Reviewerly/exHarmony huggingface dataset:
Recognize Facial Emotion Using Landmark Technique in Deep Learning

Body language and facial expressions account for 60-65% of all communication, and researchers have used a landmark algorithm to extract features from images of the face. The algorithm identifies 68 points on the face and calculates the Euclidean distance between each point, creating a file of distances which is then used as input for a deep one-dimensional model. The resulting accuracy is 0.98.
Improving user's sense of participation in robot-driven dialogue

In this study, we aimed to enhance the sense of dialogue participation (SDP), which reflects an individual's conscious engagement in dialogues, and investigated the multimodal dialogue elements that contribute to its improvement. To achieve this, we developed a dialogue system utilizing an android robot specifically for tour spot recommendations at a travel agency. The system was designed with various components aimed at enhancing SDP and was evaluated by 29 participants during the Dialogue Robot Competition 2022. After conceptually defining SDP, we employed the author's subjective evaluation to visualize the time-series changes in SDP at approximately 20-second intervals, using video recordings of the dialogues. Initially, we expected an overall decrease in SDP, as the robot had a speaking duration of around two minutes during the middle of the dialogue session when presenting the tour plan. However, the analysis of five participants' data revealed an increase in SDP during segments that involved 'Dealing with content that increases the user's interest' and 'Multimodal response requests.' Consequently, we can conclude that these elements have a discernible impact on enhancing SDP.
Blockchain for Smart Cities Improvement: an Architecture Proposal

The combination between innovative topics and emerging technologies lets researchers define new processes and models. New needs regard the definition of modular and scalable approaches, with society and environment in mind. An important topic to focus on is the smart city one. The use of emerging technologies lets smart cities develop new processes to improve services offered from various actors, either industries or government. Smart cities were born to improve quality of life for citizens. To reach this goal, various approaches have been proposed, but they lack on a common interface to let each stakeholder communicate in a simple and fast way. This paper shows the proposal of an architecture to overcome the actual limitations of smart cities: it uses Blockchain technology as a distributed database to let everyone join the network and feel part of a community. Blockchain can improve processes development for smart cities. Scalability is granted thanks to a context-aware approach: applications do not need to know about the back-end implementation, they just need to adapt to an interface. With Blockchain, it is possible to collect data anonymously to make some statistical analysis, to access public records to ensure security in the city and to guarantee the origin of products and energy.
Research on the Development of Blockchain-based Distributed Intelligent Healthcare Industry -- A Policy Analysis Perspective

As a pivotal innovation in digital infrastructure, blockchain ledger technology catalyzes the development of nascent business paradigms and applications globally. Utilizing Rothwell and Zegveld's taxonomy of twelve innovation policy tools, this study offers a nuanced comparison of domestic blockchain policies, dissecting supply, environment, and demand-driven policy dimensions to distill prevailing strategic orientations towards blockchain healthcare adoption. The findings indicate that blockchain technology has seen rapid growth in the healthcare industry. However, a certain misalignment exists between the corporate and policy layers in terms of supply and demand. While companies focus more on technological applications, existing policies are geared towards regulations and governance. Government emphasis lies on legal supervision through environmental policies, aiming to guide the standardization and regulation of blockchain technology. This maintains a balance between encouraging innovation and market and legal regulatory order, thereby providing a reference for the development of the distributed intelligent healthcare industry in our country.
E-Ticketing System to Accelerate Car Terminal Indonesia Port of Tanjung Priok Activities Using Waterfall Method

The implementation of automated gate systems (autogate systems) at ports has become imperative due to regulations set by the Director General of Customs and Excise (Decree No. PER-10/BC/2020). This mandates TSP operators to adopt autogate systems to meet operational standards. IPCC has already introduced autogate systems but required supportive infrastructure, leading to the creation of the ETicket system integrated with CEISA (Customs System). Research, conducted through the waterfall methodology and employing an Enterprise Service Bus (ESB), reveals that this new system eliminates manual input of export documents for each cargo by automating the process through validation with CEISA. It concurrently reduces the announcement time from FTP to APIs. For the future, CEISA needs to furnish cargo counts or lists for the IPCC API to validate cargo quantity, exceeding the count results in announcement rejection. This integrated approach optimizes port operations, offering efficiency and compliance.
CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition

This article proposes a semi-supervised contrastive capsule transformer method with feature-based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) techniques for wearable human activity recognition (HAR), called CapMatch. CapMatch gracefully hybridizes supervised learning and unsupervised learning to extract rich representations from input data. In unsupervised learning, CapMatch leverages the pseudolabeling, contrastive learning (CL), and feature-based KD techniques to construct similarity learning on lower and higher level semantic information extracted from two augmentation versions of the data“, weak” and “timecut”, to recognize the relationships among the obtained features of classes in the unlabeled data. CapMatch combines the outputs of the weak-and timecut-augmented models to form pseudolabeling and thus CL. Meanwhile, CapMatch uses the feature-based KD to transfer knowledge from the intermediate layers of the weak-augmented model to those of the timecut-augmented model. To effectively capture both local and global patterns of HAR data, we design a capsule transformer network consisting of four capsule-based transformer blocks and one routing layer. Experimental results show that compared with a number of state-of-the-art semi-supervised and supervised algorithms, the proposed CapMatch achieves decent performance on three commonly used HAR datasets, namely, HAPT, WISDM, and UCI_HAR. With only 10% of data labeled, CapMatch achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{1}$</tex-math> </inline-formula> values of higher than 85.00% on these datasets, outperforming 14 semi-supervised algorithms. When the proportion of labeled data reaches 30%, CapMatch obtains <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{1}$</tex-math> </inline-formula> values of no lower than 88.00% on the datasets above, which is better than several classical supervised algorithms, e.g., decision tree and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -nearest neighbor (KNN).
Re-envisioning Field Surveying as an Immersive Gamification Experience

Recent strides in immersive technologies, coupled with advancements in game development, are reshaping engineering education, and revolutionizing the design and deliv- ery of both in-class and lab activities. Traditional experiential practices in field surveying and mapping, which have historically relied on hands-on learning alongside theory concepts, have been supplemented by innovative pedagogical approaches, catalyzed by the disruptions caused by COVID-19. There has been a growing imperative to create virtual and immersive resources and shift towards virtual-based lab designs. In response to this evolving landscape, we present a novel approach that provides students with high-quality field training through an immersive virtual experience. We introduce an interactive web application for topographic surveying, embedded within a gamification framework, utilizing digital twins to recreate the field space. Our research offers compelling evidence that this approach effectively supports practical training, as demonstrated by real-world use cases conducted in the lab setting. Addi- tionally, we gauge the framework’s usability using the System Usability Scale (SUS), affirming its effectiveness as a tool in virtualizing field practice.
Movie Caption Generation with Vision Transformer and Transformer-based Language Model

The paper presents a video caption generation system using Vision Transformer (ViT) and a Transformer-based language model. A video is regarded as images with time information but it is difficult to analyze a video with ordinary image recognition techniques because of the difference between spacial information and time information. So, the system analyzes each image with ViT and integrates all image features into a video feature. ViT is a state-of-the-art object recognition system and consists of stacked Transformers. ViT is not trained with a training dataset but is employed with a pre-trained model. In a caption generation module, a caption is generated with Transformer decoders based on the video feature. The caption generation module is trained with a training dataset. In experiments, we use a large-scale video captioning dataset and train the proposed system. As experiment results, the proposed system achieves 0.27 F-measure in Rouge-L and we confirm that the trained system can generate appropriate captions according to input videos from the viewpoint of human judgment. The results show the proposed system is superior to the system with VGG16.
Investigation of Biomedical Cell Image Cryptography Based on RC4 Technique

Abstract Enormous data permeates various facets of social life, including public administration, community services, and scientific research, owing to the advent of new technologies. With the escalation of data transportation concerns, its significance in our daily lives has magnified. Particularly in the medical field, preserving information integrity during data transit has become an urgent priority. For instance, safeguarding massive electronic medical files like MRI images from cyberattacks and data loss poses a formidable challenge. Encryption and decryption techniques are employed as a solution. While numerous image types have undergone testing using cryptography techniques, biomedical images have received limited attention. In this article, We implemented RC4, pixel shuffling, and steganography to encrypt and decrypt cell images. Evaluating the encryption and decryption performance of these cell images involved testing cell viability, live/dead ratio, and morphology.
PERSONAL FACTORS OF PSYCHOLOGICAL WELL-BEING IN CONDITIONS OF SOCIOECONOMIC INSTABILITY

The relevance of the topic is in the peculiarities of the formation of psychological well-being under the influence of destructive phenomena of the socio-economic environment. The modern geopolitical space and economic trends encourage the solution of problems not only at the macroeconomic level and stabilization of the economy but also at the level of the individual, which is the prerogative of the state social policy. The factors of psychological well-being formation may differ in their nature and direction, which necessitates a methodical analysis of the available factors. The issue of using pedagogical, spiritual, and cultural practices to ensure the formation of psychological well-being is a key task in the implementation of the mechanism for creating a positive psychological climate. The psychological climate in which a person lives is an important factor in the development of psychological well-being. The purpose of the article is to study the personal factors of psychological well-being in the context of socioeconomic instability and global geopolitical challenges. The task of the research is to outline the key prospects for the development of psychological well-being and its possible formation following the conditions of socioeconomic space. The research methodology involves the implementation of a series of scientific research methods, as well as the use of correlation analysis to identify the main advantages of psychological well-being in conditions of socioeconomic instability. The results obtained can improve social policy and psychological practices of assisting people with low levels of psychological well-being. The practical significance of the article lies in the possibility of using the obtained results to improve the individual development of personal factors of psychological well-being. In the context of global geopolitical challenges, it is worth directing human cognitive activity to the analysis of the elimination of destructive phenomena in psychological well-being. The article investigates the peculiarities of the development along with the formation of characteristic features of psychological well-being and identifies the key directions of development of practices for their improvement and optimization.
Generative AI in Computing Education: Perspectives of Students and Instructors

Generative models are now capable of producing natural language text that is, in some cases, comparable in quality to the text produced by people. In the computing education context, these models are being used to generate code, code explanations, and programming exercises. The rapid adoption of these models has prompted multiple position papers and workshops which discuss the implications of these models for computing education, both positive and negative. This paper presents results from a series of semi-structured interviews with 12 students and 6 instructors about their awareness, experiences, and preferences regarding the use of tools powered by generative AI in computing classrooms. The results suggest that Generative AI (GAI) tools will play an increasingly significant role in computing education. However, students and instructors also raised numerous concerns about how these models should be integrated to best support the needs and learning goals of students. We also identified interesting tensions and alignments that emerged between how instructors and students prefer to engage with these models. We discuss these results and provide recommendations related to curriculum development, assessment methods, and pedagogical practice. As GAI tools become increasingly prevalent, it's important to understand educational stakeholders' preferences and values to ensure that these tools can be used for good and that potential harms can be mitigated.
Intelligent Model for Improving Risk Assessment in Sterilization Units Using Revised FMEA, Fuzzy Inference, k-Nearest Neighbors and Support Vector Machine.

The complex environment of the hospital and the critical operations practiced in the medical departments, such as the sterili zation unit, require implementing risk assessment plans as fundamental support for effective management. The Failure Modes Analysis and Effects (FMEA) method is one of the popular methods used to perform the risk assessment process. Fuzzy logic and machine learning techniques provide robust devices that improve the efficiency of several risk assessment methods, such as FMEA. Hence, this study aims to enhance the efficiency of risk assessment in hospital sterilization units using an intelligent model based on revised FMEA, an improved FMEA adaptable to the studied system, fuzzy inference system, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) techniques. An interesting application of the model in the central sterilization unit of the largest university hospital is presented. The performance of the proposed model is evaluated at the end to prove its efficiency.
Hand Gesture Recognition using MIMO Radar and Lightweight Convolutional Neural Network

Frequency-Modulated continuous-wave (FMCW) radar is a promising modality for gesture recognition, as it has the potential to overcome the limitations of cameras, which are commonly used for this purpose. Ambient light conditions and obstructions pose significant challenges to optical sensors. This paper introduces a method for hand gesture recognition with ten different gestures by processing the data obtained from the continuous waves captured by the radar system of Texas Instruments. Raw radar data is performed FFT (Fast Fourier Transform) to generate the Range-Doppler Heatmap (RDH), and then micro-Doppler (time-Doppler) is analyzed to extract velocity hand gesture data over time from RDH. Subsequently, we propose a lightweight Convolutional Neural Network (CNN) model designed to extract complex structures from the processed data, comprising velocity, angles, and phase features. The CNN model we developed was trained on a dataset consisting of eight individuals and evaluated on two additional individuals. The average accuracy across different gestures achieved by the model is 98.35%.
CAvatar

Human mesh reconstruction is essential for various applications, including virtual reality, motion capture, sports performance analysis, and healthcare monitoring. In healthcare contexts such as nursing homes, it is crucial to employ plausible and non-invasive methods for human mesh reconstruction that preserve privacy and dignity. Traditional vision-based techniques encounter challenges related to occlusion, viewpoint limitations, lighting conditions, and privacy concerns. In this research, we present CAvatar, a real-time human mesh reconstruction approach that innovatively utilizes pressure maps recorded by a tactile carpet as input. This advanced, non-intrusive technology obviates the need for cameras during usage, thereby safeguarding privacy. Our approach addresses several challenges, such as the limited spatial resolution of tactile sensors, extracting meaningful information from noisy pressure maps, and accommodating user variations and multiple users. We have developed an attention-based deep learning network, complemented by a discriminator network, to predict 3D human pose and shape from 2D pressure maps with notable accuracy. Our model demonstrates promising results, with a mean per joint position error (MPJPE) of 5.89 cm and a per vertex error (PVE) of 6.88 cm. To the best of our knowledge, we are the first to generate 3D mesh of human activities solely using tactile carpet signals, offering a novel approach that addresses privacy concerns and surpasses the limitations of existing vision-based and wearable solutions. The demonstration of CAvatar is shown at https://youtu.be/ZpO3LEsgV7Y.
Towards Inadequately Pre-trained Models in Transfer Learning

Transfer learning has been a popular learning paradigm in the deep learning era, especially in annotation-insufficient scenarios. Better ImageNet pre-trained models have been demonstrated, from the perspective of architecture, by previous research to have better transferability to downstream tasks[26]. However, in this paper, we find that during the same pre-training process, models at middle epochs, which are inadequately pre-trained, can outperform fully trained models when used as feature extractors (FE), while the fine-tuning (FT) performance still grows with the source performance. This reveals that there is not a solid positive correlation between top-1 accuracy on ImageNet and the transferring result on target data. Based on the contradictory phenomenon between FE and FT that a better feature extractor fails to be fine-tuned better accordingly, we conduct comprehensive analyses on features before the softmax layer to provide insightful explanations. Our discoveries suggest that, during pre-training, models tend to first learn spectral components corresponding to large singular values and the residual components contribute more when fine-tuning.
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning

The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data being produced at the edge. The integration of self-supervised learning (SSL) and federated learning (FL) into one coherent system can potentially offer data privacy guarantees while also advancing the quality and robustness of the learned visual representations without needing to move data around. However, client bias and divergence during FL aggregation caused by data heterogeneity limits the performance of learned visual representations on downstream tasks. In this paper, we propose a new aggregation strategy termed Layer-wise Divergence Aware Weight Aggregation (L-DAWA) to mitigate the influence of client bias and divergence during FL aggregation. The proposed method aggregates weights at the layer-level according to the measure of angular divergence between the clients’ model and the global model. Extensive experiments with cross-silo and cross-device settings on CIFAR-10/100 and Tiny ImageNet datasets demonstrate that our methods are effective and obtain new SOTA performance on both contrastive and non-contrastive SSL approaches.
GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction

Neural implicit representations have recently demonstrated compelling results on dense Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors in camera tracking and distortion in the reconstruction. Purposely, we present GO-SLAM, a deep-learning-based dense visual SLAM framework globally optimizing poses and 3D reconstruction in real-time. Robust pose estimation is at its core, supported by efficient loop closing and online full bundle adjustment, which optimize per frame by utilizing the learned global geometry of the complete history of input frames. Simultaneously, we update the implicit and continuous surface representation on-the-fly to ensure global consistency of 3D reconstruction. Results on various synthetic and real-world datasets demonstrate that GO-SLAM outperforms state-of-the-art approaches at tracking robustness and reconstruction accuracy. Furthermore, GO-SLAM is versatile and can run with monocular, stereo, and RGB-D input.