Reviewerly/exHarmony huggingface dataset:
Combining 2D and 3D Datasets with Object-Conditioned Depth Estimation

When detecting objects, depth sensors are not always available, requiring 3D object detection from monocular images. However, for many object classes, datasets with 3D annotations are missing. Recent monocular 3D object detection methods lack the semantic diversity needed for autonomous systems, because of missing 3D ground truth data for static classes such as poles and traffic lights. To overcome this gap we combine a large scale dataset for 2D object detection, with an unlabeled dataset containing depth measurements. We lift 2D object detections of the depth dataset into the 3D domain, associating detections with corresponding depth values. This leverages 2D annotated datasets to enable semantically rich 3D object detection, without extra labelling effort. We train an object detection model with mixed batches and evaluate it comparing the predicted depth with the projected centerpoint depth of cars manually annotated in 3D space. The result is a monocular object detector that can predict 3D positions of up to 37 static and dynamic object classes from camera only.
Capsule Networks for Hierarchical Novelty Detection in Object Classification

Hierarchical Novelty Detection (HND) refers to assigning labels to objects in a hierarchical category space, where non-leaf labeling represents a novelty detection of that category. By labeling a novel instance in at least one abstract category, more informed decisions can be made by an automated driving (AD) function, resulting in a safer behavior in novel situations. Current approaches are mainly composed of different architectures based on Convolutional Neural Networks (CNNs). Capsule Networks (CNs) were introduced as an alternative to CNNs that expand their capacity in tasks that were previously challenging. We explore the hierarchical nature of CNs and propose a novel approach for hierarchical novelty detection using a unified CN architecture. As a proof-of-concept, we evaluate it on a novelty detection task based on the Fashion-MNIST dataset. We define a misclassification matrix for evaluation of the performance based on a semantically sensible scenario for this dataset. The results show that our method outperforms the main CNN-based methods in the current literature in this task while also giving more flexibility for task-specific tuning and has the potential to reach state-of-the-art status in more complex HND use cases within the AD domain.
Detecting and Identifying Global Visual Novelties in Driving Scenarios

As a safety-critical application, automated driving aims to provide autonomy and safe navigation. Although recent advances in perception algorithms based on deep learning have boosted the progress in such field, state of the art depends on availability of large datasets. Thus, most visual analysis in this context is related to the detection and classification of previously known classes. However, dynamic environments with complex interactions among traffic elements can lead to unpredictable anomalous situations that are not registered previously. On the other hand, most of prior work on visual novelty detection points out novelty instances but does not provide additional useful information that can be used in the next levels for decision-making. In this paper we propose an approach to detect global visual novelties and their corresponding type in real driving scenarios. Based on pixel-wise and perceptual information, we use a generative adversarial network to detect novelties and a support vector machine to identify their categories. Its performance is experimentally evaluated using the Ford self-driving dataset. Experimental results show that the average area under the curve (AUC) surpasses 0.97 for novelty detection, and novelty type identification reaches an average of 92.7% of accuracy requiring only a small number of samples for training.
Solving the Deadlock Problem with Deep Reinforcement Learning Using Information from Multiple Vehicles

Autonomous driving system controls a vehicle using path planning. Path planning for automated vehicles observes a vehicle and the surrounding information and plans a trajectory on the basis of rule-based approach. However, the rule-based path planning cannot generate an appropriate trajectory for complex scenes, such as two vehicles passes each other at an intersection without traffic lights. Such complex scene is called deadlock. For avoiding the deadlock, it is very costly to create rules manually. In this paper, we propose a multi-agent deep reinforcement learning method to generate appropriate trajectories at the deadlock scenes. The proposed method consists of a single feature extractor and actor-critic branches. Moreover, we introduce a mask-attention mechanism for visual explanation. By taking a look at the obtained attention maps, we can confirm the obtained agent and the reason of the behavior. For evaluating our method, we develop a simulator environment of autonomous driving that produces a certain deadlock scene. The experimental results with the developed environment show that the proposed method can generate trajectories avoiding deadlocks.
Balanced scene graph generation assisted by an additional biased predictor

A scene graph is a structural representation of a scene comprising the objects as nodes and relationships between any two objects as edges. The scene graph is widely adopted in high-level vision language and reasoning applications. Therefore, scene graph generation has been a popular topic in recent years. However, it is limited by bias due to the long-tailed distribution among the relationships. Scene graph generators prefer to predict the head predicates, which are ambiguous and less precise. It makes the scene graph convey less information and degenerate into the stacking of objects, restricting other applications from reasoning on the graph. To make the generator predict more diverse relationships and provide a precise scene graph, we propose an additional biased predictor (ABP)-assisted balanced learning method. This method introduces an extra relationship prediction branch that is especially affected by the bias to make the generator pay more attention to the tail predicates rather than the head ones. Compared to the scene graph generator that predicts relationships between object pairs, the biased branch predicts the relationships without being assigned a certain object pair of interest, which is more concise. To train this biased branch, the region-level relationship annotation is constructed using the instance-level relationship annotation automatically. Extensive experiments on popular datasets, i.e., Visual Genome, VRD, and OpenImages, show that the ABP is effective on different scene graph generators. Besides, it makes the generator predict more diverse and accurate relationships and provides a more balanced and practical scene graph.
Klasifikasi Human Stress Menggunakan Adagrad Optimization untuk Arsitektur Deep Neural Network

According to the World Health Organization, stress is a type of mental illness that affects human health and there is no one in this world who does not suffer from stress or depression. Stress is a term that is often used synonymously with negative life experiences or life events. . Analysis of data that has an unbalanced class results in inaccuracies in predicting human stress. This study shows that using the Deep Neural Network (DNN) Architecture model by optimizing several parameters, namely the optimizer, Learning rate and epoch. The best DNN Architect results are obtained with 4 Hidden Layers, Adagard Optimization, Learning rate 0.01 and the number of epochs 100. Accuracy, precision, recall and f-measure scores get 98.25%, 83.00%, 98.25%, 91.00%, respectively.
Toward Efficient and Adaptive Design of Video Detection System with Deep Neural Networks

In the past decade, Deep Neural Networks (DNNs), e.g., Convolutional Neural Networks, achieved human-level performance in vision tasks such as object classification and detection. However, DNNs are known to be computationally expensive and thus hard to be deployed in real-time and edge applications. Many previous works have focused on DNN model compression to obtain smaller parameter sizes and consequently, less computational cost. Such methods, however, often introduce noticeable accuracy degradation. In this work, we optimize a state-of-the-art DNN-based video detection framework—Deep Feature Flow (DFF) from the cloud end using three proposed ideas. First, we propose Asynchronous DFF (ADFF) to asynchronously execute the neural networks. Second, we propose a Video-based Dynamic Scheduling (VDS) method that decides the detection frequency based on the magnitude of movement between video frames. Last, we propose Spatial Sparsity Inference, which only performs the inference on part of the video frame and thus reduces the computation cost. According to our experimental results, ADFF can reduce the bottleneck latency from 89 to 19 ms. VDS increases the detection accuracy by 0.6% mAP without increasing computation cost. And SSI further saves 0.2 ms with a 0.6% mAP degradation of detection accuracy.
RL-Assisted Energy-Aware User-Edge Association for IoT-based Hierarchical Federated Learning

The extremely heavy global reliance on IoT devices is causing enormous amounts of data to be gathered and shared in IoT networks. Such data need to efficiently be used in training and deploying of powerful artificially intelligent models for better future event detection and decision making. However, IoT devices suffer from many limitations regarding their energy budget, computational power, and storage space. Therefore, efficient solutions have to be studied and proposed for addressing these limitations. In this paper, we propose an energy-efficient Hierarchical Federated Learning (HFL) framework with optimized client-edge association and resource allocation. This was done by formulating and solving a communication energy minimization problem that takes into consideration the data distribution of the clients and the communication latency between the clients and edges. We also implement an alternative less complex solution leveraging Reinforcement Learning (RL) that provides a fast user-edge association and resource allocation response in highly dynamic HFL networks. The proposed two solutions are compared with several state-of-the-art client-edge association techniques, leveraging MNIST dataset. Moreover, we study the trade-off between minimizing the per-round energy consumption and Kullback-Leibler Divergence (KLD) of the data distribution, and its effect on the total energy consumption.
A Blockchain-Based Distributed Pruning Deep Compression Approach for Cooperative Positioning in Internet of Vehicles

Autonomous driving is a core application that greatly benefits from Internet of Vehicles (IoV). The calculation of the precise positions of Connected Autonomous Vehicles (CAVs) is mainly done using a Deep Neural Network (DNN) which requires significant computing power. Therefore, reducing the computational overhead and improving the efficiency are urgent problems to be solved. In this paper, we first propose a CAV cooperative learning architecture based on blockchain to improve the positioning accuracy of vehicles. Then, we introduce an error precision sharing model between CAVs. The proposed framework enables CAVs to train vehicle positioning accuracy models locally and exchange them via a blockchain network. Such a distributed training architecture further reduces the computing power required. Extensive simulation results show that the proposed scheme can also significantly improve the accuracy of the trajectory error compared to existing approaches.
Novel TT&amp;C Signal Acquisition Scheme for Hypersonic Aircraft

For the Telemetry, Tracking, and Command (TT&C) system of hypersonic aircraft, high-speed relative movement and long-distance between aircraft and TT&C station cause serious Doppler effects and low signal-to-noise ratio (SNR). Therefore, the fast and accurate acquisition is a core issue for the TT&C signals recovery. This paper proposes a novel algorithm to alleviate the computational burden caused by high dynamics, based on the idea of reducing order and narrowing search range. Additionally, to improve the acquisition sensitivity of the weak signals, a superposition design, which enhances the peak values at the edges of the frequency resolution, is introduced to address the scalloping loss problem. Also, numerical results demonstrate that the proposed algorithm attains greater acquisition probability and better acquisition sensitivity compared to existing acquisition algorithms.
Digital Forensic Investigation Framework for Marine Industry

A massive 400% increase of cyber-attacks was reported in the Marine industry since February 2020 up to date. Experiments and research are being conducted towards safeguarding data in various technological aspects of the marine environment with regards to navigation, transportation and system management. At present, generic investigation processes are developed for computer incidents and forensic readiness in this sector, but those do not describe the investigation process precisely. Although there are incident analysis technologies implemented for the Marine environment, it lacks reliable digital forensic tools for evidence extraction and analysis. Therefore, in this research, a comprehensive digital forensic investigation framework is proposed, answering the issues of existing frameworks, and a tool is implemented for the examination of navigational evidence. Forensic procedures of existing investigation methodologies were taken into consideration when developing the fusion framework. The Marine Geo Navigator was implemented with the technologies of Machine Learning and Visualization to evaluate geolocations and paths of vessels. Through expert analysis, the proposed investigation framework was verified as a suitable process to be utilized in the Marine industry and the tool was validated resulting an acceptable level of accuracy.
ACiS: Lightweight and Robust Homomorphic Block Cipher Additive Scheme

Recent IoT emerging systems and applications require processing over encrypted data to preserve confidentiality. However, designing efficient Homomorphic Encryption (HE) algorithm to respond better to real-time requirements of IoT applications and tiny IoT devices is primordial. Unfortunately, existing homomorphic asymmetric schemes do not provide the efficiency implementation. While, on the other hand, homomorphic symmetric approaches suffer from a low level of security. In this paper, an efficient flexible additive homomorphic symmetric cipher scheme is proposed and it is called “ACiS”. Moreover, ACiS is based on the dynamic key approach to reach a high level of security, where different cryptographic primitives are used for different sessions. In addition, it can achieve a good performance and robustness against confidentiality attacks. It is based on a simple round function that should be iterated for nr iterations and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$nr &gt; 1$</tex> . Furthermore, the proposed solution is analyzed and evaluated in terms of security and performance levels. Experimental results prove that the proposed solution reaches a good balance between performance and security level compared to the Paillier cipher scheme (a well-known asymmetric additive HE scheme).
A Study Of Voiceprint Recognition Technology Based on Deep Learning

Voiceprint recognition technology is a high-quality identification technology, which is currently widely used in all walks of life and brings great convenience to our lives. In order to better promote the actual application range and effects of voiceprint recognition technology, the research and realization of its technology has extremely far-reaching significance. This paper mainly analyzes the basic process of voiceprint recognition, technical indicators, fac-tors that affect the effect of voiceprint recognition, and the current research status of voiceprint recognition technology. In addition, it analyzes the ad-vantages of the current mature deep learning technology, as well as the main problems existing in the current voiceprint recognition research work, and gives directions for the next step in the effective combination of deep learning technology for voiceprint recognition research work.
Heterogeneity-Aware Federated Learning for Device Anomaly Detection in Industrial loT

With the popularity and application of the Industrial Internet of Things (1IoT), device anomaly detection is considered as one of the important challenges in IloT implementation. However, the privacy sensitivity of device data and the high heterogeneity of IloT devices make it impossible for traditional schemes to achieve efficient, accurate, and privacy-protected device anomaly detection in IloT networks. In this study, we propose an intelligent anomaly detection architecture for IloT networks based on federated optimization algorithms and deep learning (DL). In particular, an online, adaptive, and semi-supervised device anomaly detection model is designed, and a heterogeneity-aware federated learning algorithm, called Clustered-FedProx, is presented. The Clustered-FedProx algorithm considers the differences in computational power and data statistical distribution among IloT devices, whereby multiple devices can be coordinated to train a global DL model in highly heterogeneous networks. Simulation results show that the proposed scheme can achieve more stable and accurate performance than conventional schemes.
BeeCast: A Device-to-Device Collaborative Video Streaming System

In this paper, we propose BeeCast, a collaborative video streaming system that facilitates collaborative video streaming for a group of mobile users with limited Internet connectivity. The novelty of the proposed system is the ability to watch the video on a shared screen or to watch the video on multiple screens. The latter option entails proposing a method to exchange the downloaded video segments among the users using device-to-device communication. The proposed system is composed of two components: BeeBuzzer, and BeePlanner. The BeeBuzzer component manages and coordinates the segment exchange among devices, while BeePlanner component enhances the overall Quality of Experience (QoE) through effective segment assignments decisions for each user. Simulation results show that using BeeCast in an unstable network produces a more consistent QoE than individual streaming while eliminating 80% of redundant network traffic.
A Preliminary Performance Evaluation of Breadth-first Search on a Configurable Processor

Graph processing is a fundamental computing paradigm in the modern big data analysis domain. Although graph processing is well-known to pose challenges to general-purpose processors, most of the graph processing frameworks are still being powered by these processors. With the long-term aim of designing a high-performance processor optimized for graph processing, we conduct a preliminary performance evaluation of running breadth-first search (BFS), a typical graph processing algorithm on a general-purpose processor core. We test three kinds of BFS algorithms, namely top-down, bottom-up and hybrid ones on a configurable processor core with various micro-architecture parameters such as the number of load pipelines, size of reorder buffer, size of issue queue, and so on. The preliminary experimental results show that adding an extra load pipeline and an ALU can increase the performance obviously, while other parameters achieve little performance gains.
FPGA-based Edge Detection Algorithm for Molten Droplet Images

Metastable materials, which exhibit excellent physical properties that do not exist in the thermodynamic steady state, have recently garnered significant attention. Metastable materials are typically obtained via containerless microgravity experiments. The National Space Science Center of the Chinese Academy of Sciences combines an electrostatic levitation device and a drop tube to achieve electrostatic levitation such that a containerless environment can be created, where the free fall of objects in the drop tube can overcome the effect of gravity. Molten droplets, i.e., a metastable material, with a diameter of approximately 2 mm are obtained in such an environment. An effective approach for investigating the volume, density, and other thermal physical parameters of molten droplets is to detect and extract edge information from images acquired using high-speed cameras. To adapt to the real-time nature of edge detection, we build a field-programmable gate array (FPGA) experimental platform and improve the traditional Canny algorithm to implement edge detection for molten droplets. The Canny edge detection algorithm primarily comprises a Gaussian filtering module, gradient magnitude and direction computation module, non-maximum suppression module, and hysteresis thresholding module. However, the traditional Canny algorithm is extremely sensitive to noise in the edge detection of molten droplet images; therefore, it typically detects false edges and overlooks true edges. Hence, we replace traditional 2 × 2 templates with 3 × 3 Sobel operator templates in the gradient magnitude computation module, as well as use four-direction templates instead of two-direction templates. In the traditional Canny algorithm, thresholds cannot be adaptively selected but must be manually set. To address the threshold adaptation issue, we add a threshold selection module, in which the maximum between-class variance method is used to adaptively select the thresholds. The experimental results show that the proposed method can accurately extract the edges of molten droplet images and provide clear edge details. Additionally, the proposed algorithm affords good adaptiveness, as well as high flexibility and operating speed on the FPGA.
Credit Card Fraud Detection Using Boundary Reconstruction and Integrated Classification

With the popularity of electronic payment, it not only brings great convenience, but also increases the risk of fraudulent transactions. At present, there are two problems in the identification of credit card fraud. The number of fraud and normal transactions is extremely unbalanced and the classification boundary is fuzzy. In order to solve these problems, this paper proposes an integrated classification framework for boundary reconstruction, which uses different machine learning algorithms as base learners to compare the original data with the data modeling after boundary reconstruction. The research shows that data boundary reconstruction can not only effectively alleviate the deviation caused by data imbalance to machine learning. It can also improve the data quality, so as to improve the accuracy of model classification; The integrated classification method can accurately identify credit card transactions, and the prediction effect of decision tree is the best. The proposed model is also applicable in other abnormal situations.
Applications of Elastic Net technology in survival analysis of high-dimensional data

With the rapid development of big data technology, gene recognition, protein structure detection and other technologies, the characteristics of survival data gradually tend to be small sample size, high dimension and strong correlation. Elastic Net technology improves LASSO method and makes up for the shortcomings of LASSO method in dealing with the correlated variables. In this paper, LASSO method and Elastic Net technology are combined with Cox model, and the effects of the two methods are compared through data simulation and case analysis. The results show that the Elastic Net technology has more advantages in dealing with correlated and high-dimensional survival data.