Reviewerly/exHarmony huggingface dataset:
HMRFLR: A Hybrid Model for Sentiment Analysis of Social Media Surveillance on Airlines

Social media dominates modern life. People publish their daily activities, self-indulgent sentiments, and real-life experiences on Twitter, Instagram, Facebook, YouTube, etc. Twitter dominates social media surveillance (64%). Twitter data is useful for user viewpoints and tweets. Like other companies, the airline industry seeks to stay current and survive in the present climate. Airlines employ widespread, time-consuming consumer feedback. Feelings must be analyzed to reduce issues. When flights resume after the epidemic, the airline industry is trying harder than ever to stay in contact with consumers. The airline industry's decade-long growth is impressive. Every day, millions of users share their airline experiences, from joyful consumers uploading photographs with clouds and employees and unhappy customers complaining about unsatisfactory services and issues like lost bags, delayed flights, changes in boarding schedules, IT system failures, etc. Real-time feedback helps consumers pick a flight and helps airline management and employees assess the situation and take prompt action to enhance services for passengers. In the research paper, a hybrid model composed of Machine Learning algorithms including the classifiers of random forest and logistic regression named as HMRFLR is proposed to analyze the tweets of Airlines in the US for categorization of the posts according to positivity, neutrality, and negativity of the posts. For revealing the current level of customer satisfaction towards the airlines, sentiment analysis is undertaken. This hybrid model achieves a better accuracy score of 88.16%, however, the individual accuracy score of Logistic Regression is 79.1% and Random Forest is 76.87% respectively.
Detecting financial statement fraud using dynamic ensemble machine learning

Our study uses Machine learning to develop an advanced fraud detection model that can detect fraudulent firms. We build our model using raw financial and non-financial variables following prior literature. In addition, we introduce the Dynamic Ensemble Selection algorithm to the fraud detection literature, which combines individual classifiers dynamically to make a final prediction. Using several performance evaluation metrics, we find that our model can outperform several machine learning models used in recent studies.
Exploring the use of mobile technologies for learning: an empirical study of library and information science (LIS) students

Purpose The use of mobile technologies and learning devices has been increasing in every field of life, and library and information sciences are no exception. This study aims to explore the perceptions of library and information science (LIS) students regarding mobile learning (m-learning) along with their primary purposes, social media applications, advantages, disadvantages, barriers, impact and overall satisfaction with using these devices. Design/methodology/approach A survey method was used, and data was collected from the students of 12 library schools in Pakistan for a total sample of 250 students. Findings Their main purposes of usage, their needs, advantages, disadvantages, barriers, impacts and the level of overall satisfaction were also identified. The inferential statistics ( t -test and ANOVA) also identified the difference of opinion on the basis of gender, programs, types and number of m-learning devices. Overall, the findings highlight the need for academic libraries to give mobile access (launching mobile applications) to better use library services. To ensure this, libraries must keep an eye on new advances in mobile technology, researchers’ needs and related electronic library services and observe how these services are being used. Practical implications Theoretical and practical implications have been highlighted to understand the perceptions of LIS students about m-learning devices. Originality/value The mobile library service providers and management need to improve their services by offering services that fit the diverse needs of their users and should know how to attract modern library users.
DDIN: Deep Disentangled Interest Network for Click-Through Rate Prediction

Click-Through Rate(CTR) prediction aims to predict the possibility of users clicking on products, which has become the core task of advertising recommendation systems. Due to the richness of user historical behavior, a key to making effective prediction is to capture users' diverse interests from historical behavior. An efficient way to do this is to perform dot product of behavior and target embedding with attentive neural networks. To better model the users' diverse interests, our proposed disentangled interest extraction block decouples the unary terms modeling the impact of user behavior sequence from pairwise interactions. Specifically, the decoupled pairwise term can learn the pure pairwise interactions, whereas the unary term models the impact of behavior sequence on each target items. Meanwhile, our model emphasizes both high- and low-order feature interactions by combining Attentional Factorization Machines(AFMs) with deep learning. This work intends to accomplish our goal by proposing a novel architecture Deep Disentangled Interest Network(DDIN). We conduct comprehensive experiments on Movielens dataset and Amazon electronic dataset. The results demonstrate the effectiveness of DDIN which is superior to some state-of-art models by up to 26.271 %.
A Protocol for Testing Conscious Learning Robots

This is a theoretical paper. The theory [31] and algorithm [32] for conscious learning have been recently published. Developmental scales for human children are well developed. Such scales need to be adapted to testing conscious learning robots. Without such adaptations, future conscious robots lack a standard, even if the theory and algorithm for conscious learning are implemented and refined in the future. This paper discusses such an adaptation, but does not include actual experimental results. It first proposes that an open skull will not allow a conscious brain because the open skull allows a conscious homunculus (human) who takes over the job of consciousness. That is why the currently popular “open skull” machine learning protocols will not produce conscious robots. Then, the paper borrows some of the milestones from human mental development measured in terms of human mental ages. The author hopes that independent laboratories will conduct tests using the milestones suggested here, so as to see whether the new protocol is suited for measuring robotic consciousness. Due to space limitations, this paper does not explain conscious learning. The reader should first read [32] before reading this paper. For four aspects of transfer across milestones, the reader should read [28] (e.g., Sec. 10.3).
High Dynamic Range Imaging with Context-aware Transformer

Avoiding the introduction of ghosts when synthesising LDR images as high dynamic range (HDR) images is a challenging task. Convolutional neural networks (CNNs) are effective for HDR ghost removal in general, but are challenging to deal with the LDR images if there are large movements or oversaturation/undersaturation. Existing dual-branch methods combining CNN and Transformer omit part of the information from non-reference images, while the features extracted by the CNN-based branch are bound to the kernel size with small receptive field, which are detrimental to the deblurring and the recovery of oversaturated/undersaturated regions. In this paper, we propose a novel hierarchical dual Transformer method for ghost-free HDR (HDT-HDR) images generation, which extracts global features and local features simultaneously. First, we use a CNN-based head with spatial attention mechanisms to extract features from all the LDR images. Second, the LDR features are delivered to the Hierarchical Dual Transformer (HDT). In each Dual Transformer (DT), the global features are extracted by the window-based Transformer, while the local details are extracted using the channel attention mechanism with deformable CNNs. Finally, the ghost free HDR image is obtained by dimensional mapping on the HDT output. Abundant experiments demonstrate that our HDT-HDR achieves the state-of-the-art performance among existing HDR ghost removal methods.
Crucial Semantic Classifier-based Adversarial Learning for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA), which aims to explore the transferrable features from a well-labeled source domain to a related unlabeled target domain, has been widely progressed. Nevertheless, as one of the mainstream, existing adversarial-based methods neglect to filter the irrelevant semantic knowledge, hindering adaptation performance improvement. Besides, they require an additional domain discriminator that strives extractor to generate confused representations, but discrete designing may cause model collapse. To tackle the above issues, we propose Crucial Semantic Classifier-based Adversarial Learning (CSCAL), which pays more attention to crucial semantic knowledge transferring and leverages the classifier to implicitly play the role of domain discriminator without extra network designing. CSCAL effectively mitigates distribution shifts between the source and target domains from both intra- and inter-class perspectives. Specifically, in intra-class-wise alignment, a Paired-Level Discrepancy (PLD) is designed to transfer crucial semantic knowledge. Additionally, based on classifier predictions, a Nuclear Norm-based Discrepancy (NND) is formed that considers inter-class-wise information and improves the adaptation performance. Moreover, CSCAL can be effortlessly merged into different UDA methods as a regularizer and dramatically promote their performance.
Controlling dynamical systems to complex target states using machine learning: next-generation vs. classical reservoir computing

Controlling nonlinear dynamical systems using machine learning allows to not only drive systems into simple behavior like periodicity but also to more complex arbitrary dynamics. For this, it is crucial that a machine learning system can be trained to reproduce the target dynamics sufficiently well. On the example of forcing a chaotic parametrization of the Lorenz system into intermittent dynamics, we show first that classical reservoir computing excels at this task. In a next step, we compare those results based on different amounts of training data to an alternative setup, where next-generation reservoir computing is used instead. It turns out that while delivering comparable performance for usual amounts of training data, next-generation RC significantly outperforms in situations where only very limited data is available. This opens even further practical control applications in real world problems where data is restricted.
On-line Learning, Classification and Interpretation of Brain Signals using 3D SNN and ESN

The paper proposes a novel hierarchical recurrent neural network architecture for on-line classification and interpretation of EEG data. It incorporates two dynamic pools of neurons - one based on NeuCube three dimensional structure of spiking neurons, spatially mapping a brain template and connected via spike-timing dependent plastic synapses and another Echo state neural network (ESN) reservoir of sparsely connected hyperbolic tangent neurons that is able to learn on-line to classify continuously extracted from the Cube spike-rate features. The aim of the work was to interpret and classify in a brain-inspired manner dynamic spatio-temporal brain signals. The achieved results demonstrate improved classification accuracy on a benchmark EEG data set along with a good interpretability of the data. In future, the proposed method can be used for classification of other brain spatio-temporal data, such as ECOG and fMRI.
High-Throughput Privacy-Preserving GRU Network with Homomorphic Encryption

The deep learning technique has been applied in a wide range of applications. The development of cloud computing further expands the application scenarios of deep learning but brings privacy issue. Homomorphic Encryption (HE), as a cryptographic solution, has attracted much attention in recent years. While some research applies HE on forward neural networks (FNNs), the research on recurrent neural networks (RNN) is still rare because the deep recurrent operation is hard to implement with HE. In this paper, we propose the first gated recurrent unit (GRU) network on HE scheme without bootstrapping, which is very expensive that takes 73% calculation time in the former encrypted GRU design. We introduce several techniques to reduce the multiplicative depth to 8 per recurrent step and allow 7 recurrent steps in our model. Processing long input sequences is also available by a rearranging method to control the recurrent steps less than 7. The testing results show that there is nearly no accuracy degradation between our encrypted GRU and the original unencrypted model. Also, we highly improve the throughput by a packing strategy. On MNIST dataset, we achieve 98.6% accuracy, which is totally the same as the unencrypted result. The throughput is 439 images/hour, which is 360 times higher than the former design. Furthermore, we test more complex natural language processing (NLP) task, which is a dominant application of RNNs. Our encrypted GRU network achieves 90.0% accuracy on AG-news dataset, almost the same as the unencrypted GRU model of 90.3%. The results indicate that the encrypted GRU design can provide accurate, efficient and privacy-preserving predictions.
A Survey of Datasets, Applications, and Models for IMU Sensor Signals

Inertial Measurement Units (IMUs) are small, low-cost sensors that can measure accelerations and angular velocities, making them valuable tools for a variety of applications, including robotics, virtual reality, and healthcare. With the advent of deep learning, there has been a surge of interest in using IMU data to train DNN models for various applications. In this paper, we survey the state-of-the-art ML models including deep neural network models and applications for IMU sensors. We first provide an overview of IMU sensors and the types of data they generate. We then review the most popular models for IMU data, including convolutional neural networks, recurrent neural networks, and attention-based models. We also discuss the challenges associated with training deep neural networks on IMU data, such as data scarcity, noise, and sensor drift. Finally, we present a comprehensive review of the most prominent applications of deep neural networks for IMU data, including human activity recognition, gesture recognition, gait analysis, and fall detection. Overall, this survey provides a comprehensive overview of the stateof-the-art deep neural network models and applications for IMU sensors and highlights the challenges and opportunities in this rapidly evolving field.
Enhancing Human Activity Recognition Through Sensor Fusion And Hybrid Deep Learning Model

Wearable-based human activity recognition (HAR) is essential for several applications, such as health monitoring, physical training, and rehabilitation. However, most HAR systems presently depend on a single sensor, typically a smartphone, due to its widespread use. To improve performance and adapt to various scenarios, this study focuses on a smart belt equipped with acceleration and gyroscope sensors for detecting activities of daily living (ADLs). The collected data was pre-processed, fused and used to train a hybrid deep learning model incorporating a CNN and BiLSTM network. We evaluated the effect of window length on recognition accuracy and conducted a performance analysis of the proposed model. Our framework achieved an overall accuracy of 96% at a window length of 5 seconds, demonstrating its effectiveness in recognizing ADLs. The results show that belt sensor fusion for HAR provides valuable insights into human behaviour and could enhance applications such as healthcare, fitness, and sports training.
DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation

Depth estimation using monocular vision sensors is crucial in computer vision, with diverse applications ranging from autonomous driving to robot motion. Conventional methods suffer from the trade-off between consistency and fine-grained details due to the local-receptive field limiting their practicality. This lack of long-range dependency inherently comes from the convolutional neural network (CNN) part of the architecture. In this article, a dual window transformer-based network, namely DwinFormer, is proposed, which utilizes both local and global features for depth estimation using monocular vision sensors. The DwinFormer consists of dual window self-attention (Dwin-SA) and cross-attention transformers, dual window self-attention transformer (Dwin-SAT) and dual window cross attention transformer (Dwin-CAT), respectively. The Dwin-SAT seamlessly extracts intricate, locally aware features while concurrently capturing global context. It harnesses the power of local and global window attention to adeptly capture both short-range and long-range dependencies, obviating the need for complex and computationally expensive operations, such as attention masking or window shifting. Moreover, Dwin-SAT introduces inductive biases which provide desirable properties, such as translational equivariance and less dependence on large-scale data. Furthermore, conventional decoding methods often rely on skip connections which may result in semantic discrepancies and a lack of global context when fusing encoder and decoder features. In contrast, the Dwin-CAT employs both local and global window cross-attention to seamlessly fuse encoder and decoder features with both fine-grained local and contextually aware global information, effectively amending semantic gap. Empirical evidence obtained through extensive experimentation on the NYU-Depth-V2 and Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets demonstrates the superiority of the proposed method, consistently outperforming existing approaches across both indoor and outdoor environments.
Adaptive Resource Scheduling in Permissionless Sharded-Blockchains: A Decentralized Multiagent Deep Reinforcement Learning Approach

Existing permissionless sharded-Blockchains come on the scene. However, there is a lack of systematic formulations and experiments regarding the behaviors of individual miners. In this article, we interpret block mining in a permissionless sharded-Blockchain as a repeated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -player noncooperative game with finite actions, and propose a new multiagent deep reinforcement learning (MADRL) framework to allow the miners to maximize their profits in a decentralized fashion by scheduling their resources across the shards without centralized coordination. We formulate the rewards, and design a two-scale action space for each miner to reduce the action space and expedite convergence. We also propose a new MADRL model, named Rainbow-WoLF-PHC, which allows each miner to learn its resource allocation online and converge fast to a mixed strategy Nash equilibrium. Extensive experiments show the superiority of the Rainbow-WoLF-PHC to its alternatives in terms of convergence, stability, and profitable actions. This work provides a prosperous design of an end-user-friendly permissionless sharded-Blockchain.
Graph Analysis of Blockchain P2P Overlays and Their Security Implications

In blockchain systems, similar to any distributed system, the underlying network plays a crucial role and provides the infrastructure for communication and coordination among the participating peers. As a result, the properties of the network define the level of security, availability, and fault tolerance within a blockchain system. This study aims to improve our understanding of the structural properties of peer-to-peer overlay networks that underpin blockchain applications. Our objective is to gain insights into the security and resilience of these systems. By analyzing seven distinct blockchain overlay networks and evaluating a comprehensive set of graph characteristics, we draw important conclusions about their overall robustness. Our findings reveal that major blockchain networks have vulnerabilities that make them susceptible to exploitation by malicious actors. Furthermore, despite relying on similar protocols for node discovery and network formation, we observe dissimilar characteristics among these blockchains.
RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks

Capsule Networks (CapsNets) are able to hierarchically preserve the pose relationships between multiple objects for image classification tasks. Other than achieving high accuracy, another relevant factor in deploying CapsNets in safety-critical applications is the robustness against input transformations and malicious adversarial attacks. In this paper, we systematically analyze and evaluate different factors affecting the robustness of CapsN ets, compared to traditional Convolutional Neural Networks (CNNs). Towards a comprehensive comparison, we test two CapsNet models and two CNN models on the MNIST, GTSRB, and CIFAR10 datasets, as well as on the affine-transformed versions of such datasets. With a thorough analysis, we show which properties of these architectures better contribute to increasing the robustness and their limitations. Overall, CapsNets achieve better robustness against adversarial examples and affine transformations, compared to a traditional CNN with a similar number of parameters. Similar conclusions have been derived for deeper versions of CapsNets and CNNs. Moreover, our results unleash a key finding that the dynamic routing does not contribute much to improving the CapsNets' robustness. Indeed, the main generalization contribution is due to the hierarchical feature learning through capsules.
Multi-channel Attentive Weighting of Visual Frames for Multimodal Video Classification

Multimodal video classification aims to incorporate semantic information to regularize the visual representation learning of videos. Conventional methods typically focus on analyzing all information extracted from different modals rather than key information. However, they usually face the problem of handling the redundant video frames of little categorical information. To address this problem, this paper proposes a novel approach that employs multi-channel weighting of visual frames to mitigate the interference of redundant information. Specifically, the proposed algorithm, termed MCA-WF, includes two main modules, where the multi-channel attentive weighting of video frames (McAW) module performs the multi-granularity and multi-channel frame weighting mechanism based on visual self-attention, contrastive attention and cross-modal attention constraints to filter visual noise and redundant information. The visual frame selection (VFS) module explores the combination of multi-channel attention mechanisms to select the key visual information in the video. Experiments were conducted on MSR-VTT and ActivityNet Captions datasets in terms of performance comparison, ablation study, in-depth analysis, and case studies. The results verified that MCA-WF can notice the key information in the classification and effectively improve the ability of information complementation and integration between modals, which leads to better performance than the state-of-the-art methods.