Reviewerly/exHarmony huggingface dataset:
Towards Camera Parameters Invariant Monocular Depth Estimation in Autonomous Driving

Monocular depth estimation is an effective approach to environment perception due to simplicity of the sensor setup and absence of multisensor calibration. Deep learning has enabled accurate depth estimation from a single image by exploiting semantic cues such as the sizes of known objects and positions on the ground plane thereof. However, learning-based methods frequently fail to generalize on images collected with different vehicle-camera setups due to the induced perspective geometry bias. In this work, we propose an approach for camera parameters invariant depth estimation in autonomous driving scenarios. We propose a novel joint parametrization of camera intrinsic and extrinsic parameters specifically designed for autonomous driving. In order to supplement the neural network with information about the camera parameters, we fuse the proposed parametrization and image features via the novel module based on a self-attention mechanism. After thorough experimentation on the effects of camera parameter variation, we show that our approach effectively provides the neural network with useful information, thus increasing accuracy and generalization performance.
Post Quantum Code-Based Cryptosystems with Dual Inverse Matrix

The Shor algorithm demonstrates the significant risk that quantum attacks pose to the security of widely used cryptographic primitives. However, code-based cryptography has been shown to be resistant to these attacks. To date, no polynomial-time attack exists that can break code-based cryptosystems such as the McEliece cryptosystem. Despite this, these cryptosystems are not employed in practical applications in domains such as online banking, blockchains, and e-commerce platforms. The primary reason is the large sizes of the public and private keys associated with code-based cryptosystems. In this paper, a new code-based cryptosystem is introduced which employs a dual matrix based on the transpose and inverse of the parity check matrix to reduce the key size compared to the McEliece cryptosystem.
Research on Cloud Edge Collaborative Application Scenarios

Cloud edge collaboration refers to the complementary collaboration between cloud computing and edge computing. The proposed edge computing model provides a new solution to the shortcomings of the centralized model of cloud computing, and is the product of adapting to the needs of technological development. With the rapid development of edge computing, cloud edge collaboration technology is also widely used in various industries. This article mainly analyzes the application scenarios of cloud edge collaboration in the field of energy big data.
Experimental Course Management System Based on Web

This article introduces an experimental timetable filling and management system developed for experimental teaching, including the main functions and filling content. It uses user-defined Java class objects to complete operations such as querying, adding, deleting, modifying, and closing the background database of the system. Administrator users can add, modify, delete, and download experimental courses on the management page by calling background applications. When creating or modifying an experimental curriculum, the database has the function of checking input information and judging conflicts, preventing conflicting course items from being entered. Obtain statistical information of experimental courses through the sum function and group by clause in database queries.
AMB:Automatically Matches Boxes Module for One-Stage Object Detection

In computer vision, object detection is an important task. Whether it is a single-stage or two-stage method, it has been based on anchor-based detectors for several years. In recent years, due to multi-scale feature fusion and Focal Loss, anchor-free detectors have become popular. However, both anchor-based and anchor-free detectors have the problems of large computation, poor stability, many hyperparameters, and imbalance between positive and negative samples. This paper proposes a one-stage target detection network (AMBNet) that automatically matches ground truth boxes, avoid manual selection of anchors, which is difficult to match multi-scale targets. The size of the grid can be adjusted according to the size of the detection target. It is still regressing the target from rectangles that are closer to the ground-truth boxes, so the training is more stable. By calculation, when the number of grids is 64, no concentric rectangles are set, and the average IOU is 0.7753. When setting 4 concentric rectangles, the average value of IOU is 0.8211.
Event-Potential Method for Mobile Robot Motion Planning and Control

Motion planning and control of mobile robots is a challenging but crucial task for autonomous navigation. Since the actual environment is usually highly dynamic and has extremely high uncertainty and unpredictability, motion planning and control methods need to handle unexpected events and uncertainties in the environment, such as dynamic obstacles. Existing motion planning and control methods are usually decoupled processes. When unexpected events occur, the planner replans a new feasible plan based on new sensory information, and then the controller executes the new plan. Existing methods usually perform the perception, replanning, and control loop in real time. Replanning is an inefficient and time-consuming process since the robot needs to process new perception information and restart the planner. Also, the plan is not fully executed before the next loop which wastes most of the plan. We tackle this problem by introducing an event-potential method: a novel event-based potential field motion planning and control method that introduces event reference instead of time reference, which can locally modify the preplanned velocity along the trajectory using the resultant force generated by the attractive force from a trajectory and repulsive force from obstacles for obstacle avoidance. After completing obstacle avoidance, the event reference allows the robot to continue to track the original trajectory without replanning. We test and compare the eventpotential method with existing methods on a three-wheeled omnidirectional mobile robot. The framework can also be adapted to different robots and tasks.
Predictive Analysis Based on GBDT Deep Learning Regression Model in Wordle Games

Wordle is a popular puzzle currently offered daily by the New York Times. At present, there are daily game result files of users who have participated in this game for nearly one year. The document includes the date; Competition number; Words of the day; Number of people reporting scores on that day; Number of players entering difficult mode; The percentage of words that can't be solved after 1~6 attempts. Based on this file, this paper mainly studies the influence of word attributes on report score, and creates a model to predict the distribution of report results. The attributes of words are divided into six influencing factors (Fre, Count, Sum, Repeat, Vowel, Root), and a polynomial fitting model is established to get the specific relationship. Then an ARIMA time series model is established to verify the accuracy of the model and analyze the errors. Then, according to the six influencing factors (inputs) and the relevant percentages (outputs) of future dates (1,2,3,4,5,6,X) in the question, the GBDT deep learning regression model of gradient lifting tree is established. So as to study the influence of word attributes on the percentage of relevance, and finally determine the result.
In-Network Quality Control of IP Camera Streams

Manufacturing processes are often monitored by IP cameras. The generated video streams can be transferred via a wireless link to be processed and used by remote industrial controllers that manage and configure the industrial task in real-time. However, the link has limited bandwidth and is not capable of transmitting the aggregated traffic of all the IP cameras. Moreover, the platform provider of the remote controller (e.g., cloud) might charge extra fees for high volumes of network traffic. To optimize the data transport, we propose an in-network video quality control method for IP camera streams that drops non-essential frames from temporally irrelevant IP camera streams even when bandwidth is available. Our solution introduces a high-level API through which the operator can define which camera streams are needed with high quality at which actuator positions/states of the industrial process. Our method assumes an aggregation point that monitors the actuators' states and reduces the quality of camera streams that are not important for the control process, selectively dropping a set of video frames. We have implemented a P4-based prototype of the aggregation point and show that the remote controller can be fed with high-quality video streams while meeting bandwidth limitations and potentially saving data transfer costs without modifying the end-points.
Differentially Private Distributed Online Convex Optimization Towards Low Regret and Communication Cost

Distributed online convex optimization (DOCO) has emerged as a promising approach in scenarios where multiple learners collaboratively serve sequential (possibly untrusted) clients using AI models. However, ensuring clients' privacy and minimizing regret while keeping the communication cost reasonable poses a significant challenge. To address this issue, we propose private DOCO algorithms, termed PDOM, for both oblivious and stochastic settings. Our approach involves a mini-batch strategy that optimally balances the effects of slower model updates and differential privacy (DP) perturbation. Our theoretical analysis shows that compared to state-of-the-art algorithms, PDOM reduces the regret bounds and communication cost by an O(d/ε) factor for the oblivious setting. For the stochastic setting, the impact of DP perturbation becomes negligible if the learning time T = Ω(d4/(nε4)) provided that the loss functions are Lipschitz, convex, and smooth, where d is the model dimension, n is the number of learners, and ε is the privacy budget. Our evaluations validate these results, demonstrating that PDOM can reduce classification error rates of the state-of-the-art methods by up to 20% in distributed online logistic regression tasks while achieving communication savings of above 90%.
Poster: Unobtrusively Mining Vital Sign and Embedded Sensitive Info via AR/VR Motion Sensors

Despite the rapid growth of augmented reality and virtual reality (AR/VR) in various applications, the understanding of information leakage through sensor-rich headsets remains in its infancy. In this poster, we investigate an unobtrusive privacy attack, which exposes users' vital signs and embedded sensitive information (e.g., gender, identity, body fat ratio), based on unrestricted AR/VR motion sensors. The key insight is that the headset is closely mounted on the user's face, allowing the motion sensors to detect facial vibrations produced by users' breathing and heartbeats. Specifically, we employ deep-learning techniques to reconstruct vital signs, achieving signal qualities comparable to dedicated medical instruments, as well as deriving users' gender, identity, and body fat information. Experiments on three types of commodity AR/VR headsets reveal that our attack can successfully reconstruct high-quality vital signs, detect gender (accuracy over 93.33%), re-identify users (accuracy over 97.83%), and derive body fat ratio (error less than 4.43%).
An Approach Towards the Security Management for Sensitive Medical Data in the IoMT Ecosystem

The Internet of Medical Things (IoMT) is a network of interconnected medical devices, wearables, and sensors integrated into healthcare systems. It enables real-time data collection and transmission using smart medical devices with trackers and sensors. IoMT offers various benefits to healthcare, including remote patient monitoring, improved precision, and personalized medicine, enhanced healthcare efficiency, cost savings, and advancements in telemedicine. However, with the increasing adoption of IoMT, securing sensitive medical data becomes crucial due to potential risks such as data privacy breaches, compromised health information integrity, and cybersecurity threats to patient information. It is necessary to consider existing security mechanisms and protocols and identify vulnerabilities. The main objectives of this paper aim to identify specific threats, analyze the effectiveness of security measures, and provide a solution to protect sensitive medical data. In this paper, we propose an innovative approach to enhance security management for sensitive medical data using blockchain technology and smart contracts within the IoMT ecosystem. The proposed system aims to provide a decentralized and tamper-resistant platform that ensures data integrity, confidentiality, and controlled access. By integrating blockchain into the IoMT infrastructure, healthcare organizations can significantly enhance the security and privacy of sensitive medical data.
Anarchic Federated learning with Delayed Gradient Averaging

The rapid advances in federated learning (FL) in the past few years have recently inspired a great deal of research on this emerging topic. Existing work on FL often assume that clients participate in the learning process with some particular pattern (such as balanced participation), and/or in a synchronous manner, and/or with the same number of local iterations, while these assumptions can be hard to hold in practice. In this paper, we propose AFL-DGA, an Anarchic Federated Learning algorithm with Delayed Gradient Averaging, which gives maximum freedom to clients. In particular, AFL-DGA allows clients to 1) participate in any rounds; 2) participate asynchronously; 3) participate with any number of local iterations; 4) perform gradient computations and gradient communications in parallel. The proposed AFL-DGA algorithm enables clients to participate in FL flexibly according to their heterogeneous and time-varying computation and communication capabilities, and also efficiently by improving utilization of their computation and communication resources. We characterize performance bounds on the learning loss of AFL-DGA as a function of clients' local iteration numbers, local model delays, and global model delays. Our results show that the AFL-DGA algorithm can achieve a convergence rate of [EQUATION] and also a linear convergence speedup, which matches that of existing benchmarks. The results also characterize the impacts of various system parameters on the learning loss, which provide useful insights. Numerical results demonstrate the efficiency of the proposed algorithm.
The Upsides of Turbulence: Baselining Gossip Learning in Dynamic Settings

In dynamic settings, fully distributed gossip-based learning schemes have recently gained interest due to their better scalability, robustness, and enhanced privacy protection compared to server-based architectures. However, existing approaches to their performance characterization either assume stable connectivity among nodes or are ad-hoc for specific trace-based mobility patterns. Thus, in dynamic settings, there is currently a poor understanding of the conditions under which gossip-based learning schemes are feasible, and of their main performance tradeoffs. In this work, we start addressing this issue by performing a first baselining of Gossip Learning (GL) on random Time-Varying Graphs (TVG), to get a first-order characterization of their main performance patterns in dynamic settings. The use of random TVG enables a fine-grained and accurate characterization of GL effectiveness as a function of the main system parameters while abstracting from scenario-specific features of patterns of communication and mobility (e.g., induced by road grids or measured mobility traces). Our results suggest that GL schemes are robust to node mobility and comparable in accuracy and convergence speed to Federated Learning architectures, over a wide range of operational conditions. We show that the final model accuracy is robust against data dispersion across nodes as well as against very low rates of exchanges across nodes.
Demo: Immersive Remote Monitoring and Control for Internet of Things

The Internet of Things (IoT) interconnects a vast number of physical devices with each other and enables remote monitoring and control of the physical system. This, together with the recent progress of virtual reality (VR) technology, provides an immersive experience for the user to perform remote monitoring and control as if she observes and controls the physical system in person. In this demo, we develop an immersive remote monitoring and control system consisting of 3D virtual monitoring and control panel and the physical water pump system. Users can remotely control and monitor the water level of the physical water pump system through the 3D virtual panel in real-time. We use Fourier transform to filter out the noise when the water level remains static and the exponential running average method to make the collected data more smooth when the water level dynamically changes. The experimental evaluations demonstrate that the difference between the physical and virtual water levels is small in both static and dynamic water levels.
Poster: Leveraging Data Analysis and Machine Learning to Authenticate Yelp Reviews through User Metadata Patterns

Due to the technological revolution, online reviews have become extremely valuable to consumer decision making, with approximately 93% of all consumers relying on reviews for purchasing decisions. However, the rise of fake online reviews has raised concerns about their trustworthiness, as it is estimated that 4% of all internet reviews are estimated to be fraudulent, impacting worldwide online purchases by $152 billion. This prevalence not only misleads consumers but also challenges businesses striving to maintain their reputation. Yelp, a prominent platform hosting online reviews, plays a crucial role in shaping consumer perceptions and purchasing choices. However, the presence of fake reviews on Yelp has raised integrity concerns. This research explores the use of data analysis techniques and machine learning algorithms to determine the authenticity of Yelp reviews, contributing to the enhancement of the credibility of online review systems, enabling informed decisions, and promoting fair competition among businesses.
Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication

Anomaly detection is an important function in IoT applications for finding outliers caused by abnormal events. Anomaly detection sometimes comes with high-frequency data sampling which should be carried out at Edge devices rather than Cloud. In this paper, we consider the case that multiple IoT devices are installed in a single remote site and that they collaboratively detect anomalies from the observations with device-to-device communications. For this, we propose a fully distributed collaborative scheme for training distributed anomaly detectors. We introduce the concept of Global Anomaly which sample is not only rare to the local device but rare to all the devices in the target domain. We also propose a distributed threshold-finding algorithm for Global Anomaly detection. With our standard benchmark-based evaluation, we have confirmed that our scheme trained anomaly detectors perfectly across the devices. We have also confirmed that the devices collaboratively found thresholds for Global Anomaly detection with low false positive rates while achieving high true positive rates with few exceptions.
Difficulty-Aware Time-Bounded Planning Under Uncertainty for Large-Scale Robot Missions

We consider planning problems where a robot must visit a large set of locations to complete a task at each one. Our focus is problems where the difficulty of each task, and thus its duration, can be predicted, but not fully known in advance. We propose a general Markov decision process (MDP) model for difficulty-aware problems, and propose variants on this model which allow adaptation to different robotics domains. Due to the intractability of the general problem, we propose simplifications to allow planning in large domains, the key being constraining navigation using a solution to the travelling salesperson problem (TSP). We build a set of variant models for two domains with different characteristics: UV disinfection, and cleaning, evaluating them on maps generated from real-world environments. We evaluate the effect of model variants and simplifications on performance, and show that our models outperform a rule-based baseline.
Knowledge Discovery from Natural Languages: a Linguistic Dataset of 10K Kinship Relations

In the expansive realm of knowledge discovery, this study propels forward the subdomain of rule mining with the inception of a singular synthetic dataset – the Kinship 10K Dataset. This dataset, purpose-built for natural language rule mining, derives from the intricate relationship networks across 20 simulated families. These networks include 1,500 unique characters. The development leverages generative techniques, producing a rich array of kinship rules. Each rule is grounded in one of eight foundational Meta kinship relations. The final ensemble, a comprehensive dataset, comprises 10,526 relationship instances, 234 distinct kinship relations, and 104 learnable rules. In addition, we introduce two evaluation metrics – Rule Coverage (RC) and Directed Rule Mining Capability (DRMC) for examining rule mining algorithms in closed domains. RC quantifies the inclusiveness of rule mining datasets, while DRMC delivers nuanced analysis of algorithmic performance in discerning and extracting precise rules, taking accuracy and precision into account. Additionally, we set a benchmark by utilizing the GPT-3.5 and GPT-4 models as baselines. It is noteworthy that the GPT-4 model attained scores of 0.78 and 0.35 on the RC and DRMC metrics respectively. These scores underscore the inherent challenges of the task and signify the merit in pursuing further research to advance this domain. Collectively, this investigation presents a substantial contribution to knowledge discovery. By introducing an innovative dataset, formulating novel evaluation metrics, and instituting a robust baseline model, it not only highlights the prospects for deeper insights and increased automation in the wider field of knowledge discovery but also sets the stage for upcoming advancements in rule mining research.
Communication Resources Limited Decentralized Learning with Privacy Guarantee through Over-the-Air Computation

In this paper, we propose a novel decentralized learning algorithm, namely DLLR-OA, for resource-constrained over-the-air computation with formal privacy guarantee. Theoretically, we characterize how the limited resources induced model-components selection error and compound communication errors jointly impact decentralized learning, making the iterates of DLLR-OA converge to a contraction region centered around a scaled version of the errors. In particular, the convergence rate of the DLLR-OA algorithm in the error-free case [EQUATION] achieves the state-of-the-arts. Besides, we formulate a power control problem and decouple it into two sub-problems of transmitter and receiver to accelerate the convergence of the DLLR-OA algorithm. Furthermore, we provide quantitative privacy guarantee for the proposed over-the-air computation approach. Interestingly, we show that network noise can indeed enhance privacy of aggregated updates while over-the-air computation can further protect individual updates. Finally, the extensive experiments demonstrate that DLLR-OA performs well in the communication resources constrained setting. In particular, numerical results on CIFAR-10 dataset shows nearly 30% communication cost reduction over state-of-the-art baselines with comparable learning accuracy even in resource constrained settings.