Reviewerly/exHarmony huggingface dataset:
In-air Handwriting System Based on Improved YOLOv5 algorithm and Monocular Camera

In-air handwriting is a new and more humanized human-computer interaction way. The existing in-air handwriting systems are mainly based on three-dimensional sensors, which are expensive, too large and not conducive for integration and application promotion. To solve this problem, this paper proposes a new in-air handwriting system using cheap and portable monocular camera which allows users writing freely in the air. Additionally we develop an end-to-end fingertip detection algorithm based on improved YOLOv5 algorithm to form in-air handwritten characters. Concretely, we first build a fingertip images dataset. After preprocessing and fingertip labeling, we use the dataset to train the improved YOLOv5 model, and then use the trained model to detect the coordinates of the fingertip in each video frame. After that, we connect the coordinates of the fingertip of each frame to form the character, and finally utilize the classifiers to recognize characters. The experimental results show that proposed in-air handwriting system allows user write freely in the air, and can obtain over 92 % in fingertip detection and character recognition.
Deep-Cogn: Skeleton-based Human Action Recognition for Cognitive Behavior Assessment

Skeleton-based human action recognition has received increasing attention in recent years. It aims at extracting features on top of human skeletons and estimating human pose. However, existing methods capture only the action information while in a real world application such as cognitive assessment, we need to measure the executive functioning that helps psychiatrists to identify some mental disease such as Alzheimer, Schizophrenia and ADHD. In this paper, we propose a skeleton-based action recognition named Deep-Cogn for cognitive assessment. Deep-Cogn integrates a pose estimator to extract the human body joints and then automatically measures the executive functioning employing the distance and elbow angle calculation. Three score functions were designed to measure the executive functioning: the accuracy score, the rhythm score and the functioning score. We evaluate our model on two different datasets and show that our approach significantly outperforms the existing methods.
Dual-Stream Transformer With Distribution Alignment for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification(VI-ReID) aims to match the person images captured by visible and infrared cameras and suffers from severe cross-modality discrepancy and intra-modality variations. Existing approaches mainly use convolution neural network (CNN)-based architectures to extract pedestrian features, which fail to capture the long-range dependencies within an image. In addition, previous works usually attempt to bridge the modality gap by using adversarial learning to generate style-consistent images or designing different feature-level metric learning constraints. However, few works consider the cross-modality disparity from the perspective of assessing overall distance distribution discrepancy. To address these problems, we design a pure Transformer-based Visible-Infrared (TransVI) network with a conventional two-stream structure, which can explicitly capture modality-specific representations and learn multi-modality sharable knowledge. TransVI can efficiently address the lack of global dependency in CNN-based architectures due to the multi-head self-attention modules in the transformer, which allows us to capture the long-range dependencies of pedestrian images. Furthermore, we introduce the Cross-Modality Dissimilarity-based Maximum Mean Discrepancy (CMD-MMD) constraint to handle the cross-modality discrepancy at the distance distribution level. Specifically, CMD-MMD leverages intra-modality distribution separability to guide inter-modality distribution separability learning, aligning pair-wise distance distributions of intra- and inter-modality for within-class and between-class, respectively. In this way, the distance distributions of intra- and inter-modality become more similar, significantly mitigating the cross-modality discrepancy and learning more modality invariant representations. Extensive experimental results on two public VI-ReID datasets confirm that our proposed framework can achieve state-of-the-art performance.
Practical Cloud-Edge Scheduling for Large-Scale Crowdsourced Live Streaming

Even though conventional wisdom claims that in order to improve viewer engagement, the cloud-edge providers should serve the viewers with the nearest edge nodes, however, we show that doing this for crowdsourced live streaming (CLS) services can introduce significant costs inefficiency. In this paper, we first carry out large-scale measurement analysis by using the real-world service data from Huawei Cloud, a representative cloud-edge provider in China. We observe that the massive number of channels has proposed great burdens to the operating expenditure of the cloud-edge providers, and most importantly, unbalanced viewer distribution makes the edge nodes suffer significant costs inefficiency. To tackle the above concerns, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><monospace>AggCast</monospace></i> , a novel CLS scheduling framework to optimize the edge node utilization for the cloud-edge provider. The core idea of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><monospace>AggCast</monospace></i> is to aggregate some viewers that are initially scattered on different regions, and assign them to fewer pre-selected nodes, thereby reducing bandwidth costs. In particular, by integrating the useful insights obtained from our large-scale measurement, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AggCast</monospace> can not only ensure that quality of experience (QoS) does not suffer degradation, but also satisfy the systematic requirements of CLS services. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AggCast</monospace> has been A/B tested and fully deployed. The online and trace-driven experiments show that, compared to the most prevalent method, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AggCast</monospace> saves over 16.3% <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">back-to-source</i> (BTS) bandwidth costs while significantly improving QoS (startup latency, stall frequency and stall time are reduced over 12.3%, 4.57% and 3.91%, respectively).
Digital transformation in education

In modern conditions, the content of education requires updating and reaching the educational process to a completely new qualitative level. Many educational institutions are actively using digital technologies in their educational process. Based on the study of domestic and foreign literary sources, examples of the use of digital resources in the field of preschool, secondary and higher education are given. The authors of this article focus on the need not only to improve the quality of subject knowledge and master new competencies, but also the special role of working with young people in terms of educating and shaping a young person as a responsible and harmonious personality. The important directions of educational work in the process of training and education are determined. The paper presents approaches to the individual design of education and the requirements of digital education in general. The qualities that schoolchildren and students must possess for successful educational activities are listed. The authors have identified the main advantages and disadvantages of the digitalization of the educational process in educational institutions. The relevance of the study is dictated by the need to determine the main trends in the development of education in modern conditions. Innovations in the educational process associated with the introduction of digital technologies, at this stage of the development of education, need to be finalized and changed, taking into account the existing experience and strategic goals for the education and upbringing of the younger generation.
Outlier Detection Algorithms for Open Environments

The high dimensionality and massive amount of data in open environments make the existing low-dimensional outlier detection methods time-consuming. The support vector machine (SVM) is a commonly used outlier detection method. However, the SVM still faces the problem of difficulty in obtaining optimal parameters quickly and effectively, resulting in low detection efficiency, poor stability, and difficulty in applying to open environment datasets. In order to improve the efficiency and stability of outlier detection, this paper proposes an improved sparrow search algorithm and uses it to optimize SVM parameters. First, the traditional sparrow search algorithm is improved by using improved backtracking learning and variable logarithmic spirals. Then, the improved sparrow search method is used to optimize SVM parameters, and the optimized support vector machine is applied to the field of outlier detection. Simulation experimental results show that the proposed method is significantly better than the compared classification algorithms in multiple evaluation indicators, with better detection efficiency, stability, and generalization ability.
Immersive virtual reality in AECO/FM to enhance education: Systematic literature review and future directions

Abstract Immersive virtual reality (IVR) is considered as an effective and efficient instructional tool for architecture, engineering, construction, operation, and facility management (AECO/FM) education. However, it is essential to systematically evaluate them to obtain consolidated evidence on the impact of IVR applications. Thus, the aim of this article is to present the state‐of‐the‐art on all AECO/FM use cases, including learning theories/models, all key factors, and experimental design, and to suggest future directions for IVR research. This review follows a systematic literature review methodology to analyze 79 papers between 2013 and 2022. The results show that learning theories/models are not sufficiently applied. There is consensus that IVR influences affective factors positively. It is necessary to consider task type, task complexity, virtual environment design, and ergonomics of IVR tools in terms of cognitive factors. Cognitive/affective factors associated with learning performance should be examined in depth to develop more effective learning methods. Future studies should design and conduct experimental procedures with more scientific rigor.
Gate-Shift-Fuse for Video Action Recognition

Convolutional Neural Networks are the de facto models for image recognition. However 3D CNNs, the straight forward extension of 2D CNNs for video recognition, have not achieved the same success on standard action recognition benchmarks. One of the main reasons for this reduced performance of 3D CNNs is the increased computational complexity requiring large scale annotated datasets to train them in scale. 3D kernel factorization approaches have been proposed to reduce the complexity of 3D CNNs. Existing kernel factorization approaches follow hand-designed and hard-wired techniques. In this paper we propose Gate-Shift-Fuse (GSF), a novel spatio-temporal feature extraction module which controls interactions in spatio-temporal decomposition and learns to adaptively route features through time and combine them in a data dependent manner. GSF leverages grouped spatial gating to decompose input tensor and channel weighting to fuse the decomposed tensors. GSF can be inserted into existing 2D CNNs to convert them into an efficient and high performing spatio-temporal feature extractor, with negligible parameter and compute overhead. We perform an extensive analysis of GSF using two popular 2D CNN families and achieve state-of-the-art or competitive performance on five standard action recognition benchmarks.
A System for Language Translation using Sequence-to-sequence Learning based Encoder

Hindi is the mother tongue of nearly 133 crore Indians. Along with India, it is spoken in Nepal, Fiji, and Bangladesh. Since good knowledge of English is not common, there is a good opportunity for machine translation from English to Hindi and vice versa. Language translation is one task in which machines lag behind human power [1]. One task where machines fall short of human ability is language translation. Rule-Based Translation (RBT) systems and Statistical Machine Translation (SMT) systems are the conventional systems used for language translation. Rule Based Translation requires in-depth knowledge of the language. RBT is a fairly complicated system that can and must include many rules in order to improve quality. SMT is one of the traditional approaches to the machine translation issue. This technique works well with pairs of languages with comparable grammatical structures and requires enormous data sets. This paper proposes a better approach - a neural network model that uses “Recurrent Neural Network” (RNN) and “Gated Recurrent Unit” (GRU). The system consists of an RNN-encoder and RNN-decoder architecture and an attention mechanism to deal with long sentences.
A Study of Machine Learning Techniques based on Human Daily Living Activities via Inertial Sensors

The recognition of human daily living activities has attained significant attention in recent times. As a result, many methods have been used in the literature to detect and monitor human lifelogs. Despite the plethora of applications, the classification and detection of human behaviours, which may result in inappropriate reactions and responses, still need to be more accurate. For instance, conventional machine learning techniques employ hand-crafted features based on a classification strategy. However, deep learning methods have shown improved recognition rates with better performance. Thus, this study was aimed at presenting a detailed overview of recent and state-of-the-art supervised and unsupervised human daily lifelog classification techniques. In addition, a comprehensive analysis will be presented of how various design parameters, such as the volume of features and other data fusion techniques from different sensor locations, can affect the overall recognition performance. Furthermore, with the rapid advancement of body-worn sensing technology and modelling approaches, the widespread usage of wearable sensors is anticipated to provide countless opportunities for precise and reliable inferences across a broad range of human activities.
Arabic Sign Language Recognition Using EfficientnetB1 and Transfer Learning Technique

Deaf and mute people rely on signing as a means of communication with others and with themselves. The value of sign language, which is the sole way for the deaf and mute communities to communicate, is often overlooked by regular people. Because of these limitations or impairments, these people are experiencing considerable setbacks in their life, including joblessness, serious depression, and a variety of other symptoms. Sign language interpreters are one of the communication services they use. However, paying these interpreters is prohibitively expensive, necessitating a low-cost solution to the problem. As a result, a system has been built that would interpret the Arabic Sign Language-based visual hand dataset into written information. The dataset used for this model Arabic Alphabets Sign Language dataset consists of 32 classes, each category has 506 images, resulting in a total of 506 * 32 = 16192 images. On the provided dataset, the tests have been run using a variety of pre-trained models. Most of them carried out their duties rather normally, and in the end, we constructed the Convolutional Neural Networks model with EfficientnetB1 scaling loaded with weights pre-trained on the ImageNet model, Based on this observation. Using a simple but highly effective compound coefficient, it equally scales all width/depth/resolution dimensions. Based on the model's results, we demonstrated the efficacy of this strategy. The percentage of accuracy that we obtained from this model is 99% accuracy and 97.9 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> validation accuracy
Hybrid Binary Grey Wolf with Jaya Optimizer for Biomarker Selection from Cancer Datasets

GWO (Grey Wolf optimizer) is the leading metaheuristic algorithm preferred by most researchers to solve different feature selection and optimization problems. Stagnation in local optima is a major concern that exists and it affects the performance of the machine-learning model. Exploration (search field) and exploitation (identifying optimal solutions) are the two most vital concepts in each metaheuristic algorithm. Properly balancing both to achieve a better result is a critical task as GWO is stuck in local optima. This paper presents a hybrid GWO with the Jaya algorithm (JA) as a local search (exploitation) to solve stagnation issues. Most of these methods are designed for solving complex continuous problems. A sigmoid transfer function is used to convert continuous search space to binary, which creates an environment for feature selection. The high-dimensional datasets are used to evaluate the most prominent features. To evaluate the performance of the proposed model, three different cancer datasets were used. The performance of the proposed model is compared with different state-of-the-art machine learning models. The analysis of the results shows that GWO optimized with local search (Jaya) performs better in terms of accuracy, feature size (selected), and computation time.
Rapid Detection of Pilgrims Whereabouts During Hajj and Umrah by Wireless Communication Framework : An application AI and Deep Learning

Human injuries and deaths occur often during public events like concerts, religious services, and political rallies because of a lack of proper crowd safety oversight. A small disaster can trigger panic in a huge crowd. Many intelligent video surveillance solutions can identify things, but despite the recent developments in artificial intelligence approaches and deep learning processes, it is very probable to track congested crowds and their mobility to avoid future detection disasters. Searching for points of interest makes use of movement analytics and classification to provide a superior platform for monitoring large crowds. The purpose of point-of-interest explorations is to aid in the management of the safety of moveable crowd events by assisting in the prediction and prevention of future disasters through the classification and analysis of real-time information gathered from crowds. Current surveillance cameras are insufficient for monitoring large crowds in outdoor locations due to their inability to scale. We believe that by using our proposed crowd analysis strategy, we may help enhance the current state of crowd safety management. Among the many aspects of crowd motion, we pay special attention to the difficulties of determining the identity, velocity, and direction of individuals inside the group. We then used these crowd-level semantics to monitor test POI searches in both a controlled lab environment and a real-world crowd. Findings from this study imply that POI searching can be utilized to help prevent harmful circumstances brought on by the movement of large crowds by recognizing the characteristics of mobile crowds in real time.
Recognition of Leukemia Sub-types Using Transfer Learning and Extraction of Distinguishable Features Using an Effective Machine Learning Approach

Although extensive research has been conducted on leukemia, the disease still accounts for more than 350,000 fatalities annually. Automated Leukemia diagnosis may alter the situation because actions can be taken immediately; as a result, accurate detection of Leukemia has been a subject of interest for researchers. As statistics grow and expand, the need for precise leukemia identification continues to increase. In this study, we investigated a dataset of leukemia that used the WHO classification scheme. We developed a modified DenseNet201 design that achieved an overall accuracy of 99.69% without relying on data augmentation. Additionally, we identified and validated key features for leukemia classification by utilizing three feature extraction approaches (i.e., hu moments, haralick texture and parameter-free threshold adjacency statistics) and several machine learning classifiers (i.e., Gaussian Process, Support Vector Machine, K-Nearest Neighbor or KNN, Extra Trees Classifier, and Logistic regression) that outperformed earlier feature extraction-based techniques.
Multi-Modal Feature Pyramid Transformer for RGB-Infrared Object Detection

RGB-Infrared multi-modal object detection utilizes diverse and complementary information, showing some advantages in intelligent transportation field. The main challenge of RGB-Infrared object detection is how to fuse the two modalities. The difficulty of fusion is reflected in two aspects: 1) large visual differences between modalities make it difficult to learn effective complementary features, 2) some misaligned RGB-Infrared images increase the difficulty of fusion. To this end, based on feature pyramid commonly used in object detection, we propose Multi-modal Feature Pyramid Transformer (MFPT) to fuse the two modalities. The proposed MFPT learns semantic and modal complementary information to enhance each modal features via intra-modal feature pyramid transformer and inter-modal feature pyramid transformer. The intra-modal feature pyramid transformer enables features to interact across space and scales, improving the semantic representations of features in each modality. The inter-modal feature pyramid transformer conducts feature interaction between modalities, enabling each modality to learn complementary features from other modalities. Meanwhile, the inter-modal feature pyramid transformer can also learn distance independent dependencies between modalities, which are not sensitive to misaligned images. Furthermore, a local attention mechanism is introduced within different windows into MFPT to achieve efficient correlation between regions of different scales or different modalities. Experimental results on two RGB-Infrared detection datasets demonstrate the proposed method is superior to state-of-the-art methods.
Resilient Watermarking Algorithm to Printing and Scanning Combining Local Feature Regions and DWT-SVD

Resilient digital watermarking to printing and scanning is of great significance in protecting important paper documents. How to improve the robustness of digital watermark is a difficult research point. In order to achieve a more robust resilient algorithm to printing and scanning and improve the correct detection rate of watermark, a resilient watermarking algorithm combining stable local feature regions and DWT-SVD is proposed. This algorithm is mainly divided into two parts, the first is to construct local feature regions, the second is to embed watermark information repeatedly, and the watermark detection is an inverse process. Experiments show that the algorithm has achieved good results in resisting conventional attacks and printing and scanning.