Reviewerly/exHarmony huggingface dataset:
A Near-Field Area Object Detection Method for Intelligent Vehicles Based on Multi-Sensor Information Fusion

In order to solve the difficulty for intelligent vehicles in detecting near-field targets, this paper proposes a near-field object detection method based on multi-sensor information fusion. Firstly, the F-CenterFusion method is proposed to fuse the information from LiDAR, millimeter wave (mmWave) radar, and camera to fully obtain target state information in the near-field area. Secondly, multi-attention modules are constructed in the image and point cloud feature extraction networks, respectively, to locate the targets’ class-dependent features and suppress the expression of useless information. Then, the dynamic connection mechanism is used to fuse image and point cloud information to enhance feature expression capabilities. The fusion results are input into the predictive inference head network to obtain target attributes, locations, and other data. This method is verified by the nuScenes dataset. Compared with the CenterFusion method using mmWave radar and camera fusion information, the NDS and mAP values of our method are improved by 5.1% and 10.9%, respectively, and the average accuracy score of multi-class detection is improved by 22.7%. The experimental results show that the proposed method can enable intelligent vehicles to realize near-field target detection with high accuracy and strong robustness.
Train@Train – A Case Study of Using Immersive Learning Environments for Health and Safety Training for the Austrian Railway Company

Train@Train (2021–2022) is a project funded by the WST3 (Funding programs for Lower Austrian companies in the areas of business, tourism and technology) and focuses on the health and safety training for the Austrian Railway Company. In this project participants were presented a digital twin of a real locomotive and had to carry out regular health and safety training before the locomotive being put into operation.
Double Rainbows: A Promising Distributed Data Sharing in Augmented Intelligence of Things

The Augmented Intelligence of Things enables many edge or end devices in the Internet of Things (IoT) to perform machine reasoning to make decisions, thus become more intelligent. For healthcare enterprises, huge physical data generated by smart devices facilitate to iterate their products. However, traditional data sharing models based on cloud outsourcing meet many security challenges, such as data confidentiality, reliability, and privacy protection, and most existing schemes have high computational complexity for the utilization of time-consuming cryptographic operations, such as bilinear pairing, which is not suitable for those resource-constrained IoT devices. To tackle the abovementioned issues, we present Double Rainbows, a promising data sharing scheme based on pairing-free searchable encryption. It is constructed on the cloud-edge-end architecture, supporting computing task transfer and reliable data storage and retrieval. The experimental results show that it outperforms in efficiency and exhibit more security functionalities.
Crowd Counting Model Using Convolutional Neural Network

Crowd Counting is being used for public safety, effective management of the crowd in elections or pilgrimages, music concerts. Recently there was a stampede in January 2022 in a temple in India where due to overcrowd many people were killed. Also, to stop the spread of pandemic crowd counting is proving to be beneficial in public places. Counting manually is a tedious task and it may produce false results, since it takes a long time. In crowded photos, objects appear to be partially surrounding each other as the density of people increase in the frame. Crowd counting is having limitations such as occlusion and background clutter. To solve this difficulty, earlier approaches relied on labelling complex density maps to understand the scale variation implicitly. Data preparation can be time expensive, and training these deep models might be problematic owing to a shortage of training data. As a result, we suggest an alternate and new method for crowd counting. The proposed model in this paper counts the number of people in the given image using a convolutional neural network based on ResNet50.
A multi-Bernoulli Gaussian filter for track-before-detect with superpositional sensors

This paper presents a Gaussian implementation of a multi-Bernoulli track-before-detect filter for multi-target tracking with superpositional sensors. The proposed filter runs independent Bernoulli filters for each potential target. At each update step, each Bernoulli filter shares its predicted measurement information with the rest of the Bernoulli filters so that they can account for the influence of this target in the likelihood. The Bernoulli filters are implemented using unscented Kalman filters. Simulation results show the benefits of the proposed algorithm.
Research on Informed-RRT* with Improved Initial Solution

RRT* is a common motion planning algorithm with asymptotic optimality, which is a variant of RRT (Rapidly-exploring Random Tree), but its convergence speed is slow. This paper proposes an improved Informed-RRT* algorithm to promote planning efficiency. Informed- RRT*samples within a hyper-ellipsoid built from an initial feasible solution, speeding up convergence to an optimal solution. To find the initial feasible solution more quickly, an improved Metropolis acceptance criterion for Informed-RRT* is introduced to filter the new nodes generated by each iteration. By comparing Informed-RRT* with the improved algorithm through numerical simulation, it is verified that the improved algorithm can find the initial solution efficiently. At the same time, the simulation results also verify that the improved algorithm can find the same solution in a shorter time, so it can be concluded that the algorithm is effective.
Uncertainty Quantification-Based Unmanned Aircraft System Detection using Deep Ensembles

Robust and accurate unmanned aircraft system (UAS) detection is pivotal in restricted air spaces. Deep learning-based object detection has been proposed to identify the presence of UASs, but it introduces two key challenges. Specifically, deep learning detectors (i) provide point estimates at test-time with no associated measure of uncertainty, and (ii) easily trigger false positive detections for birds and other aerial wildlife. In this work, we propose a novel detection algorithm, which is capable of providing uncertainty quantification (UQ) metrics at test time while also significantly reducing the false positive rate on natural wildlife. Our proposed method consists of using an ensemble of object detectors to generate a distributive estimate of each input prediction. In addition, we measure multiple UQ-based scoring metrics for each input to further validate our model’s effectiveness. Through evaluation on our custom generated UAS dataset, consisting of images captured from deployed cameras, we show that our model provides robust UQ estimates, low false positive rates on wildlife, and significantly improved error rates over singular deep learning detection models.
Research on Asymptotic Image Compression Algorithm for Beidou Short Message

In order to realize the narrow-band communication of BeiDou satellite short message, the technology of BeiDou satellite navigation system, image compression, embedded system, geographic information system and other technologies is integrated, and an image progressive transmission technology based on BeiDou short message is constructed. Among them, in the field of electric power emergency communication, in order to ensure the high-quality transmission of real-time data such as voice and pictures at the fault site, it is necessary to compress and enhance coding for low-rate voice, anti-error under narrow bandwidth, high compression ratio picture compression, and Beidou short message Carry out indepth research on transmission technology to achieve efficient compression and high-quality transmission of voice packets and standard-definition pictures. In order to ensure the emergency communication of the power system without operator network coverage, it is necessary to conduct in-depth research on the emergency cluster ad hoc network technology and cluster management strategy to realize the intelligent selection of the optimal communication strategy.
Towards Group Fairness via Semi-Centralized Adversarial Training in Federated Learning

As federated learning increasingly performs better on tasks of decision-making scenarios such as medical care or commercial area, there have been concerns about discrimination against certain populations with sensitive attributes (e.g., race, gender). In this work, we propose to improve group fairness with semi-centralized adversarial training. And we adopt Variational AutoEncoder (VAE) for federated learning scenarios to generate adversarial samples. We keep VAE decoder at server side and leave encoder at client side to encode local samples into feature dimensions for transmitting, which ensures the privacy of user data. Our proposal further performs sensitive attribute alignment to improve group fairness. Our experimental evaluation shows that our approach outperforms the state-of-the-art federated learning frameworks in terms of group fairness and communication resource consumption.
A Convolutional Neural Network Model for Credit Card Fraud Detection

Nowadays, online transactions through various ecommerce platforms are becoming more prevalent, and Credit Card (CC) is significantly used in various online transactions. However, Credit Card Fraud (CCF) strategies continue to evolve with the business transformation, causing customers as well as the financial institutions to lose billions of dollars annually. Hence, effective detection of fraudulent transactions initiated by fraudsters from the voluminous array of normal transactions is ever necessary. Hence, a Convolutional Neural Network (CNN) model for credit card fraud detection is proposed in this study using Adaptive Synthetic (ADASYN) sampling technique to address the imbalance dataset. The proposed model has achieved 0.9982, 0.9965, and 0.9999, accuracy, precision, and recall, respectively compared to other existing studies.
Adaptive Discontinuous Reception in 5G Advanced for Extended Reality Applications

Extended Reality (XR) applications introduce challenging requirements for radio mobile systems in terms of capacity and latency. At the same time, XR devices needs to minimize power consumption to extend battery lifetime and limit dissipated heat. Addressing capacity and latency requirements without excessively increasing power consumption of user devices requires the evolution of 5G power saving schemes. In this work, we propose an Adaptive DRX (ADRX) scheme and design a control policy to optimally adjust active duration of the DRX cycle in order to satisfy XR requirements and minimize energy consumption. To this end, we formulate the problem of selecting the user active time as a stochastic online optimization problem and present a control policy to optimally solve it. Numerical results confirm that the proposed solution increases the number of satisfied users and reduce power consumption compared to standard DRX mechanism. In particular, in a Dense Urban scenario where standard DRX cannot fulfill the requirements of any XR user, ADRX can serve up to 4-5 users per cell with 10&#x0025; power saving gain.
Mine track obstacle detection method based on information fusion

Abstract As an important part of the coal mine transportation system, coal mine underground rail transportation undertakes the core transportation task of coal mine underground. Its safe and efficient operation is directly related to the efficiency of coal mine production and transportation. In view of this, this paper proposes a mine track environmental obstacle detection system which integrates camera and Lidar information to realize real-time automatic detection of obstacles in front of the underground track mine cart. The specific research results are as follows: first of all, a point cloud clustering algorithm for the mine environment is designed to extract the obstacle information, and then the YOLOv5 algorithm is used to identify the obstacle information in the image. Finally obstacle information from image and point cloud are fused at the decision-level. The obstacle detection method proposed in this paper can be successfully identified to meet the requirements of the obstacle object detection function of the rail vehicle.
Satellite-relayed intercontinental quantum network

Abstract In recent years, with the development of long-distance communication, the encryption method has become one of the crucial topics. Quantum communication, with the advantage of no-cloning theorem, is far more secure than traditional communication and could be a key method in encrypted communication. In this paper, we simulate the whole process of quantum encryption, we simulate the satellite-relayed quantum key distribution, the transmission of encrypted information, and the decoding of the received message, meanwhile using Hamming code error correction method to reduce the noise. During the simulation, we successfully realize the transmission of an encrypted picture and efficiently reduce the noise associated with it. In conclusion, our thesis shows that quantum communication could be an ideal method to realize encrypted communication. However, our thesis is only based on the simulation of the satellite-relayed quantum network and the channel noise still destroys some part of the decoded picture, thus more practical testing should be done to confirm this theory and some advanced methods should be used to reduce the channel noise.
StoryQ

StoryQ is a web-based machine learning and text mining tool that allows young learners (Grade 6-12) to engage in machine learning practices and work with unstructured text data without coding. StoryQ features dynamically linked data representations that promote meaningful inquiries across tables, graphs, and texts--a unique user experience that makes machine learning models transparent, explainable, and fun to explore. This demo provides a brief tutorial on how to use StoryQ to train, test, and troubleshoot text classification models using both standard feature extractors (e.g., N-grams) and special feature extraction tools and visualizations designed to support young learners and non-computing teachers. This demo also includes sample learning activities designed for high school English Language Arts classes to showcase how machine learning concepts and practices can be introduced in non-computing classes. This work is supported by an NSF I-TEST project (DRL-1949110).
Experience report streamlining complex website design using a content audit selection heuristic

In this project experience report, we describe our experience working as researchers specializing in technical communication that informed the risk communication decisions for an interdisciplinary, grant-funded, risk communication website called HazardAware. We first discuss how content audits serve as a website design research method. Next, we provide our Content Audit Selection heuristic in a process flowchart format to enable communicators to understand how practical application of content audits serve as a formative tool to streamline the decision-making processes for complex website design content. Finally, we describe how we used the Content Audit Selection heuristic to inform the risk communication decisions for HazardAware.
The role of cue utilization in the detection of phishing emails

This study was designed to examine the roles of cue utilization, phishing features and time pressure in the detection of phishing emails. During two experiments, participants completed an email sorting task containing both phishing and genuine emails. Participants were allocated to either a high or low time pressure condition. Performance was assessed via detection sensitivity and response bias. Participants were classified with either higher or lower cue utilization and completed a measure of phishing knowledge. When participants were blind to the nature of the study (N = 191), participants with higher cue utilization were better able to discriminate phishing from genuine emails. However, they also recorded a stronger bias towards classifying emails as phishing, compared to participants with lower cue utilization. When notified of phishing base rates prior to the email sorting task (N = 191), participants with higher cue utilization were better able to discriminate phishing from genuine emails without recording an increase in rate of false alarms, compared to participants with lower cue utilization. Sensitivity increased with a reduction in time pressure, while response bias was influenced by the number of phishing-related features in each email. The outcomes support the proposition that cue-based processing of critical features is associated with an increase in the capacity of individuals to discriminate phishing from genuine emails, above and beyond phishing-related knowledge. From an applied perspective, these outcomes suggest that cue-based training may be beneficial for improving detection of phishing emails.
Decompositions of stratified institutions

Abstract The theory of stratified institutions is a general axiomatic approach to model theories where the satisfaction is parameterized by states of the models. In this paper we further develop this theory by introducing a new technique for representing stratified institutions, which is based on projecting to such simpler structures. On the one hand this can be used for developing general results applicable to a wide variety of already existing model theories with states, such as those based on some form of Kripke semantics. On the other hand this may serve as a template for defining new such model theories. In this paper we emphasize the former application of this technique by developing general results on model amalgamation and on the existence diagrams for stratified institutions. These are two most useful properties to have in institution theoretic model theory.
Lazy Aggregation for Heterogeneous Federated Learning

Federated learning (FL) is a distributed neural network training paradigm with privacy protection. With the premise of ensuring that local data isn’t leaked, multi-device cooperation trains the model and improves its normalization. Unlike centralized training, FL is susceptible to heterogeneous data, biased gradient estimations hinder convergence of the global model, and traditional sampling techniques cannot apply FL due to privacy constraints. Therefore, this paper proposes a novel FL framework, federated lazy aggregation (FedLA), which reduces aggregation frequency to obtain high-quality gradients and improve robustness in non-IID. To judge the aggregating timings, the change rate of the models’ weight divergence (WDR) is introduced to FL. Furthermore, the collected gradients also facilitate FL walking out of the saddle point without extra communications. The cross-device momentum (CDM) mechanism could significantly improve the upper limit performance of the global model in non-IID. We evaluate the performance of several popular algorithms, including FedLA and FedLA with momentum (FedLAM). The results show that FedLAM achieves the best performance in most scenarios and the performance of the global model can also be improved in IID scenarios.
Multi-Objective Hybrid Flower Pollination Resource Consolidation Scheme for Large Cloud Data Centres

Cloud Computing has rapidly emerged as a successful paradigm for providing Information and Communication Technology (ICT) infrastructure. Resource allocation is used to execute user applications in the form of requests for consolidated resources in order to minimize energy consumption and violation of the Service Level Agreement (SLA) for large-scale data centers resource utilization. The energy consumption is usually caused due to local entrapment and violation of SLA during resource assigning and execution. Several researchers have proposed solutions to reduce local entrapments and violations of SLA, to minimize the energy consumption of the entire data center. However, strategies employed in their solutions face entrapment in either local searches or at the global search level with a certain level of SLA violation. In this light, a Multi-Objective Hybrid Flower Pollination Resource Consolidation (MOH-FPRC) scheme for efficient and optimal resource consolidation of data center resources is put forward. The Local Neighborhood Search (LNS) algorithm has been employed for addressing entrapment at the local search level, while the prominent flower pollination algorithm is used to solve the problem of entrapment at the global search level. This, in turn, reduces the energy consumption of the data centers. In addition, clustering strategies have been introduced with a robust migration mechanism to minimize the violation of SLA while also satisfying minimum energy consumption. The simulation results using the MultiRecCloudSim simulator have shown that our proposed MOH-FPRC demonstrates an improved performance on the data center energy consumption, resource utilization, and SLA violation with a 20.5% decrease, 23.9% increase, and 13.5% reduction, respectively, as compared with the benchmarked algorithms. The proposed scheme has proven its efficiency in minimizing energy consumption while at the same time improving the data center resource allocation with minimum SLA violations.
Emergency Management System for Coal Mine Safety Based on IoT Technology

The Internet of Things (IoT) has brought technological innovation to various industries, and it has also brought great impact to coal industry. It is important to study the safety supervision system of coal in the IoT mode to improve effectiveness of safety supervision and make suggestions to improve safety production in coal. In this paper, based on analysis of influence factors and emergency supervision system in the IoT mode, system dynamics modeling theory and method are used to construct a system dynamics model of influence factors and emergency supervision in the IoT mode. The validity and reliability of the model are verified by the system dynamics software to reach more than 98.6%, and the support for the development of coal Internet of things is appropriately increased to support the development of coal Internet of things, ensure the healthy and long-term development of coal Internet of things, improve the level of supervision, and ensure the safe production of coal
Towards the Development of Disaster Management Tailored Machine Learning Systems

Natural disasters such as cyclones and floods recur frequently in certain parts of the world. In this work, we provide a framework to build an easily deployable disaster management application, over five stages. First, we interview four categories of people to understand current problems and approaches in disaster management. Next, we analyze responses and establish that identified disaster management efforts are hitherto unable to effectively harness existing technology. We accordingly build a guided recommendation toolbox of existing Machine Learning (ML), Internet of Things (IOT), and NLP technologies, that satisfies critical system requirements for the identified efforts; this is qualitatively evaluated by senior data scientists and disaster management researchers, and found to reduce development time, as well as increase reliability and user-friendliness. Finally we provide a model to decentralize disaster management. Our work promotes the development of NLP systems tailored for disaster management, bridging the gap between research and real world applications.