Reviewerly/exHarmony huggingface dataset:
Multi-source domain adaptation method for textual emotion classification using deep and broad learning

Existing domain adaptation methods for classifying textual emotions have the propensity to focus on single-source domain exploration rather than multi-source domain adaptation. The efficacy of emotion classification is hampered by the restricted information and volume from a single source domain. Thus, to improve the performance of domain adaptation, we present a novel multi-source domain adaptation approach for emotion classification, by combining broad learning and deep learning in this article. Specifically, we first design a model to extract domain-invariant features from each source domain to the same target domain by using BERT and Bi-LSTM, which can better capture contextual features. Then we adopt broad learning to train multiple classifiers based on the domain-invariant features, which can more effectively conduct multi-label classification tasks. In addition, we design a co-training model to boost these classifiers. Finally, we carry out several experiments on four datasets by comparison with the baseline methods. The experimental results show that our proposed approach can significantly outperform the baseline methods for textual emotion classification.
Developing an integrated approach based on geographic object-based image analysis and convolutional neural network for volcanic and glacial landforms mapping

Rapid detection and mapping of landforms are crucially important to improve our understanding of past and presently active processes across the earth, especially, in complex and dynamic volcanoes. Traditional landform modeling approaches are labor-intensive and time-consuming. In recent years, landform mapping has increasingly been digitized. This study conducted an in-depth analysis of convolutional neural networks (CNN) in combination with geographic object-based image analysis (GEOBIA), for mapping volcanic and glacial landforms. Sentinel-2 image, as well as predisposing variables (DEM and its derivatives, e.g., slope, aspect, curvature and flow accumulation), were segmented using a multi-resolution segmentation algorithm, and relevant features were selected to define segmentation scales for each landform category. A set of object-based features was developed based on spectral (e.g., brightness), geometrical (e.g., shape index), and textural (grey level co-occurrence matrix) information. The landform modelling networks were then trained and tested based on labelled objects generated using GEOBIA and ground control points. Our results show that an integrated approach of GEOBIA and CNN achieved an ACC of 0.9685, 0.9780, 0.9614, 0.9767, 0.9675, 0.9718, 0.9600, and 0.9778 for dacite lava, caldera, andesite lava, volcanic cone, volcanic tuff, glacial circus, glacial valley, and suspended valley, respectively. The quantitative evaluation shows the highest performance (Accuracy > 0.9600 and cross-validation accuracy > 0.9400) for volcanic and glacial landforms and; therefore, is recommended for regional and large-scale landform mapping. Our results and the provided automatic workflow emphasize the potential of integrated GEOBIA and CNN for fast and efficient landform mapping as a first step in the earth's surface management.
Chatbot‐mediated learning of cardiac auscultation

Abstract Previous research suggested cardiac auscultation is underdeveloped in physicians‐in‐training. Developing proficiency requires wide exposure to signs, practice and feedback, which may not regularly occur in clinical environments. Our novel pilot study using a mixed‐methods approach ( n = 9) suggests chatbot‐mediated learning of cardiac auscultation is accessible and possesses unique advantages, including immediate feedback, helping in the management of cognitive overload and facilitating deliberate practice.
Understanding online fake review production strategies

Extending the literature that has, thus far, mostly focused on the detection of fake online reviews, this paper explores a more fundamental yet hitherto-unanswered question: How do people go about creating fake reviews in the first place? To delve deeper, it further investigates the role of individuals’ cognitive style—the way one gathers, processes, structures, and applies information. While cognitive style was measured using a quantitative scale, a qualitative approach was adopted to explore fake review production strategies. Fifty participants imagined that they had been hired by a marketing agency to write fake reviews, completed the writing task, shared their experiences, and then filled out a cognitive style questionnaire. Writing fake reviews involved four stages: gathering information, assimilating information, drafting the fake review, and finalizing the fake review production. Through a cognitive lens, the paper uncovers three fake review production strategies and explains why someone would adopt a certain strategy.
Feature fusion based deep neural collaborative filtering model for fertilizer prediction

With the advent of the modern era, deep neural networks have dominated recommender systems, as they can effectively capture complex interactions. Nevertheless, there is still a research gap in fertilizer recommendations based on soil nutrients content. In this work, we propose nutrient-centered deep collaborative filtering technique to determine the required amount of fertilizers for sustainable crop growth. The undetermined fertilizer's amount is treated as a data sparsity problem that is solved primarily by adding side features such as soil fertilizer level, land size, and soil chemical properties. We specifically introduce a new method of wide matrix factorization that maps the embedded land and nutrient vectors linearly to obtain the feature representation. Furthermore, the fully connected multi-layer perceptron captures the non-linear higher-order interaction between land and nutrients to deeply learn the historical fertilizer recommendations of the land. An exploratory outcome on two real-time datasets indicates the superior performance of the proposed method over recent state-of-the-art approaches. It reveals that the proposed deep network predicts well on nutrients data for precise fertilizer recommendation, which support farmers to increase the crop yield.
Fast and robust spatial fuzzy bounded k-plane clustering method for human brain MRI image segmentation

Fuzzy k-plane clustering (FkPC) is a soft plane-based clustering that efficiently clusters non-spherically distributed data. However, the FkPC method is sensitive to noise and provides unbounded cluster planes. To overcome these two limitations, we propose two modifications in the conventional FkPC method, referred as fuzzy bounded k-plane clustering method with local spatial information (FBkPC_S1). We introduce FCM objective function to bound the cluster planes and local spatial information in the objective function of FkPC to handle noise. The proposed FBkPC_S1 clustering method is fast and robust as it produces bounded cluster planes and can provide accurate segmentation in presence of noise. To show the effectiveness of the proposed FBkPC_S1 method, extensive experiments are performed on one synthetic image dataset and three publicly available human brain MRI datasets. The performance of the proposed FBkPC_S1 method is compared with 19 related methods in terms of average segmentation accuracy and Dice score. The proposed method achieves 91%, 65% and 75% average segmentation accuracy in the presence of noise artifacts on BrainWeb, IBSR and MRBrainS18 MRI datasets, respectively. Experimental results and statistical test demonstrate superior performance of the proposed FBkPC_S1 method in comparison to related methods.
UniColor

We propose the first unified framework UniColor to support colorization in multiple modalities, including both unconditional and conditional ones, such as stroke, exemplar, text, and even a mix of them. Rather than learning a separate model for each type of condition, we introduce a two-stage colorization framework for incorporating various conditions into a single model. In the first stage, multi-modal conditions are converted into a common representation of hint points. Particularly, we propose a novel CLIP-based method to convert the text to hint points. In the second stage, we propose a Transformer-based network composed of Chroma-VQGAN and Hybrid-Transformer to generate diverse and high-quality colorization results conditioned on hint points. Both qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art methods in every control modality and further enables multi-modal colorization that was not feasible before. Moreover, we design an interactive interface showing the effectiveness of our unified framework in practical usage, including automatic colorization, hybrid-control colorization, local recolorization, and iterative color editing. Our code and models are available at https://luckyhzt.github.io/unicolor .
Social media aesthetics as part of academic library merchandising

Purpose The subject of this paper is the phenomenon of social media aesthetics, which can be perceived as a tool for promoting and building the image of libraries, especially in terms of merchandising. The aim of this paper is to analyse the potential of the dark academia social media trend in the promotion of academic libraries. Design/methodology/approach The article is based on a review of the social networking sites YouTube and Instagram and an analysis of network resources using the Brand24 tool. Findings Resources that are described by Internet users as “dark academia” are popular in social media. Dark academia as an aesthetic concept creates potential for the promotion of academic libraries, especially those that are more traditional in terms of their architecture, décor or how they offer their services. Research limitations/implications The paper concerns a phenomenon which, although popular socially, has not yet been scientifically analysed in the literature on the subject. Since the topic is new and there is no scientific literature on it, the author had to base the paper on less standard sources of information (e.g. analysis of the content of social media). The article is a review, an introduction, as well as an invitation to further discussion. The author's aim is not to comprehensively cover this topic but only to draw attention to an interesting and rarely discussed issue that has great potential for practical activities. Practical implications The topic has great potential for the practical improvement of the promotional activities of libraries, especially older, more traditional libraries, to create a strong and positive image on the basis of characteristics often perceived as weaknesses. Social implications Social media services are powerful social impact tools. Showing the potential role of social media aesthetics for cultural institutions could serve to make the public more aware of the role of the proper use of social media for promotion and image building. Originality/value The use of social media aesthetics is very rarely discussed in the subject literature.
End-to-End Multi-Modal Speech Recognition on an Air and Bone Conducted Speech Corpus

Automatic speech recognition (ASR) has been significantly improved in the past years. However, most robust ASR systems are based on air-conducted (AC) speech, and their performances in low signal-to-noise-ratio (SNR) conditions are not satisfactory. Bone-conducted (BC) speech is intrinsically insensitive to environmental noise, and therefore can be used as an auxiliary source for improving the performance of an ASR at low SNR. In this paper, we first develop a multi-modal Mandarin corpus, which contains air- and bone-conducted synchronized speech (ABCS). The multi-modal speeches are recorded with a headset equipped with both AC and BC microphones. To our knowledge, it is by far the largest corpus for conducting bone conduction ASR research. Then, we propose a multi-modal conformer ASR system based on a novel multi-modal transducer (MMT). The proposed system extracts semantic embeddings from the AC and BC speech signals by a conformer-based encoder and a transformer-based truncated decoder. The semantic embeddings of the two speech sources are fused dynamically with adaptive weights by the MMT module. Experimental results demonstrate the proposed multi-modal system outperforms single-modal systems with either AC or BC modality and multi-modal baseline system by a large margin at various SNR levels. It also shows the two modalities complement with each other, and our method can effectively utilize the complementary information of different sources.
Unlock the black box of remote e-working effectiveness and e-HRM practices effect on organizational commitment

Organizations realizes that, after COVID-19, remote e-working will be the new working arrangement for a sizable portion of their workforce. In light of this, this study attempted to unlock the black box of remote e-working effectiveness and e-HRM practices by theoretically and empirically establishing the path by which they may deliver value to the business. Accordingly, the study investigates the effects of remote e-working and e-HRM practices on organizational commitment via the mediation of job satisfaction. The data used for this research were collected from 273 participants from 25 commercial banks in Jordan using self-administrated electronic questionnaire. The PLS-SEM method was used to analyze the data. The results supported the study hypotheses revealing that e-HRM practices positively affect remote e-working effectiveness by enabling a real-time information based communication, interactive work environment, e-training, and e-performance appraisal. The findings show that since it gives employees more control over how much time they devote to work and family life, remote e-working improves employee focus levels while they are working from home. Their satisfaction is therefore increased. Furthermore, Job satisfaction is significant predictors of whether remote work enhances organizational commitment. A satisfied e-worker will feel more emotionally connected to the organization and more obligated remain in the organization. Our research provides evidence that remote e-working effectiveness and e-HRM practices have an effect on organizational commitment and this relationship becomes stronger when it mediated by Job satisfaction. Conclusion, limitations, and future research directions are included.
Framework for the Use of Extended Reality Modalities in AEC Education

The educational applications of extended reality (XR) modalities, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), have increased significantly over the last ten years. Many educators within the Architecture, Engineering, and Construction (AEC) related degree programs see student benefits that could be derived from bringing these modalities into classrooms, which include but are not limited to: a better understanding of each of the subdisciplines and the coordination necessary between them, visualizing oneself as a professional in AEC, and visualization of difficult concepts to increase engagement, self-efficacy, and learning. These benefits, in turn, help recruitment and retention efforts for these degree programs. However, given the number of technologies available and the fact that they quickly become outdated, there is confusion about the definitions of the different XR modalities and their unique capabilities. This lack of knowledge, combined with limited faculty time and lack of financial resources, can make it overwhelming for educators to choose the right XR modality to accomplish particular educational objectives. There is a lack of guidance in the literature for AEC educators to consider various factors that affect the success of an XR intervention. Grounded in a comprehensive literature review and the educational framework of the Model of Domain Learning, this paper proposes a decision-making framework to help AEC educators select the appropriate technologies, platforms, and devices to use for various educational outcomes (e.g., learning, interest generation, engagement) considering factors such as budget, scalability, space/equipment needs, and the potential benefits and limitations of each XR modality. To this end, a comprehensive review of the literature was performed to decipher various definitions of XR modalities and how they have been previously utilized in AEC Education. The framework was then successfully validated at a summer camp in the School of Building Construction at Georgia Institute of Technology, highlighting the importance of using appropriate XR technologies depending on the educational context.
Twin SVM for conditional probability estimation in binary and multiclass classification

In this paper, we estimate the conditional probability function by presenting a new twin SVM model (CPTWSVM) in binary and multiclass classification problems. The motivation of CPTWSVM is to implement the empirical risk minimization on training data, which is hard to realize in traditional twin SVMs. In each subproblem of CPTWSVM, it measures the empirical risk and outputs the corresponding probability estimate of each class, which eliminates the problems of inconsistent measurement in twin SVMs. Though an additional discriminant objective function is introduced, the optimization problem size of each subproblem is smaller than conditional probability SVM, and is solved by block decomposition algorithm efficiently. In addition, we extend CPTWSVM to multiclass classification by estimating the conditional probability of each class, and maintaining the above properties. Numerical experiments on benchmark and real application datasets demonstrate that CPTWSVM outputs the estimate of probability and the data projection well, resulting in better generalization ability than some leading TWSVMs communities, in terms of binary and multiclass classification.
Bitcoin Price Forecasting and Trading: Data Analytics Approaches

Currently, the most popular cryptocurrency is bitcoin. Predicting the future value of bitcoin can help investors to make more educated decisions and to provide authorities with a point of reference for evaluating cryptocurrency. The novelty of the proposed prediction models lies in the use of artificial intelligence to identify movement cryptocurrency prices, particularly bitcoin prices. A forecasting model that can accurately and reliably predict the market’s volatility and price variations is necessary for portfolio management and optimization in this continually expanding financial market. In this paper, we investigate a time series analysis that makes use of deep learning to investigate volatility and provide an explanation for this behavior. Our findings have managerial ramifications, such as the potential for developing a product for investors. This can help to expand upon our model by adjusting various hyperparameters to produce a more accurate model for predicting the price of cryptocurrencies. Another possible managerial implication of our findings is the potential for developing a product for investors, as it can predict the price of cryptocurrencies more accurately. The proposed models were evaluated by collecting historical bitcoin prices from 1 January 2021 to 16 June 2022. The results analysis of the GRU and MLP models revealed that the MLP model achieved highly efficient regression, at R = 99.15% during the training phase and R = 98.90% during the testing phase. These findings have the potential to significantly influence the appropriateness of asset pricing, considering the uncertainties caused by digital currencies. In addition, these findings provide instruments that contribute to establishing stability in cryptocurrency markets. By assisting asset assessments of cryptocurrencies, such as bitcoin, our models deliver high and steady success outcomes over a future prediction horizon. In general, the models described in this article offer approximately accurate estimations of the real value of the bitcoin market. Because the models enable users to assess the timing of bitcoin sales and purchases more accurately, they have the potential to influence the economy significantly when put to use by investors and traders.
Hybrid attention-oriented experience replay for deep reinforcement learning and its application to a multi-robot cooperative hunting problem

Multiple robots complete a cooperative hunting task by obtaining environmental information and autonomously learning hunting decision-making strategies. However, with the increase in the number of environment participants, it becomes difficult for robots to process a large amount of environmental information. Thus, a multi-robot cooperative hunting decision-making method called hybrid attention-oriented experience replay in multi-agent deep deterministic policy gradient (HAER-MADDPG) is proposed. First, a hybrid attention module is designed to pay greater attention to key information in a large amount of environmental information by integrating it with the multi-agent deep deterministic policy gradient (MADDPG). The method then combines hybrid attention and prioritized experience replay to improve the utilization of experience samples. Finally, the proposed algorithm is tested through a predator–prey game. The results show that the effectiveness, convergence speed, and scalability of the proposed algorithm are better than those of the baseline algorithms. In addition, HAER-MADDPG is effectively applied to a hunting task with real robots.
Determine the number of unknown targets in the open world from the perspective of bidirectional analysis using Gap statistic and Isolation forest

How to ascertain the number of unknown targets in open world is a crucial issue in generalized evidence theory (GET). To this, limited works have been done, and they may trap in low accuracy, multi-choice issue, complex implementation, etc. To overcome the above limitations, a new method which introduces Isolation forest and Gap statistic into K-means method to perform "bidirectional" analysis is proposed in this paper. According to the "forward" clustering result, isolation-based anomaly detection could achieve the partition of normal domain and abnormal domain, then "reverse" clustering could directly focus on the abnormal domain to pick out those novel clusters using the newly defined novelty validation standard. Further, the defective frame of discernment (FOD) could be completed since the sum of base clusters and novel clusters is the actual target numbers. The proposed method could guarantee the integrity of FOD even the features of the samples are highly-correlated. Various simulation experiments illustrate its effectiveness and wide applicability.
Control Distance IoU and Control Distance IoU Loss for Better Bounding Box Regression

Numerous improvements in feedback mechanisms have contributed to the great progress in object detection. In this paper, we first present an evaluation-feedback module, which consists of an evaluation system and feedback mechanism. Then we analyze and summarize traditional evaluation-feedback modules. We focus on both the evaluation system and the feedback mechanism, and propose Control Distance IoU and Control Distance IoU loss function (CDIoU and CDIoU loss) without increasing parameters in models, which make significant enhancements on several classical and emerging models. Finally, we propose Automatic Ground Truth Clustering (AGTC) and Floating Learning Rate Decay (FLRD) for faster regression in object detection. Experiments show that a coordinated evaluation-feedback module can effectively improve model performance. Both CNN and transformer-based detectors with CDIoU + CDIoU loss, AGTC, and FLRD achieve excellent performances. There are a maximum AP improvement of 2.9%, an average AP of 1.1% improvement on MS COCO, a maximum AP improvement of 8.2%, and an average AP improvement of 3.7% on Visdrone dataset.