Reviewerly/exHarmony huggingface dataset:
Autonomous Robot Motion Decision Algorithm Based on Decision Tree Algorithm and Deep Learning Technology

Generally, the traditional robot path planning method can solve the modeling problem well, but the common feature is that there is a lack of search ability in the planning process, which often needs to be combined with other search algorithms. Intelligent method can solve the problem of path planning in complex environment, but some problems will be encountered in the application process. In order to change the problems of traditional robots, such as fixed movements and lack of adaptability, Deep learning (DL) has been gradually introduced into intelligent robot technology. Because the amount of data of environmental parameters that make up the robot is not large, it is easy to obtain a simple data classification rule, and the robot can recognize the environment according to this classification rule. In this paper, an autonomous robot motion decision algorithm based on decision tree and DL technology is proposed, which can complete the functions of robot motion, information collection and processing. Through the overall design of the tracking system, the design of data and robot’s bottom-level instruction transformation and the choice of data transmission mode, a test environment is set up to test the target, and the tracking performance of the improved algorithm is verified.
On the classification of codes over non-unital ring of order 4

In the last 60 years coding theory has been studied a lot over finite fields [Formula: see text] or commutative rings [Formula: see text] with unity. Although in [Formula: see text], a study on the classification of the rings (not necessarily commutative or ring with unity) of order [Formula: see text] had been presented, the construction of codes over non-commutative rings or non-commutative non-unital rings surfaced merely two years ago. In this letter, we extend the diverse research on exploring the codes over the non-commutative and non-unital ring [Formula: see text] by presenting the classification of optimal and nice codes of length [Formula: see text] over [Formula: see text], along with respective weight enumerators and complete weight enumerators.
Falling People Detection in Real Time Video Using Convolution Neural Network

Nowadays accidents can happen anywhere, anytime, especially in private areas, houses or condos. Use of surveillance camera technology and machine learning-based object identification to develop applications for alerting family members in the event of accidents is important if a person living alone while his family is abroad has an accident and no one is aware of the situation to the point of death. The purpose of this research is to create applications for warning family members of accidents using the technology of surveillance cameras and machine learning-based object detection in perspective of the technology that makes today's development possible. By using such an application, users would be able to alert others to accidents by sending images to those who were involved as well as phone or alarm-send emergency services or hospitals. As a result of the program's development, it was discovered that an application could warn when a user fell. An average accuracy of 89.62% can be achieved using the suggested method. By using a convolution neural network, the proposed methodology outperformed than state-of-the-art techniques for detecting falling persons in real-time video.
METHODOLOGY FOR CREATING "TRUSTWORTHINESS" ARTIFICIAL INTELLIGENCE IN SYSTEMS BASED ON THE REGULARIZING BAYESIAN APPROACH

The article proposes a methodology for creating a system for evaluating the quality of artificial intelligence (AI) solutions, in particular, evaluating the trustworthiness of solutions, which determines the degree of confidence in the solutions obtained. The methodology is based on the regularizing Bayesian approach and implemented as a system of metrological support of AI solutions. Within the framework of this methodology and technologies of metrological support, complexes of metrological characteristics are proposed that determine accuracy, reliability (level of errors of the 1st and 2nd kind), reliability, risk, entropy, amount of information for each stage of the algorithms, which ensures traceability and transparency of the solutions obtained. Practical examples of determining metrological complexes for solving applied artificial intelligence systems are given.
Fundamentals Of Artificial Intelligence And Machine Learning

This book fills in the knowledge gaps in the apparently insurmountable fields of artificial intelligence and machine learning, and is aimed primarily at undergraduate and graduate students in computer science and engineering. It is claimed that more case studies and fully-solved examples are included in this book than in any other of its kind. An engaging reading experience is ensured by the text's lively style. After a brief introduction to AI, the material delves into more advanced topics like heuristic search and game playing. The machine learning section starts out with some general information on how learning works and the different association rule learning methods. Numerous case studies and application exercises are given, along with several forms of learning including reinforced, supervised, unsupervised, and statistical. The book is helpful for students since each subject has its own well-explained algorithms and pseudo codes.
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation

Brain tumor segmentation is an active research area due to the difficulty in delineating highly complex shaped and textured tumors as well as the failure of the commonly used U-Net architectures. The combination of different neural architectures is among the mainstream research recently, particularly the combination of U-Net with Transformers because of their innate attention mechanism and pixel-wise labeling. Different from previous efforts, this paper proposes a novel network architecture that integrates Transformers into a self-adaptive U-Net to draw out 3D volumetric contexts with reasonable computational costs. We further add a residual connection to prevent degradation in information flow and explore ensemble methods, as the evaluated models have edges on different cases and sub-regions. On the BraTS 2021 dataset (3D), our model achieves 87.6% mean Dice score and outperforms the state-of-the-art methods, demonstrating the potential for combining multiple architectures to optimize brain tumor segmentation.
Leveraging Hard Positives for Contrastive Learning in Semi-Supervised Medical Image Segmentation

Semi-supervised learning (SSL) has been a popular technique to resolve the annotation scarcity problem in image segmentation. Recently, contrastive learning (CL), which encourages intra-class compactness and inter-class dispersion, has shown great potential in helping SSL learn discriminative features. However, vanilla CL tends to focus on negative samples, while ignoring those hard positives (i.e., samples have dissimilar feature representations with respect to anchors) which could also deliver discriminative knowledge. In this paper, we propose to inject hard positives oriented contrastive (HPC) learning into SSL and present an effective three-stage framework for medical image segmentation. Specifically, the first and second stages pre-train an encoder-decoder architecture through bi-level HPC learning, including unsupervised image-level HPC (IHPC) learning and supervised pixel-level HPC (PHPC) learning. Notably, the PHPC loss is implemented in a region-based manner so that the model is competent to capture both global contextual and local semantic information with less memory consumption. In the third stage, the well-pre-trained architecture is adapted to a semi-supervised segmentation framework. Experiments on two public datasets demonstrate that our proposed method surpasses the state-of-the-art methods.
Oriented Object Detection Based on CNN and Transformer for Remote Sensing Images

Remote sensing image object detection has broad application prospects in civil fields, such as environmental monitoring and resource exploration. Since remote sensing images are characterized by complex and diverse imaging scenes, there is no generalized and universal remote sensing image object detection method yet. This paper proposes an anchorless fixed-frame remote sensing image object detection method using CNN and transformer, and the detection head based on centroid localization and boundary-aware vectors. The method is a single-stage algorithm, which does not depend on the object candidate frame and avoids the complicated anchor frame design and tedious anchor frame parameter tuning process. Experiments were conducted on the object detection dataset DOTA 2.0, and the results show the superiority of our proposed method when compared with state-of-the-art methods.
Stealthy Backdoor Attack on RF Signal Classification

Recently, deep learning (DL) has become one of the key technologies supporting radio frequency (RF) signal classification applications. Given the heavy DL training requirement, adopting outsourced training is a practical option for RF application developers. However, the outsourcing process exposes a security vulnerability that enables a backdoor attack. While backdoor attacks have been explored in the computer vision domain, it is rarely explored in the RF domain. In this work, we present a stealthy backdoor attack that targets DL-based RF signal classification. To realize such an attack, we extensively explore the characteristics of the RF data in different applications, which include RF modulation classification and RF fingerprint-based device identification. Particularly, we design a training-based backdoor trigger generation approach with an optimization procedure that not only accommodates dynamic application inputs but also is stealthy to RF receivers. Extensive experiments on two RF signal classification datasets show that the average attack success rate of our backdoor attack is over 99.2%, while its classification accuracy for the clean data remains high (i.e., less than a 0.6% drop compared to the clean model). Additionally, we demonstrate that our attack can bypass existing defense strategies, such as Neural Cleanse and STRIP.
New Scalable and Efficient Online Pairwise Learning Algorithm

Pairwise learning is an important machine-learning topic with many practical applications. An online algorithm is the first choice for processing streaming data and is preferred for handling large-scale pairwise learning problems. However, existing online pairwise learning algorithms are not scalable and efficient enough for large-scale high-dimensional data, because they were designed based on singly stochastic gradients. To address this challenging problem, in this article, we propose a dynamic doubly stochastic gradient algorithm (D2SG) for online pairwise learning. Especially, only the time and space complexities of O (d) are needed for incorporating a new sample, where d is the dimensionality of data. This means that our D2SG is much faster and more scalable than the existing online pairwise learning algorithms while the statistical accuracy can be guaranteed through our rigorous theoretical analysis under standard assumptions. The experimental results on a variety of real-world datasets not only confirm the theoretical result of our new D2SG algorithm, but also show that D2SG has better efficiency and scalability than the existing online pairwise learning algorithms.
Harnessing a real-time location system for contact tracing in a busy emergency department

With the persistent threat of emerging infectious diseases (EIDs), digital contact tracing (CT) tools can augment conventional CT for the prevention of healthcare-associated infectious disease transmission. However, their performance has yet to be evaluated comprehensively in the fast-paced emergency department (ED) setting.This study compared the CT performance of a radiofrequency identification (RFID)-based real-time location system (RTLS) with conventional electronic medical record (EMR) review against continuous direct observation of close contacts ('gold standard') in a busy ED during the coronavirus disease 2019 pandemic period.This cross-sectional study was conducted at the ED of a large tertiary care hospital in Singapore from December 2020 to April 2021. CT performance [sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and kappa] of the RTLS, EMR review and a combination of the two approaches (hybrid CT) was compared with direct observation. Finally, the mean absolute error (MAE) in the duration of each contact episode found via the RTLS and direct observation was calculated.In comparison with EMR review, both the RTLS and the hybrid CT approach had higher sensitivity (0.955 vs 0.455 for EMR review) and a higher NPV (0.997 vs 0.968 for EMR review). The RTLS had the highest PPV (0.777 vs 0.714 for EMR review vs 0.712 for hybrid CT). The RTLS had the strongest agreement with direct observation (kappa=0.848). The MAE between contact durations of 80 direct observations and their respective RTLS contact times was 1.81 min.The RTLS was validated to be a high-performing CT tool, with significantly higher sensitivity than conventional CT via EMR review. The RTLS can be used with confidence in time-strapped EDs for time-sensitive CT for the prevention of healthcare-associated transmission of EIDs.
Migration-based algorithm library enrichment for constrained multi-objective optimization and applications in algorithm selection

It is of necessity to select appropriate optimization algorithms from an algorithm library due to the universality of constrained multi-objective optimization problems and the suitability of intelligent optimization algorithms, which requires a rich optimization algorithm library. This paper proposes a migration-based method of enriching the algorithm library for constrained multi-objective optimization problems. After calculating the similarity between problems based on their landscape features, the proposed method calculates the migration probabilities of intelligent optimization algorithms solving similar problems based on the performance of each algorithm and the similarity between problems. According to the redundancy and compatibility of components, the algorithms with large migration probabilities enrich the algorithm library for solving the current problem. Based on the enhanced algorithm library, a Softmax regression model is trained to generate an optimal intelligent algorithm to solve the current problem. The proposed method is applied to solve a series of constrained multi-objective optimization benchmark problems and the operation optimization problems of an integrated coal mine energy system, and the experimental results verify its effectiveness and feasibility.
Study of Immersion in Augmented Reality Through Analysis of Immersion Elements in Museums

With the advent of the information age, augmented reality technology can enhance the sense of reality in the virtual world and immerse people in the real and virtual world. People have always been interested in virtual space or augmented reality technology, especially in the face of historical development trends. Museums have always been open to the public, they are not just a collection, but also an exhibition for the public. Through analyzing different museums, it is found that museums with augmented reality technology exhibitions are non-profit museums for the purpose of emotional experience. In order to find out the immersion factors of virtual reality in museums, on the basis of previous research, this paper divides immersion in augmented reality into story immersion, five-sense experience immersion, and spatial interaction immersion. Through the analysis of different museums, it is found that immersion through five-sense experience is the most commonly used method when all display media are included. This method provides educational content for museum visitors and projects virtual presentations through monitors and portable IT devices, thereby increasing visitors’ viewing pleasure.
A GPU-friendly hybrid occlusion culling algorithm for large scenes

In this paper, we present a novel hybrid occlusion culling method for large scale scenes. The basic idea is to use an iterative hierarchical Z-buffer occlusion culling algorithm to execute a coarse-grained culling via compute shader, followed by a fine-grained culling via rasterization. We also propose a forward warping method to generate a low resolution approximated depth map to accelerate the culling process. Our solution requires only one indirect multidraw command and runs on GPU totally without any CPU read-back operations. Our experimental results indicate that our algorithm outperforms the existing solutions both in performance and culling rate.
PREDIKSI KELULUSAN MAHASISWA PROGRAM STUDI MATEMATIKA UNIVERSITAS UDAYANA MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK

Based on the rules of the BAN-PT Study Program Accreditation Instrument 4.0 (IAPS 4.0), one of the study program accreditation assessment indicators is the percentage of students who graduate on time. The percentage of Udayana University Mathematics Study Program students who graduated on time during the graduation period from 2002 to 2019 was 52.5%. It can be seen that many students fail to complete their studies on time, which has an impact on the study program accreditation assessment. Based on these problems, this study aims to help academics increase the percentage of Udayana University Mathematics Study Program graduates by predicting the graduation of mathematics study program students using the backpropagation neural network method. This study uses data on alumni of students of the 2002-2017 mathematics study program. With the BNN 5-3-1 architecture, the predicted results of graduation for students of the Udayana University mathematics study program are 73%.
ABLA: Application-Based Load-Balanced Approach for Adaptive Mapping of Datacenter Networks

Cloud-based services are growing more rapidly than ever, and so does the management challenge on their providers’ side. Cloud-based datacenter networks are built with nodes of huge processing power, connected by high bandwidth capacities to carry their interior traffic requirements. However, such cloud networks still have limits that are imposed not necessarily by their physical components, but by the schemes of resource management being deployed. Traditionally, for an institute to provide services, it needs to have its own datacenter facility that interconnects its servers through a topology that matches its desired administrative policies and scaling objectives. With the theme of cloud-based IaaS, such datacenter topologies can be created virtually over the cloud. Nowadays, a significant part of those institutes who provide us with our daily services have their infrastructures hosted over cloud ones. Therefore, resources of such cloud networks need to be efficiently utilized, in order to keep their performance and hosting prices competitive. A typical datacenter network mainly consists of server nodes and network links. Besides the resources of the server nodes, the network bandwidth resources are considered a crucial key determinant for the whole datacenter performance. Indeed, a server without sufficient bandwidth capacities is almost useless. Proposals in the literature present schemes for resource utilization on either side of the problem at a time: the nodes or the links. Working in isolation can never deliver efficient mapping solutions. ABLA is an Application-Based, and Load balancing Approach for adaptive mapping proposal. ABLA’s methodology tackles both sides of the datacenter, the nodes and links. It starts by (1) breaking down the node’s resource requirement for the requested applications to be hosted over the virtual server machines besides (2) reading the topological connectivity and bandwidth requirements for each virtual node to all other nodes in the virtual datacenter topology. Compared to other models in the literature, the simulation results show that our proposed ABLA model provides for complete mapping services via load-balanced hosting networks. This allows for competitive hosting prices, with higher performance and service satisfaction rates.
An Intelligent Device to Analyze and Detect Driver Alertness using Media Pipe and Raspberry Pi

Distracted driving is an important problem that plagues the world, and is the cause of thousands of deaths in the United States. Distracted driving has cost an estimate of $44.2 billion to the US in damages. A way I can help solve this problem is creating a device that can detect if the driver is distracted. This device contains an Arducam Day n Night 1080p camera, a Raspberry Pi 3, and a buzzer. The device utilizes python and Mediapipe, a library which can locate important landmarks on the driver’s face [1]. During development, I had to face many challenges such as the driver possibly looking down subtlety with their eyes, or the camera being positioned in a weird way. During experimentation, I tested the delay, and accuracy of the device. After 35 trials, the device is accurate around 94%, and the delay is around 9 seconds. Overall, this device can be used by everyone who wants to be kept responsible and alert while driving.
GFS-CNN: A GPU-friendly Secure Computation Platform for Convolutional Neural Networks

Outsourcing convolutional neural network (CNN) inference services to the cloud is extremely beneficial, yet raises critical privacy concerns on the proprietary model parameters of the model provider and the private input data of the user. Previous studies have indicated that some cryptographic tools such as secure multi-party computation (MPC) can be used to achieve secure outsourced inferences. However, MPC-based approaches often require a large number of communication rounds across two or more non-colluding servers, which make them hard to exploit GPU acceleration. In this paper, we propose GFS-CNN, a GPU-friendly secure computation platform for convolutional neural networks. The following two specific efforts of GFS-CNN have been made by combining machine learning and cryptography techniques. Firstly, We use quadratic activation functions to replace most of the ReLU functions without losing much accuracy, so as to create a mixed linear layer for better efficiency by integrating convolution, batch normalization, and quadratic activation. Secondly, for the rest ReLU functions, we implement the secure ReLU protocol using function secret sharing, enabling GFS-CNN to evaluate the secure comparison function via a single interaction during the online phase. Extensive experiments demonstrate that GFS-CNN is accuracy-preserving and reduces online inference time by 16.4% on VGG-16 models compared to Delphi (USENIX Security’20).