Reviewerly/exHarmony huggingface dataset:
FITIC: A Few-shot Learning Based IoT Traffic Classification Method

With the rapid development and wide application of Internet of Things (IoT) technology, Internet Service Providers need to accurately classify IoT traffic to provide hierarchical network management and network protection for highly het-erogeneous IoT devices. Currently, popular traditional machine learning and deep learning-based approaches to IoT traffic classification require large amounts of labeled traffic to build classification models. However, in practice simple IoT traffic with simple operating modes can be identified with only a small amount of labeled traffic and some classes of IoT devices only generate a limited amount of traffic, therefore, the aforementioned methods is not applicable in such scenarios. In this paper, we propose FITIC, a novel IoT traffic classification method based on few-shot learning. FITIC proposes a feature construction method for IoT traffic characteristics and can classify IoT traffic with only a limited number of labeled traffic samples. We evaluate FITIC on two publicly available datasets, and the experimental results show that FITIC has excellent classification accuracy and outperforms the state-of-the-art traffic classification methods.
Differentially Private Distributed Multi-Task Relationship Learning

In many real-world applications, data are distributed across different geographical regions, and may come from different distributions which result in multiple learning tasks. In such cases, distributed multi-task learning is usually used to learn the multiple related tasks to improve the generalization performance for each task. Among the distributed multi-task learning algorithms, distributed multi-task relationship learning (DMTRL) attracts much attention in the community as it learns task relationships from data, instead of imposing a prior task relatedness assumption. To perform DMTRL, task model or its gradient is transferred between task node and central server. There will be a potential privacy violation when the distributed data are possessed by different institutes and contain sensitive information (e.g., medical records). In this paper, we propose a distributed multi-task relationship learning approach under differential privacy called DRUPE, where privacy protection is achieved through perturbing the gradients at each task node. In particular, to reduce the high variance of the perturbed gradients and achieve fast convergence rate, we develop a variance reduction based gradient calibration method, which first estimates the gradient error from the previous perturbed gradients and then calibrates the current perturbed gradient by subtracting the gradient error term. Moreover, to alleviate the negative effect caused by the inaccurate task relationships that are inferred from the private task models, we present a task relationship calibration method which uses the least-squares approximation algorithm to calibrate the inaccurate pairwise relationships between the tasks. Experimental results on real-world datasets confirm the effectiveness of our approach.
Real-time Application Identification of RTC Media Streams via Encrypted Traffic Analysis

The globalization of the economy and the increase in network bandwidth have contributed significantly to the development and popularity of real-time communication (RTC) social applications. RTC media streams, such as video meetings and calls, require more network resources and real-time performance than other services. In order to meet the requirements of RTC application providers to offer a higher level of service to their subscribers, Internet Service Providers (ISPs) need to identify the application to which the RTC media stream belongs. There are already some studies on traffic identification. However, the extant work is not yet able to distinguish the corresponding applications from the same type of media streams in real time. In addition, most of the work is not validated with actual data containing massive background traffic. Hence, we propose a real-time application identification method for meeting and calling RTC media streams in social networks. By analyzing the encrypted traffic, the method extracts features from the unit-time traffic aggregation without using payload and related information fields. The generated feature sequences are fed to our lightweight model. Our proposed method does not depend on initial packets or whole flows, and only an arbitrary 3-second traffic block is needed to achieve over 99% accuracy. Moreover, experiments using high-speed network traffic reflect that our approach can identify corresponding applications from RTC media streams in real time. Besides, comparisons with similar work show that this method requires only 1/160th of the memory and 1/10th of the processing time.
Decision Making for Autonomous Driving Via Multimodal Transformer and Deep Reinforcement Learning

On the basis of environmental information processed by the sensing module, the decision module in automatic driving integrates environmental and vehicle information to make the autonomous vehicle produce safe and reasonable driving behavior. Considering the complexity and variability of the driving environment of autonomous vehicles, researchers have begun to apply deep reinforcement learning (DRL) in the study of autonomous driving control strategies in recent years. In this paper, we apply an algorithm framework combining multimodal transformer and DRL to solve the autonomous driving decision problem in complex scenarios. We use ResNet and transformer to extract the features of LiDAR point cloud and image. We use Deep Deterministic Policy Gradient (DDPG) algorithm to complete the subsequent autonomous driving decision-making task. And we use information bottleneck to improve the sampling efficiency of RL. We use CARLA simulator to evaluate our approach. The results show that our approach allows the agent to learn better driving strategies.
Collision-Free Motion Planning Method Based on Online Trajectory Generation in High Dimensional Dynamic Workspace

This paper proposes a novel and effective online trajectory generation method to help 6 DOF non-redundant manipulators avoid dynamic obstacles. The proposed method decouples the robot motion planning in the task space into front-end path search and back-end trajectory optimization modules. The path planning module uses the constraint-based kinodynamic path search approach to generate a safe and feasible initial trajectory. In the following stage, the cubic B-spline-based trajectory optimization method is adopted to minimize the penalty of collision cost, smoothness, and dynamical feasibility. The optimization method of the links collision avoidance based on constraint relaxation is integrated into the online trajectory planning task. The task space trajectory is converted to the joint space based on the robot inverse kinematics. Detailed simulations and real-world experiments are reported to demonstrate the effectiveness of our approach.
A Lightweight Serial CNN Model for Remote Sensing Ship Target Recognition on FPGA

Ship target recognition with remote sensing is an important task for earth observation. The excessive recognition delay may render the results meaningless for time-sensitive targets such as ships. Therefore, rapid and timely analysis of image data on the space-borne platform is required. With the development of deep learning, Convolution Neural Network (CNN)-based methods have achieved high accuracy in the ship recognition task. The huge model size and computational complexity make the CNN-based methods difficult to be deployed on the space-borne platform with limited resources. Hence, model compression techniques are adopted in some on-board methods to obtain the real-time information of ships. However, these methods can only locate ships and cannot give specific category information. To address this problem, a lightweight ship recognition method based on the subgraph cascade classification is proposed. Firstly, the entire image is divided into small subgraphs. Secondly, a lightweight CNN is adopted to select ships from backgrounds. Then, the accurate coordinates of ships are obtained through position regression. At last, ship targets are classified as civil ships and other ships. The experiments show that the method has a promising application prospect in on-board intelligent processing.
Highly-Robust Feature Detection Method in Shape-Coded Structured Light Based on End-to-End Deep Neural Network

Shape-coded structured light is one of the most important technologies in the field of structured light. However, due to its poor robustness in feature extraction, its application in practical scenes is greatly limited. To solve this problem, this paper developed a highly-robust feature extraction method based on the deep learning method. First, a shape-coded structured light pattern was designed. The grid points formed by a series of horizontal and vertical lines were designated as the coding feature points. Then, the famous U-net network was utilized to detect the location of the grid feature points. At last, to varify the effectiveness of the proposed method, a large number of experiments were carried out. Experimental results showed that our method outperforms traditional feature detection method, especially in the case of noise interference. For structured light pattern with higher coding density, our method performs much better than the traditional method in both detection robustness and detection accuracy.
Spatiotemporal Authentication System Architecture for Smart Campus Safety

A Smart Campus is a protected area within Smart Cities (Cities 2.0) where physical security of assets is vital for the continuous operation of the university. Concretely, there are specific mission-critical areas on the campus, which should be protected from unauthorized and malicious individuals. This paper describes a sustainable Smart Campus system architecture based on individuals’ spatiotemporal authentication fingerprint, generated by fusing data from mobile GPS devices and CCTV cameras infrastructure to detect malicious user behavior. The system incorporates unobtrusive monitoring to collect data from such individuals. While the system monitors for unauthorized access to restricted locations within the campus area, data are analyzed by an intrusion detection algorithm that sets off alarms and prompts physical evacuation. The efficiency of the proposed system is evaluated by gauging the prediction accuracy of alarms triggered and response time to the actual incidents on the campus. Results are promising for the adoption of the proposed system architecture by universities in Cities 2.0.
Penetration Testing Procedure using Machine Learning

The main aim of this study is to determine the effectiveness of a penetration testing tool, GyoiThon as a Machine Learning tool by conducting penetration tests on websites, including the websites that have Content Management System (CMS) frameworks, to identify their vulnerabilities and assess the effectiveness of GyoiThon features in penetration testing. This experiment will determine how well the automation framework is executed for penetration testing. This research hypothesized, that if the feature of a penetration tool consists of any form of Machine Learning algorithm, the more effective the feature can search for more vulnerabilities. To achieve the aim of this paper, an experiment was conducted to evaluate the effectiveness of the two features of GyoiThon, the Default and Machine Learning modes. In the end, it was revealed that the Machine Learning mode of GyoiThon discovered more types of vulnerabilities than using the Default mode of GyoiThon, proving the hypothesis to be right.
Design of Image Acquisition System Based on Visual Intelligent Sensor and Video Signal Processing

With the rapid development and iteration of CMOS image sensor and other related technologies, in addition to the more advanced technology, the corresponding sensor also integrates the image signal processing system, so it is a leap for the image acquisition system. Based on this, this paper will take the visual intelligent sensor produced and developed by a semiconductor company as the hardware to complete the hardware design of the image acquisition and transmission system. In addition to the core processor of the system, this paper will also use audio and video processing chips and image data processing chips for collaborative analysis, so as to ensure the processing capacity and processing quality of the image acquisition system at the hardware design level. Compared with the traditional system, at the corresponding system software level, in order to further improve the quality of video signal processing, this paper creatively proposes a switch weighted median filter denoising algorithm, which has a significant impact on the processing of random impulse noise in video and the overall image filtering. At the same time, the algorithm also protects the edge details of the video image, thus improving the authenticity of the image and video. Based on this, this paper has built a verification platform based on supporting hardware and corresponding algorithms. Through hardware debugging and system operation analysis, when the operating frequency of the visual intelligent sensor is 100 MHz, the set acquisition depth is 10 bits, and the corresponding system collects data at a rate of 251 fps per second. In terms of the corresponding performance, the iteration number of the algorithm proposed in this paper is about 40% more than that of the traditional algorithm in unit time. In terms of the corresponding algorithm performance, the algorithm proposed in this paper is about 30% higher than the traditional algorithm. In terms of the efficiency of the corresponding image and video acquisition system, the algorithm proposed in this paper is about 44% higher than the traditional algorithm. Based on this, the system proposed in this paper has obvious advantages over the traditional algorithm.
An experimental and comparative benchmark study examining resource utilization in managed Hadoop context

Transitioning cloud-based Hadoop frameworks from IaaS to PaaS, which are commercially conceptualized as pay-as-you-go or pay-per-use, often reduces the associated system costs. However, the managed Hadoop systems obscure the inner performance dynamics of the platform and present a black-box behavior to the end-users. The aim of this study was to investigate the resource utilization of current managed Hadoop platforms. Thus, we explored three prominent Hadoop-on-PaaS proposals as they come out-of-the-box and conducted Hadoop-specific workloads using the HiBench Benchmark Suite. During the benchmark executions, the system resource utilization data from the worker nodes were collected and analyzed. The results indicated that the same property specifications among cloud services neither do guarantee similar performance outputs, nor produce consistent results based on different workloads within themselves. We anticipate that the managed systems’ architectures and pre-configurations play a crucial role in the performance outcomes.
Real-time prediction of cardiovascular diseases using reservoir-computing and fusion with electronic medical record

Cardiovascular diseases (CVDs) are a leading cause of death in USA and globally, but many people suffering from CVDs are asymptomatic in the early stages leading to reduced awareness, and less chances of managing the disease. This work presents a potential solution for at-home monitoring by leveraging predictive power of artificial intelligence (AI) for developing a fusion framework that combines patient electrocardiogram (ECG) and electronic medical record (EMR) for predicting risk of CVDs at an early stage. To improve energy-efficiency of wearable ECG sensor, in-sensor analog reservoir-computing is proposed that precludes need for front-end digitization and transmission of raw sensor data. The fusion framework predicts ischemic heart disease (I20–I25 ICD codes) with area under the receiver operating characteristic (AUROC) of 0.91, and other heart diseases (I30–I52 ICD codes) with AUROC of 0.95 which is better than state-of-the-art while not requiring laboratory test results.
Learning Monotone Dynamics by Neural Networks

Feed-forward neural networks (FNNs) work as standard building blocks in applying artificial intelligence (AI) to the physical world. They allow learning the dynamics of unknown physical systems (e.g., biological and chemical) to predict their future behavior. However, they are likely to violate the physical constraints of those systems without proper treatments. This work focuses on imposing two important physical constraints: monotonicity (i.e., a partial order of system states is preserved over time) and stability (i.e., the system states converge over time) when using FNNs to learn physical dynamics. For monotonicity constraints, we propose to use nonnegative neural networks and batch normalization. For both monotonicity and stability constraints, we propose to learn the system dynamics and corresponding Lyapunov function simultaneously. As demonstrated by case studies, our methods can preserve the stability and monotonicity of FNNs and significantly reduce their prediction errors.
Intrinsic Sparse LSTM using Structured Targeted Dropout for Efficient Hardware Inference

Recurrent Neural Networks (RNNs) are useful for speech recognition but their fully-connected structure leads to a large memory footprint, making it difficult to deploy them on resource-constrained embedded systems. Previous structured RNN pruning methods can effectively reduce RNN size; however, it is difficult to find a good balance between high sparsity and high task accuracy or the pruned models only lead to moderate speedup on custom hardware accelerators. This work proposes a novel structured pruning method called Structure Targeted Dropout (STD)-Intrinsic Sparse Structures (ISS) that stochastically drops grouped rows and columns of the weight matrices during training. The compressed networks are equivalent to a smaller dense network, which can be efficiently processed by Graphics Processing Units (GPUs). STD-ISS is evaluated on the TIMIT phone recognition task using Long Short-Term Memory (LSTM) RNNs. It outperforms previous state-of-the-art hardware-friendly methods on both accuracy and compression ratio. STD-ISS achieves a size compression ratio of up to 50× with <1 % accuracy loss, leading to a 19.1× speedup on the embedded Jetson Xavier NX GPU platform.
Improving Embedded Target Tracking Systems Based on Siamese Networks with Infrared Images

Whether it is for hunting prey, or modern racing, games, or war, how to accurately track the target is the key to the victory. When we can have a machine to help us track the target, we can naturally solve more problems. Not only it can be used in military applications, such as warplane radar and missiles but it can be applied in civilian purposes including home security, event broadcasting, smart home. The target tracking is becoming increasingly mature. In this paper, we aim to develop a practical three-axis target tracking system by simulating the hunting ability of snakes in nature, and we add thermal infrared images to increase the model modality and optimize the ability of existing Siamese tracking network. Multi-Modal Machine Learning (MMML) is modified with the Siamese tracking network into a multi-modal fusion model, so as to improve the model's perception. At the same time, the Siamese networks models for training normal images and thermal infrared images are separated and a pseudo-Siamese network is used as the main framework to process the different the databases. This paper successfully improves the stability and accuracy of the model. With the embedded development version and three-axis servo motor, our system can be more flexible in tracking applications.
Design Exploration of An Energy-Efficient Acceleration System for CNNs on Low-Cost Resource-Constraint SoC-FPGAs

Deep convolutional neural networks (CNNs) require enormous computation capacity, great amounts of memory accesses and data movement among parallel processing elements (PEs). From an energy perspective, CNNs are difficult to be fully deployed to low-cost resource-constraint edge devices because of both memory-intensive and computation-intensive workloads. In this paper, energy-efficient software/hardware co-design is explored for CNN acceleration on a Xilinx resource-constraint SoC-FPGA device. The acceleration system is optimized based on the constraints of DRAM bandwidths, BRAM resources, computing resources, optimal frequency and the complexity of wire routing. Moreover, the efficient workload distribution and dataflow control are also implemented by both software and hardware to achieve the maximum resource utilization. Based on a low-cost Xilinx Zynq XC7Z020 SoC-FPGA device, the proposed acceleration system achieves the throughput of VGG16 and YOLOv3-tiny by 4.3 frame/s and 21 frame/s, respectively. Moreover, 34 GOPS/W and 38.9 GOPS/W can be realized for VGG16 and YOLOv3-tiny. Compared to other state-of-art designs on resource-constraint SoC-FPGA devices, the proposed acceleration system achieves the best energy efficiency with high resource utilization.
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning

The prevalence and success of Deep Neural Network (DNN) applications in recent years have motivated research on DNN compression, such as pruning and quantization. These techniques accelerate model inference, reduce power consumption, and reduce the size and complexity of the hardware necessary to run DNNs, all with little to no loss in accuracy. However, since DNNs are vulnerable to adversarial inputs, it is important to consider the relationship between compression and adversarial robustness. In this work, we investigate the adversarial robustness of models produced by several irregular pruning schemes and by 8-bit quantization. Additionally, while conventional pruning removes the least important parameters in a DNN, we investigate the effect of an unconventional pruning method: removing the most important model parameters based on the gradient on adversarial inputs. We call this method Greedy Adversarial Pruning (GAP) and we find that this pruning method results in models that are resistant to transfer attacks from their uncompressed counterparts. Code is available at [1].
PrivOpt: an intrinsically private distributed optimization algorithm

A critical factor for expanding the adoption of networked solutions is ensuring local data privacy of in-network agents implementing a distributed algorithm. In this paper, we consider privacy preservation in the distributed optimization problem in the sense that local cost parameters should not be revealed. Current approaches to privacy preservation normally propose methods that sacrifice exact convergence or increase communication overhead. We propose PrivOpt, an intrinsically private distributed optimization algorithm that converges exponentially fast without any convergence error or using extra communication channels. We show that when the number of the parameters of the local cost is greater than the dimension of the decision variable of the problem, no malicious agent, even if it has access to all transmitted-in and -out messages in the network, can obtain local cost parameters of other agents. As an application study, we show how our proposed PrivOpt algorithm can be used to solve an optimal resource allocation problem with the guarantees that the local cost parameters of all the agents stay private.