage and two-stage methods, have limits and are inadequate for usage in a real-world setting where each step must be thoroughly checked. This thesis investigates advanced object detection models and frameworks. It offers an in-depth analysis of the most recent object detection models, their frameworks, and the performance criteria used to evaluate such models. The object detection models selected are YOLOv5, Faster R-CNN using Detectron 2, and SSD using TensorFlow 2, and the dataset selected is the Vehicles-Open Images Dataset. The performance of the selected models in relation to many metrics is analyzed, and the findings are reported. In conclusion, the benefits and limits of the selected models, as well as their relative performance, are discussed.
Model scale versus domain knowledge in statistical forecasting of chaotic systems

Chaos and unpredictability are traditionally synonymous, yet large-scale machine-learning methods recently have demonstrated a surprising ability to forecast chaotic systems well beyond typical predictability horizons. However, recent works disagree on whether specialized methods grounded in dynamical systems theory, such as reservoir computers or neural ordinary differential equations, outperform general-purpose large-scale learning methods such as transformers or recurrent neural networks. These prior studies perform comparisons on few individually chosen chaotic systems, thereby precluding robust quantification of how statistical modeling choices and dynamical invariants of different chaotic systems jointly determine empirical predictability. Here, we perform the largest to-date comparative study of forecasting methods on the classical problem of forecasting chaos: we benchmark 24 state-of-the-art forecasting methods on a crowdsourced database of 135 low-dimensional systems with 17 forecast metrics. We find that large-scale, domain-agnostic forecasting methods consistently produce predictions that remain accurate up to two dozen Lyapunov times, thereby accessing a long-horizon forecasting regime well beyond classical methods. We find that, in this regime, accuracy decorrelates with classical invariant measures of predictability like the Lyapunov exponent. However, in data-limited settings outside the long-horizon regime, we find that physics-based hybrid methods retain a comparative advantage due to their strong inductive biases.
Few-Shot Image Classification via Mutual Distillation

Due to their compelling performance and appealing simplicity, metric-based meta-learning approaches are gaining increasing attention for addressing the challenges of few-shot image classification. However, many similar methods employ intricate network architectures, which can potentially lead to overfitting when trained with limited samples. To tackle this concern, we propose using mutual distillation to enhance metric-based meta-learning, effectively bolstering model generalization. Specifically, our approach involves two individual metric-based networks, such as prototypical networks and relational networks, mutually supplying each other with a regularization term. This method seamlessly integrates with any metric-based meta-learning approach. We undertake comprehensive experiments on two prevalent few-shot classification benchmarks, namely miniImageNet and Caltech-UCSD Birds-200-2011 (CUB), to demonstrate the effectiveness of our proposed algorithm. The results demonstrate that our method efficiently enhances each metric-based model through mutual distillation.
MulticloudFL: Adaptive Federated Learning for Improving Forecasting Accuracy in Multi-Cloud Environments

Cloud computing and relevant emerging technologies have presented ordinary methods for processing edge-produced data in a centralized manner. Presently, there is a tendency to offload processing tasks as close to the edge as possible to reduce the costs and network bandwidth used. In this direction, we find efforts that materialize this paradigm by introducing distributed deep learning methods and the so-called Federated Learning (FL). Such distributed architectures are valuable assets in terms of efficiently managing resources and eliciting predictions that can be used for proactive adaptation of distributed applications. In this work, we focus on deep learning local loss functions in multi-cloud environments. We introduce the MulticloudFL system that enhances the forecasting accuracy, in dynamic settings, by applying two new methods that enhance the prediction accuracy in applications and resources monitoring metrics. The proposed algorithm’s performance is evaluated via various experiments that confirm the quality and benefits of the MulticloudFL system, as it improves the prediction accuracy on time-series data while reducing the bandwidth requirements and privacy risks during the training process.
From Turing to Transformers: A Comprehensive Review and Tutorial on the Evolution and Applications of Generative Transformer Models

In recent years, generative transformers have become increasingly prevalent in the field of artificial intelligence, especially within the scope of natural language processing. This paper provides a comprehensive overview of these models, beginning with the foundational theories introduced by Alan Turing and extending to contemporary generative transformer architectures. The manuscript serves as a review, historical account, and tutorial, aiming to offer a thorough understanding of the models’ importance, underlying principles, and wide-ranging applications. The tutorial section includes a practical guide for constructing a basic generative transformer model. Additionally, the paper addresses the challenges, ethical implications, and future directions in the study of generative models.
Quantum data learning for quantum simulations in high-energy physics

Quantum machine learning with parametrised quantum circuits has attracted significant attention over the past years as an early application for the era of noisy quantum processors. However, the possibility of achieving concrete advantages over classical counterparts in practical learning tasks is yet to be demonstrated. A promising avenue to explore potential advantages is the learning of data generated by quantum mechanical systems and presented in an inherently quantum mechanical form. In this article, we explore the applicability of quantum data learning to practical problems in high-energy physics, aiming to identify domain specific use-cases where quantum models can be employed. We consider quantum states governed by one-dimensional lattice gauge theories and a phenomenological quantum field theory in particle physics, generated by digital quantum simulations or variational methods to approximate target states. We make use of an ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states in the Schwinger model, (de)confinement phases from time-evolved states in the ${\mathbb{Z}}_{2}$ gauge theory, and that it can extract fermion flavor/coupling constants in a quantum simulation of parton shower. The observation of nontrivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum data learning architecture in high-energy physics.
Managing Libraries and Information Centres using Cloud Computing

Cloud computing is basically a new phenomenon for providing services over the internet. The biggest plus point of cloud computing is that it uses third-party hardware and software applications for providing services. It is very much costeffective and easy to maintain. This type of emerging technology is being adopted by the 21st century libraries and information centres. Cloud computing can be used in libraries to provide better services. Cloud computing allows users of the library to access information from any geographic location. Cloud computing is helpful for libraries and information centres in automating and managing their services.&amp;nbsp;&lt;br&gt;
Light field reconstruction with decoupled fusion and angular attention mechanism

Light field image (LFI) processing is a hot topic of current study; however, light field (LF) cameras’ poor sensor resolution forces a trade-off between spatial resolution and angular resolution. We propose an angular reconstruction network based on decoupled fusion and angular attention to achieve a dense LF with high spatial and angular resolution simultaneously. The angular self-attention module of the network is designed to capture the correspondence between different views and deeply refine the effective angular information. The network may obtain the related angular information non-locally and rebuild the dense LF by decoupling fusion of spatial and angular information. Furthermore, the feature information of the LFI in the spatial, angular, and epipolar plane image dimensions is entirely dissociated and merged, resulting in a higher perceptual quality of the reconstructed view. The design refinement module utilizes the correlation between spatial and angular information to recover the texture details of the LFI. Experimental results show that the method outperforms several current state-of-the-art angular reconstruction methods on a variety of LF datasets.
YouTubeVOS Challenges

Video segmentation is an important research area in computer vision with numerous applications such as video editing, autonomous driving, and robotics. However, little effort has been put to develop large-scale video segmentation benchmarks to evaluate existing methods.
Multimedia Security in Audio Signal

The security of Digital media has been varying continuously due to advanced malware attacks. Multimedia security has become one of the major concerns since new technologies are introduced. The proposed paper applied the watermarking technique in digital audio signals in which unique data is inserted in one-dimensional data in such a way that it must not affect the major information of the audio signal. The hybrid decomposition scheme has been applied to the audio data in order to extract features in terms of energy bands. The data is kept hidden in a low significant energy band that contains less information. This watermarking technique ensures the ownership of the multimedia data. Only authorized authors can be able to claim ownership of the audio data. The correct authorization of audio data can be proven by the extraction method in which the hidden watermark data has been extracted back to its original form without leaving any distortion in audio data. The proposed work introduces a hybrid approach to watermarking 2D data into an audio file. A hybrid audio decomposition technique was introduced by the proposed scheme in which a dual form of audio decomposition method has been applied containing Fast Fourier transform (FFT) and Cordic QR scheme. The correct location from the energy band has been found to embed the watermark data. Before the embedding procedure, the watermarking data has been selected. The proposed method selects an image containing information as a watermark that is first encrypted before initiating the embedding process. Watermark Encryption has been done using a cyclic coding algorithm and Arnold’s cat map. The disintegration of the audio file will finally result in Q and R matrices. Both such matrices are of orthogonal type. Then, the encrypted watermark data has been implanted in a random fashion in the R component of decomposed audio data during the embedding process. The inverse procedure has been applied for the watermark extraction and decryption process.&lt;br&gt;
Switchable-Encoder-Based Self-Supervised Learning Framework for Monocular Depth and Pose Estimation

Monocular depth prediction research is essential for expanding meaning from 2D to 3D. Recent studies have focused on the application of a newly proposed encoder; however, the development within the self-supervised learning framework remains unexplored, an aspect critical for advancing foundational models of 3D semantic interpretation. Addressing the dynamic nature of encoder-based research, especially in performance evaluations for feature extraction and pre-trained models, this research proposes the switchable encoder learning framework (SELF). SELF enhances versatility by enabling the seamless integration of diverse encoders in a self-supervised learning context for depth prediction. This integration is realized through the direct transfer of feature information from the encoder and by standardizing the input structure of the decoder to accommodate various encoder architectures. Furthermore, the framework is extended and incorporated into an adaptable decoder for depth prediction and camera pose learning, employing standard loss functions. Comparative experiments with previous frameworks using the same encoder reveal that SELF achieves a 7% reduction in parameters while enhancing performance. Remarkably, substituting newly proposed algorithms in place of an encoder improves the outcomes as well as significantly decreases the number of parameters by 23%. The experimental findings highlight the ability of SELF to broaden depth factors, such as depth consistency. This framework facilitates the objective selection of algorithms as a backbone for extended research in monocular depth prediction.
Calibration of the Ångström–Prescott Model for Accurately Estimating Solar Radiation Spatial Distribution in Areas with Few Global Solar Radiation Stations: A Case Study of the China Tropical Zone

The Ångström–Prescott formula is commonly used in climatological calculation methods of solar radiation simulation. Aiming at the characteristics of a vast area, few meteorological stations, and uneven distribution in the tropical regions of China, in order to obtain the optimal parameters of the global solar radiation calculation model, this study proposes a suitable monthly global solar radiation model based on the single-station approach and the between-groups linkage of the A–P model, which utilizes monthly measured meteorological data from 80 meteorological stations spanning the period from 1996 to 2016 in the tropical zone of China, considering the similarity in changes of monthly sunshine percentage between stations. The applicability and accuracy of the correction parameters (a and b coefficients) were tested and evaluated, and then the modified parameters were extended to conventional meteorological stations through Thiessen polygons. Finally, the spatial distribution of solar radiation in the tropical region of China was simulated by kriging, IDW, and spline interpolation techniques. The results show the following: (1) The single-station model exhibited the highest accuracy in simulating the average annual global solar radiation, followed by the model based on the between-groups linkage. After optimizing the a and b coefficients, the simulation accuracy of the average annual global solar radiation increased by 5.3%, 8.1%, and 4.4% for the whole year, dry season, and wet season, respectively. (2) Through cross-validation, the most suitable spatial interpolation methods for the whole year, dry season, and wet season in the tropical zone of China were IDW, Kriging, and Spline, respectively. This research has positive implications for improving the accuracy of solar radiation prediction and guiding regional agricultural production.
PUBLIC POLICIES FOR DIGITAL INCLUSION AND INDIGENOUS PEOPLES

The goal of this article is to discuss the public policies applied to the digital inclusion of indigenous communities in Tocantins through bibliographical research, so we did a bibliography survey, made records and brought it up in the theoretical discussion. To answer the question: How can public policies through digital technologies influence indigenous communities? We used the following authors to support our research: LAKATOS (1991), ARBOS(2010), PEDROSA(2017), PINTO(2008), DE SOUZA &amp; TOMIZAWA(2014) et al. Thus, we discuss three topics that we consider important to achieve results: Public policies for digital inclusion, Digital inclusion in indigenous communities and the digital insertion of people from Tocantins. Therefore, we came to the conclusion that two factors are important with regard to public policies for digital inclusion: education and cultural diffusion.