Reviewerly/exHarmony huggingface dataset:
Elder Fall Detection System Based on Two-level SVM Algorithm

With the acceleration of population aging, the health and life safety of the elderly has gradually become a common problem facing the whole society. In this paper, an elderly fall detection system based on embedded microcontroller system and SVM algorithm is studied. In terms of hardware design, acceleration sensor, communication module and positioning module are integrated. In the algorithm part, THE SVM algorithm and threshold algorithm are integrated. The threshold algorithm is used for preliminary judgment of the action, and the SVM algorithm is used for accurate judgment, which can effectively distinguish the fall and daily behavior, reduce misjudgment and improve the accuracy of the fall judgment. Can effectively protect the life safety of the elderly
Semantic Segmentation Network Based on Lightweight Feature Pyramid Transformer

Transformer has excellent global expression ability. Recently, researchers have proposed many Transformer-based image semantic segmentation networks, and most of them have achieved considerable results. However, they ignore the multiscale modeling of the decoder, multi-scale feature representation is crucial for segmenting objects of different scales. To improve the decoder of the transformer network for semantic segmentation, we propose a lightweight feature pyramid Transformer. Specifically, an up-sampling method of feature pixel rearrangement is proposed to up-sample high-level features, and feature pyramid fusion is performed to form a rough multi-scale representation; secondly, a lightweight multi-head attention with multi-level feature fusion is proposed. The coarse multiscale features are refined, and the multi-head attention allows the model to focus on the differences between scales and learn the subspace information of each scale from each other. Therefore, a unified image semantic segmentation network is formed, which can capture contextual information at multiple feature scales with only a small increase in computational overhead. Our method is effectively validated on Cityscapes and ADE20K datasets and achieves good results.
Path Planning of Six-wheel-drive Rescue Robot Based on A* Algorithm and Artificial Potential Field Method

In view of the rescue robot path planning under complicated environment and unknown disturbance failure problem, path planning of complex environment for rescue robot based on the improved Hybrid algorithm is proposed in this paperThe hybrid algorithm improves the traditional A* and introduces artificial potential field into A*. More and more reliable path planning algorithms need to be studied to ensure that the rescue robot can reach the rescue scene quickly, including the heuristic function H(n) is dynamically weighted, and redundant nodes in the path are removed. In my paper, the search speed of A* algorithm is improved by dynamically changing the weight size, the redundant nodes are eliminated to reduce the path length, and the APF is introduced to avoid obstacles to make the rescue robot reach the end point successfully and efficiently. We can see from the simulation results-the hybrid algorithm has better performance, the Search time reduced by 34.09%, the length is shortened by 5.75%, and the turning points are shortened by 36.28%. The results show that this method has obvious optimization effect and certain feasibility.
Billet Number Recognition Based on ESRGAN and Improved YOLOv5

Aiming at solving the problem of billet number recognition and further improve the rate of recognition, this paper proposes a billet number recognition algorithm based on ESRGAN and improved YOLOv5, which is referred to as YOLOv5-Billet in this paper. According to the actual situation, we propose a target recognition algorithm that uses the lightweight network MobileNetv3 to replace the backbone feature extraction network of YOLOv5. We also introduce ESRGAN to improve the quality of the input image and further improve the recognition effect. A series of rigorous experiments show that YOLOv5-Billet has a certain improvement in billet recognition speed and precision. Specifically, the average precision of YOLOv5-Billet is improved by 10.8%, and the detection speed is also improved by 5.12fps, reaching 40.35fps. Through the experimental verification of real-time collection of data sets, compared with a variety of classical target detection methods, the recognition precision and detection speed are improved to varying degrees. This model maintains the lightweight characteristics of YOLOv5 and meets the requirements of real-time monitoring of the number of blanks.
Face Detection and Tracking Using Raspberry Pi based on Haar Cascade Classifier

Face detection and tracking technology is the key link for intelligent service robot to complete visual perception and human-robot interaction. This paper research the problems of slow speed and low accuracy for face detection and tracking on the low performance of embedded devices. In this paper, face detection and tracking are implemented on a smart car controlled by Raspberry Pi 4B. The face detection algorithm used in this research is the haar cascade classifier method in the OpenCV library which is run using the python programming languages, and the scaling process is used to the image to speed up the detection speed and improve the frame rate. Face tracking is based on the idea of keeping the detected face frame in the central area of the detection window, the coordinates of the center of the face frame in the X direction are calculated firstly, and the corresponding tracking commands are executed by judging the range of the coordinates. Experiments show that the face detection and tracking of the Raspberry Pi car based on the haar cascade classifier method in this paper can identify and locate faces in real time and accurately, and can effectively track the detected faces.
Security Threats and their Mitigations on the Operating System of Internet of Medical Things (IoMT)

The potential security dangers of IoMT influence the privacy, uprightness, realness and accessibility of its information and the related assets. In spite of the fact that IoMT is engaged with enormous scope administrations provisioning in the clinical worldview; nonetheless, the asset compelled nature of these gadgets makes them powerless against colossal security and protection issues. These weaknesses are lamentable for IoMT as well as compromise the entire medical care environment, which can thusly get human lives risk. The Internet of Medical Things (IoMT) is a mixture of clinical gadgets and applications that can associate with medical care data innovation frameworks utilizing organizing advances. Despite the fact that all gadgets are totally controlled for patient effect, the business has been sub-par in managing dangers of potential online protection dangers in the beyond couple of years. This paper basically aims at providing facts on the security threats and their mitigation to operating system of medical devices, such as identity authentication, cryptography etc.
S³MPL:Semi-supervised Semantic Segmentation with Mixed Pseudo Label

The introduction of semi-supervised methods reduces the cost of labeling training data. In this regard, semi-supervised semantic segmentation methods have received great attention. In this paper, a consistent regularization method called Semi-supervised Semantic Segmentation with Mixed Pseudo Label (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> MPL) is proposed. In this approach, consistency is applied by feeding the same input image into two segmented networks that are initialized differently and encouraging both networks to make the same predictions. The one with higher confidence in the pseudo one-hot label maps generated by the two segmentation networks is selected as the mixed pseudo label map to supervise the above segmentation networks simultaneously. There are two advantages to this: 1) the quality of pseudo labels is improved; and 2) the segmentation network can not only use divergence between networks to locate its own errors, but also continuously consolidate what it has learned; In addition, our approach also incorporates ClassMix augmentation to mix input images to obtain more diverse data. The experimental results show that our method has achieved competitive performance on Cityscapes and PASCAL VOC 2012.
M-ary Hopfield neural network for storage and retrieval of variable length sequences: Multi-limit cycle approach

In this paper, we present M-ary Hopfield neural network based auto-associative memory formulation for variable length sequences. Each sequence is composed of variable length of multi-valued pattern vectors. Recently, we presented M-ary Hopfield neural network for storage and retrieval of fixed length sequences. In this paper, we focus on two important issues (i) storage and retrieval mechanism for variable length pattern sequences in a single network (ii) analysis of storage capacity of the network for variable length sequences. We use Pseudo-Inverse learning based dual weight learning for storing variable length sequences as multiple limit cycles in a single M-ary Hopfield neural network. We propose a novel recall mechanism based on energies of End-of-Sequence markers to retrieve the stored sequence (complete sequence) and sequence id, corresponding to the trigger state. We present results of experiments on a large dataset of variable length sequences constructed from movie clips (i) to establish the efficacy of the proposed retrieval mechanism for sequence retrieval under zero error tolerance, given any particular state of the sequence as trigger state to the network, (ii) to demonstrate trade off between number of cycles and average length of cycle which the network supports for storage and retrieval (iii) to demonstrate that the system retains O(N) storage capacity for variable length sequences modelled in its limit cycles. This is attempted for the first time in the area of attractor neural networks.
Motivational benefits and usability of a handheld Augmented Reality game for anatomy learning

To explore the benefits of scalable, interactive handheld Augmented Reality (AR) serious games for anatomical terminology learning, an AR learning game for German-Latin terminology of bone-structures and areas of the female pelvis was developed and evaluated. The evaluation of the game with 36 midwifery students was conducted with a focus on intrinsic motivation, usability and perceived usefulness of the app. The results indicated strong effect sizes of increased perceived competence and intrinsic motivation, and a medium effect size for increased pressure (Intrinsic Motivation Inventory) after completing the AR game. Furthermore, students perceived the usability of the game as "best imaginable" (contextualization of SUS scores) and strongly agreed that the app is very useful to them.
Hybrid security scheme for a colored image using DNA and chaotic map

this paper proposes a color image encryption algorithm based on DNA encoding in conjunction with a 1D logistic map and a high-dimensional chaotic map. The XOR function is used to combine low and high-dimensional chaotic maps, resulting in a cipher image. For image encryption, the paper also proposes a system of confusion and diffusion. Confusion and Diffusion apply to high-dimensional chaotic maps. In a low-dimensional logistic map, the keyspace is smaller, and chaotic sequences are used directly from the chaotic map, which has an impact on the encryption process. Recent image encryption focuses primarily on a gray image scale. We present color image encryption that uses non-RGB color spaces like YCbCr, YIQ, HSV, and L*a*b*. The high connection between RGB color spaces red, green, and blue is commonly used in data storage and handling. RGB color spaces merge Luminance and Chroma channels so color analysis becomes more complex so we use non -RGB spaces. Using the logistic sine map or tent map association, different chaotic systems can be found by random number attacks and dictionary attacks. Gives the security analysis of encryption using DNA encoding and chaotic map and explains that it's vulnerable to plaintext-choice attacks we propose a new and more efficient approach and reduce time complexity then- existing approach and become encryption more secure.
Sample-Efficient Deep Reinforcement Learning via Balance Sample

In this paper, we propose two algorithms to improve sample efficiency by focusing on late stage samples in episodes. The first algorithm is Balanced Sample Experience Replay (BSER). Unlike the traditional random sampling approach, this algorithm improves the final score and stability in environment by learning more late stage experience in the corresponding episode. The second algorithm is weight-corrected DQN (WCDQN). This algorithm differs from the traditional undifferentiated update approach by differentially updating the samples used for training to improve the final score and stability in environment. We tested both algorithms on a classic Atari game environment and demonstrated the effectiveness of the algorithms.
VRVideos: A flexible pipeline for Virtual Reality Video Creation

Recent advances in Neural Radiance Field (NeRF)-based methods have enabled high-fidelity novel view synthesis for video with dynamic elements. However, these methods often require expensive hardware, take days to process a second-long video and do not scale well to longer videos. We create an end-to-end pipeline for creating dynamic 3D video from a monocular video that can be run on consumer hardware in minutes per second of footage, not days. Our pipeline handles the estimation of the camera parameters, depth maps, 3D reconstruction of dynamic foreground and static background elements, and the rendering of the 3D video on a computer or VR headset. We use a state-of-the-art visual transformer model to estimate depth maps which we use to scale COLMAP poses and enable RGB-D fusion with estimated depth data. In our preliminary experiments, we rendered the output in a VR headset and visually compared the method against ground-truth datasets and state-of-the-art NeRF-based methods.
Comparing Meditation and Immersive Virtual Environment for Relaxation

Traditionally, meditation has been used to help people change their emotional states, and most beginners practice meditation by closing their eyes to imagine a relaxing scenario. In this study, we explore the use of sensory information provided by immersive Virtual Reality Environments (VRE) to help people relax. We have designed two scenes in the VRE to help people transform from an anxious state into a more relaxed state. Previous workhas confirmed that the emotions of humans can be detected by observable quantifiable data. In this work, the emotional states of users are detected through physiological measurements. The results show that meditation makes participants relaxed possibly through a physiological mechanism while immersing in a natural VR environment can help users achieve better relaxation through positive sensory information. The users also indicated preferences on the types of virtual scenes that can help relaxation, which sheds some light on the future direction of customizing virtual scenes to achieve better effects for different users.
A Chatbot to Search for Similar Fashion Photos and Reference Fashion Coordinators via Body Part and Fashion Item Segmentations

In the past decade, a variety of apparel e-commerce sites have appeared. Although such sites enable users simply and easily to purchase fashion items, there frequently occur matters such that ordered items differ from what the user expected. To avoid such matters, fashion coordination sites have also been used, to which users can post photo images of their fashion coordination and appreciate the fashion photo images posted by other users. However, due to the vast amount of information posted on such sites, it is not always easy for inexperienced users to search for reference users (fashion coordinators) and photo images matching their tastes. To support such searching, we construct a prototype for a fashion chatbot that accepts a user's fashion photo image as input and outputs similar fashion photo images to the input image and account URL links of recommendable reference users (fashion coordinators). In this chatbot, we first conduct a body part segmentation to extract color features of body parts from the input fashion photo image. However, such a way does not properly consider item categories. Therefore, this chatbot also performs a fashion item segmentation to recognize item categories. Then, this chatbot searches for similar fashion photo images and recommendable reference users (fashion coordinators) based on the extracted color features of body parts and the recognized item category information. In this study, such a searching procedure for similar fashion photo images is also validated using 4,571 images posted on a fashion coordination site in Japan. Moreover, we test the chatbot with a use-case scenario.
Beta-Liouville and Inverted Beta-Liouville Based Predictive Models for Occupancy Detection using Small Training Data

In this paper, we propose two predictive models based on Beta-Liouville (BL) and inverted Beta-Liouville (IBL) mixture models. The choice of the BL and IBL mixture models is motivated by their flexibility. The proposed predictive models are dedicated to classification tasks where the training datasets are non-Gaussian and small which is generally the case in real-life scenarios. A principled variational approach is proposed to learn the proposed models. Extensive experimental results based on both synthetic data and a real application that concerns occupancy detection in smart buildings prove that our predictive framework achieves promising results especially with extremely small training data sets.
Control of Decentralized Autonomous Organizations

Blockchain technology has facilitated the emergence of a new type of organizational form, Decentralized Autonomous Organizations (DAOs). In contrast to corporations and other types of business organizations, DAOs do not have managers or directors with formal decision rights. Instead, the members of DAOs collectively make decisions by voting on proposals. The governance process is encoded and executed via a smart contract, negating the need for centralized leadership. We examine the control of DAOs by analyzing 10,639 proposals across 151 DAOs. We find that, rather than democratizing decision-making, a small number, often three or fewer, of entities exert control over most decisions.
INTERNAL INDEPENDENT ASSESSMENT OF THE QUALITY OF EDUCATION: FORMS, NORMATIVE AND METHODOLOGICAL SUPPORT

The creation of a system for assessing the quality of education ensures the achievement of high performance indicators of organizations engaged in professional training of specialists. The quality assessment system is a necessary tool for monitoring the results of the development of educational programs by students in accordance with the requirements of the Federal State Educational Standard. The article deals with the issues of ensuring a comprehensive assessment of the quality of education, while the assessment of the quality of education is considered as a necessary condition for the training of highly qualified specialists. The authors reveal the content of the updated system of state accreditation, control and supervision in the field of quality assurance of education. The issue of the organization of an internal system for assessing the quality of education, including its normative and methodological support, is covered in sufficient detail. The key areas within which the quality control of education should be carried out are indicated. Separately, the authors dwell on the problem of organizing an independent assessment of the quality of students within the framework of the state final certification. To solve such a problem, it is proposed to use self-examination methods, involve employers, other legal entities and individuals, as well as teachers. The issue of complex preparation of funds of evaluation tools for carrying out diagnostic measures, including with the use of remote technologies, is touched upon. The completion of an internal independent assessment of the quality of education involves the formation of accounting documentation, which is an important element in the organization of the quality system of education. The authors emphasize the need to ensure the quality of education in an educational organization when training highly qualified personnel for all industries and spheres of economic and social activity of the state.
Performer: A Resource Demand Forecasting Method for Data Centers

Predicting the resource demands of online tasks plays an important role in data centers, which can help cloud providers to better allocate resources and to schedule tasks. To cope with the huge number of workloads in a data center, workloads are usually clustered first and then prediction is conducted for each cluster. However, training different models for different clusters separately reduces the overall utilization of the data in the data center, potentially reducing the prediction ability of the whole predicting system. Inspired by federated learning, we propose Performer, a Transformer-based forecasting model for clustered massive time-series. Each cluster of workloads is viewed as a local dataset owned by a training worker and all workers cooperate to train a global prediction model, while local models are trained by workers respectively. By combining global model and local models in an encoder-decoder architecture, Performer can learn global information and local information to perform predictions while keeping low model deployment costs. By splitting time-series into blocks and calculating self-attention inner-blocks, Performer keeps good prediction accuracy with lower computation cost than other Transformer-based time-series forecasting methods, making it more suitable for data center usage. Experiments on an online tasks workload dataset show Performer is an effective method in the scenario of cluster-based forecasting.
Hybrid method to automatically extract medical document tree structure

A huge and rapidly growing quantity of medical documents is available in an electronic versions. These informing documents mostly have textual content in natural language. These facts can make the documents difficult to read, ambiguous, or even contain mistakes. Consequently, when a doctor decides on treatment, many medical critical errors can happen. The information extraction in unstructured documents can handle this problem. In our paper, we introduce an automatic section detection method in the medical field SDM (Section Detection in Medical field) to improve information extraction tasks by providing more context. Accordingly, we have constructed some rules to prepare automatically the training set. Then, we benefit from numerous features such as formatting style, syntactic, and semantic features to train a machine learning model to find titles. Then, a section tree is generated that can be useful for other tasks. As anticipated, our experiments show that merging these features using a Convolutional Neural Network (CNN) can lead to a better result in real medical documents according to the F1-score measure. Thus, we benefit from the layout information and our method can provide the document sections in a tree form. It is worth noting that our method can be easily applied in other fields since it is not strongly dependent on the document type or language.