Reviewerly/exHarmony huggingface dataset:
A Deep Joint Network for Monocular Depth Estimation Based on Pseudo-Depth Supervision

Depth estimation from a single image is a significant task. Although deep learning methods hold great promise in this area, they still face a number of challenges, including the limited modeling of nonlocal dependencies, lack of effective loss function joint optimization models, and difficulty in accurately estimating object edges. In order to further increase the network’s prediction accuracy, a new structure and training method are proposed for single-image depth estimation in this research. A pseudo-depth network is first deployed for generating a single-image depth prior, and by constructing connecting paths between multi-scale local features using the proposed up-mapping and jumping modules, the network can integrate representations and recover fine details. A deep network is also designed to capture and convey global context by utilizing the Transformer Conv module and Unet Depth net to extract and refine global features. The two networks jointly provide meaningful coarse and fine features to predict high-quality depth images from single RGB images. In addition, multiple joint losses are utilized to enhance the training model. A series of experiments are carried out to confirm and demonstrate the efficacy of our method. The proposed method exceeds the advanced method DPT by 10% and 3.3% in terms of root mean square error (RMSE(log)) and 1.7% and 1.6% in terms of squared relative difference (SRD), respectively, according to experimental results on the NYU Depth V2 and KITTI depth estimation benchmarks.
Sequence Prediction and Classification of Echo State Networks

The echo state network is a unique form of recurrent neural network. Due to its feedback mechanism, it exhibits superior nonlinear behavior compared to traditional neural networks and is highly regarded for its simplicity and efficiency in computation. In recent years, as network development has progressed, the security threats faced by networks have increased. To detect and counter these threats, the analysis of network traffic has become a crucial research focus. The echo state network has demonstrated exceptional performance in sequence prediction. In this article, we delve into the impact of echo state networks on time series. We have enhanced the model by increasing the number of layers and adopting a different data input approach. We apply it to predict chaotic systems that appear ostensibly regular but are inherently irregular. Additionally, we utilize it for the classification of sound sequence data. Upon evaluating the model using root mean squared error and micro-F1, we have observed that our model exhibits commendable accuracy and stability.
DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media

SocialMedia Marketing (SMM) has become a mainstream promotional scheme. Almost every business promotes itself through social media, and an educational institution is no different. The users’ responses to social media posts are crucial to a successful promotional campaign. An adverse reaction leaves a long-term negative impact on the audience, and the conversion rate falls. This is why selecting the content to share on social media is one of the most effective decisions behind the success of a campaign. This paper proposes a Data-Driven Promotional Management System (DPMS) for universities to guide the selection of appropriate content to promote on social media, which is more likely to obtain positive user reactions. The main objective of DPMS is to make effective decisions for Social Media Marketing (SMM). The novel DPMS uses a well-engineered and optimized BiLSTM network, classifying users’ sentiments about different university divisions, with a stunning accuracy of 98.66%. The average precision, recall, specificity, and F1-score of the DPMS are 98.12%, 98.24%, 99.39%, and 98.18%, respectively. This innovative Promotional Management System (PMS) increases the positive impression by 68.75%, reduces the adverse reaction by 31.25%, and increases the conversion rate by 18%. In a nutshell, the proposed DPMS is the first promotional management system for universities. It demonstrates significant potential for improving the brand value of universities and for increasing the intake rate.
Attention guided cross-modal multispectral object detection

The spatial offset between the visible and thermal images can pose challenges for accurately matching objects in both modalities, thus affecting the performance of object detection algorithms. To address this, we propose a feature alignment-based algorithm for unaligned RGB-T image object detection. Our approach admits the coarse-to-fine feature alignment strategy, incorporating an attention-guided feature offset prediction module. Additionally, a multi-headed self-attention mechanism is introduced to predict and correct the feature map offsets for visible images during the feature extraction stage. To further correct the offset between RGB-T features, a region of interest alignment module performs quadratic regression for each candidate frame in the pooling stage. Furthermore, our algorithm introduces a light-aware weighting module to adaptively adjust the contributions of different modalities by reweighting the region of interest features. Experimental results on the FLIRADAS dataset demonstrate that the proposed method achieves high accuracy and stability.
A dynamic gesture recognition method based on R(2+1)D-transformer network

Efficient spatial-temporal feature extraction from input video streams is crucial for dynamic gesture recognition. In the task of video classification, convolutional neural networks (CNNs) are widely used as feature extractors, while methods based on recurrent neural networks (RNNs) are commonly employed for sequence modeling. However, RNNs lack the ability to model global dependencies and have a limited attention span in the temporal dimension. This becomes a performance bottleneck for dynamic gestures that require sensitivity to temporal correlations. To address this issue, this paper proposes a dynamic gesture recognition model called R(2+1)D-Transformer. It is a Transformer-based approach that focuses on global modeling. Firstly, the R(2+1)D network is employed as a spatial-temporal feature extractor to capture the spatiotemporal information. Then, self-attention-based Transformer is used to map the spatiotemporal feature sequence to the semantic representation of gesture movements, considering both the temporal and spatial context. Finally, the gesture recognition results are obtained through an MLP classification head. Experimental results demonstrate the effectiveness and potential of the proposed R(2+1)D-Transformer model on two publicly available dynamic gesture datasets, IPN-Hand and NvGesture. The promising performance of the proposed approach provides valuable insights and reference for further research and applications in dynamic gesture recognition.
Comparison of Pre-trained vs Custom-trained Word Embedding Models for Word Sense Disambiguation

The prime objective of word sense disambiguation (WSD) is to develop such machines that can automatically recognize the actual meaning (sense) of ambiguous words in a sentence. WSD can improve various NLP and HCI challenges. Researchers explored a wide variety of methods to resolve this issue of sense ambiguity. However, majorly, their focus was on English and some other well-reputed languages. Urdu with more than 300 million users and a large amount of electronic text available on the web is still unexplored. In recent years, for a variety of Natural Language Processing tasks, word embedding methods have proven extremely successful. This study evaluates, compares, and applies a variety of word embedding approaches to Urdu Word embedding (both Lexical Sample and All-Words), including pre-trained (Word2Vec, Glove, and FastText) as well as custom-trained (Word2Vec, Glove, and FastText trained on the Ur-Mono corpus). Two benchmark corpora are used for the evaluation in this study: (1) the UAW-WSD-18 corpus and (2) the ULS-WSD-18 corpus. For Urdu All-Words WSD tasks, top results have been achieved (Accuracy=60.07 and F1=0.45) using pre-trained FastText. For the Lexical Sample, WSD has been achieved (Accuracy=70.93 and F1=0.60) using custom-trained GloVe word embedding method.
A Novel Interaction Convolutional Network Based on Dependency Trees for Aspect-Level Sentiment Analysis

Aspect-based sentiment analysis aims to identity the sentiment polarity of a given aspect-based word in a sentence. Due to the complexity of sentences in the texts, the models based on the graph neural network still have issues in the accurately capturing the relationship between aspect words and viewpoint words in sentences, failing to improve the accuracy of classification. To solve this problem, the paper proposes a novel Aspect-level Sentiment Analysis model based on Interactive convolutional network with the dependency trees, named ASAI-DT in short. In particular, the ASAI-DT model first extracts the aspect words representation from the sentence representation trained by the Bi-GRU model. Meanwhile, the self-attention score of both the sentence and aspect representation are calculated separately by the self-attention mechanism, in order to reduce the attention to the irrelevant information. Afterward, the proposed model constructs the sub-tree of the dependency trees for the word, while the attention weight scores of the aspect representations will be integrated into the sub-tree. Therefore, the acquired comprehensive information about aspect words is processed by the graph convolutional network to maximize the retention of valid information and minimize the interference of noise. Finally, the effective information can be preserved more completely in the integrated information through the interactive network. Through a large number of experiments on various data sets, the proposed ASAI-DT model shows both the effectiveness and the accuracy of aspect sentiment analysis, which outperforms many aspect-based sentiment analysis models.
Gradient Coupled Flow: Performance Boosting on Network Pruning by Utilizing Implicit Loss Decrease

Network pruning prior to training makes generalization more challenging than ever, while recent studies mainly focus on the trainability of the pruned networks in isolation. This paper explores a new perspective on loss implicit decrease of the data to be trained caused by one-batch training during each round, whose first-order approximation we term gradient coupled flow. We thus present a criterion sensitive to gradient coupled flow (GCS), which is hypothesized to capture those weights most sensitive to performance boosting at initialization. Interestingly, our explorations show there exists a linear correlation between generalization and implicit loss decrease based measurements on previous works as well as GCS, which ideally describes causes of accuracy fluctuation in a fine-grained manner. Our code is made public at: https://github.com/kangxiatao/pruning_before_training .
Dual Channel Graph Neural Network Enhanced by External Affective Knowledge for Aspect Level Sentiment Analysis

Aspect-level sentiment analysis is a prominent technology in natural language processing (NLP) that analyzes the sentiment polarity of target words in a text. Despite its long history of development, current methods still have some shortcomings. Mainly, they lack the integration of external affective knowledge, which is crucial for allocating attention to aspect-related words in syntactic and semantic information processing. Additionally, the synergy between syntactic and semantic information is often neglected, with most approaches focusing on only one dimension. To address these issues, we propose a knowledge-enhanced dual-channel graph neural network. Our model incorporates external affective knowledge into both the semantic and syntactic channels in different ways, then utilizes a dynamic attention mechanism to fuse information from these channels. We conducted experiments on Semeval2014, 2015, and 2016 datasets, and the results showed significant improvements compared to existing methods. Our approach bridges the gaps in current techniques and enhances performance in aspect-level sentiment analysis.
Efficient Collaboration via Interaction Information in Multi-agent System

Cooperative multi-agent reinforcement learning (CMARL) has shown promise in solving real-world scenarios. The interaction information between agents contains rich global information, which is easily neglected after perceiving other agents' behavior. To tackle this problem, we propose Collaboration Interaction Information Modelling via Hypergraph (CIIMH), which first perceives the behavior of other agents by mutual information optimization and constructs the dynamic interaction information via hypergraph. Perceived behavioral features of other agents are further aggregated in the hypergraph convolutional network to obtain interaction information. We compare our method with three existing baselines on StarCraft II micromanagement tasks (SMAC), Level-based Foraging (LBF), and Hallway. Empirical results show that our method outperforms baseline methods on all maps.
MRRC: Multi-agent Reinforcement Learning with Rectification Capability in Cooperative Tasks

Motivated by the centralised training with decentralised execution (CTDE) paradigm, multi-agent reinforcement learning (MARL) algorithms have made significant strides in addressing cooperative tasks. However, the challenges of sparse environmental rewards and limited scalability have impeded further advancements in MARL. In response, MRRC, a novel actor-critic-based approach is proposed. MRRC tackles the sparse reward problem by equipping each agent with both an individual policy and a cooperative policy, harnessing the benefits of the individual policy's rapid convergence and the cooperative policy's global optimality. To enhance scalability, MRRC employs a monotonic mix network to rectify the state-action value function Q for each agent, yielding the joint value function $${Q_{tot}}$$ to facilitate global updates of the entire critic network. Additionally, the Gumbel-Softmax technique is introduced to rectify discrete actions, enabling MRRC to handle discrete tasks effectively. By comparing MRRC with advanced baseline algorithms in the "Predator-Prey" and challenging "SMAC" environments, as well as conducting ablation experiments, the superior performance of MRRC is demonstrated in this study. The experimental results reveal the efficacy of MRRC in reward-sparse environments and its ability to scale well with increasing numbers of agents.
Latent Causal Dynamics Model for Model-Based Reinforcement Learning

Learning an accurate dynamics model is the key task for model-based reinforcement learning (MBRL). Most existing MBRL methods learn the dynamics model over states. But in most cases, the relationships among states are complex because the states are affected by the interaction of various factors in the environment. Recently some works are proposed to learn the dynamics model on latent representations space. But the learned model is dense and may contain spurious associations between latent representations. To deal with these problems, we introduce a latent causal dynamics model over latent representations and provide a learning method for MBRL. Specifically, we first learn the latent representations from the observed state space. Second, we learn a latent causal dynamics model among latent representations by a causal discovery method. Finally, the latent causal dynamics model is used to aid policy learning. The above steps are iterative to update the unified loss function until convergence. Experimental results on four tasks show that the performance of our proposed method benefits from the causality and the learned latent representations.
Modeling of class imbalance handling with optimal deep learning enabled big data classification model

Big data is the amount of data that surpasses the ability to process the data of a system concerning memory usage and computation time. It is commonly applied in several domains like healthcare, education, social networks, e-commerce, etc., as they have progressively obtained a massive quantity of input data. A major research problem is big data analytics, which can be carried out using expert systems and deep structured architectures. Besides, data wrangling and class imbalance data handling are challenging issues that need to be resolved in big data analytics. Class imbalance data degrade the performance of the classification model, which remains a challenging process due to the heterogeneous and complex structure of the comparatively huge datasets. Thus, the research focused on presenting a Class Imbalance Handling with Optimal Deep Learning Enabled Big Data Classification (CIHODL-BDC) framework. The core perception of the CIHODL-BDC framework helps to classify the big data in the Hadoop MapReduce framework. To accomplish this, the presented CIHODL-BDC model initially performs a data wrangling process is performed to alter the unrefined data into a useful layout. Next, the CIHODL-BDC model handles the class imbalance problem using a grey wolf optimizer (GWO) with Synthetic Minority Oversampling (SMOTE) technique. Besides, the Adam optimizer procedure with the Bidirectional Long Short Term Memory (BiLSTM) approach is performed to categorize the big data. The result analysis of the proposed CIHODL-BDC model is evaluated by two standard datasets. The simulation outcomes revealed the elevated performance of the CIHODL-BDC approach over existing methods.
A quality assessment of Korean–English patent machine translation

Abstract This paper aims to investigate the quality Korean–English patent translations by three machine translation (MT) engines based on automatic and human evaluations of Korean to English Patent Automatic Translation (K2E-PAT), a pattern-based statistical MT; and Patent Translate and WIPO Translate, both neural MTs. For title translations, WIPO Translate scored the highest in automatic and human evaluations, while results were mixed for the other two MTs. K2E-PAT slightly outperformed Patent Translate in automatic evaluation, whereas Patent Translate outperformed K2E-PAT in human evaluation. For abstract translations, Patent Translate scored the highest in automatic evaluation, followed by WIPO Translate and K2E-PAT. In human evaluation, the ranking order was the same as that of title translations, with WIPO Translate scoring the highest on average. The results indicated correlations between automatic and human evaluations, and the NMTs subject to the current study still do not render satisfactory gist translation from Korean to English.
Accelerated Genetic Algorithm with Population Control for Energy-Aware Virtual Machine Placement in Data Centers

Energy efficiency is crucial for the operation and management of cloud data centers, which are the foundation of cloud computing. Virtual machine (VM) placement plays a vital role in improving energy efficiency in data centers. The genetic algorithm (GA) has been extensively studied for solving the VM placement problem due to its ability to provide high-quality solutions. However, GA's high computational demands limit further improvement in energy efficiency, where a fast and lightweight solution is required. This paper presents an adaptive population control scheme that enhances gene diversity through population control, adaptive mutation rate, and accelerated termination. Experimental results show that our scheme achieves a 17% faster acceleration and 49% fewer generations compared to the standard GA for energy-efficient VM placement in large-scale data centers.
TEACHING AND LEARNING QUALITY IN A HIGHER EDUCATION CONTEXT IN CHINA

Higher education institutions around the world have seen a surge in quality evaluations as a result of an increase in public sector accountability. Student learning is facilitated by high-quality teaching. The quality of teaching and learning at higher education institutions is being closely monitored. Academic quality in higher education has been the subject of new policy instruments introduced in many nations during the last decade. These include the UK, the US, and Australia. There was increasing pressure on UK's higher education system, for example, due to the growth of tertiary institutions and increased consumer expectations. The Quality Assurance Agency for Higher Education (QAAHE) was established in 1997 to implement a dual system of quality assurance that included quality audits and quality assessments. As a result of the establishment of the Australian University Quality Agency in 2001 to monitor the quality of the country's higher education system, a quality assurance program was implemented, requiring universities to incorporate quality assurance and improvement planning into their annual published educational plans. And this is a review paper. For Chinese universities, quality assurance is a novel notion. The fast growth of Chinese tertiary institutions in the last 15 years has resulted in an increased focus on teaching quality. The number of students enrolled in China's higher education institutions has increased dramatically since 1999 as a result of increased enrolment.
Constructing hermeneutical relations: a postphenomenological inquiry into immersive VR memory palaces

Abstract While VR adaptations of the mnemonic Method of Loci (or the ‘Memory Palace’ technique) show promising results in increasing mnemonic capabilities, little to no research has explored the use and integration of Virtual Memory Palaces over time in a context of self-initiated studying. To explore the use of Virtual Memory Palaces (VMPs) contextually, we conducted an in-the-wild study where we gave ten participants a VR Head-Mounted Display through which they could access and furnish their VMP over eight weeks. We conducted go-along interviews in our participants’ VMPs at various intervals throughout the eight-week study, exploring their creations and querying them about their experience. Based on our findings, this article discusses individual and contextual factors that come into play when a VMP is approached as a personal project in the midst of an already-established study routine. We frame our study as a postphenomenological inquiry into the mediating effects of VMPs, where our primary interest lies in what relationship the students developed to the VMP.
CAS-NN: A Robust Cascade Neural Network Without Compromising Clean Accuracy

Adversarial training has emerged as a prominent approach for training robust classifiers. However, recent researches indicate that adversarial training inevitably results in a decline in a classifier's accuracy on clean (natural) data. Robustness is at odds with clean accuracy due to the inherent tension between the objectives of adversarial robustness and standard generalization. Training a single classifier that combines high adversarial robustness and high clean accuracy appears to be an insurmountable challenge. This paper proposes a straightforward strategy to bridge the gap between robustness and clean accuracy. Inspired by the idea underlying dynamic neural networks, i.e., adaptive inference, we propose a robust cascade framework that integrates a standard classifier and a robust classifier. The cascade neural network dynamically classifies clean and adversarial samples using distinct classifiers based on the confidence score of each input sample. As deep neural networks suffer from serious overconfident problems on adversarial samples, we propose an effective confidence calibration algorithm for the standard classifier, enabling accurate confidence scores for adversarial samples. The experiments demonstrate that the proposed cascade neural network increases the clean accuracies by 10.1%, 14.67%, and 9.11% compared to the advanced adversarial training (HAT) on CIFAR10, CIFAR100, and Tiny ImageNet while keeping similar robust accuracies.