Miracle-dz/UniFAR-FTUs huggingface dataset:
A multi-view 3D reconstruction method that integrates patchmatch and efficient channel attention cascades

Multi-view 3D reconstruction refers to reconstructing the 3D structure and dynamic changes of the real world from images captured at different viewpoints. It is critical for in-depth analysis and understanding of real-world scenes. To address issues like insufficient feature extraction capabilities and weak correlation between feature extraction and depth estimation in existing multi-view 3D reconstruction methods, this paper proposes a multi-view 3D reconstruction approach integrating block matching and cascaded effective channel attention mechanisms. First, standard convolutions in the feature pyramids are replaced with grouped ghost convolutions in the feature extraction module, enabling the model to better capture subtle feature changes and improve its capability on complex tasks. Second, block matching networks at different scales are cascaded with the effective channel attention mechanism in the depth estimation layer to further enhance the network's learning ability. Finally, experimental results demonstrate that compared to the baseline network, the proposed algorithm improves point cloud reconstruction accuracy and overall performance on the DTU dataset by 5.9% and 1.9% respectively. It also shows competitive advantages on the custom dataset. This validates that the proposed network achieves high reconstruction accuracy and can be applied to practical high-precision 3D reconstruction tasks.
On Cyber-Physical Fault Resilience in Data Communication: A Case From A LoRaWAN Network Systems Design

Systems offering fault-resilient, energy-efficient, soft real-time data communication have wide applications in Industrial Internet-of-Things (IIoT). While there have been extensive studies for fault resilience in real-time embedded systems, investigations from cyber-physical systems (CPS) perspective are still much needed, as CPS faults occur not just from abnormal conditions in the software/hardware of the system, but also from the physical environment in which the system operates. At the same time, in addition to conventional fault tolerance strategies embedded in the software/hardware of the target system, CPS faults could be mitigated via some strategic systems re-configuration made available by the physical environment. This paper presents a design and implementation for CPS fault-resilient data communication, in the context of IIoT networks running LoRaWAN, a low-power wide-area networking standard. The proposed design combines collaborative IIoT end devices plus a network gateway piggybacked on a third-party cruising object that is part of the environment. With the focus on data communication, the study illustrates challenges and opportunities to address CPS fault resilience while meeting the needs for energy efficiency and communication timeliness that are common to IIoT systems. The implementation of the design is based on ChirpStack, a widely used open source framework for LoRaWAN. The results from experiment and simulation both show that the proposed scheme can tolerate limited errors of data communication while saving operating energy and maintaining timeliness of data communication to some extent.
Robotic Terrain Classification with Loss Regularization

Locomotion mechanics of legged robots are suitable when pacing through difficult terrains. Recognising terrains for such robots are important to fully yoke the versatility of their movements. Consequently, robotic terrain classification becomes significant to classify terrains in real time with high accuracy. The conventional classifiers suffer from overfitting problem, low accuracy problem, high variance problem, and not suitable for live dataset. On the other hand, classifying a growing dataset is difficult for convolution based terrain classification. Supervised recurrent models are also not practical for this classification. Further, the existing recurrent architectures are still evolving to improve accuracy of terrain classification based on live variable-length sensory data collected from legged robots. This paper proposes a new semi-supervised method for terrain classification of legged robots, avoiding preprocessing of long variable-length dataset. The proposed method has a stacked Long Short-Term Memory architecture, including a new loss regularization. The proposed method solves the existing problems and improves accuracy. Comparison with the existing architectures show the improvements.
Automated Security Evaluations for IoT Deployments

Our everyday lives are being dramatically changed by the Internet of Things (IoT), but it is also introducing significant security vulnerabilities that might be misused by bad actors. This paper provides a thorough summary of past studies on security of Internet of Things devices by delving into a wide range of methods used for IoT device evaluation and the related issues. We also review 1oT-based security trials that have been described in scholarly works. In this study, we provide a free platform made to detect communicative and network issues in IoT systems. Its versatility and adaptability enable the simple incorporation of extra security inspection features and tests. Our novel method simplifies security assessments by enabling automated testing without direct human involvement. The lab claims the capacity to detect and avert possible inbound assaults in addition to being skilled at locating and making public security flaws in closely examined equipment. It also has the ability to keep an eye on how the machines in the testbed interact with one another and to step in and stop operations if it notices any suspicious activity. In this work, we examine two separate Internet of Things (or IoT) devices-a smart light and a linked video camera-for privacy vulnerabilities. This illustrates the efficacy of our suggested Internet of Things, also known as IoT, safety testbed.
Investigating the role of genetic variation in long run economic outcomes

This dissertation empirically tests whether adaptations resulting from the Neolithic Revolution, or the widespread adoption of sedentary agriculture for sustenance, have led to economic differences. The development of sedentary agriculture constituted an environmental shift from the previous sustenance method of hunting and gathering. This environmental shift resulted in the natural selection of certain traits. I seek to exploit differences in these traits to measure differing economic outcomes. Two main adaptations are considered in this work: the ability to consume milk, or lactose tolerance, and resistance to infectious Eurasian diseases, which is the result of genetic variation. The first essay establishes a link between lactose tolerance and economic conditions in the pre-colonial era. The ability to digest milk, or to be lactose tolerant, is conferred by a gene variant, which is unequally distributed across the Old World. Digesting milk conferred qualitative and quantitative advantages to early farmers’s diets, which ultimately, led to differences in the carrying capacities of respective countries. The second essay investigates the role of genetic differences in resistance to infectious diseases on contemporary health outcomes. The Neolithic Revolution led to the initiation and sustainability of new infectious diseases. The differential timing of the Neolithic Revolution created differences in exposure to these infectious pathogens. Ultimately, this led to differential selection of genetic resistance, in which diversity within a key component of the immune system,
An Efficient and More Secure Searchable Encryption Algorithm Based On Fully Homomorphic Encryption

The increased use of cloud service platforms (CSP) has led to a growing need for private information retrieval (PIR) methods and searchable encryption (SE) methods. This under-scores the importance of securing clients’ private information while using CSP services. In this paper, we set more stringent goals for searchable encryption (SE) than prior work. We then present an index-based searchable encryption scheme using fully homomorphic encryption, which can be used in conjunction with the services provided by CSPs. Our scheme has a low calculation complexity under certain conditions (when the total number of queried files is small). Our scheme enables data inquirers to search their desired private information while preserving their privacy and keeping them from being frequency analysis attack.
An adversarial defense algorithm based on image feature hashing

Due to the black-box nature of neural network models and the existence of adversarial examples, AI security issues have attracted much attention. Adversarial examples are attack methods that cause the model to reach incorrect conclusions through invisible interference. This article proposes a feature perception hash defense algorithm based on wavelet transform and cosine transform, utilizing the interpretability, stability, and efficiency of traditional manual algorithms. Through experiments combined with a vegetable dataset, the algorithm is verified to effectively resist attacks from adversarial examples.
Robot-oriented 6G satellite-UAV networks: Requirements, paradigm shifts, and case studies

Networked robots can perceive their surroundings, interact with each other or humans, and make decisions to accomplish specified tasks in remote/hazardous/complex environments. Satelliteunmanned aerial vehicle (UAV) networks can support such robots by providing on-demand communication services. However, under traditional open-loop communication paradigm, the network resources are usually divided into user-wise mostly-independent links, via ignoring the task-level dependency of robot collaboration. Thus, it is imperative to develop a new communication paradigm, taking into account the highlevel content and values behind, to facilitate multirobot operation. Inspired by Wiener's Cybernetics theory, this article explores a closed-loop communication paradigm for the robot-oriented satellite-UAV network. This paradigm turns to handle group-wise structured links, so as to allocate resources in a taskoriented manner. It could also exploit the mobility of robots to liberate the network from full coverage, enabling new orchestration between network serving and positive mobility control of robots. Moreover, the integration of sensing, communications, computing and control would enlarge the benefit of this new paradigm. We present a case study for joint mobile edge computing (MEC) offloading and mobility control of robots, and finally outline potential challenges and open issues.
Research on Optimization of Lane Detection Algorithm under Complex Road Background

In order to improve the effect of lane detection in complex and changeable road background, this paper proposes a lane detection algorithm that combines attention mechanism and deformable convolution. The algorithm can adaptively adjust the weight of each pixel position in the feature map, so that the shape and position information of lane lines in the image can be captured more quickly and accurately. In addition, the algorithm can better learn the continuous changes of lane lines in the image, thus improving the detection effect. The experimental results show that the detection effect of the algorithm proposed in this paper is better than the traditional lane detection method on Tusimple dataset, and the algorithm can accurately detect lane lines in the face of complex road scenes. Therefore, the optimization and research of this algorithm improve the lane detection performance in the complex and changeable road background.
Integrating Differential Privacy and HE for Enhanced Privacy and Security in FL without Compromising Model Utility

Training machine learning models in a federated fashion across numerous devices or servers while maintaining data locality is an exciting new paradigm. However, maintaining data confidentiality throughout the aggregating process is still difficult. By combining DP with HE, this research presents a fresh strategy for implementing privacy-protecting FL. To avoid any accidental disclosure of private information, our technique keeps the data encrypted throughout the training process. Using DP in the big model keeps it safe from sharing details about any single data point. This is done by adding random signals into how updates are made to the model. The balance between making sure privacy is safe, the amount of work needed and how accurate a model can be are discussed here. The results of the tests show that our method works better than others and gives good model performance with strong protection for privacy. This study paves the way for a new time of private and secure FL. In this era, user privacy is taken into account without affecting how well these models work at all.
Human Pose Estimation Using Commodity WiFi and Deep Learning Approach

In this paper, we designed a method to extract human body pose coordinates from WiFi signals. We utilize OpenPose as the teacher network and convolutional residual architecture as the student network to design a novel human pose estimation model based on WiFi signals. Human pose images captured by the camera are used by the teacher network to generate pose annotations, supervising the learning of the student network. Finally, the student network achieves human pose estimation. We use PCK (percentage of correct keypoints) to assess the performance of the model, achieving a PCK@40 of 97.81%, and PCK@10 reaching 34.65%.
Impact on Efficiency of Reactors via Bubbles

With an emphasis on battery-comparable network theories and the function of power control devices, the current study investigates the effect of bubble on the electrical energy transformation effectiveness of chemical reactors. A thorough knowledge of chemical conditions for storing is necessary because to the growing dependence on charging devices, particularly in electronics that can be carried. The models that now exist are divided into four categories by the literature review: Electrical, A number, Logical, and Point Input Systems. Each category has pros and cons of its own. The proposed approach describes in detail the electric modelling technique for lithium-polymer, lithium-ion, and nickel-metal hybrid power sources, emphasising certain characteristics for examination. The section on findings and discussion highlights the trade-off amongst reliability and system brevity, recognising the difficulties in striking a balance of computing viability and accuracy. Evaluation of batter comparable network models is now necessary due to the development of power controllers. The benefit of this kind of simulation is that it can precisely predict the kinetic properties of a charger. The goal of identifying circuit characteristics is to predict electrochemical storage conditions. The various circuit types presented in this article are taken from published books. The Metals Combination, Potassium, Lead-Acid, and Rechargeable battery varieties are examined.
Obstacle-Avoidance X-Architecture Steiner Minimal Tree Algorithm Based on Deep Reinforcement Learning

In recent years, we have witnessed the rapid development of the Internet of Things (IoT) paradigm. A new computing paradigm, predictive intelligence, has been introduced into many IoT applications. In IoT applications, predictive intelligent devices must consider power and delay consumption. The goal of Very Large Scale Integration (VLSI) circuit routing problem is to minimize the wirelength to optimize chip delay. Therefore, VLSI routing problems have been widely applied in IoT. The X-architecture routing technology presents a promising solution that can reduce the number of vias, wirelength, and mold size significantly, thereby proving to be a highly practical approach. Moreover, with the shift of modern VLSI design towards system-on-chip paradigm, many uncrossable obstacles appear on the plane, thus Obstacle-Avoiding X-architecture Steiner Minimal Tree (OAXSMT) problem has attracted more and more attention. Most existing algorithms are troubled by poor wirelength quality or high construction time cost. Therefore, this paper proposes a method based on Deep Reinforcement Learning to construct an OAXSMT. First, an encoder based on heterogeneous graph transformer is designed to generate fixed-length encodings for each node. Second, an actor-critic decoder based on pointer network is designed to generate obstacle-avoiding X-architecture Steiner redundant tree. Finally, a fast evaluation strategy is designed to evaluate the quality of the redundant tree. Experimental results show that the routing results generated by the algorithm are superior to existing heuristic algorithms in terms of wirelength and runtime, and the algorithm also has certain generalization ability.
SAM based Computational Analysis of Root Mean Square Error for SPES Using LR, RF & ARIMA Machine Learning Models

Renewable energy is crucial in addressing various global challenges like climate change, global warming, human development index, etc. To address these challenges, wind and solar energy are the mature technologies to harness green power. Integrating machine learning into the renewable energy sector can lead to more efficient, reliable, and sustainable energy systems. The work proposed for the solar photovoltaic energy system. The solar photovoltaic system of 500kW AC is simulated in the System Advisor Model (SAM). The data for annual energy generation is fetched to form the dataset. This dataset from SAM contains the hourly energy generation data for a particular year. The comparative analysis is proposed after mining the dataset. The machine learning algorithms are Linear Regression, Random Forest and ARIMA. To evaluate the performance of these algorithms, the statistical measure, namely Root Mean Square Error (RMSE), was used. The comparative analysis shows the effectiveness of the proposed work and its dataset for the machine learning algorithms.
ORB-YOLO: An Indoor IMU-aided Visual-Inertial SLAM System for Dynamic Environment

Visual Simultaneous Localization and Mapping (Visual SLAM) is a methodology that empowers a robot to construct a map of an unfamiliar environment and determine its own position within the map solely based on visual data. However, Visual SLAM faces many challenges in indoor scenarios, such as low lighting, dynamic objects, textureless surfaces, and motion blur. To overcome these difficulties, we propose an indoor Visual SLAM system that combines ORB_SLAM3 with YOLOv81 to achieve high accuracy while maintaining robust performance in dynamic environments. Besides, the proposed system also uses the Inertial Measurement Unit (IMU) to aid the SLAM process. The IMU provides complementary information to the camera, such as orientation, acceleration, and angular velocity, which can improve the robustness and accuracy of the Visual SLAM system. Our system undergoes evaluation on two public datasets, as well as in real-world scenarios. To assess its performance, we compare it against alternative Dynamic SLAM approaches. The outcomes demonstrate that our system effectively handles intricate dynamic scenes that involve multiple moving objects, yielding state-of-the-art (SOTA) results. We have released our code on: https://github.com/SimonWXW/ORB-YOLO.
Dynamic Optimal Graph Learning for Unsupervised Feature Selection

Various graph-based unsupervised feature selection (UFS) methods have recently been developed to exploit the hidden structure information in data, achieving remarkable results. However, there are still some challenges: (1) most of them construct a suboptimal similarity graph to capture the global or local structure individually; (2) they ignore the high-order relations (i.e., the relation between the neighbor structure among samples). To tackle these challenges, Dynamic Optimal Graph Learning for Unsupervised Feature Selection (DOGL) is proposed to learn an optimal graph, which can simultaneously capture the global and local structure in embedding subspace. In particular, we project the original data into a low-dimensional embedding subspace to remove the corrupted information (i.e., noisy features, redundant features, and outliers) in the data. Then, a global similarity graph is learned based on data reconstruction in the embedding subspace. Meanwhile, we learn a mixed-order similarity graph from the predefined local similarity graph to exploit the high-order neighborhood information. Furthermore, we incorporate the two processes into a united framework, which can learn an optimal graph to capture the global and local structure while eliminating the influence of corrupted information. Last but not least, we design an iterative algorithm to solve the objective function of DOGL. Extensive experiments on six datasets demonstrate the effectiveness and superiority of DOGL.
Significance of Convolutional Neural Network in View of Lip Reading for Speech-Impaired People

One way to understand what another person is saying is by lip reading. This is done by observing the speaker's lips movements and determining the words being spoken. One of the key problems with lip reading methods is visual uncertainty, making it difficult to implement in practice. When homophones have similar or nearly identical lip movement, this occurs at the word level. ([p], [m], [b] are all examples). One of the primary factors behind this effort is the intention to aid those who struggle with speech problems. Many people throughout the globe have difficulties communicating because of speech impediments, but visual speech recognition has the potential to change that. In the past ten years, as deep learning has developed, there has been a steady increase in the requirement for effective speech-to-text systems. Intelligent devices that can read lips are called lip reading systems. In order to convert lip movement into text, it is necessary to identify the face, find the lips, identify the characteristics, and then train the classifier to detect lip movement. This article presents an extensive examination of the latest advancements in deep learning model-based Visual speech recognition (VSR). The emphasis is on addressing data problems, task-specific complexities like homophones and absence of context, and providing existing results. Progress in these areas will accelerate the implementation of the silent speech interface from concept to reality. In addition to that, we also summarised the statistical results of standard datasets of VSR.
Research on Knowledge Enhancement Recommendation Methods

Traditional recommendation algorithms have the problems of data sparsity and cold start. Researchers solve these problems by introducing external information. To enhance the recommendation system, the integration of the knowledge graph is implemented due to its extensive connectivity and abundance of prior knowledge. In this research, the knowledge graph serves as a valuable resource for obtaining edge information. In this paper, a multi-task recommendation method ECAKR, which combines attribute classification and attention, is proposed. Features from other attributes undergo extraction in the knowledge graph embedding component, considering both user and project specifics. A cross-compression segment links the feature extraction portion with the recommendation component of the knowledge graph for simultaneous learning. This cross compression section combines the attention mechanism to improve recommendation performance. A large number of experiments have been conducted on authentic datasets, which unequivocally demomstrate the superiority of ECAKR over several state-of-the-art baselines in terms of CTR prediction and Top-N recommendation.