Reviewerly/exHarmony huggingface dataset:
Toward Fast and Accurate Violence Detection for Automated Video Surveillance Applications

Surveillance cameras are increasingly being used worldwide due to the proliferation of digital video capturing, storage, and processing technologies. However, the large volume of video data generated makes it difficult for humans to perform real-time analysis, and even manual approaches can result in delayed detection of events. Automatic violence detection in surveillance footage has therefore gained significant attention in the scientific community as a way to address this challenge. With the advancement of machine learning algorithms, automatic video recognition tasks such as violence detection have become increasingly feasible. In this study, we investigate the use of smart networks that model the dynamic relationships between actors and/or objects using 3D convolutions to capture both the spatial and temporal structure of the data. We also leverage the knowledge learned by a pre-trained action recognition model for efficient and accurate violence detection in surveillance footage. We extend and evaluate several public datasets featuring diverse and challenging video content to assess the effectiveness of our proposed methods. Our results show that our approach outperforms state-of-the-art methods, achieving approximately a 2% improvement in accuracy with fewer model parameters. Additionally, our experiments demonstrate the robustness of our approach under common compression artifacts encountered in remote server processing applications.
Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review

Computer vision research in detecting and classifying the subtype Acute Lymphoblastic Leukemia (ALL) has contributed to computer-aided diagnosis with improved accuracy. Another contribution is to serve as an assistant and second opinion for doctors and hematologists in diagnosing the ALL subtype. Early detection can also rely on computer-aided diagnosis to determine initial treatment. The purpose of this study is to review the progress of research in the detection and classification of ALL subtypes. The method’s discussion focuses on the application of deep learning to the domain of object detection and classification. Motivations, challenges, and future research recommendations are thoroughly discussed to improve understanding and progress in this field of study. The study was carried out methodically by analyzing a collection of papers on the detection and classification of ALL subtypes published in science direct, IEEE, and PubMed from 2018 to 2022. The analysis of this paper field is included in the results of the selected paper. The paper selection from among 65 papers was based on inclusion and exclusion methods. Based on research methods and objectives, papers are divided into two large groups. The first group discusses the classification of ALL subtypes, while the second group discusses the detection of ALL subtypes. The discussion of prior research reveals some challenging issues and future work, such as the limited availability of the ALL subtypes dataset, the high computational complexity of the deep learning model, and further exploration of transformers in computer vision as a reference for research gaps that can contribute to future research.
Barriers to blockchain adoption: Empirical observations from securities services value network

Blockchain can support the transition to a sustainable business by facilitating the traceability of activities that drives responsible behavior in a value delivery network. Despite its potential utility, the adoption rate is still relatively slow, and there are barriers to adopting this radical new technology. This research explores these barriers both from the organizational and business network perspectives. A qualitative study with interview data from financial service sector experts was conducted. In particular, informants represented securities services value network (SSVN). Using abductive reasoning, we found barriers that are further categorized into four dimensions: psychological-internal, functional-internal, psychological-external, and functional-external. For managers, this study offers new in-depth insights for facilitating new technology adoption, taking into account both company's internal and external viewpoints.
Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes

Pedestrian detection (PD) is a vital computer vision (CV) problem that is highly employed in several real-time applications, namely autonomous driving methods, robotics, and security observing methods. Simulated by deep learning (DL) approaches to the recognition of generic objects, several investigation mechanisms have attained maximum recognition accuracy for acceptable scale and non-blocked pedestrians. However, the detection efficiency needed to be improved for complex cases like rare pose samples, crowd scenes, and cases with worse visibility due to daytime or weather. Therefore, this study develops a multimodal pedestrian detection system in crowded scenes using metaheuristics and a deep convolutional neural network (MMPD-MDCNN) technique. The MMPD-MDCNN technique's goal is to identify pedestrians in crowd scenes using different deep-learning models effectively. The proposed MMPD-MDCNN technique integrates three deep learning models: the residual network (ResNet-50), Inception v3, and the capsule network (CapsNet). In addition, the Harris Hawks Optimization (HHO) algorithm is applied for optimal hyperparameter tuning of the deep learning models. For pedestrian detection, the MMPD-MDCNN technique uses the long short-term memory (LSTM) model, and its hyperparameters can be adjusted by the shark smell optimization (SSO) algorithm. To demonstrate the superior performance of the MMPD-MDCNN approach, A comprehensive set of simulations on the INRIA and UCSD datasets was performed to illustrate the superior performance of the MMPD-MDCNN approach. The experimental results suggest that the MMPD-MDCNN model performs well on both datasets.
Optimization of Tree-Based Machine Learning Models to Predict the Length of Hospital Stay Using Genetic Algorithm

The length of hospital stay (LOS) is a significant indicator of the quality of patient care, hospital efficiency, and operational resilience. Considering the importance of LOS in hospital resource management, this research aims to improve the accuracy of LOS prediction using hyperparameter optimization (HPO). Expert physicians and related studies were reviewed to determine the variables affecting LOS. The electronic medical records of 200 patients in the department of internal medicine of a hospital in Iran were collected randomly. As the performance of machine learning (ML) models can vary based on the characteristics of the features, several models were applied and evaluated in this study. In particular, k-nearest neighbors (KNN), multivariate regression, decision tree (DT), random forest (RF), artificial neural network (ANN), and XGBoost have been evaluated and improved. The genetic algorithm (GA) was applied to optimize the tree-based models. In addition, the dummy coding technique, sometimes called the One-Hot encoding, was used to encode categorical features to increase prediction accuracy. Compared with other algorithms, the XGBoost model optimized by GA (XGB_GA) achieved higher accuracy and better prediction performance. The mean and median of absolute errors in the test dataset for this model were 1.54 and 1.14 days, respectively. In other words, the XGB_GA model reduced the mean absolute error by 37%, which is beneficial in the reliable design of a clinical decision support system.
Adaptive Wireless Video Streaming: Joint Transcoding and Transmission Resource Allocation

The emerging mobile edge computing (MEC) technology has been recently applied to improve adaptive bitrate (ABR) streaming service quality under time-varying wireless channels. In this paper, we consider a heterogeneous multi-user MEC-enabled video streaming network with time-varying wireless channels in sequential time frames. In particular, we aim to design an online joint transcoding and transmission resource allocation algorithm to maximize the ABR streaming user’s quality of experience (QoE) subject to the bandwidth and CPU constraints. The algorithm is “online” in the sense that the bitrate and resource allocation decisions made at each frame depend only on the observation of past events. We formulate the problem as a mixed integer non-linear programming (MINLP) that jointly determines bitrate adaptation, bandwidth allocation, and CPU cycle assignment. To cope with the challenge arising from the coupling decisions of adjacent frames, we propose a low-complexity online algorithm, named OCCA. Specifically, by introducing queueing model constraints, we transform the offline non-conex MINLP problem into a multi-frame problem. Then, we analytically decouple the multi-stage(frame) problem to multiple per-frame convex subproblems that can be solved with high robustness and low computational complexity. We perform simulations with realistic scenarios to evaluate the performance of the proposed algorithm. Results manifest that compared with state-of-the-art approaches, our proposed algorithm can provide 97.84% (on average) extra QoE.
Multi-Robot Exploration of Unknown Space Using Combined Meta-Heuristic Salp Swarm Algorithm and Deterministic Coordinated Multi-Robot Exploration

Multi-robot exploration means constructing a finite map using a group of robots in an obstacle chaotic space. Uncertainties are reduced by distributing search tasks to robots and computing the best action in real time. Many previous methods are based on deterministic or meta-heuristic algorithms, but limited work has combined both techniques to consolidate both classes’ benefits and alleviate their drawbacks. This paper proposes a new hybrid method based on deterministic coordinated multi-robot exploration (CME) and the meta-heuristic salp swarm algorithm (SSA) to perform the search of a space. The precedence of adjacent cells around a robot is determined by deterministic CME using cost and utility. Then, the optimization process of the search space, improving the overall solution, is achieved utilizing the SSA. Three performance measures are considered to evaluate the performance of the proposed method: run time, percentage of the explored area, and the number of times when a method failed to continue a complete run. Experimental results compared four different methods, CME-GWO, CME-GWOSSA, CME-SCA, and CME, over seven maps with extra complexity varying from simple to complex. The results demonstrate how the proposed CME-SSA can outperform the four other methods. Moreover, the simulation results demonstrate that the proposed CME-SSA effectively distributes the robots over the search space to run successfully and obtain the highest exploration rate in less time.
Federated-Learning-Empowered Semi-Supervised Active Learning Framework for Intrusion Detection in ZSM

Exponential growth of novel radical applications and services in sixth-generation (6G) networks is expected to increase the complexity of managing existing network infrastructures. In this context, the zero touch network and service management (ZSM) paradigm, which leverages AI, SDN, and NFV techniques, is seen as a promising solution to automatically manage and orchestrate network resources. However, due to the closed-loop operation and automated end-to-end framework in a distributed 6G network, the ZSM architecture, along with its potential benefits, is exposed to various security threats. A recently proposed solution to address privacy concerns is federated learning (FL), whereby distributed training is performed, and the aggregated model parameters, instead of clients' raw data, are forwarded to the global server. However, most of the existing FL and semi-supervised learning (SSL) models for intrusion detection are based on the assumption that fully labeled data are always available at the server and client sides, which is not practical due to the high labeling costs and privacy constraints in the 6G network. In this article, we propose a novel FL-empowered semi-supervised active learning (FL-SSAL) security orchestration framework for the Label-at-Client scenario where, along with unlabeled samples, clients have a small portion of labeled data. The entropy-based active learning selects the most informative samples for data annotation and leverages the unlabeled data using a semi-supervised approach. The results of our experimental evaluations performed on the private, not independent and identically distributed (non-IID) dataset demonstrate that FL-SSAL achieves higher intrusion detection accuracy and has less communication overhead than baseline schemes with less labeled data.
Adjacent Bi-Hierarchical Network for Scene Parsing of Remote Sensing Images

Driven by the rapid development and application of earth observation sensors, the scene parsing of remote sensing images (RSIs) has attracted extensive research attention in recent years. Restricted by the limited local receptive field of successive convolution layers, traditional models of scene parsing cannot effectively and interactively utilize the local-global information and digital surface model (DSM) of RSIs. Comparatively, accurate scene parsing faces more challenges because of unbalanced categories, small targets, and more complex scenes. To address these challenges, herein, we propose a novel adjacent bi-hierarchical network (ABHNet). Specifically, we introduce a DSM-enhanced (DSE) module to excavate characteristic DSM information from DSM images and enhance the red, green, and blue (RGB) features by exploiting informative cues between RGB and DSM modalities. In addition, an adjacent context exploration (ACE) module is proposed, which contains current and adjacent branches. The branches first exploit multiscale complementary characteristics of multilevel features and then integrate these features by applying adjacent exploration. Our model includes five ACE modules—three are deployed to activate detailed features and two obtain deep-guided features. The mutual collaboration of deep and detailed features is more beneficial to the segmentation of small objects. Extensive experiments on two remote sensing benchmark datasets (ISPRS Potsdam and Vaihingen) showed that the proposed ABHNet qualitatively and quantitatively outperformed other methods.
Multi-Label Multimodal Emotion Recognition With Transformer-Based Fusion and Emotion-Level Representation Learning

Emotion recognition has been an active research area for a long time. Recently, multimodal emotion recognition from video data has grown in importance with the explosion of video content due to the emergence of short video social media platforms. Effectively incorporating information from multiple modalities in video data to learn robust multimodal representation for improving recognition model performance is still the primary challenge for researchers. In this context, transformer architectures have been widely used and have significantly improved multimodal deep learning and representation learning. Inspired by this, we propose a transformer-based fusion and representation learning method to fuse and enrich multimodal features from raw videos for the task of multi-label video emotion recognition. Specifically, our method takes raw video frames, audio signals, and text subtitles as inputs and passes information from these multiple modalities through a unified transformer architecture for learning a joint multimodal representation. Moreover, we use the label-level representation approach to deal with the multi-label classification task and enhance the model performance. We conduct experiments on two benchmark datasets: Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Carnegie Mellon University Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) to evaluate our proposed method. The experimental results demonstrate that the proposed method outperforms other strong baselines and existing approaches for multi-label video emotion recognition.
Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

The increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal.The present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran.In this study, four machine learning methods, including extreme gradient boosting (XGBoost), random forest (RF), neural networks (NNs), and logistic regression (LR), were applied to analyze the data of 630 women with confirmed BC. Also, different statistical methods, including chi-square, p-value, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC), were utilized in different steps of the survey.Thirty percent of patients had a delayed BC diagnosis. Of all the patients with delayed diagnoses, 88.5% were married, 72.1% had an urban residency, and 84.8% had health insurance. The top three important factors in the RF model were urban residency (12.04), breast disease history (11.58), and other comorbidities (10.72). In the XGBoost, urban residency (17.54), having other comorbidities (17.14), and age at first childbirth (>30) (13.13) were the top factors; in the LR model, having other comorbidities (49.41), older age at first childbirth (82.57), and being nulliparous (44.19) were the top factors. Finally, in the NN, it was found that being married (50.05), having a marriage age above 30 (18.03), and having other breast disease history (15.83) were the main predicting factors for a delayed BC diagnosis.Machine learning techniques suggest that women with an urban residency who got married or had their first child at an age older than 30 and those without children are at a higher risk of diagnosis delay. It is necessary to educate them about BC risk factors, symptoms, and self-breast examination to shorten the delay in diagnosis.
An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning

This paper discusses a hybrid grey wolf optimizer utilizing a clone selection algorithm (pGWO-CSA) to overcome the disadvantages of a standard grey wolf optimizer (GWO), such as slow convergence speed, low accuracy in the single-peak function, and easily falling into local optimum in the multi-peak function and complex problems. The modifications of the proposed pGWO-CSA could be classified into the following three aspects. Firstly, a nonlinear function is used instead of a linear function for adjusting the iterative attenuation of the convergence factor to balance exploitation and exploration automatically. Then, an optimal α wolf is designed which will not be affected by the wolves β and δ with poor fitness in the position updating strategy; the second-best β wolf is designed, which will be affected by the low fitness value of the δ wolf. Finally, the cloning and super-mutation of the clonal selection algorithm (CSA) are introduced into GWO to enhance the ability to jump out of the local optimum. In the experimental part, 15 benchmark functions are selected to perform the function optimization tasks to reveal the performance of pGWO-CSA further. Due to the statistical analysis of the obtained experimental data, the pGWO-CSA is superior to these classical swarm intelligence algorithms, GWO, and related variants. Furthermore, in order to verify the applicability of the algorithm, it was applied to the robot path-planning problem and obtained excellent results.
Art marketing in the metaverse world: Evidence from South Korea

This study aims to describe the structural relationship between audience attitude and behavioral intention based on an audience’s experience value of a performance on a metaverse platform and their intention to accept this platform. A questionnaire was administered to the study sample audience who watched the performance on a metaverse platform at least once; we included 323 valid questionnaires in our final analysis. For the analysis, a structural equation model was performed to identify the above-mentioned relationship. Overall, the study findings demonstrate that aesthetic, which is the value of performing performances on a metaverse platform, had a major influence on performance viewing, and escapism significantly affected the use of a metaverse platform. Conversely, the educational experience of the audience did not appear significantly in the performance using the metaverse platform. Therefore, this study will be helpful in understanding how to utilize space and experience marketing functions in relation to metaverse performance and how to communicate in the future.
CLSEP: Contrastive learning of sentence embedding with prompt

Sentence embedding, which aims to learn an effective representation of the sentence, is beneficial for downstream tasks. By utilizing contrastive learning, most recent sentence embedding methods have achieved promising results. However, these methods adopt simple data augmentation strategies to obtain variants of the sentence, limiting the representation ability of sentence embedding. In addition, these methods simply adopt the original framework of contrastive learning developed for image representation, which is not suitable for learning sentence embedding. To address these issues, we propose a method dubbed unsupervised contrastive learning of sentence embedding with prompt (CLSEP), aiming to provide effective sentence embedding by utilizing the prompt mechanism. Meanwhile, we propose a novel data augmentation strategy for text data named partial word vector augmentation (PWVA), which augments the data in the word embedding space, retaining more semantic information. Finally, we introduce supervised contrastive learning of sentence embedding (SuCLSE) and verify the effectiveness of the PWVA on the natural language inference (NLI) task. Extensive experiments are conducted on the STS dataset, demonstrating that the proposed CLSEP and SuPCSE are superior to the previous best methods, by utilizing the proposed PWVA strategy. The code is available at https://github.com/qianandfei/CLSEP-Contrastive-Learning-of-Sentence-Embedding-with-Prompt.
Applying Distributed Ledger Technology to Facilitate IIoT Data Exchange: An Approach Based on IOTA Tangle

Data interoperability is a fundamental dimension in enterprise interoperability. The interoperability of data is concerned with exchanging information coming from heterogeneous sources among different partners. Under the Industry 4.0 context, Internet of Things (IoT) technology has been widely implemented in manufacturing factories which enables the concept of smart factory. The huge amount of Industrial IoT (IIoT) devices are generating large volume of data related to all aspects of the enterprise. The free exchange of these big IIoT data is crucial to enterprise interoperability. However, in practice, the overwhelming part of the industrial data remains siloed preventing the full use of the big IIoT data. Concerns about data security and privacy bring more obstacles to industrial data sharing. With the decentralized and consensus-driven characteristics, distributed ledger technologies (DLT), represented by blockchain, provide reliable solutions to improving enterprise interoperability. This paper explores the application of IOTA in IIoT data exchange. IOTA is a tangle-based distributed ledger designed specifically for the IoT applications. A prototype data exchange system is developed based on IOTA and its data communication protocol, masked authenticated messaging (MAM), to demonstrate the feasibility of the proposed approach.