Reviewerly/exHarmony huggingface dataset:
HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations

Emotion detection in conversations (EDC) is to detect the emotion for each utterance in conversations that have multiple speakers. Different from the traditional non-conversational emotion detection, the model for EDC should be context-sensitive (e.g., understanding the whole conversation rather than one utterance) and speaker-sensitive (e.g., understanding which utterance belongs to which speaker). In this paper, we propose a transformer-based context- and speaker-sensitive model for EDC, namely HiTrans, which consists of two hierarchical transformers. We utilize BERT as the low-level transformer to generate local utterance representations, and feed them into another high-level transformer so that utterance representations could be sensitive to the global context of the conversation. Moreover, we exploit an auxiliary task to make our model speaker-sensitive, called pairwise utterance speaker verification (PUSV), which aims to classify whether two utterances belong to the same speaker. We evaluate our model on three benchmark datasets, namely EmoryNLP, MELD and IEMOCAP. Results show that our model outperforms previous state-of-the-art models.
Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images

Abstract Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still experience tumor recurrence at 5 years post-surgery. We developed and validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary resection, directly from hematoxylin and eosin-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections. Our model achieved concordance indices of 0.724 and 0.683 on the internal and external test cohorts, respectively, exceeding the performance of the standard Tumor-Node-Metastasis classification system. The model’s risk score stratified patients into low- and high-risk subgroups with statistically significant differences in their survival distributions, and was an independent risk factor for post-surgical recurrence in both test cohorts. Our results suggest that deep learning-based models can provide recurrence risk scores which may augment current patient stratification methods and help refine the clinical management of patients undergoing primary surgical resection for HCC.
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting

In this paper, we propose two modified neural networks based on dual path multi-scale fusion networks (SFANet) and SegNet for accurate and efficient crowd counting. Inspired by SFANet, the first model, which is named M-SFANet, is attached with atrous spatial pyramid pooling (ASPP) and context-aware module (CAN). The encoder of M-SFANet is enhanced with ASPP containing parallel atrous convolutional layers with different sampling rates and hence able to extract multi-scale features of the target object and incorporate larger context. To further deal with scale variation throughout an input image, we leverage the CAN module which adaptively encodes the scales of the contextual information. The combination yields an effective model for counting in both dense and sparse crowd scenes. Based on the SFANet decoder structure, M-SFANet`s decoder has dual paths, for density map and attention map generation. The second model is called M-SegNet, which is produced by replacing the bilinear upsampling in SFANet with max unpooling that is used in SegNet. This change provides a faster model while providing competitive counting performance. Designed for high-speed surveillance applications, M-SegNet has no additional multi-scale-aware module in order to not increase the complexity. Both models are encoder-decoder based architectures and are end-to-end trainable. We conduct extensive experiments on five crowd counting datasets and one vehicle counting dataset to show that these modifications yield algorithms that could improve state-of-the-art crowd counting methods. Codes are available at https://github.com/Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting.
Dynamic Memory based Attention Network for Sequential Recommendation

Sequential recommendation has become increasingly essential in various online services. It aims to model the dynamic preferences of users from their historical interactions and predict their next items. The accumulated user behavior records on real systems could be very long. This rich data brings opportunities to track actual interests of users. Prior efforts mainly focus on making recommendations based on relatively recent behaviors. However, the overall sequential data may not be effectively utilized, as early interactions might affect users' current choices. Also, it has become intolerable to scan the entire behavior sequence when performing inference for each user, since real-world system requires short response time. To bridge the gap, we propose a novel long sequential recommendation model, called Dynamic Memory-based Attention Network (DMAN). It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users. To improve memory fidelity, DMAN dynamically abstracts each user's long-term interest into its own memory blocks by minimizing an auxiliary reconstruction loss. Based on the dynamic memory, the user's short-term and long-term interests can be explicitly extracted and combined for efficient joint recommendation. Empirical results over four benchmark datasets demonstrate the superiority of our model in capturing long-term dependency over various state-of-the-art sequential models.
Security Enhancement in Smart Vehicle Using Blockchain-based Architectural Framework

The concept of interconnecting smart vehicles and advancements in automotive automation leads to beneficial outcomes, such as a reduction in road fatalities and congestion. However, including a chain of automation in the attack surface will expand the attack surface and expose the security of automobiles to malicious infiltration. The proposed methodology provides access to specific users while restricting the third party requests. Moreover, it also makes use of data exchange that takes place between the roadside units and vehicle to track the vehicle status without compromising the in-vehicle network. To ensure a valid and authentic communication, vehicles with a proper and verifiable record will only be allowed to exchange messages in the blockchain network. Using qualitative arguments, we have identified that the proposed work is resilient to identified attacks. Similarly, quantitative experimentation indicates that this methodology shows a storage size compatibility and suitable response time in realistic scenarios. Simulation results indicate that, the proposed work shows positive results to secure vehicular networks, vehicular forensics and trust management.
Human-Level Interpretable Learning for Aspect-Based Sentiment Analysis

This paper proposes human-interpretable learning of aspect-based sentiment analysis (ABSA), employing the recently introduced Tsetlin Machines (TMs). We attain interpretability by converting the intricate position-dependent textual semantics into binary form, mapping all the features into bag-of-words (BOWs). The binary form BOWs are encoded so that the information on the aspect and context words are nearly lossless for sentiment classification. We further adapt the BOWs as input to the TM, enabling learning of aspect-based sentiment patterns in propositional logic. To evaluate interpretability and accuracy, we conducted experiments on two widely used ABSA datasets of SemEval 2014: Restaurant 14 and Laptop 14. The experiments show how each relevant feature takes part in conjunctive clauses that contain the context information for the corresponding aspect word, demonstrating human-level interpretability. At the same time, the obtained accuracy is competitive with existing neural network models, reaching 78.02% on Restaurant 14 and 73.51% on Laptop 14.
Mitigating information asymmetry in inventory pledge financing through the Internet of things and blockchain

Purpose Risks resulted from asymmetric information have become crucial barriers for commercial banks to implement supply chain finance (SCF) – mainly the inventory pledge financing (IPF). At the same time, online financial service providers (OFSPs) are emerging as strong competitors in the SCF market. As a result, commercial banks need to update their traditional SCF business models and alleviate their over-dependence on OFSPs. Design/methodology/approach The authors employ a multi-case-study method to investigate how the Internet of things (IoT) and blockchain technologies can be jointly leveraged to mitigate SCF risks. In-depth interviews were conducted to depict the business models and their novel ecosystem to reinforce traditional banks' ability in SCF services. Findings From the perspective of information asymmetry, the authors categorize IPF risks into three groups based on the principal-agent theory: collateral, warehousing and liquidity risk. The findings suggest that IoT can primarily improve traditional banks' information acquisition ability, and blockchain can facilitate credible information transformation, enabling banks to acquire knowledge from collaterals. Besides, the e-platform in the new architecture increases banks' involvement in the supply chain and builds a fair network to curtail warehousing risks. The employment of smart contracts and collaborative mechanism ensure process and outcome control in mitigating liquidity risks. Originality/value The research contributes to the literature by confirming the role of emerging technologies in reducing information asymmetry risks. Besides, the findings provide valuable insights for practitioners to promote effective practices and approaches in IPF.
Dynamical Variational Autoencoders: A Comprehensive Review

Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an unsupervised manner. In the original VAE model, the input data vectors are processed independently. Recently, a series of papers have presented different extensions of the VAE to process sequential data, which model not only the latent space but also the temporal dependencies within a sequence of data vectors and corresponding latent vectors, relying on recurrent neural networks or state-space models. In this paper, we perform a literature review of these models. We introduce and discuss a general class of models, called dynamical variational autoencoders (DVAEs), which encompasses a large subset of these temporal VAE extensions. Then, we present in detail seven recently proposed DVAE models, with an aim to homogenize the notations and presentation lines, as well as to relate these models with existing classical temporal models. We have reimplemented those seven DVAE models and present the results of an experimental benchmark conducted on the speech analysis-resynthesis task (the PyTorch code is made publicly available). The paper concludes with a discussion on important issues concerning the DVAE class of models and future research guidelines.
Service robots are an option for contactless services due to the COVID-19 pandemic in the hotels

The development of robotics in the current COVID-19 pandemic scenario can change the face of the industries. Robots are becoming more prominent in the hospitality industry. In this scenario, the usage of service robots for hotels is the best option. This study is performed using TAM and TRI theories. The constructs selected for the study are perceived ease of use, perceived usefulness, attitudes, behavioural intention, discomfort, insecurity, and trust. Survey-based research is carried out with the help of a questionnaire. The target population are the employees working in the hotels. Ten hypotheses are proposed for the study. This study highlights the acceptance of service robots in the hotels of India. Out of ten proposed hypothesis, five hypotheses were accepted, and the rest were rejected. For data analysis, structural equation modelling in AMOS 20.0 was carried out. This study will help the managers and the top management in the adoption of service robots.
A Transformer-Based Feature Segmentation and Region Alignment Method for UAV-View Geo-Localization

Cross-view geo-localization is a task of matching the same geographic image from different views, e.g., unmanned aerial vehicle (UAV) and satellite. The most difficult challenges are the position shift and the uncertainty of distance and scale. Existing methods are mainly aimed at digging for more comprehensive fine-grained information. However, it underestimates the importance of extracting robust feature representation and the impact of feature alignment. The CNN-based methods have achieved great success in cross-view geo-localization. However it still has some limitations, e.g., it can only extract part of the information in the neighborhood and some scale reduction operations will make some fine-grained information lost. In particular, we introduce a simple and efficient transformer-based structure called Feature Segmentation and Region Alignment (FSRA) to enhance the model’s ability to understand contextual information as well as to understand the distribution of instances. Without using additional supervisory information, FSRA divides regions based on the heat distribution of the transformer’s feature map, and then aligns multiple specific regions in different views one on one. Finally, FSRA integrates each region into a set of feature representations. The difference is that FSRA does not divide regions manually, but automatically based on the heat distribution of the feature map. So that specific instances can still be divided and aligned when there are significant shifts and scale changes in the image. In addition, a multiple sampling strategy is proposed to overcome the disparity in the number of satellite images and that of images from other sources. Experiments show that the proposed method has superior performance and achieves the state-of-the-art in both tasks of drone view target localization and drone navigation.
Short-term PV power forecasting using variational mode decomposition integrated with Ant colony optimization and neural network

In this paper, Artificial Neural Network (ANN) is integrated with data processing, input variable selection, and external optimization techniques to forecast the day ahead output power of a PV system. Variational mode decomposition (VMD) is used to decompose the highly fluctuating original data into relatively stable components with periodic characteristics which can be logically interpreted. The VMD parameters are optimally set through a methodology that involves index of orthogonality (IO) and correlation measures. The input variable selection is accomplished through mutual information (MI). A neural network with technically decided architecture is the core of the forecasting model. The weights and biases of the ANN are externally optimized through Ant colony optimization (ACO) during training. The forecasted components are used as input for a second level forecasting of the PV power through another ANN. The proposed hybrid method, labeled as VMD-ACO-2NN, was evaluated based on a 100kW PV system in Beijing, China. It is compared against NN, GA-NN, ACO-NN and VMD-ACO-NN and proved to outperform all with each of the added features contributing a part. The forecasting model is able to outstandingly explain 97.68% of the total variation in the forecasted PV power.
A Narrative Review of the Current State of Extended Reality Technology and How it can be Utilised in Sport

Extended reality is an umbrella term used to describe three computer-generated technologies including virtual reality, augmented reality and mixed reality. Extended reality is an emerging technology that has been utilised in many high-performance domains including psychology, medicine and the military, with the aim of enhancing perceptual-cognitive skills and motor skills. However, the use of extended reality in sport, particularly at the elite level, has only recently started to receive attention. While the growth of extended reality technology continues to accelerate at a rapid rate, empirical evidence aimed at understanding how these devices can best be applied in high-performance sport has not followed suit. Therefore, the purpose of this review is to provide clarity for high-performance sport organisations, researchers, sport scientists, coaches and athletes about the current state of extended reality technology and how it has been utilised in sport. In doing so, we first define and give examples of the types of extended reality technology including virtual reality, augmented reality and mixed reality that are available at the present time. Second, we detail how skill acquisition principles underpinned by the theoretical framework of ecological dynamics can be used to help inform the design and assessment of extended reality training tools. Third, we describe how extended reality has been utilised in sport, including how extended reality tools have been assessed for their level of representativeness, and the effectiveness of extended reality training interventions for improving perceptual-cognitive skills and motor skills. Finally, we discuss the future utilisation of extended reality in sport, including the key learnings that can be drawn from other domains, future research directions, practical applications and areas for consideration related to the use of extended reality for training skills in sport.
Blockchain in accounting practice and research: systematic literature review

Purpose This study aims to review the academic literature on the utilization of blockchain in accounting practice and research to identify potential opportunities for further scientific investigation and to provide a framework for how accounting practices are impacted by blockchain. Design/methodology/approach This study is based on a systematic literature review (SLR) of 346 research products available on Scopus, which were mapped with bibliometric analyses and critically discussed in relation to three main topics: the impact of blockchain on accounting and auditing, cryptoassets and finance, business models and supply chain management. Findings Blockchain has many potential implications for accounting practice and research. In addition to providing the state-of-the-art of accounting research on blockchain and additional avenues for further studies, this study discusses why practitioners are interested in this technology: triple-entry bookkeeping, the inalterability of transactions, the automation of repetitive tasks that do not require discretionary choices, the representation of cryptocurrencies in financial statements, value-chain management, social and environmental auditing and reporting and business model innovation. Originality/value The novel contribution of this study is integrated and threefold. First, this SLR provides a clear picture of the state of the accounting research on blockchain using bibliographic and narrative analyses. Second, it investigates how accounting and auditing practices are impacted by blockchain. Third, it contributes to the accounting literature with its discussion of the potential future research trends related to blockchain for accounting.
Factorials and Integers for Applications in Computing and Cryptography

Mathematical formulae with integers or prime numbers are used as cryptographic algorithms like RSA algorithm and elliptic-curve cryptography. These methodological advances in computational science and mathematics play a vital role in communication and cybersecurity. This article is prepared for applications in computing and cybersecurity using the theorems in factorials and binomial identity. Also, this paper focuses on the analysis of relationship between factorials and integers.
Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using internet of medical things (IoMT) devices. However, due to the centralized training approach of artificial intelligence (AI), mobile and wearable IoMT devices raise privacy issues concerning the information communicated between hospitals and end-users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm, has opened up new opportunities for privacy preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to end-users as only gradients are shared during training. For these specific properties of FL, in this paper, we present privacy-related issues in IoMT. Afterwards, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures by incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Moreover, we present some practical opportunities for FL in IoMT. In the end, we conclude this survey by discussing open research issues and challenges while using FL in future smart healthcare systems.
Boundary-Guided Camouflaged Object Detection

Camouflaged object detection (COD), segmenting objects that are elegantly blended into their surroundings, is a valuable yet challenging task. Existing deep-learning methods often fall into the difficulty of accurately identifying the camouflaged object with complete and fine object structure. To this end, in this paper, we propose a novel boundary-guided network (BGNet) for camouflaged object detection. Our method explores valuable and extra object-related edge semantics to guide representation learning of COD, which forces the model to generate features that highlight object structure, thereby promoting camouflaged object detection of accurate boundary localization. Extensive experiments on three challenging benchmark datasets demonstrate that our BGNet significantly outperforms the existing 18 state-of-the-art methods under four widely-used evaluation metrics. Our code is publicly available at: https://github.com/thograce/BGNet.