Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of fatigue and distraction driving detection system based on line of sight impact

Fatigue driving and distracted driving both belong to bad driving behaviors, but their hazard levels to safe driving are different. Through accurate detection and classification warning, not only driving safety can be ensured, but also transportation efficiency can be improved. According to the view angle, the driver's view is divided into 17 areas, which is more in line with the actual human physiological situation. The experiment was conducted on 30 drivers based on the indicators such as line of sight impact point, number of stay frames, and number of regional impact points, and the original data was analyzed by independent sample t-test. It was concluded that there was a significant difference between fatigue driving and distracted driving. By building a double objective prediction BP neural network model of driving state, two types of bad driving behaviors could be classified and detected with high accuracy. The warning system of fatigue driving and distracted driving was designed to realize the hierarchical warning of two types of bad driving behaviors.
A Reinforcement Learning Approach for Trajectory Planning of Intelligent Vehicles

Intelligent vehicles have received significant attention from numerous research academics and technology businesses, following the rapid growth of artificial intelligence and machine learning. In dynamic environments, motion planning is critical for autonomous vehicles. However, handling these issues of active obstacle avoidance in dynamic environments and real-time optimal trajectory planning are current challenges for traditional motion planning algorithms. To address these issues, this paper proposes a reinforcement learning method for real-time near-optimal trajectory planning of intelligent vehicles in the presence of moving obstacles. To achieve real-time trajectory planning, a dual heuristic programming (DHP) algorithm is adopted. Furthermore, for achieving active obstacle avoidance in dynamic scenarios, the potential field function is incorporated into the designed actor-critic network. Simulation results illustrate that the proposed method has an excellent capacity to follow a specified reference trajectory. Also, the proposed approach can wonderfully plan a real-time collision-free path and has excellent capabilities in the aspect of the position error and the smoothness of the planned trajectory compared with the RRT* algorithm.
Profitability Analysis for Voice-Over-IP (VoIP) Service Providers Using Simulation

Abstract Voice-over-Internet Protocol (VoIP) calling-card service providers are facing critical challenges in the decision - making process to increase its profit margin. A simulation model using ARENA is developed to evaluate different business plans and system parameters affecting net profit. Data from a real — world VoIP calling-card system are used in the simulation experiments. The results indicate that there are alternative policies or business plans that the service provider can implement to increase its net profit, and the selection of suppliers is critical.
Named Entity Recognition of TCM Classics Based on SiKuBERT and Multi-Feature Embedding

NER of traditional Chinese medical (TCM) classics is the basis for constructing knowledge graphs of Chinese medicine. However, the current research in this field is insufficient, and there are problems due to the corpus is sparse and the rich semantic information in the special structural features of ancient Chinese characters is not considered. Therefore, the Herb dataset is constructed. Meanwhile, we propose a model based on SiKuBERT and multi-feature embedding by combining the characteristics of the corpus and considering the special structural information of ancient Chinese characters for the first time in the filed of TCM classics named entity recognition. The results demonstrate that our model can effectively identify five distinct types of entities with an F1-score of 86.66%, a precision rate of 86.95%, and a recall rate of 86.37%, which outperforms other popular deep learning models.
NAF: Deeper Network/Accelerator Co-Exploration for Customizing CNNs on FPGA

Recently, algorithm and hardware co-design for neu-ral networks (NNs) has become the key to obtaining high-quality solutions. However, prior works lack consideration of the underlying hardware and thus suffer from a severely unbalanced neural architecture and hardware architecture search (NA-HAS) space on FPGAs, failing to unleash the performance potential. Nevertheless, a deeper joint search leads to a larger (multiplicative) search space, highly challenging the search. To this end, we propose an efficient differentiable search framework NAF, which jointly searches the networks (e.g., operations and bitwidths) and accelerators (e.g., heterogeneous multicores and mappings) under a balanced NA-HAS space. Concretely, we design a coarse-grained hardware-friendly quantization algorithm and integrate it at a block granularity into the co-search process. Meanwhile, we design a highly optimized block processing unit (BPU) with key dataflow configurable. Afterward, a dynamic hardware generation algorithm based on modeling and heuristic rules is designed to perform the critical HAS and fast generate hardware feedback. Experimental results show that compared with the previous state-of-the-art (SOTA) co-design works, NAF improves the throughput by $1.99\times\sim 6.84\times$ on Xilinx ZCU102 and energy efficiency by 17%~88% under similar accuracy on the ImageNet dataset.
Fast Performance Evaluation Methodology for High-speed Memory Interfaces

An increase in the data rate of memory interfaces causes higher inter-symbol interference (ISI). To mitigate ISI, recent high-speed memory interfaces have started employing complex datapath, utilizing equalization techniques such as continuous-time linear equalizer and decision-feedback equalizer. This incurs huge overhead for design verification with conventional methods using transient simulation. This paper proposes a fast and accurate verification methodology to evaluate the voltage and timing margin of the interface, based on the impulse sensitivity function. To take nonlinear circuit behavior into account, the small- and large-signal responses were separately calculated to improve accuracy, using the data obtained from the periodic AC and periodic steady-state analyses. This approach achieves high accuracy, with shmoo similarity rates of over 95 %, while also significantly reducing verification time, up to 23 × faster. Moreover, two different methods are proposed for evaluating the multi-stage Rx performance, providing a trade-off between accuracy and efficiency that can be tailored to the specific purpose, e.g., the verification or design process.
EAST Experimental Data Gateway Based on RESTful and Microservice Architecture

This paper designs and implements the EAST ex-perimental data gateway based on RESTful and microservice architecture as a centralized control point for traffic and services. Different from the single-architecture UDA method with high complexity and limited scalability, our method aims to realize the decoupling of data services at the business logic and technology stack levels, thereby improving the usability and maintainability of data services, and Unified provision of protocol conversion and adaptation, service routing and governance, traffic management and fault tolerance, log recording and monitoring and other functions. To achieve this goal, this paper designs and implements the EAST experimental data gateway based on the technical solution of the combination of Nginx and Flask backend frame-work. On the one hand, the gateway provides a unified RESTful experimental data query service for various external terminal devices, enabling cross-platform data access without maintaining various programming language clients. On the other hand, the gateway internally accesses microservices implemented by different technology stacks to realize the decoupling of data access services. In order to further improve the maintainability of the system, this paper also implements registration discovery and health detection of microservices through Consul, and implements distributed link tracing based on Elastic APM to record relevant logs and display global topology relationships. The test results show that the EAST experimental data gateway meets the established functional requirements and performance requirem-ents.
PRADA: Point Cloud Recognition Acceleration via Dynamic Approximation

Recent point cloud recognition (PCR) tasks tend to utilize deep neural network (DNN) for better accuracy. Still, the computational intensity of DNN makes them far from real-time processing, given the fast-increasing number of points that need to be processed. Because the point cloud represents 3D-shaped discrete objects in the physical world using a mass of points, the points tend for an uneven distribution in the view space that exposes strong clustering possibility and local pairs' similarities. Based on this observation, this paper proposes PRADA, an algorithm-architecture co-design that can accelerate PCR while reserving its accuracy. We propose dynamic approximation, which can approximate and eliminate the similar local pairs' computations and recover their results by copying key local pairs' features for PCR speedup without losing accuracy. For accuracy good, we further propose an advanced re-clustering technique to maximize the similarity between local pairs. For performance good, we then propose a PRADA architecture that can be built on any conventional DNN accelerator to dynamically approximate the similarity and skip the redundant DNN computation with memory accesses at the same time. Our experiments on a wide variety of datasets show that PRADA averagely achieves 4.2×, 4.9×, 7.1×, and 12.2× speedup over Mesorasi, V100 GPU, 1080TI GPU, and Xeon CPU with negligible accuracy loss.
Dam Intelligent Extraction Technology Based on UAV Remote Sensing Image

China is rich in hydraulic resources. In order to make full use of water resources, many reservoirs have been built. Taking Guilin as an example, water conservancy facilities naturally become the focus and development object of Guilin. There are more than 130 large and small reservoirs in Guilin, which are used for irrigation, flood control, water supply, power generation and other purposes. Therefore, it is necessary to strengthen the safety warning of reservoir dams to ensure the safe operation. At present, it is an effective technical means to use UAV remote sensing data for intelligent extraction of reservoir dams and dam change monitoring. Firstly, this paper uses a variety of image preprocessing methods to reduce noise and external interference and improve the subsequent extraction rate and quality of the image. Secondly, LSC (Linear Spectral Clustering) superpixel segmentation combined with improved GrabCut algorithm is used to extract intelligent dams from reservoirs. The experimental results show that compared with SM algorithm and the original GrabCut algorithm, the proposed method for dam segmentation extraction effect not only improves the accuracy, but also greatly increases the running speed, especially for large resolution scenes.
Application of Deep Learning in Skin Disease Diagnosis: A Review

The most prevalent causes of skin diseases include bacterial infections, viruses, fungal infections, allergic reactions, etc. Recent advances in photonics-based medical technology and laser technology have enabled the rapid and precise diagnosis of skin disorders. However, diagnostic tools in medicine are limited and, in general, quite expensive. However, the use of an image-based diagnostic system may reduce the required amount of time and money. Image processing and deep learning are two distinct but complementary fields of study that could be coupled for the early detection of skin conditions. However, feature extraction is essential for the classification of skin conditions.
Expanding In-Cone Obfuscated Tree for Anti SAT Attack

Logic locking is a hardware security technology to protect circuit designs from overuse, piracy, and reverse engineering. It protects a circuit by inserting key gates to hide the circuit functionality, so that the circuit is functional only when a correct key is applied. In recent years, encrypting the point function, e.g., AND-tree, in a circuit has been shown to be promising to resist SAT attack. However, the encryption technique may suffer from two problems: First, the tree size may not be large enough to achieve desired security. Second, SAT attack could break the encryption in one iteration when it finds a specific input pattern, called remove-all DIP. Thus, in this paper, we present a new method for constructing the obfuscated tree. We first apply the sum-of-product transformation to find the largest AND-tree in a circuit, and then insert extra variables with the proposed split-compensate operation to further enlarge the AND-tree and mitigate the remove-all DIP issue. The experimental results show that the proposed obfuscated tree can effectively resist SAT attack.
Millimeter-Wave Wide-Angle Scanning Phased Array Antenna Based on Heterogeneous Beam Elements

By imitating the element-inclined beam distribution of a curved surface conformal (CSC) phased array antenna (PAA), a pseudo-CSC (PCSC) PAA realizes wide-angle scanning while maintaining a planar low profile. Inspired by the PCSC PAA, this communication proposes a PAA based on heterogeneous beam elements (HBEs) to realize wide-angle scanning. Using HBEs introduces a new design degrees of freedom (DoFs), namely the element beam shape, to PAA. By optimizing HBEs and their arrangement, the effective aperture of a PAA can be increased at large scanning angles, and thus the scanning range can be improved. To verify the idea, first, a wide-beam <inline-formula> <tex-math notation="LaTeX">$1\times4$ </tex-math></inline-formula>-element subarray is developed. Then, a standard <inline-formula> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula>-element PAA is implemented using four uniform <inline-formula> <tex-math notation="LaTeX">$1\times4$ </tex-math></inline-formula>-element subarrays, achieving a <inline-formula> <tex-math notation="LaTeX">$>\pm 60^{\circ }$ </tex-math></inline-formula> 3-dB gain variation scanning range. At last, based on the standard PAA, by adjusting its structure to tailor four subarrays’ beam inclinations, a <inline-formula> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula>-element HBE PAA is designed. It further improves the scanning range to <inline-formula> <tex-math notation="LaTeX">$>\pm 70^{\circ }$ </tex-math></inline-formula> at an average cost of ~1.5 dBi broadside scanning gain within the 5G mmWave band (24.25–29.5 GHz, 19.5%). To the authors’ best knowledge, compared with the former planar mmWave PAAs, the proposed design shows the widest scanning range in a wide bandwidth.
Exploration of Machine Learning Algorithms for Development of Intelligent Intrusion Detection Systems

For anomaly-based intrusion detection systems, software implementation of several string-matching algorithms using deep/machine learning approaches are frequently available. The research paper aims to explore the use of machine learning algorithms in developing intelligent intrusion detection systems for preventing unauthorized access to computer networks. A complete intrusion detection system consists of packet decoding, capturing, pre-processing, and pattern or string matching. However, string matching is computationally the most intensive part. Consequently, several architectures/designs have been proposed to accelerate the performance of string matching. This paper provides an architectural evaluation of the existing deep/machine learning-based solutions of string-matching algorithms. First, the most recent state-of-the-art solutions are carefully identified. Then, the identified solutions are compared in terms of the used dataset, number of classes, and accuracy. The architectural evaluation of multiple classification algorithms in this paper allows users of the domain to select an appropriate deep/machine learning algorithm for string matching according to their needs.
DEL: Dynamic Symbolic Execution-based Lifter for Enhanced Low-Level Intermediate Representation

This work develops an approach that lifts binaries into an enhanced LLVM Intermediate Representation (IR) including indirect jumps. The proposed lifter combines both static and dynamic methods and strives to fully recover the Control-Flow Graph (CFG) of a program. Using Satisfiability Modulo Theories (SMT) supported by memory and register models, our lifter dynamically symbolically executes IR instructions after translating them into SMT expressions.
Using Edge-based Deep Learning Model for Early Detection of Cancer

Cancer is one of the most frequent causes of death in the world. Usually, cancer can be easily diagnosed if characteristic symptoms occur. However, many people who are suffering from cancer have no symptoms. Early diagnosis of tumors is essential to contrast their progression, helping to define more effective treatments to provide long-term survival. Early cancer detection is effective if sensible data can be investigated through high-performance technologies like edge computing. Edge computing is a new paradigm for analyzing data as close to the source as possible, avoiding exporting them outside. Hence, edge-based deep learning models can be applied to improve early cancer detection. This paper provides an use case of a classification task on tumor-related data based on the famous UCI machine learning data sets repository using a deep learning approach based on edge computing. In addition, the manuscript provides an overview of the edge computing paradigm, highlighting its advantages and usability. We also described a small experiment with real tumor data to characterize performance considerations. Moreover, the presented model can be used with different data types, such as images, EGC, and ECC signals.
A Novel Approach for Enhancing Customer Retention Using Machine Learning Techniques in Email Marketing Application

Customers are becoming more concerned about the QoS (Quality of Service) that businesses can give them in today’s modern world. Because the services offered by many vendors are not very distinguishable, businesses have increased rivalry to maintain and improve their quality of service. Customer Relationship Management (CRM) systems are used to provide businesses with the capacity to boost their profitability by acquiring new consumers, establishing a continuous relationship with existing customers, as well as keeping more of their existing customers. Machine Learning (ML) algorithms are used in CRM (Customer Relationship Management) systems to evaluate personal & behavioral data from clients. This gives a company a competitive edge by improving the percentage of consumers they keep as clients. This research attempts to evaluate and assess the performance of several machine learning (ML) approaches to solve the subscriber prediction issue in email marketing. Different analytical machine learning methods that belong to diverse types of learning are selected for this work, especially classification and regressor techniques. Models were used on the dataset of emails that comprises twenty-three features. The experimental outcome demonstrates that RF (Random Forest) & Adaboost outperform all other machine learning methods with an almost similar accuracy of 95%. KNN and ensemble approach achieved the highest 89.8% and 91% R2 scores. The comparison found that the ensemble approach outperforms state-of-arts machine learning methods regarding accuracy, error value, and R2 score.
Graph Convolutional Neural Networks based Marked Point Process

A point process is a collection of positional variables in a given space, while a marked point process (MPP) is basically a point process that measures some additional feature or value at each point. Existing applications such as financial market models and hospitalization models have demonstrated that MPPS are suitable for modeling random events with meaningful values. A big challenge is to study the relationship between different types of events, or how different types of markers affect each other. While Hawkes procedures are an effective and efficient way to model point processes with internal influences, inferential methods of intensity functions can be expensive, especially for multi-dimensional processes. Graph convolutional Network (GCN) is a powerful neural network designed to directly process graphs. A GCN-based model is presented to study potential patterns in the marking process and to predict the location of future events without estimating the Hawkes intensity function. A real data set of water pipe leakage and rupture records over the last 50 years was used in the experiment, modeled as a marker point process, where leakage and rupture are two marker forms. Experimental results on synthetic data sets and real water pipe data sets show that the proposed model is superior to recent state-of-the-art methods.
Throughput maximization of EH-CRN-NOMA based on PPO

The cognitive radio system with non-orthogonal multiple access and energy harvesting (EH-CRN-NOMA) is an important research aspect in 6G network. To maximize the total throughput of multiple secondary users (SUs) in the EH-CRN-NOMA a novel deep reinforcement learning algorithm (DRL) based on proximal policy optimization (PPO) is proposed in this paper. Firstly, we simplify the optimization variables through mathematical, so as to reduce the burden of agents. Furtherly, we use a new decoding sequence for better successive interference cancellation (SIC) in the base station (BS). Finally, experimental results show that the proposed algorithm achieved faster convergence and higher throughput. At the same time, it simplified the action space of DRL, but the time-consuming performance is poor.
Integrating AI With Cyber Security for Smart Industry 4.0 Application

The advent of Smart Industry 4.0 has brought about significant advances in automation, efficiency, and productivity in industrial environments. However, these technological advances also pose new challenges for cybersecurity. The increasing interconnectivity of devices and systems within Smart Industry 4.0 environments makes them vulnerable to cyber attacks and security breaches. To address these challenges, there is a need to integrate AI with cybersecurity in Smart Industry 4.0 applications. AI can be used to analyze large amounts of data from sensors and devices within the industrial environment to detect and prevent cyber threats. Machine learning algorithms can be trained to identify patterns of normal behavior and flag any deviations as potential security threats. There are different AI-based cybersecurity techniques that can be used, such as anomaly detection, intrusion detection, and predictive analytics. These techniques can be used in conjunction with other cybersecurity measures, such as firewalls, intrusion detection systems, and security information and event management (SIEM) tools, to provide a comprehensive approach to cybersecurity for Smart Industry 4.0 applications. Integrating AI with cybersecurity for Smart Industry 4.0 applications can help improve the security posture of industrial environments and reduce the risk of cyber attacks and data breaches. However, it is important to ensure that these technologies are properly implemented and configured to avoid unintended consequences and potential vulnerabilities. By carefully designing and implementing AI-based cybersecurity solutions, organizations can protect their industrial environments from cyber threats and ensure the reliability and safety of their operations.